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A comparative study of soil erosion modelling in Lom Kao-Phetchabun, Thailand

Bamutaze Yazidhi March, 2003

A comparative study of soil erosion modelling in Lom Kao-Phetchabun, Thailand

by

Bamutaze Yazidhi

Thesis submitted to the International Institute for Geo-information Science and Earth Obser-vation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Land Degradation and Conservation specialisa-tion

Degree Assessment Board Dr. D. Rossiter (Chairman) ESA Department, ITC Dr. V. Jetten (External examiner) University of Utrecht Dr. D.P.Shrestha (Supervisor) ESA Department, ITC Dr. A Farshad (Student advisor) ESA Department, ITC Dr. P Van Dijk (Programm Director, EREG) ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

I

Abstract

Soil erosion is a serious environmental problem in the world. With the generally high erosion rates in many parts of the world, efforts should be directed towards curtailing its hazard. This requires quan-titative data to identify critical areas where urgent conservation is needed. Traditional approaches based on runoff plots are expensive, time consuming and generate point-based data. A comparative study was conducted to estimate soil loss under landscapes, major land uses and slope gradients in Lom Kao, Phetchabun district, Thailand. Two soil erosion models i.e. the Revised Soil Loss Equation (RUSLE) and the Revised Morgan Morgan and Finney (RMMF) models were applied in a GIS envi-ronment. A soil study characterising the soil-landscape relation, chemical and physical properties was also carried out following the geo-pedological approach. Two techniques were applied in gener-ating the slope length (LS) factor of RUSLE. Estimates were made for surface cover factor (C) of RUSLE and RMMF models to compare with the typical values used in Thailand. Data was analyzed using the Kruskal Wallis test, students t-test, Pearson Product momentum correlation and partial cor-relation. Descriptive statistics like the mean, standard deviation, and coefficients of variation were also used in the analysis. To evaluate the accuracy and reliability of the models, a sensitivity analysis was performed on models parameters namely surface cover, slope gradient and organic matter con-tent. The results show that the predicted soil loss rates of RUSLE model were greater than those of the RMMF. The average soil loss prediction by the RUSLE model was 6 t ha-1 yr-1, while the average soil loss prediction by the RMMF model was 2.1 t ha-1 yr-1for the study area. In terms of land use the RUSLE model predicted rates of 24.9 t ha-1 yr-1 for annuals, 0 t ha-1 yr-1 for rice, 6.9 t ha-1 yr-1 for orchards, 0.9 t ha-1 yr-1 for grassland, 3.3 t ha-1 yr-1 for disturbed forest and 4.9 t ha-1 yr-1 for tree plantations. The RMMF model gave 9.7 t ha-1 yr-1 for annuals, 0 t ha-1 yr-1 for rice, 2.8 t ha-1 yr-1 for orchards, 0.7 t ha-1 yr-1 for grassland, 0.3 t ha-1 yr-1 for disturbed forest and 1.7 t ha-1 yr-1 for tree plantations. There was a significant difference in predictions between the two models (P<0.05). With regard to landscape, higher rates of soil loss were estimated for Hilland, while the lowest were esti-mated for the valleys. RUSLE model was more sensitive to the cover factor and organic matter, while RMMF was more sensitive to slope gradient and rainfall amount. Overall, the RUSLE model per-formed better than the RMMF and is recommended for the study area.

II

Acknowledgement

First and foremost, praise be to the Merciful God who enabled me accomplish this work. Secondly, I would like to thank the Netherlands government, which through NFP granted me a scholarship that enabled me to study in this peaceful and beautiful country, and is also supporting my organization at home in capacity building. I am greatly indebted to my supervisors Dr. D.P Shrestha and Ir. R Hennemann for scientific guidance and supervision in a calm manner. I also wish to thank Dr. A Farshad for the parental care as my stu-dent adviser during the entire duration of the program. I am grateful to Dr. D Rossiter for the con-structive criticism and advise especially on ortho-photo, which enabled me to improve my work. I ac-knowledge received from Mr. B Krol and Mr. T Loran. I would like to thank the entire staff of the Department of Earth Systems Analysis for the knowledge and skills I have acquired especially GIS and Remote sensing. I also express my gratitude to the Programm Director Dr. V Dijk, the secretary Ms N Aneke and the Cluster manager Mr. Aiko. I am grateful to all my fellow students for the support during the pre-fieldwork, fieldwork and post fieldwork activities. I would also like to appreciate the support given to me by the Land Development Department of Thailand during fieldwork and after fieldwork. In particular, I would like to thank Ms Parida, Ms Funeepong and Mr Anukul for the assistance offered. I would like to thank the Ugandan community at ITC for all the support and encouragement. I would also like to thank Mr. Paul Mukwaya, Mr. Shuaib Lwasa and Mr. Nasser Lukyamuzi for all the care at home during my absence. Thanks also go to my colleagues in the Department of Geography, Maker-ere University for the encouragement and support. Lastly I want to appreciate the efforts of my par-ents who laid the education foundation for me. For Ms Nantege, I appreciate your company.

III

Table of contents

Abstract_______________________________________________________________________i

Acknowledgement _____________________________________________________________ ii

Table of contents _____________________________________________________________ iii

List of Figures_________________________________________________________________vi

List of Tables ________________________________________________________________ vii

List of Equations ____________________________________________________________ viii

List of Abbrevations____________________________________________________________ix

1. INTRODUCTION ____________________________________________________________ 1

1.1. Background ____________________________________________________________ 1

1.2. Problem formulation ____________________________________________________ 2

1.3. Objectives _____________________________________________________________ 3

1.4. Research questions ______________________________________________________ 4

1.5. Hypotheses_____________________________________________________________ 4

1.6. Structure of the thesis____________________________________________________ 4

2. LITERATURE REVIEW ______________________________________________________ 6

2.1. Soil erosion ____________________________________________________________ 6 2.1.1. Rainfall ______________________________________________________________ 6 2.1.2. Soils ________________________________________________________________ 7 2.1.3. Vegetation ___________________________________________________________ 7 2.1.4. Management __________________________________________________________ 8 2.1.5. Topography___________________________________________________________ 8

2.2. Scale of erosion assessment _______________________________________________ 8

2.3. Soil erosion modeling ____________________________________________________ 8 2.3.1. Empirical models ______________________________________________________ 9 2.3.2. Physical based models _________________________________________________ 11 2.3.3. Rule based expert systems ______________________________________________ 11 2.3.4. Hybrid approach ______________________________________________________ 11

2.4. Geo-information system and erosion modeling ______________________________ 12

3. MATERIALS AND METHODS________________________________________________ 13

3.1. Materials used _________________________________________________________ 13

3.2. Description of the study area_____________________________________________ 13 3.2.1. Location ____________________________________________________________ 13 3.2.2. Topography__________________________________________________________ 14

IV

3.2.3. Climate _____________________________________________________________ 15 3.2.4. Geology and geomorphology ____________________________________________ 16 3.2.5. Soils _______________________________________________________________ 16 3.2.6. Vegetation and land use ________________________________________________ 17

3.3. Data collection_________________________________________________________ 18 3.3.1. Pre field work ________________________________________________________ 20 3.3.2. Fieldwork stage ______________________________________________________ 20

3.4. Erosion modeling ______________________________________________________ 24 3.4.1. The RUSLE model ____________________________________________________ 24 3.4.2. The RMMF model ____________________________________________________ 27

4. DATA PROCESSING ________________________________________________________ 30

4.1. Data input ____________________________________________________________ 30

4.2. Ortho-photo generation _________________________________________________ 30

4.3. DEM generation and correction __________________________________________ 30

4.4. Cover factor (C) estimation ______________________________________________ 31 4.4.1. Generation of sub-factor Canopy cover ____________________________________ 31 4.4.2. Generation of surface cover sub factor ____________________________________ 32 4.4.3. Generation of surface roughness sub-factor_________________________________ 32

4.5. Determining RUSLE GIS parameters _____________________________________ 33 4.5.1. Rainfall erosivity (R) layer______________________________________________ 33 4.5.2. Slope length (LS) layer_________________________________________________ 34 4.5.3. Soil erodibility (K) layer _______________________________________________ 35 4.5.4. Cover and support practice (CP) layer _____________________________________ 36 4.5.5. Running the RUSLE model _____________________________________________ 37

4.6. Determining RMMF GIS input parameters ________________________________ 40 4.6.1. Estimation of rainfall energy ____________________________________________ 40 4.6.2. Estimation of runoff ___________________________________________________ 41 4.6.3. Estimation of Soil particle detachment ____________________________________ 41 4.6.4. Estimation of transport capacity of runoff __________________________________ 42 4.6.5. Running the RMMF model _____________________________________________ 42

4.7. Data analysis and evaluation of soil erosion models __________________________ 42

5. RESULTS AND DISCUSSION ________________________________________________ 44

5.1. Soils of Lom Kao_______________________________________________________ 44 5.1.1. Soil landscape relation _________________________________________________ 44 5.1.2. Variability of soil properties ____________________________________________ 51

5.2. Soil loss estimation _____________________________________________________ 59 5.2.1. Estimation Using RUSLE model _________________________________________ 59 5.2.2. Estimation using RMMF _______________________________________________ 64 5.2.3. Comparison between RUSLE and RMMF soil loss predictions _________________ 68

V

5.3. Sensitivity of model parameters __________________________________________ 73 5.3.1. Sensitivity of models to cover factor ______________________________________ 73 5.3.2. Sensitivity of models to slope gradient ____________________________________ 75 5.3.3. Sensitivity of models to rainfall amount ___________________________________ 76 5.3.4. Sensitivity of models to soil erodibility ____________________________________ 76

5.4. Model performance ____________________________________________________ 77

6. CONCLUSIONS AND RECOMMENDATIONS __________________________________ 79

6.1. Conclusions ___________________________________________________________ 79

6.2. Limitations of the study _________________________________________________ 79

6.3. Recommendations______________________________________________________ 80

References _____________________________________________________________________ 81

Appendices_____________________________________________________________________ 85

Appendix 1: FAO (1994) chart for estimating surface cover. _______________________ 85

Appendix 2 Soil properties __________________________________________________ 86

APPENDIX 3 SOIL PROFILE DESCRIPTION________________________________ 88

Appendix 4: Sample soil profile _______________________________________________ 92

VI

List of Figures

Figure 3-1 Location of study area _________________________________________________ 14 Figure 3-2 Digital elevation model of the study area __________________________________ 15 Figure 3-3 Annual rainfall and temperature of the study area ___________________________ 16 Figure 3-4 Methodological flow chart ______________________________________________ 19 Figure 3-5 Estimation of plant height for tall crops ___________________________________ 23 Figure 4-1 Ortho-corrected segment map with label points _____________________________ 36 Figure 4-2 GIS based analytical methodology for RUSLE model _________________________ 38 Figure 4-3 Cover (C) factor map __________________________________________________ 39 Figure 4-4 Slope length (LS) factor map ____________________________________________ 40 Figure 4-5 Soil erodibility (K) factor map ___________________________________________ 40 Figure 5-1 Geo-pedological map of Lom Kao ________________________________________ 45 Figure 5-2 Cross-sectional view (A-B) of the Low Mountains from West to North East________ 49 Figure 5-3 Cross-sectional view (C-D) of the Hilland from South to the East _______________ 50 Figure 5-4 Cross sectional view (E-F) of the Valley and Piedmont _______________________ 51 Figure 5-5 Variation of soil organic matter with landscape _____________________________ 52 Figure 5-6 Variations of Ca, Mg, Na and K with Landscape ____________________________ 52 Figure 5-7 Variation of CEC with landscape_________________________________________ 53 Figure 5-8 Variation of soil pH with landscape_______________________________________ 53 Figure 5-9 Variation of sand, silt and clay with landscape ______________________________ 56 Figure 5-10 Variations of soil physical properties with slope position____________________ 56 Figure 5-11 Variation of soil physical properties with soil depth________________________ 58 Figure 5-12 RUSLE soil erosion hazard map for Lom Kao ____________________________ 60 Figure 5-13 Severity of soil erosion based on RUSLE model ___________________________ 61 Figure 5-14 RUSLE predicted soil loss rates under slope Gradient ______________________ 63 Figure 5-15 Correlation between soil loss and slope gradient __________________________ 63 Figure 5-16 RMMF Soil erosion hazard map (with erosive rain) for Lom Kao _____________ 66 Figure 5-17 Severity of soil erosion based on RMMF model with erosive rain _____________ 67 Figure 5-18 RMMF predicted soil loss rates under slope Gradient ______________________ 68 Figure 5-19 Showing distribution of pixels following the RUSLE model __________________ 69 Figure 5-20 Showing the distribution of pixels following the RMMF model _______________ 70 Figure 5-21 Histogram of pixel distribution in the High Mountain ______________________ 70 Figure 5-22 Histogram of Pixel distribution in the Low Mountain_______________________ 71 Figure 5-23 Histogram of pixel distribution in the Hilland_____________________________ 71 Figure 5-24 Histogram of pixel distribution for Piedmont _____________________________ 72 Figure 5-25 Sensitivity of Soil loss ratios to ground cover _____________________________ 74

VII

List of Tables

Table 3-1 Permeability class allocations ___________________________________________ 22 Table 4-1 Computed canopy cover sub factor for annual land use _______________________ 31 Table 4-2 Computed CC values for other land uses___________________________________ 32 Table 4-3 Derived values for surface cover sub factor ________________________________ 32 Table 4-4 Computed surface roughness values ______________________________________ 32 Table 4-5 Derived C values in comparison with LDD derived C_________________________ 33 Table 4-6 RMMF plant inputs ___________________________________________________ 42 Table 5-1 Geo-pedological map legend updated from Ekkanit (1998) ____________________ 46 Table 5-2 Selected soil chemical properties and their CV at various landscapes ____________ 54 Table 5-3 Variation of soil chemical properties with slope position ______________________ 54 Table 5-4 Variations of soil chemical properties with soil depth_________________________ 55 Table 5-5 RUSLE Annual soil loss predictions ______________________________________ 62 Table 5-6 Soil loss predictions with slope form ______________________________________ 64 Table 5-7 Predicted annual soil detachment, transport capacity and soil loss by RMMF model 64 Table 5-8 Comparative summary statistics of RUSLE and RMMF models _________________ 68 Table 5-9 Sensitivity of RUSLE and RMMF models to cover factor change ________________ 75 Table 5-10 Sensitivity of RUSLE and RMMF model to slope gradient ___________________ 75 Table 5-11 Sensitivity of RUSLE and RMMF models to rainfall amount _________________ 76 Table 5-12 Partial correlation coefficients (r) between K and input variables_____________ 77 Table 5-13 Sensitivity of RUSLE and RMMF to organic matter content__________________ 77

VIII

List of Equations

Equation 3-1 H1 = tan (H1)*d____________________________________________________ 23 Equation 3-2 H2 = tan (H2)*d____________________________________________________ 23 Equation 3-3 A = R*K*L*S*C*P _________________________________________________ 24 Equation 3-4 R = 0.4669X-12.1415________________________________________________ 25 Equation 3-5 K=(2.1*10-4)*(12-OM)*M1.14+3.25*(S-2)+2.5*(P-3)/100*7.59_______________ 25 Equation 3-6 S = 10.8 sin θθθθ + 0.03 (For slope gradient less than 9%) ____________________ 26 Equation 3-7 S= 16.8 sin θθθθ - 0.50 (For slope gradient equal or above 9%)_________________ 26 Equation 3-8 L = (λλλλ/22.12) m _____________________________________________________ 26 Equation 3-9 SLR = PLU*CC*SC*SR _____________________________________________ 26 Equation 3-10 PLU = Cf*Cb*exp [(-Cur*Bur)+(Cus*Bus/Cf

cuf)] __________________________ 26 Equation 3-11 CC = 1-Fc. exp (-0.1.H) ____________________________________________ 26 Equation 3-12 SC = exp (-b. Sp. (0.24/Ru)

0.08 )_______________________________________ 27 Equation 3-13 SR = exp (-0.66*(Ru-0.24)) _________________________________________ 27 Equation 3-14 ER = R*A_______________________________________________________ 27 Equation 3-15 LD = ER*CC ____________________________________________________ 27 Equation 3-16 DT=ER-LD _____________________________________________________ 28 Equation 3-17 KE (DT) = DT (9.81+10.60Log10I) ___________________________________ 28 Equation 3-18 KE (LD) = (15.8*PH0.5)-5.87 _______________________________________ 28 Equation 3-19 RC = 1000MS*BD*EHD*(Et/Eo) ____________________________________ 28 Equation 3-20 RO = R/RN______________________________________________________ 28 Equation 3-21 Q = R*exp (-Rc /Ro) ______________________________________________ 28 Equation 3-22 F=K*KE*10-3 ___________________________________________________ 28 Equation 3-23 Z = 1/(0.5*COH__________________________________________________ 28 Equation 3-24 H = ZQ1.5sinS (1-GC)*103 __________________________________________ 29 Equation 3-25 D= F+H________________________________________________________ 29 Equation 3-26 TC = CQ2sinS*10-3 _______________________________________________ 29 Equation 4-1 DEM_modified = Iff (Land use=”rice”, 0,DEM) __________________________ 31 Equation 4-2 S=Iff(slope<5.14,10.8*sin(degrad(slope))+0.03,16.8*sin(degrad(slope))-0.50) __ 34 Equation 4-3 ([Flow accumulation]>12)*12 ________________________________________ 35 Equation 4-4 ([Flow accumulation]<12)*flow accumulation)___________________________ 35 Equation 4-5 LS = (Flow accumulation*cell size/22.13)^0.4*(sin slope/0.0896)^1.3 _________ 35 Equation 4-6 Soil loss = Min (D/1000*(10000), TC/1000*(10000)) ______________________ 42

IX

List of Abbrevations

ANSWERS Areal Non-point Source Watershed Environment Response Simulation API Air Photo Interpretation BMP Best Management Practices CP Surface cover and Support Practice CREAMS Chemical Runoff and Erosion from Agricultural Management Systems CV Coefficient of Variation DUSLE Differential Universal Soil loss Equation EUROSEM European Soil Erosion Model FAO Food and Agriculture Organisation LDD Land Development Department, Min of agriculture and cooperatives, Thailand LS Slope Length MMF Morgan Morgan and Finney MUSLE Modified Universal Soil Loss Equation RMMF Revised Morgan Morgan and Finney RUSLE Revised Universal Soil Loss Equation SEMMED Soil Erosion Model for MEDiterranean Areas SLEMSA Soil Loss Estimation Equation for Southern Africa SLR Soil Loss Ratios USDA United States Department of Agriculture USLE Universal Soil Loss Equation WEPP Water Erosion Prediction Project

A comparative study of soil erosion modeling in Lom Kao-Phetchabun, Thailand

Introduction

1

1. INTRODUCTION

1.1. Background

Water erosion is one of the most serious forms of land degradation in the world (Nanna, 1996; Sohan and Lal, 2001). More than 56% of land degradation is caused by water erosion, raising a global concern on land productivity (Elirehema, 2001). Changes in land use due to urbanization, agricultural expansion and monoculture productions have led to accelerated and spatial increase in erosion. Soil erosion is a detrimental process both on-site and off-site. Soil erosion not only reduces soil depth, but also reduces the capacity of soils to hold water due to sealing, and depletes plant nutrients in the soil. This reduces soil productivity and causes long term reduction in crop yields (Nanna, 1996), since the necessary plant nutrients are washed away. It is esti-mated that crop production becomes uneconomical on 20 million hectares of land annually (Elirehema, 2001). This raises concern about the ability of land to feed the ever-increasing population. Moreover, water erosion also creates off site environmental problems, such as water pollution, siltation of reservoirs and degradation of coastal ecosystems. It is thus neces-sary to understand where erosion is taking place in order to design sound conservation meas-ures (Kadupitiya, 2002a). Controlling erosion requires data on relative erosion rates, spatial extents, vulnerable areas, current sources, relative contributions from different sources and likely effects on land use (Meijerink and Lieshout, 1996). In many areas, quantitative data on erosion rates is severely lacking (Nadeem, 1999). This data is necessary for land management decisions in assigning priorities for erosion control (Jack, 2002; Moore and Burch, 1986). It is financially impracti-cal to have conservation in all areas, rendering the need to identify and prioritize critical ar-eas (Wessels et al., 2001). Such information enables prevention of various forms of degrada-tion before they cause irreparable damage (Wessels et al., 2001). There are several methods of assessing soil erosion. Traditional approaches are centered on quantifying soil erosion from experimental plots (Harmsen, 1996). Experimental plots pro-vide the most accurate runoff and soil loss data. However, they have practical disadvantages that limit their application. They are expensive, time consuming and generate point-based data, which in strict sense may be valid for only the plot location (Harmsen, 1996). On the other hand, qualitative methods of erosion assessment such as GLASOD, SOTER, etc are based on scoring systems for rainfall erosivity, soil erodibility, slope and land use. Although they provide good information on the spatial distribution of erosion and areas at risk, they are subjective, yield limited data on soil erosion rates and do not produce information required to design erosion control measures or to evaluate their effect. Economists using GLASOD data indicated that the method overestimates land degradation (Olderman and Vanlynden, 2001).


Introduction

2

These deficiencies in erosion assessment are rectified in erosion models (Chisci and Morgan, 1988). Soil erosion modeling has proved to be a sound approach in generating quantitative data (Shigeo et al., 1998). Models are effective predictive tools of soil loss (Nearing et al., 1994). Since the development of the Universal Soil Loss Equation, USLE (Wishmeier and Smith, 1978), modeling has increasingly been used to estimate soil loss in many parts of the world. They are useful tools for understanding erosion processes and their interaction (Nearing et al., 1994). Models are particularly useful for evaluating the impacts of intensified land use on soil loss, water quality and for evaluating the potential effectiveness of mitigation or remedial measures before large sums of money are invested in such measures (Moore and Burch, 1986). The development of computer hardware and software has meant that it is now possible to apply these models in a computer-based environment. According to Wolfgang (2002), remote sensing complemented with field ground truthing and GIS; provide the best methodological toolset to investigate soil erosion. Remote sensing techniques are very effective in providing input data required in erosion modeling. Visual and digital image interpretation can be used to derive input parameters such as land use and land cover and to a less extent the conserva-tion and erodibility factors (Jaroslav et al., 1996). Remote sensing techniques are further ad-vantageous in that, they generate model parameters on spatial scales, yet conventional meth-ods only offer point-based information (Mohamed et al., 2002). GIS techniques also allow scaling data and results to either local or regional level. Several studies (Shrestha, 1997; Shrestha, 2000; Wessels et al., 2001) have shown that GIS is an excellent tool in erosion modeling. GIS modeling does not only predict consequences of human actions on erosion, but it is also useful in the conceptualization and interpretation of complex systems as it allows decision-makers to easily view different scenarios. Most of the data used in models i.e. vegetation, soil, relief, climatic, etc can be processed in a GIS and used as first stage input to identify and map degraded lands (Jaroslav et al., 1996; Shigeo et al., 1998). But application of RS and GIS technology in erosion assessment has not been fully utilized in Asia (Eiumnoh, 2001). The study is therefore aimed at estimating soil erosion in Thailand, using RS and GIS techniques.

1.2. Problem formulation

Soil erosion is a major environmental problem in Thailand. Over 59% of the country is re-ported to be experiencing severe erosion, yet only half of the country is suitable for agricul-ture (Samran, 2001). Scattered reports of the magnitude of soil erosion in Thailand build up to alarming levels especially in hilly areas. For example, studies by Eiumnoh (2001) and (Shrestha et al., 1996) revealed soil losses amounting to 48 and 631 t ha-1 y-1 respectively. Causative factors to this phenomenon have pointed to human population increase, deforesta-


Introduction

3

tion, improper agricultural practices, cultivation of hilly areas and urbanization (Boonma et al., 2001). Efforts to assess soil erosion spatially have only picked up recently in Thailand after the introduction of the manual methodology (Mongkolsawat et al., 1994). The applica-tion of geo-information techniques in assessing soil erosion in Thailand has not extensively been used, despite its advantages like high accuracy and speed. In assessing soil erosion, researchers are always confronted with the problem of selecting the appropriate model to use in a given area (Meijerink and Lieshout, 1996). It is important to adapt a model that can be applied to the critical conditions of an area (Chisci and Morgan, 1988). Some models are area-specific and may not perform well in other areas, since they are designed with a specific application in mind (Shrestha, 2000). Understanding the proper model to use in an area should therefore be the first step in erosion modeling. In areas where certain models have been tested, it’s relatively easy to adopt that model or to select the rec-ommended one for that area. In other areas, it is important to evaluate the applicability of models as a basis for further studies. Details of the existing soil erosion prediction models can be found in Roo (1993), Nanna (1996), Petter (1992) and Shrestha (2000). These include the USLE, SLEMSA, MUSLE, RUSLE, DUSLE, ANSWERS, CREAMS, WEPP, EU-ROSEM, SEMMED, ROSE, and MMF. Each of these models has its advantages and limita-tions. The USLE has been the most widely applied erosion model due to its simplicity. However, the model has been criticized heavily as being inefficient especially for areas outside the USA (Chisci and Morgan, 1988; Shrestha, 2000). Several modifications were incorporated in the USLE model culminating into the Revised Universal Soil Loss Equation (RUSLE) (Renard et al., 1991). On the other hand the MMF model is reported to be successful in a wide range of environments (Morgan, 2001). Despite its success (MMF), some weaknesses have been identified with regard to data collection in some input parameters, while progress on data availability that hitherto was lacking has enabled improvement in deriving soil de-tachability, culminating into the Revised MMF model (RMMF) (Morgan, 2001). Agricultural development requires improved prediction of soil loss. This in turn requires simple, reliable, flexible and appropriate models (Evans, 1990). Although a soil loss map has been prepared in Thailand using USLE for the objective of implementing land management for the country, the application of other models with higher precisions is indicated to be important (LDD, 2000). In the present study, the Revised USLE and the RMMF models are selected from a large list of soil erosion models to assess their applicability in Thailand. The reasons for the choice of these models lie in their simplicity in use, easiness of integration with GIS and lastly because their performance at a catchment level in Thailand is not yet known.

1.3. Objectives

The general objective was to test the applicability of RUSLE and RMMF models in Thailand using geo-information techniques. The specific objectives were: -


Introduction

4

• To study the soil landscape relationship and characterize soil properties in Lom Kao area.

• To estimate the cover factor (C) and compare soil loss predictions with Land Devel-opment Department (LDD) typical C values used in Thailand.

• To estimate soil loss under major land uses, landscapes and slopes gradients using GIS techniques.

• To evaluate the applicability of RUSLE and RMMF models in Lom Kao using sensi-tivity analysis.

1.4. Research questions

• Can estimated field cover factor (C) values give better soil loss predictions than LDD typical values?

• Which landscapes and land uses have the highest soil loss rates in Lom Kao? • Which model has the most sensitive parameters?

1.5. Hypotheses

• The estimated C values give better predictions of soil loss than the LDD typical val-ues for Lom Kao.

• Predicted soil loss rates are higher in the Mountains landscapes in Lom Kao. • The RMMF model is more sensitive to parameter change

1.6. Structure of the thesis

The thesis is organized in six chapters. In chapter one, a brief background of the study is given highlighting the need and relevance of the research. The specific research objectives, formulated research questions and hypotheses to be tested are also given in chapter one. Chapter two provides a theoretical background to the problem investigated through a review of existing literature. Literature reviewed included erosion at global level, major factors af-fecting soil erosion, scales and methods of erosion assessment as well as soil erosion models. In chapter three, a description of the study area in terms of physical aspects is given. The ma-terials, methods and techniques of data collection are described. The chapter also gives an overview of the stages gone through during the study. Details of the conceptual background of the applied models (RUSLE and RMMF) and the derivate equations are explained. In chapter four, details of how the data was in put and processed are explained. This includes application of GIS to generate parameters of the applied models and the running of models to generate soil loss results. The estimated values of the cover (C) factor are presented in this


Introduction

5

chapter. In addition the methods of data analysis and evaluation techniques of model applica-tion are explained. In chapter five, the results of the study are presented. They include the soil landscape relation study for Lom Kao area including description of the main landscapes, relief forms, landforms as well as the dominant soil types. The predicted soil loss rates by RUSLE and RMMF mod-els, and the sensitivity of model parameters are also presented in chapter five. Lastly the dis-cussion of obtained results is presented. In chapter six, the conclusions drawn from the study, limitations of the study and recommen-dations of the study are given.


Literature review

6

2. LITERATURE REVIEW

2.1. Soil erosion

Soil erosion is as old as the earth itself. Roo (1993) defines soil erosion as the removal of soil by forces of nature more rapidly than various soil-forming processes can replace it. It is caused by the interaction between rainfall as an erosive agent and soil as a medium that is detached and transported (Nanna, 1996). These processes are generally determined by loca-tional factors including climate, soil, relief, vegetation and man made soil conservation measures. Soil erosion by water is a serious problem in many parts of the world. It is categorized as the most serious environmental problem because it threatens agriculture and the natural environ-ment (Hagos, 1998). Erosion degrades soil by removing topsoil, decreasing plant nutrients, rooting depth and water reserve. Augmentation of population, overgrazing, agricultural ac-tivities on steep slopes with marginal soils in combination with heavy and sporadic rainfall, make huge areas extremely sensitive to erosion (Petter, 1992). The degradation of soil by erosion is of particular concern because soil formation is extremely slow (Hagos, 1998). Among the consequences of soil erosion is the reduced ability of cultivating possibilities on eroded hill slides and sedimentation of water reservoirs, which reduces irrigation possibilities and leads to decreased agricultural production. The potential erosion risks are higher under intensive arable land use than under forestry or pasture land uses. Soil water erosion is very dynamic and spatial phenomenon which depends on relief geome-try and surface properties influencing overland flow (Jaroslav et al., 1996). Generally, there are six major erosion factors i.e. rainfall, slope gradient and steepness, soil, surface cover and management as explained in the following sections.

2.1.1. Rainfall

Soil loss is closely related to rainfall through the combined effect of detachment by raindrops striking the soil surface and by runoff (Mkhonta, 2000). The ability of rainfall to cause ero-sion (erosivity) depends on characteristics such as rainfall energy and rainfall intensity par-ticularly half-hour rainfall. These characteristics determine the ability of raindrops to detach soil particles and the possible occurrence of surface runoff, a primary means for transporta-tion and deposition of detached soil particles (Nanna, 1996). The amount of rainfall governs the overall water balance and the relative proportion that becomes runoff (Hagos, 1998). Ero-sion is related to two types of rainfall events, the short-lived intense storm where the infiltra-tion capacity of the soil is exceeded, and the prolonged storm of low intensity, which satu-rates the soil before runoff begins. In addition to the rainfall amount, drop size distribution,


Literature review

7

kinetic energy and depth of overland flow are important characteristics affecting splash de-tachment. Detachment is due to the size of the raindrop and its velocity. Big raindrops have high erosive power to detach the soil particles.

2.1.2. Soils

The effect of soil on erosion is reflected through the resistance of soil to both detachment and transport, defined through the soil erodibility factor (Morgan, 1995). Soils with high erodibil-ity index are more sensitive to erosion than soils with low erodibility index. Soil erodibility (K-factor), varies with soil characteristics, e.g., texture, bulk density, shear strength, organic matter content, aggregate stability, infiltration capacity, chemical properties and transport-ability of loosened soil particles (Mkhonta, 2000). The aggregate stability of a soil deter-mines how easily soil particles can be detached. Transportability determines how easily these loosened soil particles can be washed away. Particle size is an important element in soil erodibility. Larger particles are more resistant to transport due to greater force entailed to move them. However, in soils with particles less than 0.06 mm, the erodibility is limited by the cohesiveness of the particles. This is a re-versed relationship compared to that of particle size. The particles that are less resistant to erosion are therefore silt and fine sand (Petter, 1992). Soil texture also influences the infiltra-tion capacity. This is defined as the maximum sustained rate at which soil can absorb water, and depends on pore size, pore stability and the form of the soil profile. Clay soils have a low infiltration capacity and produce more overland flow than soils consisting of coarser material, with higher infiltration capacity (Petter, 1992).

2.1.3. Vegetation

Vegetation covers is a very crucial factor in reducing soil loss (Petter, 1992). In general, as the protective canopy of land cover increases, the erosion hazard decreases (Mkhonta, 2000). It protects the soil against the action of falling raindrops, increases the degree of infiltration of water into the soil, maintains the roughness of the soil surface, reduces the speed of the surface runoff, binds the soil mechanically, diminishes micro-climatic fluctuations in the up-permost layers of the soil, and improves the physical, chemical and biological properties of the soil (Petter, 1992). As long as vegetation cover is unbroken, erosion and runoff are small despite erosivity of the rainfall, slope steepness and soil instability. The effects of vegetation cover on erosional processes especially on surface erosion are varied depending on the type of vegetation cover, density, undergrowth cover and litter. These determine the interception loss, absorption of kinetic energy and increasing water infiltration. Land with good cover al-lows soil retardance to overland flow. Vegetation acts as a protective layer or buffer between the atmosphere and the soil. The above ground cover absorbs energy of falling raindrops, running water and wind, so that less is directed at the soil. The below ground components comprising the root system contribute to the mechanical strength of the soil (Hagos, 1998).


Literature review

8

2.1.4. Management

In circumstances where farmers cultivate in marginal and very steep slopes, soil erosion can be accelerated if there are no proper conservation techniques applied. Proper management practices such as terracing on steep slopes, mulching, crop rotation can significantly reduce soil erosion. On the other hand improper use of land, such as reclamation of forest area, cul-tivation of steep slopes without conservation can drastically promote soil erosion.

2.1.5. Topography

Slope steepness and slope length are considered to have a strong relationship to erosional process (Nanna, 1996). Therefore both of them are useful in quantitative evaluation of ero-sion. Slope gradient and slope length are the common parameters used in erosion modeling (Petter, 1992). Slope gradient has an exponential relationship with erosion. Steep slopes are more susceptible to soil erosion because the erosive forces splash, scour and transport all have a greater effect on steep slopes (Hudson, 1995). On the other hand, longer slopes are more susceptible to soil loss due to greater build up of surface runoff, velocity and depth.

2.2. Scale of erosion assessment

Soil erosion has been assessed at different levels using a variety of methods (Mainam, 1999). These can be grouped into three levels following the objectives of assessment. They include: - micro-plot level, plot level and watershed level. At micro-plot level (0.5 to 2 m2), evalua-tions are conducted under controlled conditions to study erosion processes such as splash or interill erosion and the effects of soil properties on them. Studies at plot level are conducted mainly under natural conditions. The scale varies from a few square meters to a few hectares. Soil erosion scale at field scale allows the evaluation of the effects of farming practices, land use systems or topographic factors. The study of soil erosion at watershed level involves ar-eas covering hundreds and thousands of square kilometers and deals with streams and river basins. It is used to assess the denudation rates of major river basins, mountain system, conti-nents, and ecological regions (Mainam, 1999).

2.3. Soil erosion modeling

Renschler (1996) defines a model as a simplification of processes and their interactions with the aim of extracting, evaluating and simulating the relevant processes. The objective of soil erosion models is either predictability or explanatory (Petter, 1992). Erosion models are cur-rently the most feasible approach in generating data on erosion hazard (Meijerink and Li-eshout, 1996). Models elucidate on erosion through mathematical equations in a simplified form. However the reality to be represented can differ from model predictions (Nanna, 1996). This can be due to the way of representation of particular models as well as the spatial and temporal scales model.


Literature review

9

Several models have been developed and many new ones are in the process of being devel-oped. The main categories of erosion models are empirical, physical, stochastic, hybrid and rule based. Most erosion models are of empirical type. Stochastic models are models in which any of the variables included in the model are regarded as random variables having distributions in probability. If all variables are regarded as free from random variation, the model is regarded as deterministic (Roo, 1993). Models can be lumped or distributed. Lumped models take no account of the spatial distribu-tion in the input variables (S), or of the spatial variation in parameters characterizing the physical processes acting upon input. Procedures may be used to calculate effective values for the entire area. Distributed models incorporate data concerning the spatial distribution of variables together with computational algorithms to evaluate the influence of the distribution on simulated behavior (Roo et al., 1994). Furthermore, models can be conceptual or empiri-cal. A model is conceptual if the physical processes acting upon the input variable to produce the output variable are considered in terms of the physical laws. Empirical models are by strict definition based on observation and experiment, not on theory. The term physically based models is used to replace conceptual distributed models, because if models are physi-cally based, meaning firmly based in our understanding of the physics of the processes, they are necessarily distributed because the equations on which they are defined generally involve one or more space coordinates.

2.3.1. Empirical models

Empirical models describe erosion using statistically significant relationships between as-sumed important variables where a reasonable database exists (Kadupitiya, 2002a). Empirical models are based on defining important factors through field observation, measurement, ex-perimentation and statistical techniques relating erosion factors to soil loss (Petter, 1992). In empirical models, the inherent processes involved are not used and the models can only be operated in the designed direction where inputs go into one side of the equation and the out-put on the other side. Empirical models are quick in predicting erosion, but are site specific and require long-term data (Elirehema, 2001). Most models used in soil erosion studies are empirical models. The most widely used empirical model is the USLE. Others include SLEMSA, DUSLE, RUSLE, MUSLE etc, which are based on modifications made on USLE. Details of selected empirical models are briefly discussed. The USLE (Wishmeier and Smith, 1978) is the most widely used model in predicting soil erosion. It is used in education and research as a starting point in developing understanding of erosion hazard prediction because of its simplicity and clarity (Hagos, 1998). Many scientists have proposed changes, but all are woven around the same concept of rainfall erosivity, soil erodibility, slope length, slope class, land cover and land management factors are taken as directly proportional to the rate of annual soil erosion (Sohan and Lal, 2001).


Literature review

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Since the model was developed based on simulation in the East of the Rocky Mountains, its validity in areas outside the USA is regularly questioned (Roo, 1993). The USLE model es-timates average annual soil loss by sheet and rill on those portions of landscape profiles where erosion but not deposition is occurring. The model does neither predict single storm event nor does it predict gully erosion (Foster, 1982; Keneth et al., 1991). The model is also one-dimensional and static with limited possibilities for analysis of phenomenon dynamics (Jaroslav et al., 1996). The modified universal soil loss equation is one of the modified versions of the USLE. In MUSLE, the rainfall energy factor was replaced with runoff. The runoff factor includes both total storm runoff volume and peak runoff rate. Compared with USLE, this model is applica-ble to individual storms, and eliminates the need for sediment delivery ratios, because the runoff factor represents energy used in detaching and transporting sediment. The main limita-tion is that it does not provide information on time distribution of sediment yield during a runoff event. It is strictly a sediment yield equation and should not be used where detachment controls sediment yield (Roo, 1993). RUSLE is a revised version of USLE, intended to provide more accurate estimates of erosion (Renard et al., 1994). It contains the same factors as USLE, but all equations used to obtain factor values have been revised. It updates the content and incorporates new material that has been available informally or from scattered research reports and professional journals. The major revisions occur in the C, P, and LS factors. The C or cover management factor is now the product of 4 sub factors: prior land use, canopy cover, soil surface cover and surface roughness. The MMF model is an empirical model for predicting annual soil loss from field-sized area on hill slopes (Morgan et al., 1984). It was aimed at bridging the gap between models such as USLE and CREAMS. The model has a stronger physical base than USLE and is simple and more flexible than CREAMS. The model separates the soil erosion process into two phases i.e. the water phase and the sediment phase. In the water phase annual rainfall is used to de-termine the energy of the rainfall for splash detachment and the volume of runoff, assuming that runoff occurs whenever the daily rainfall exceeds a critical value representing moisture storage capacity of the soil-crop complex and that the daily rainfall amounts approximate an exponential frequency distribution. In the sediment phase, splash detachment is modeled us-ing a power relationship with rainfall energy modified to allow for the rainfall interception effect of the crop. The model has been revised with new changes incorporated owing to the rise in data availability and difficulties in estimating certain parameters as in the original ver-sion. In the revised version, changes have been made to the way soil particle detachment by raindrop impact is simulated, which now takes account of plant canopy height and leaf drain-age, and a component has been added for soil particle detachment by flow (Morgan, 2001).


Literature review

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2.3.2. Physical based models

Physically based models are based on knowledge of the fundamental erosion processes and incorporate the laws of conservation of mass and energy (Petter, 1992). Ideally physically based models are developed to replace conceptually distributed models because they are firmly based on understanding the physics of processes involved. The physically based mod-els consider subtle spatial and temporal changes of contributing factors and are more appro-priate for dynamic modeling (Jaroslav et al., 1996). Examples include CREAMS, AN-SWERS, WEPP, EUROSEM, AGNPS. The main limitation of these models is that they are data hungry. WEPP (Nearing et al., 1994) was developed for use in soil and water conservation and envi-ronmental planning and assessment. Spatial distributions of net soil loss can be calculated, and spatial variability in topography, surface roughness, soil properties, hydrology and land use is taken into account (Nanna, 1996). The WEPP erosion model computes estimates of net detachment and deposition using a steady state sediment continuity equation. The net soil loss detachment in rills, i.e., rill erosion rate, is calculated for the case when hydraulic shear stress exceeds the critical shear stress of the soil and when sediment load is less than sedi-ment transport capacity (Nanna, 1996). WEPP uses a static approach describing a steady state erosion and deposition caused by overland flow in dynamic equilibrium. However this situa-tion is rather rare in real landscape due to relief configuration and land cover and roughness properties. Also the equilibrium in overland flow on slopes within a catchment is reached at different time (Jaroslav et al., 1996). The disadvantage of the model is that they are data de-manding, so it’s difficult to reasonably get the data required running the model in a short time span.

2.3.3. Rule based expert systems

These are based on logical reasoning and construction of decision rules using information expressed in if-then form (Kadupitiya, 2002a). Expert knowledge of processes occurring in watershed and survey information on topography, soil, water and cover are essential factors in these models. Rule based models reach inside the black box of the classical stimulus-response model. These models receive information describing the internal environment, process that information using a set of rules, and produce a specific response as their output. While the internal workings of many of these models are complex, the models may involve multiple agents or sub models.

2.3.4. Hybrid approach

The Hybrid approach modeling uses a combined approach through model base reinforcement with relational rule-base (Kadupitiya, 2002b). Relational rules can be used to define the physical boundaries of each unit and to classify straightforwardly up to some extent as high and low erosion risks units.


Literature review

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2.4. Geo-information system and erosion modeling

Soil erosion is a spatial phenomena, thus geo-information techniques play an important role in erosion modeling. Remotely sensed data and existing maps provide a lot of data for model input (Petter, 1992). Data generated from RS can be linked with their spatial location for GIS applications (Mkhonta, 2000). GIS systems can deal with information about features that is geo-referenced. Generally geo-information techniques offer the following advantages in ero-sion modeling: - (1) Fast and cost effective estimates, (2) Possibilities to investigate larger areas, (3) Greater possibilities of continuous monitoring of these areas, (4) Possibilities to refine the soil erosion model depending on the required output scale i.e. rough global to more precise local scale. The use of digital elevation models and GIS offers possibilities to estimate more relevant to-pographical parameters. The size of a drainage basin, the mean slope, or the amount of water passing a certain point on the land surface (runoff), can be computed from a DEM (Petter, 1992).


Materials and Methods

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3. MATERIALS AND METHODS

3.1. Materials used

The materials used in the study included; • Black and white aerial photographs of 1967 (1:15,000) and 1978 (1:50,000). • Landsat TM data with seven bands for March 1988. • Topographic map sheets at 1:50000 scales of Lom Kao, Ban Tha Chang, Ban Sila,

Ban Dan Du area. The contour interval on the topographic maps was 20 m, and in flat areas, 10 m.

• Digital Land use map of the study area (2001), obtained from the Land Development Department at a scale of 1:50,000.

• Soil map at a scale of 1:100,000 (Ekkanit, 1998). • Equipments used in the field included: - GPS receiver for geo-referencing, clinometer

for slope measurement, pH meter, ruler, tape measure for slope length and plant height measurement, soil auger, spade, knife, altimeter, Munsell color chart and soil description guidelines.

The software’s used included ILWIS 3.1 and ArcView 3.3 for map processing, Minitab 11 and MS Excel for statistical analysis, and MS Word for word processing.

3.2. Description of the study area

Details of the location, topography, climate, geology, geomorphology, soils and vegetation of the study area are explained in the following sections.

3.2.1. Location

The study area is located in Lom Kao area, Phetchabun province, Thailand (Figure 3-1). Lom Kao is located approximately 400km north of the capital city, Bangkok. It lies between 16054’13 and 17004’54 latitudes north and between 101009’34 and 1010 24’21 longitudes east, covering an area of approximately 520 km2. The area is accessible with the highway from Phetchabun to the north of the country. There are several roads and tracks connecting villages to the commercial centers of Lom Kao. However accessibility within the mountain-ous area is poor.



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Figure 3-1 Location of study area

3.2.2. Topography

The topography of Lom Kao is part of the “central highlands” (Ojeda, 2000). The region has a complex physiography, composed of high and low Mountains, Hillands, Piedmonts and Valleys at varying levels. There are also Plateaus (undulating to rolling), intervening hilly areas which are steep with some very steep areas of craggy limestone (Ekkanit, 1998). The area also consists of flood plains, alluvial terraces and a relatively young river system. The elevation in the area ranges between 160 to 760 m above sea level (Figure 3-2).



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Figure 3-2 Digital elevation model of the study area

The nearly level to flat part consists of flood plain, alluvial terraces and the adjacent surfaces along the Huai Nam Phung River, with slopes between 1 to 5% (Ekkanit, 1998). The flood plain of the relatively younger river system, the PaKao, is quite narrow and hard to map. The part of the river in the district of Lom Kao is known as the upper PaKao, passing through the mountaneous area, forming a portion of the watershed.

3.2.3. Climate

Lom Kao has humid tropical climate, influenced by northeastern and southwestern mon-soons, with dry, hot and rainy seasons. The rainy season starts from May and extends to Oc-tober. High humidity and a moderate to high temperature characterize the Lom Kao area. The annual precipitation is approximately 1200 mm (averaged for the period 1986 to 2001), maximum monthly rainfall recorded is 219 mm and the average number of rain days is 114 per year. The driest period is from November to February (Figure 3-3). The mean annual temperature is approximately 270 C with a mean monthly maximum of 370c in April and mean monthly minimum of 15.50 C in December (Ekkanit, 1998). The relative humidity is approximately 71%, while the annual evaporation is about 1687 mm.

m.a.s.l



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Figure 3-3 Annual rainfall and temperature of the study area

3.2.4. Geology and geomorphology

The Mountains form part of the Indonesian oregeny or fold belt. Tectonic movements, which occurred in Cretaceous and Tertiary, led to the formation of horsts and grabens. The Pa Kao Valley occurs in one of the grabens. The main geological formations in the study area oc-curred in three geological eras namely:- Cenozoic, Mesozoic and Paleozoic (Ekkanit, 1998). The rock formations of the Paleozoic are dominantly gray massive to thick-bedded limestone, black chert with intercalation of thin-bedded gray shale. The Mesozoic group consists of the “Korat” group of rocks which are the most extensive in the area (Ekkanit, 1998). They com-prise of conglomerates, sandstome, shale, mudstone and argillaceous limestone. In the Ceno-zoic, alluvial deposits of Quaternary varying in texture from gravel, sand, silt to clay were formed which occur along rivers in the middle of the Valley and Piedmonts as colluvial de-posits (Ekkanit, 1998). After stable conditions prevailed during the Jurassic to the Tertiary, orogenic activity was renewed in the Tertiary with the uplift of the Khorat plateau. The tectonic activity was re-sponsible for the typical horst and graben structure present in the area. However the accom-panying denudational and aggradational processes are responsible for the present configura-tion of the area (Ojeda, 2000). Thus the present geomorphic configuration of the main rivers in the area is a result of several processes such as tectonism, denudation, and sedimentation. The geomorphologic configuration of the Pa Kao and Huai Nam Phung rivers is the result of such processes (Ekkanit, 1998). There are four major landscapes namely: - Mountains, Hil-lands, Piedmonts and Valleys in the study area.

3.2.5. Soils

There are three major soil moisture regimes in Lom Kao namely; Ustic, Udic and Aquic moisture regimes. Soils with Ustic moisture regime are dry more than 90 but not exceeding 180 days annually and are moist for at least 45 days. Soils of the Udic moisture regime have

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drier periods not exceeding 90 days. They support more than one crop season without irriga-tion annually. Soils of the Aquic moisture regime are circulated with water for prolonged pe-riods. These soils commonly exhibit gray color with reddish or brownish mottles (EKKanit, 1998). The soil temperature regime of Lom Kao is Isohyperthermic, as the seasonal differ-ence between the coolest and warmest is greater than 50C. The main diagnostic surface and subsurface horizons are; - Mollic, Argillic, Ochric, Cambic, Umbric, Aquic, Udic, Ustic, Histic and Folic horizons. The soils of Lom Kao area include Entisols, Mollisols, Inceptisols, Alfisols and Ultisols (Ekkanit, 1998). The most dominant are the Alfisols and Inceptisols. Following the geopedological approach (Zinck, 1988), the soils of Lom Kao can be described under the main landscapes: Mountain, Hilland, Piedmont, and Valley. The elevation of Mountains ranges from 300 to 1300 m above sea level. Soils are mostly formed on material derived from the rocks, which belong to lithological formations of the carboniferous age. The Hillands lie between the Mountain and the Piedmont with the altitude ranging between 200 and 300 m above sea level. They are diverse genetically as well as lithologically. Soil parent materials are mainly sedimentary rocks such as shales, siltstones, sandstones and con-glomerates. Soils are shallow to moderately deep, well drained to somewhat excessively drained and strongly acidic. Piedmont landscapes include inclined surfaces lying at the foot of mountains or hills. By ori-gin, some parts of these units are primarily depositional, but have been dissected and thus turned into denudation relief form (Ojeda, 2000). Due to tectonic activities, some portion of Piedmont has undergone some uplift to higher topographic position. On the other hand, there are also erosion levels of glacis terrace formed on various bedrock types. The topography varies from nearly level (0-2%) in the area near to the Valley to gently undulating (2-4%) next to the hills of Mountains. Two main rivers pass through the Valley in the study area; the river Huai Nam Phung flows in the west, and Mae Nam Pa Kao in the East. The Valley forms a narrow zone between Mountains. The main relief types are terraces, leeves, overflow mantles and basins. These depositional types of relief result from fluvial transport of sediments. The soils of higher ter-race have had a longer time to undergo horizon differentiation, hence are developed com-pared to the soils of the lower terraces and the floodplain. The topography is mostly classi-fied as nearly level (0 to 2%).

3.2.6. Vegetation and land use

There is a reasonable vegetation cover especially on the Mountains and Hilland. However vegetation is being deforested to create land for agriculture and other human activities. The



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existing forest is classified as deciduous and dry forest with medium to small trees such as Bambix, Danbergia, Pterocarpus, Bamboo and Shorhea. The major land use types are maize, tamarind monoculture plantation, tamarind intercropped with maize, and forest/bush. In the Piedmont, the flat areas are under rice cultivation, which is mostly rain-fed. The Valleys are mostly used for rice cultivation. Other crops grown in the valley include tobacco, mungbean and cabbage. There are two cropping systems for maize i.e. single crop and maize, followed by mungbean. The planting of maize occurs during the end of April or beginning of May, and harvesting is done in August or September. Thereafter, mungbean is planted.

3.3. Data collection

The data required for this study generally consisted of climate, topography, soil, land use and land cover. The methodological approach used in the study is summarised in figure 3-4. Data collection activities consisted of two phases, namely the pre field and fieldwork phases. De-tails of activities conducted during these stages are explained in the proceeding sections.



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Literature review

Problem formulation

Research objectives

Research proposal

Soil survey

Land use analysis

Secondary data

Correction of interpretation

Raw data

LDD offices

Data collection

Parametirization

Suitabilitymodel

Erosion modelling

ModelComparison

Conclusions and recommendations

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Data analysis

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Photointerpretation Image analysis Gathering available

material

Geo-pedological map

Profile description, Bulkdensity, soil permeability,soil texture, soil chemical

properties, surfaceroughness

Elevation, Slope gradient,Slope aspect, Land forms

and relief types

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Vegetation characterization

Topographic characterization

Surface cover, Canopycover, Plant height

Effective plant heightCrop type, land use map

Temperature,RainfallHumidity,Crop calender

Crop patternmanagement techniques

Digital database

Agricultural offices

Meteorologicalcenters

Soil properties

Figure 3-4 Methodological flow chart



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3.3.1. Pre field work

The main activities during this stage were reviewing relevant literature on main issues of the study, which included literature on soil erosion problem in Thailand, soil erosion assessment techniques, soil loss assessment models and geo-information as tools in modelling erosion. The second activity of this phase was selecting fieldwork area. A familiarization of the area was carried out by visual inspection of air photos and Landsat TM images. The area assumed to have land degradation problem after visual analysis on the images and air photos was se-lected. During this phase, the image was geo-referenced and geo-coded using topographic maps. In addition, preliminary air photo interpretation, photo mosaic making and preliminary legend construction following the geo-pedological approach was conducted. Digitising and generation of a digital elevation model was also done in this phase.

3.3.2. Fieldwork stage

This involved collection of primary data using various data collection methods. The field-work phase begun with a reconnaissance survey of the area to understand the soil-landscape relationship. During the reconnaissance, the soil pattern in relation to the landscape was stud-ied and the main landscapes and soil types were identified. This was followed by soil pit de-scription along transects. Thereafter, collection of data required for the study was conducted following along slope profiles and transects. It was assumed initially that erosion potential is high, owing to the presence of Mountain, and Hillands. However, during the reconnaissance survey it was found that the steeper areas (Mountains) are largely under forest and most of the Hillands are under bush land. Most of the cultivated areas are on the Piedmonts and Val-ley and considering the slope factor for these landscapes, there seems to be no soil erosion problem. Therefore, data collection was concentrated in the Mountains, Hillands and Pied-monts. During this stage, informal interviews were also conducted with farmers, and officers at the Land Development Department offices in Phetchabun and Bangkok. Also meteorological sta-tions in Lom Kao, Phetchabun and Bangkok were visited. From these activities, data on land use, cropping systems and seasons, tillage systems, implements used, management practices as well as the climatic of the study area were gathered. The techniques used to collect topog-raphic, climatic, soils and vegetation data used in the study are described below. (a) Climatic data To run the erosion models, detailed climatic data is required. This includes rainfall amount, intensity, number of rain days, storm durations etc. Climatic data was collected from mete-orological stations at Phetchabun and Lom Kao. The data collected included daily rainfall data, temperature, humidity and evapotranspiration for the period 1986-2001. However storm duration data necessary to evaluate the erosive power of rainfall on daily basis to know its intensity was not available.



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(b) Soil data Soil data was collected directly from the field. A soils inventory based on photo interpreta-tion (1: 50,000) was carried out following the geo-pedological approach (Zinck, 1988). Soil observations were carried out largely from mini pits, auger holes and full profiles of representative sites. Soil descriptions were made according to the FAO guidelines for soil description(FAO, 1990). Since the existing soil map of Ekkanit (1998) used, which followed the USDA soil classification system (Soil_Survey_Staff, 1998), the same classification sys-tem was adopted for this study. To evaluate the soil landscape relation, as well as characterizing soil properties, samples were taken at different depths and slope positions (summit, shoulder, back slope and foot slope) along transects. At each site, mini pits were made following Rhue’s approach (Mainam, 1999), from the summit to the Valley to evaluate similarity along the profiles. 36 Soils samples were collected for laboratory analysis of soil particle distribution, texture, or-ganic matter content, bulk density, moisture content at field capacity and cation exchange capacity. Bulk density determination To obtain samples for bulk density, core rings of known volume were driven into the soil us-ing a wooden stick put on the top of the ring. The rings were carefully removed and a knife used to level the soils in the ring in cases where the ring came out with soils beyond its limit. The rings were covered and taken for laboratory analysis. The sample was then oven dried and thereafter weighed and the bulk density was determined by dividing the dried weight with the volume of the sample before drying. Surface cohesion A shear vane test was used to measure surface cohesion. The surface was first moistened wa-ter. The shear vane was driven approximately 2 cm deep into the soil and then rotated and removed. The cohesion values were then read from the graduated instrument cylinder of the shear vane. After each reading, the scale of the shear vane was reset again to zero and meas-urements were repeated 10 times at each site. The average of the measurements was taken as the surface cohesion values at each site. Permeability This was estimated from soil hydraulic conductivity results. The auger hole method (Kadupitiya, 2002a) was used to measure hydraulic conductivity. An auger was driven into the soil and a hole of known diameter and radius obtained. A ruler was placed in the hole and filled with water. Recordings were taken initially after every 2 minutes due the high initial infiltration of water, and later after every 5 minutes. The permeability code was assigned as follows.



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Table 3-1 Permeability class allocations

Hydraulic conductivity Permeability code >6 1 1-6 2 0.5-1 3 0.2-0.5 4 0.1-0.2 5 <0.1 6 Source: Kadupitiya (2002) Surface roughness In addition to the soil data, surface roughness was also estimated in two ways. In areas where reasonable micro-topography was observed, measurements were taken using a measuring tape. Measured values were then fitted in the random roughness chart (Renard et al., 1997) to derive the random values. These were then used to compute surface roughness. In other cases, surface roughness was estimated based on the tillage equipment. The climatic data and frequency of tillage enabled the estimation of surface roughness. (c) Vegetation and land use data Vegetation parameters collected included surface cover (%), plant canopy (%) and plant height. Surface cover was estimated using the FAO surface cover estimation chart (Appendix 1). The chart consists of 10 box squares with four quarters in each box square and a corre-sponding percentage value for each box square. The field surface cover was visually com-pared with the box squares of the chart. The value of the box square with a distribution that closely corresponds with the observed field surface cover was taken for the site. Canopy cover Canopy cover was estimated using a measuring tape. A tape was laid on the ground at the base of the plant and measurement taken basing on the visual sight of the upper canopy. A radius value was derived from the measurements and the canopy of each plant was computed as πr2. The distance between individual plants was measured and an average area was estab-lished based on 10 samples. The percentage canopy was finally computed as plant canopy divided by area and multiplied by 100. For short crops, the sighting technique was used to estimate canopy. A mirror was put on the surface and the percentage canopy estimated basing on how much is reflected on the mirror surface area. Plant height Both RUSLE and RMMF models require data on plant height for estimating the cover factor and leaf drainage respectively. For crops like tamarind, which is a dominant in the study area, it is difficult to decide where the appropriate height for the model input should be measured due to the conical nature of the canopy and the fact that the leaves are spread. In this study, two heights were measured for tamarind crops. The first one involved general measurements



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based on the overall height of the plant. In the second measurement, measurements were taken on what was visually assumed to be the effective plant height for leaf drainage (Plate 3.1) in the field. This was based on the concentration of leaves and spread before intercepted rainfall is dropped. The differences in heights were then evaluated to assess their impact on soil loss predictions. For short crops like maize or mungbean, plant height was measured directly using a measur-ing tape. For taller crops like tamarind, a clinometer was used to measure the height. The an-gles at the top (A1) and bottom (A2) of the plant were measured at a known distance; com-monly at 5 m (Figure 3-5). These were used to derive two heights as follows:

Equation 3-1 H1 = tan (H1)*d

Equation 3-2 H2 = tan (H2)*d

Where; H1: height of the top angle H2: height of the bottom angle d: estimation distance The sum of H1 and H2 gave the plant height.

Figure 3-5 Estimation of plant height for tall crops



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Plate 3-1 Determining the effective plant height for Tamarind

The estimations of plant height were taken on 10 randomly selected samples. The average of all the samples was taken as the height for a particular site.

3.4. Erosion modeling

RUSLE and RMMF models were selected for this study from a variety of models. These models had not been applied at catchment levels in Thailand and were therefore deemed worth for the research. Other models especially process based models are data demanding, which would have been difficult to get in Thailand.

3.4.1. The RUSLE model

The revised universal soil loss equation (RUSLE) is an empirical model, modified from the USLE (Renard et al., 1997). It estimates sheet and rill erosion as a function of 6 major fac-tors. It maintains the basic structure of USLE and computes annual soil loss in t/ha/yr as fol-lows:

Equation 3-3 A = R*K*L*S*C*P

Where R: rainfall-runoff erosivity factor, K: soil erodibility factor, L: slope length factor, S: slope steepness factor, C: cover-management factor,



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P: supporting practices factor.

Rainfall Erosivity factor RUSLE assumes that when other factors are constant, soil losses from cultivated fields are directly proportional to a rainstorm parameter. Rainfall Erosivity (R) is calculated as a prod-uct of storm kinetic energy (E) and the maximum 30-minute storm depth (I30) summed for all storms in a year. This relationship (Wishmeier and Smith, 1978) quantifies the effect of rain-drop impact and reflects the amount and rate of runoff likely to be associated with the rain. However, data on rainfall intensity is difficult to get in many developing nations. Relational equations are commonly used to estimate R from annual rainfall amount (X) such as the one below for Thailand (Srikhajon et al., 1994).

Equation 3-4 R = 0.4669X-12.1415

Soil erodibility factor Soil erodibility factor (K) measures the inherent erodibility of soil under the standard RUSLE unit plot maintained in continuous fallow (22.1 m long and has a 9 % slope) (Renard et al., 1997). The K factor integrates the effect of processes that regulate rainfall acceptance and the resistance of the soil to particle detachment and subsequent transport. It is practically the long term soil and soil profile response to the erosive powers of rainstorms; i.e. it is a lumped parameter that represents an integrated average annual value of the total soil and profile reac-tion to a large number of erosion and hydrologic processes. RUSLE takes into account the availability of rock fragments in estimating K. K is commonly estimated using the soil nomograph. However, for soils where the silt fraction does not exceed 70%, an algebraic approximation of Wischmeier and smith (1978) is recom-mended. The equation for computing soil erodibility (K) is as follows.

Equation 3-5 K=(2.1*10-4)*(12-OM)*M1.14+3.25*(S-2)+2.5*(P-3)/100*7.59

Where; OM: % organic matter S: soil structure class (1-6) P: soil permeability class M: (% silt +%very fine sand) *(100-clay%) Slope length The slope length (LS) factor represents the topographic effect in the RUSLE model i.e. slope gradient and slope length. Slope length is defined as the horizontal distance from the origin of overland flow to the point where either the slope gradient decreases enough that deposition begins or runoff becomes concentrated in a defined channel (Wishmeier and Smith, 1978). Surface runoff will usually concentrate in less than 400 ft, which is the recommended practi-cal slope-length limit in RUSLE. Within RUSLE, separate equations are used to calculate the slope factor for areas with gradients less than 9%, and areas of equal or above 9% as shown below.



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Equation 3-6 S = 10.8 sin θ + 0.03 (For slope gradient less than 9%)

Equation 3-7 S= 16.8 sin θ - 0.50 (For slope gradient equal or above 9%)

Where; θ Is the slope gradient in degrees. The equation for calculating slope length (L) is as follows:

Equation 3-8 L = (λ/22.12) m

Where; m: is a variable slope-length exponent (Wischmeier and smith, 1978), and 22.12 is the RUSLE plot length (m). m is estimated by; m = β/(1+β), where,

β=(Sin θ/0.0896)/(3.0(sin θ) 0.8+0.56) with θ being the slope angle in degrees

Land cover factor The land cover-management factor (C) is the most important in RUSLE because it represents conditions that can easily be managed to reduce erosion (Renard et al., 1994). The C factor reflects the effect of cropping and management practices on erosion rates. It indicates how conservation affects the average annual soil loss and how soil loss potential will be distrib-uted during cropping and other management schemes (Renard et al., 1997). It is based on the concept of deviation from a standard plot under clean continuous fallow cultivations. In RUSLE, C is computed from soil loss ratios (SLR) i.e. prior land use (PLU), canopy cover (CC), surface cover (SC) and surface roughness (SR). Soil loss ratio is calculated as shown below.

Equation 3-9 SLR = PLU*CC*SC*SR

The prior land use (PLU) sub factor expresses the influence on soil erosion of subsurface re-sidual effects from previous crops and the effect of previous tillage practices on soil consoli-dation. The PLU sub factor is expressed in the following relationship.

Equation 3-10 PLU = Cf*Cb*exp [(-Cur*Bur)+(Cus*Bus/Cfcuf)]

Where; Cf: surface soil consolidation factor Cb: relative effectiveness of subsurface residue in consolidation Bur: Mass of live and dead roots in the upper inch of soil (lb.acre-1*in-1) Bus: Mass density of incorporated residue in the upper inch of soil (lb.acre- 1*in-1) Cuf: Impact of soil consolidation on the effectiveness of incorporated residue Cur and Cus: Calibration coefficients indicating the impact of subsurface residues. Canopy cover (CC; 0-1) sub factor expresses the effectiveness of vegetative canopy in reduc-ing the energy of rainfall striking the soil surface. The relationship is based on the assump-tion that the rainfall fraction intercepted by the canopy is equal to the fraction of the land sur-face beneath the canopy (FC; %) and that intercepted rainfall will leave the canopy at a height (H) with a mean drop diameter of 0.1 inch (Renard et al., 1997). The canopy sub factor is computed as follows:

Equation 3-11 CC = 1-Fc. exp (-0.1.H)



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The surface cover sub factor (SC; 0-1) is the single most important factor in determining soil loss ratios (SLR) values. It’s calculated as a function of ground cover (Sp; %) including crop residue, rocks, cryptogams and other non-erodible material that are in direct contact with the soil surface. Surface cover sub factor is calculated as shown below.

Equation 3-12 SC = exp (-b. Sp. (0.24/Ru) 0.08 )

Where b: an empirical coefficient Ru: surface roughness Surface roughness affects soil erosion through the impact of residue effectiveness. The sur-face roughness sub factor (SR; 0-1) is estimated as a function of the surface’s random rough-ness (RU), which is defined as the standard deviation of the surface elevations when changes due to land slope or non-random tillage marks. Surface roughness is calculated as follows;

Equation 3-13 SR = exp (-0.66*(Ru-0.24))

Where Ru: Random roughness value. The support factor (P) represents the ratio of soil loss with a specific practice to the corre-sponding loss with up and down slope tillage. These practices principally affect erosion by modifying the flow pattern, grade, or direction of surface runoff and by reducing the amount and rate of runoff.

3.4.2. The RMMF model

The RMMF was developed to cater for difficulties realized in collecting data on rooting depth and as a result of improvements in data availability especially soil detachability since the original MMF model (Morgan, 2001). In the revised version, effective hydrological depth is considered instead of rooting depth as in the original version. New detachability values have also been provided as an improvement from the soil detachability index of the original version, while the revised model also caters for leaf drainage, ability of runoff to detach as well as transport by rainfall. The model separates the soil erosion process into two phases i.e. the water and sediment phase. In this way, the model recognizes that erosion can either be transport or detachment limited. Estimation of rainfall energy The energy of rainfall is calculated by taking into account the way rainfall is partitioned dur-ing interception and the energy of the leaf drainage. The model takes the annual rainfall total (R; mm) and computes the proportion that reaches the ground surface after allowing for rain-fall interception (A; 0-1). R and A are multiplied together to derive effective rainfall (ER) as follows.

Equation 3-14 ER = R*A

The model then distributes effective rainfall into rainfall that reaches the ground without in-terception, and rainfall that reaches later as leaf drainage (LD) after being intercepted by plant canopy (CC; %) using equations below.

Equation 3-15 LD = ER*CC



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Leaf drainage is then used to calculate direct through fall of effective rainfall (DT) as in equation below.

Equation 3-16 DT=ER-LD

Kinetic energy is then calculated for effective rainfall of leaf drainage (KE (LD); j/m2) and effective rainfall of direct through fall (KE (DT); j/m2). KE (LD) and KE (DT) are a function of plant height (PH) and intensity (I) respectively. Kinetic energy of direct through fall is computed as follows.

Equation 3-17 KE (DT) = DT (9.81+10.60Log10I)

The kinetic energy of leaf drainage is the;

Equation 3-18 KE (LD) = (15.8*PH0.5)-5.87

KE (DT) and KE (LD) are added together to give the total energy of effective rainfall (KE; j/m2). Estimation of runoff Annual runoff (Q) is calculated using a relational equation between annual rainfall (R; mm), mean rainy day (RO; mm) and moisture storage capacity. Soil moisture storage capacity (RC; mm) is in turn a function of bulk density (BD; mg/m3), soil moisture content at field capacity (MS; %ww), effective hydrological depth (EHD; m), and ratio of actual to potential evapotranspiration (ET/EO). The equations below show the computation of soil moisture stor-age capacity;

Equation 3-19 RC = 1000MS*BD*EHD*(Et/Eo)

Mean rain per day is calculated as shown below

Equation 3-20 RO = R/RN

Where RN: Number of rain days in a year

Equation 3-21 Q = R*exp (-Rc /Ro)

Estimation of soil particle detachment by raindrop Soil particle detachment by raindrop impact (F; kg/m2) is calculated as a function of kinetic energy (KE; j/m2) and soil erodibility (K; g/j) as follows.

Equation 3-22 F=K*KE*10-3

The equation used to compute soil particle detachment by runoff (H; kg/m2) is based on ex-perimental work of Quansah (1982) as cited by Morgan (2001) and is calculated as a function of runoff (Q: mm), slope steepness (S; 0), soil resistance (Z) and ground cover (GC; %). Soil resistance is in turn dependent on surface cohesion (COH; kpa). The model assumes that soil particle detachment by runoff only occurs where soil is not protected by ground cover. The equations below show the computation of soil resistance as;

Equation 3-23 Z = 1/(0.5*COH

And of runoff detachment as;



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Equation 3-24 H = ZQ1.5sinS (1-GC)*103

Total particle detachment (D; Kg/m2) is finally computed as a sum of soil particle detachment by runoff and soil particle detachment by raindrop impact as shown below.

Equation 3-25 D= F+H

Transport capacity of runoff (TC; kg/m2) is estimated as a function of runoff (Q) surface cover factor (C), runoff and slope gradient (0) as follows.

Equation 3-26 TC = CQ2sinS*10-3

TC is the compared with D and the lower of the two is taken as the annual soil loss (kg/m2).


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4. DATA PROCESSING

4.1. Data input

All the data collected was entered into a raster-based GIS database. During processing, the data was transformed into a format acceptable to run the models in a GIS environment. From the data, various thematic layers were generated. Required parameters for running map calculations were entered and encoded according to each model.

4.2. Ortho-photo generation

Photo interpretation map and ground truthing of geomorphology and pedology resulted into geo-pedological map. The interpreted overlays were corrected for tilt, radial and relief dis-placement, and geo-referenced using Ortho-photo method as described by Rossiter and Hengl (2001). The derived DEM, topographic maps as well as photo mosaics for Lom Sak were used to geo-reference the overlays. The corrected overlays were then digitized to obtain a segment map. Using the legend of the interpreted overlays, a point map was digitized on top of the seg-ment map. The two maps were overlaid and polygonized to obtain polygon-landform map.

4.3. DEM generation and correction

Topographic information required to run the erosion models include slope gradient and slope length. These were obtained in two ways i.e. from topographic maps (1: 50, 000) and in the field through direct measurement using a slope meter (clinometer) and a measuring tape. Con-tour lines and spot heights were digitized as segments and points respectively from the topog-raphic maps at a scale of 1:50,000. The digitized maps were combined and interpolated at 10 m pixel size since the contour interval in the low land areas was 10 m. This resulted into a digital elevation model (DEM) where hilltops and peaks were well represented. Linear filtering in X and Y (Shrestha, 2002) directions was applied on the DEM to generate height differences in X and Y directions. Slope gradient, slope aspect and slope length data were then extracted from the DEM. To estimate the accuracy of the DEM, field measurements were compared with the interpolated values yielding a correlation of 0.98 and mean error of 4 meters. Generally, the values of the DEM were lower than the measured values, but considering that field measure-ments were taken with GPS, which also has some errors, the mean error was deemed to be ac-ceptable. The DEM values were adjusted in upland areas where rice is cultivated and assigned zero value. This is because fields for rice cultivation in upland areas are terraced to maintain


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moisture for the rice crop. The following ILWIS command was used to modify the DEM for rice fields.

Equation 4-1 DEM_modified = Iff (Land use=”rice”, 0,DEM)

4.4. Cover factor (C) estimation

One of the objectives of the study was to estimate the cover factor (C) and compare it with typical values used in Thailand (LDD, 2002), so as to ascertain the reliability of the methodol-ogy used by LDD. For particular land uses, LDD used a simple method based on percentage vegetative cover (g), including mulch and roughness. In this study, estimation of the cover fac-tor was based on sub factors i.e. canopy cover, surface cover and surface roughness following the RUSLE method explained in chapter3.

4.4.1. Generation of sub-factor Canopy cover

The derivation of canopy cover (CC; 0-1) for the annual land use was based on maize and mungbean, which are the dominant crops in the study area. The cropping pattern in Lom Kao is that maize is planted in the first season, followed by mungbean in the second season. Maize is harvested between 90-120 days, while the growth duration of mungbean in Lom Kao is between 60 to 70 days. It’s important to note that the canopy cover for maize before harvest reduces due to drying of leaves. Thus CC was estimated on a time varying interval for two seasons. The canopy cover was computed using time intervals of 15 days. Values used are based on field es-timates as well as crop growth information obtained from Agricultural Development office in Phetchabun. The average seasonal values calculated for maize and mungbean were then aver-aged to obtain the CC value for the annuals as presented in table below.

Table 4-1 Computed canopy cover sub factor for annual land use

Maize Mungbean Days Canopy cover frac-

tion (%) Height

(m) CC Canopy cover frac-

tion (%) Height

(m) CC

15 5 0.1 0.95 5 0.1 0.95 30 10 0.5 0.90 20 0.2 0.80 45 35 1 0.68 40 0.5 0.62 60 70 1.7 0.41 70 1.0 0.37 75 90 2 0.26 90 1.1 0.19 90 90 2.5 0.30 - - - 120 70 2.5 0.45 - - -

Mean CC 0.57 0.59 Annual

CC (0.57+0.59/2) 0.58

For other land uses the canopy sub factor was computed on a time invariant basis. Information obtained from Land development Department and literature indicates that annual canopy cover


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for these land uses does not vary significantly. The computed CC values are presented in Table 4-2.

Table 4-2 Computed CC values for other land uses

Land use Canopy cover fraction (%) Height (M) CC Grassland 5.0 1.0 0.95

Rice 35 1.3 0.69 Orchards 24 5.4 0.86

4.4.2. Generation of surface cover sub factor

This was derived from field data on ground cover (sp; %), and random roughness (RU) and an empirical coefficient (b). For annuals and rice a coefficient value of 0.035 recommended by Renard et al (1997) for typical cropland erosion conditions was assigned. For other land uses, the coefficient value was assigned basing on erosion site conditions. For fields dominated by interill erosion a coefficient of 0.025, and for grassland, 0.045 were used. The computed sur-face cover sub factor values based on equation 3-9 are given in Table 4-3.

Table 4-3 Derived values for surface cover sub factor

Land use Sp Ru -b SC Annuals 10 0.35 0.035 0.71 Rice 50 0.30 0.035 0.18 Orchards 90 0.24 0.025 0.11 Grassland 95 0.24 0.045 0.014

4.4.3. Generation of surface roughness sub-factor

The computed surface roughness (SR) values are based on measurements of micro-elevations and type of tillage equipment used. Values of micro-elevations measured in the field were fitted on the random roughness curve (Renard et al., 1997) and appropriate surface roughness values were selected from the curve. The computed surface roughness values are shown in table 4-4.

Table 4-4 Computed surface roughness values

Land use Ru SR Annuals 0.35 0.92 Rice 0.30 0.96 Orchards 0.24 1.00 Grassland 0.24 1.00 For orchards and grassland a random roughness value of 0.24 was assigned since no micro re-lief was observed and there is almost no tillage in these land uses. This value is based on the assumption that the roughness left after field operations are smoothened by the effects of rain-drop impact approaching a random roughness as the cumulative rainfall increases.


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The prior land use (PLU) factor was not estimated, as it was considered insignificant in deriv-ing C specifically for the study area and the maximum PLU value of 1 was assigned. The prac-tice in the area is either to burn or collect the residue from the farmlands. In Lom Kao, crop residue is either removed or burned because it creates problems during ploughing, while in or-chards, herbicide use renders this factor very insignificant in the study area. Using the calcu-lated SLR values, cover factor (C) values were finally computed as a function of the sub factors as shown in Table below.

Table 4-5 Derived C values in comparison with LDD derived C

Land use PLU CC SC SR Derived C C (LDD) Annuals 1 0.58 0.71 0.92 0.379 0.47 Rice 1 0.69 0.18 0.96 0.119 0.28 Orchards 1 0.86 0.11 1 0.095 0.15 Grassland 1 0.95 0.014 1 0.013 0.015 Planted trees* 0.088 0.088 Disturbed forest* 0.048 0.048 *Values from LDD. The cover factor (C) for forest and planted trees was not estimated, as the values obtained from LDD were deemed fit and accurate for this study. These values were obtained by LDD from the Royal Forest Department of Thailand after experimental research over an extended period.

4.5. Determining RUSLE GIS parameters

Four GIS layers in raster format suitable to run ILWIS 3.1 were generated as inputs for the RUSLE model. Details of the techniques used to generate rainfall; slope length, cover and erodibility input parameters in GIS are explained in the following sections.

4.5.1. Rainfall erosivity (R) layer

The generation of the rainfall erosivity layer was based on daily rainfall amounts for Lom Kao and Phetchabun aggregated into annual rainfall amount, and the isohyet map of Thailand. As is common to many weather stations in developing countries, the rain gauges measuring precipita-tion in the vicinity of Lom Kao do not collect data pertaining to rainfall intensity. The weather stations at Lom Kao and Phetchabun record daily rainfall amounts, but storm duration data as well as storm power needed to calculate rainfall intensity were not available. Because of this limitation, a relational equation (3-4) that estimates rainfall Erosivity using annual rainfall to-tals was applied. The equation is based on the works of Srikhajon et al (1994) who studied and validated Wischmeier rainfall Erosivity equation and obtained a relationship with the annual rainfall amount for Lom Kao.


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Two data sets of daily rainfall amount were obtained from Lom Kao and Phetchabun weather stations, which are about 30 km apart, but within the study area climatically. The data from Phetchabun and Lom Kao is for a duration of 36 and 15 years respectively. The data was first aggregated into annual rainfall amounts, and overall averages were computed for the respective durations. Another data set used was an isohyet map (1:2000,000) generated by Thailand mete-orological department based on rainfall data (1966-1995). A segment map was created from the isohyet map using the coordinate boundary of the study area. Since the scale of the original iso-hyet map is too small, the rainfall data from Phetchabun and Lom Kao was used to improve on the accuracy in values of the rainfall map. The averaged annual rainfall amounts for Lom Kao and Phetchabun were entered as point values using the geo-references of the two stations cul-minating into a point map. The segment isohyet map and the point map were both rasterized and then combined together using map calculation functions in ILWIS 3.1. The combined map was then interpolated yielding a map with continuous surface annual rainfall values depicting varied rainfall regimes in the study area. The Erosivity layer was finally derived using ILWIS 3.1 map calculation functions with equation 3-4.

4.5.2. Slope length (LS) layer

The LS factor is the most difficult one to derive in GIS, because the length aspect is not direct. The digital elevation model (DEM) of the study area was used in generating the LS factor. Be-cause of the difficulties commonly experienced in generating the LS factor, two methods were used. In the first method, the slope steepness (S) and length (L) factor layers were generated separately and later overlaid to get the RUSLE slope length factor layer (Mongkolsawat et al., 1994). To derive the S factor layer, the slope gradient map in degrees, generated from DEM was used. RUSLE models offers separate equations for calculating the slope factor for areas with gradients less than 9% (Equation 3-6), and areas with slope gradients equal and greater than 9% (equation 3-7). Since the input slope gradient map was in degrees, the 9% critical limit for RUSLE slope factor equation distinction was translated into degrees to obtain an equivalent value of 5.14 in degrees. The respective equations for calculating slope factor were integrated into a single ILWIS 3.1 map calculation command to generate the slope (s) map as follows.

Equation 4-2 S=Iff(slope<5.14,10.8*sin(degrad(slope))+0.03,16.8*sin(degrad(slope))-0.50)

Where; Degrad is an ILWIS function used in combination with trigonometric function sin and converts degrees into radians and slope is the input slope map in degrees.

The slope length factor (L) was derived using a technique described by (Mongkolsawat et al., 1994). To compute the L factor a slope aspect map was calculated from the DEM using hori-zontal and vertical filters. The aspect map was then overlaid on the slope gradient map resulting into a polygonal layer. The polygon layer comprised of area units used for determination of slope length factor. The polygons were however exaggerated in many areas of the map. The exaggerated areas were corrected by applying the RUSLE slope length practical limit of 120m. In the second method, an ArcView based technique designed by Bernie (1999) was used. The


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technique uses DEM as the input layer and the hydrological and spatial analyst extensions in ArcView 3.2 for processing. To estimate LS, flow accumulation and slope layers are required. The flow accumulation layer was used to estimate slope length (L). The technique is based on the principle that flow algorithm distributes flow according to relative slope of downhill pixels. This layer was calculated from the DEM following logical steps with GIS processing capabili-ties in the hydrologic extension of ArcView 3.2. From the DEM, depressions and sinks were delineated using the fill sinks option under the hydrologic extension. The operation resulted in a new DEM with depressions or sinks which indicate the route of flow. The new DEM with de-pressions was used to compute flow direction layer. The flow direction layer was in turn used as an input for computing a flow accumulation layer which shows how much area flows through each grid. The slope layer was directly extracted automatically under the derive slope option on the surface extension. Since RUSLE is suitable for estimating interill and rill erosion processes, there is a limit on the slope length at 120m. Therefore, the flow accumulation map was modified to enforce this limit. To modify the flow accumulation map, a layer was created where value 12 was assigned to all pixels that had flow accumulation greater than 12 (120/pixel size (10)). The ArcView 3.1 map calculator was used to derive this layer as below.

Equation 4-3 ([Flow accumulation]>12)*12

Another layer was also derived where value 1 was assigned to pixels that had flow accumula-tion below 12 as follows.

Equation 4-4 ([Flow accumulation]<12)*flow accumulation)

The two modified flow accumulation layers were added together to obtain a new flow accumu-lation map with a flow accumulation maximum of 12, that when multiplied with the map pixel size of 10 m translates to the RUSLE maximum slope length limit of 120 m. The new flow ac-cumulation layer and the slope steepness layer were then used as inputs for computing LS. The following equation proposed by Moore and Burch (1986) was used to calculate LS.

Equation 4-5 LS = (Flow accumulation*cell size/22.13)^0.4*(sin slope/0.0896)^1.3

Where; Flow accumulation is the grid layer expressed as number of grid cells, and cell size is the length of a cell size taken as 10 m.

4.5.3. Soil erodibility (K) layer

Air photos (1:50,000) were interpreted following the geo-pedological approach (Zinck, 1988) and resulting into a geo-pedological map after fieldwork. The interpreted and corrected over-lays of basic units were scanned and ortho-corrected using a DEM obtained after contour inter-polation. The units were digitised resulting into a segment map. Geomorphic attributes of the segment units were created in a point map that was subsequently polygonized with the segment resulting into a landform map, which after soil interpretation and classification culminated into a geo-pedological map. An attribute table consisting of map unit names was created and linked to the geo-pedological map. Computed soil erodibility Values (K), were assigned for units of


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the geo-pedological map and soil erodibility layer was generated from the table linked to the geo-pedological map as an attribute layer. The assigned K values were determined using results from laboratory analysis of soil texture, soil structure, organic matter content and soil permeability. To compute K values, the algebraic approximation (equation 3-5) of Wischmeier and smith (1978) was used. This was justified on the basis that the silt fraction of the soils samples did not exceed 70%, and therefore not rec-ommended for the soil nomograph. Secondly, observations of previous studies in Thailand indi-cate that that K values obtained using the soil nomograph over estimates soil erosion especially for steep slopes (Shrestha et al., 1996).

Figure 4-1 Ortho-corrected segment map with label points

4.5.4. Cover and support practice (CP) layer

A recently compiled land use map (2001) was obtained from the Land Development Depart-ment covering the Phetchabun province. The land use map of the study area was extracted as a sub map from the Phetchabun province land use map using coordinate references. The map was modified and updated following the field survey and the land uses were generalized into 6


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classes consisting of annuals, rice, orchards, grassland, planted trees and disturbed forest. The cover factor (C) was based on two values i.e. derived (section 4.4) and typical values from LDD. For the support practice (P) factor, the RUSLE maximum P value of 1 was allocated. In the study area, farmers do not practice any soil conservation measures. The reason given by farmers is that conservation measures are labor intensive, costly and directly not beneficial to conserva-tion (Woldu, 1998). Thus the P component was built together with C to attain the CP layer.

4.5.5. Running the RUSLE model

The GIS analytical methodology employed for running the RUSLE model is shown in Figure 4-2. The analysis was done using ILWIS 3.1. Each of the RUSLE factors with associated attribute data were digitally encoded in a GIS database producing 4 thematic layers (LS, R, K, and CP) as explained in the previous sections. These were spatially overlaid using map calculation func-tions in ILWIS 3.1 using RUSLE soil loss estimation equation (3-3). The operation resulted into a single RUSLE predicted soil loss map. The output soil loss map was crossed with the landscape, land use, and slope maps, and average annual soil loss rates per landscape, land use and slope were obtained using the aggregation operation in the resultant cross table. The soil loss map was further classified into five soil loss severity classes.


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Figure 4-2 GIS based analytical methodology for RUSLE model

Rainfall data

Isoerodent map(1: 1000,000)

Land use map(1: 50,000)

Topographic maps(1: 50,000)

Aerial photos(1: 50,000)

Pointmap map

Segment map

CP Factor

DEM

R Factor

Slope map

Aspect map

LS Factor

GPmap

Soilmap K Factor

Inputs Resultant GIS layers

API

Ortho-photoMini soil survey

Soil lab analysisEncode

Ove

rlayDigitize

Interpolate

Update and groupingEncode

Inte

rpol

ate

Encode

Submap

Ove

rlay

Soil loss map and tables

API


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Figure 4-3 Cover (C) factor map


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Figure 4-4 Slope length (LS) factor map

Figure 4-5 Soil erodibility (K) factor map

4.6. Determining RMMF GIS input parameters

Several parameters were necessary to run the RMMF model. Details of their estimation in a GIS are given in the following sections.

4.6.1. Estimation of rainfall energy

The inputs used to estimate rainfall energy were annual rainfall amount, plant rainfall intercep-tion, canopy cover and plant height. The annual rainfall map derived for RUSLE was used. The annual rainfall amount in the study area varies between 800-1200 mm. Plant rainfall intercep-tion rates ranging between 0-1 for the respective covers in the study area were taken from Mor-gan (1995). The plant rainfall interception rates, together with field-measured values of canopy cover and plant height were entered in an attribute Table linked to the land use map. Maps for plant rainfall interception, canopy cover and plant height were then generated as attributes from the Land use from the linked table.


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The annual rainfall layer was overlaid with the crop rainfall interception layer following equa-tion (3-14) culminating into the effective rainfall map. Since the RMMF model distinguishes kinetic energy into two i.e. kinetic energy of direct through fall and kinetic energy of leaf drain-age, the effective rainfall map was split into two maps. These were leaf drainage map obtained as a function of kinetic energy and canopy cover (equation 3-15), and the direct through fall map computed as effective rainfall minus leaf drainage (equation 3-16). A Kinetic energy of rainfall map was then calculated using map calculation functions with relational equation (Onaga, 1998, cited in Morgan, 2001) that is more reflective of the rainfall energy-intensity re-lationship of the study area than that of Wischmeier and Smith (1978). The rainfall intensity value of 25 suggested by Morgan (2001) for tropical countries was used. Another map for ki-netic energy of leaf drainage was also generated as a function of plant height. The two maps were added together to obtain the rainfall energy map of the study area.

4.6.2. Estimation of runoff

Runoff was estimated from annual rainfall, moisture storage capacity of the soil and annual number of rain days. The annual rainfall map derived in section 4.2.1 was used. The annual rainfall map was divided by the average rain days of Lom Kao to obtain the mean rain per day. The number of rainy days was obtained by averaging the annual rain days of the study area for a period of 15 years (1986-2001). Soil moisture storage capacity was estimated as a function of bulk density, soil moisture content at field capacity, effective hydrological depth, and the ratio of actual to potential evapotranspiration. Values for parameters used to calculate soil moisture storage capacity were obtained after laboratory analysis of soil samples from the field and from values suggested by Morgan (1995, 2001). Respective values for each parameter were entered in ILWIS tables linked to the soil and land use maps. Maps for these parameters were then gen-erated as attributes from the soil and land use maps. These maps were then overlaid using ap-propriate derivate equation (3-19) to obtain the soil moisture storage capacity layer. The annual runoff layer was finally generated as a combination of annual rainfall map, soil moisture stor-age capacity and mean rain day as per equation (3-21).

4.6.3. Estimation of Soil particle detachment

Soil particle detachment was obtained in two phases. In the first phase, a soil particle detach-ment map by raindrop impact was computed by overlaying the total kinetic energy layer with the soil erodibility map following equation (3-22). The soil erodibility layer was adopted from RUSLE (section). In the second phase, a soil particle detachment map by runoff was computed using the slope gradient layer obtained from DEM, runoff layer (section 4.3.2), resistance of the soil layer and ground cover layer. The surface cover values used in RUSLE were taken for the ground cover layer. The soil resistance map was derived from surface cohesion values obtained from the field using a torvane. Total soil particle detachment was finally obtained by adding the soil particle detachment layer by runoff to the soil particle detachment map by raindrop impact.


Data processing

____________________________________________________________________________

42

4.6.4. Estimation of transport capacity of runoff

The transport capacity layer was derived as a function of surface cover (C) (section 4.4.3), run-off (section 4.5.2) and slope gradient generated from the DEM, as per equation (3-26).

4.6.5. Running the RMMF model

The model was applied in a GIS environment using map calculation procedures in ILWIS 3.1. The estimated transport capacity map (TC) represents soil rates reflecting the transport poten-tial in the study area while the total detachment map (D) represents soil loss rates manifesting the detachment capability by raindrop impact and overland flow (runoff). To obtain actual an-nual soil loss predictions of the RMMF model, these two (D and TC) maps were compared in each grid and the minimum of the two was taken as the estimated annual soil loss denoting whether soil detachment or transport capacity by runoff is the limiting factor. The RMMF model gives soil loss rates in Kg/m2, but for comparison with the estimates of RUSLE model, the results of RMMF (Kg/m2) were converted to tons/ha/yr. Therefore, a combined equation was given in ILWIS to give soil loss estimates in tons/ha/yr as follows.

Equation 4-6 Soil loss = Min (D/1000*(10000), TC/1000*(10000))

Where; Min is an ILWIS function that returns the minimum of two values.

Table 4-6 RMMF plant inputs

Land use Et/Eo C CC RI PH EHD GC Annual 0.70 0.379 0.50 0.25 2.5 0.12 0.10 Disturbed forest 0.90 0.048 0.70 0.30 7.0 0.20 0.90 Grassland 0.85 0.013 0.05 0.25 0.5 0.14 0.85 Orchards 0.90 0.095 0.24 0.30 5.4 0.20 0.90 Rice 1.35 0.119 0.35 0.20 1.3 0.12 0.50 Tree plantation 0.90 0.088 0.70 0.30 7.0 0.20 0.70

4.7. Data analysis and evaluation of soil erosion models

Data analysis comprised of various statistical operations to test the formulated hypotheses. They include Kruskal Wallis test, students’ t-test, and the Pearson Product Moment Correlation and partial correlation. Descriptive statistics such as mean, standard deviation, coefficients of variation, as well as confidence limits were also extensively used.

The evaluation of model performance was based on sensitivity analysis. In sensitivity analysis, parameters of the model i.e. crop cover; slope gradient, rainfall amount and organic matter were changed by a percentage value while holding others constant. The models were then run using changed values in a particular parameter and soil loss values generated. Percentage changes in soil loss as a result of the changes were then calculated. The model, which is less sensitive to changes, was considered better in this study. This is based on the assumption that that model is more stable so that changes in parameters does not overshoot or undershoot soil loss rates.


Data processing

____________________________________________________________________________

43

A comparative study of soil erosion modeling in lom kao-Phetchabun, Thailand

Results and discussion

44

5. RESULTS AND DISCUSSION

5.1. Soils of Lom Kao

5.1.1. Soil landscape relation

Air photo interpretation (API) and ortho-photo generation resulted into a geometrically cor-rected landform map shown in the previous chapter (Figure 4-1). The geometric accuracy of the resultant landform map was checked by overlaying the interpreted overlays on different topog-raphic maps that comprise the study area. The geomorphologic map was used to generate a geo-pedological map (Figure 5-1) of the study area after field studies of soil horizon characteristics and laboratory analysis of soil properties. The output geo-pedological map of the study area is a result of soil studied along various transects and using previous works (Ekkanit, 1998). Results from previous studies were incorporated for areas in which it was not possible to collect data. Soils were classified according to the USDA soil classification system (Soil survey staff, 1998). This was done to enable comparison and standardization with earlier studies, which used the same classification system. The study area falls into four dominant landscape units namely; Mountains (high and low) in the western and the northeast part of the area; Hilland (dissected and un-dissected) largely in the western part of the study area; Piedmont sandwiching the Mountain and Hilland in the mid-dle, and Valley (elongated and trench) in the center, west and northeast of the study area. The Hilland covers the largest part of the study area, followed by the Piedmont, Mountain and Val-ley. Within these landscapes, 49 landforms and 30 relief types were delineated (Figure 5-1 and Table 5-1).



45

Figure 5-1 Geo-pedological map of Lom Kao



46

Table 5-1 Geo-pedological map legend updated from Ekkanit (1998)

Landscape Relief Type Lithology Landform

Map unit Dominant taxa Area (%)

Reversal HM111 Lithic stropepts, Ultic Haplustalfs 0.96

Front HM112

Lithic ystropepts, Typic Ustropepts, Typic Argiustolls 1.69

Talus HM113 Typic aplustults, Lithic torthrthents 1.82

Ridge Sandstone and shale

Slope facet complex

HM114 Lithic dystropepts 0.6

High Mountain

Vale Alluvio-colluvium Botton side complex

HM211 Fluventic ustropepts 0.4

Conglomerate, sandstone, mud-stone and limestone

Summit com-plex

LMO111 Paleustalfs, Dystropepts

2.06

Highly dis-sected slope complex

LMO112 Typic aplustults, Typic Paleustults, Lithic Ustorthents

4.43 Sandstone, lime-stone and shale

Slope facet complex

LMO113 Typic aplustults, Typic Paleustults, Lithic Ustorthents

3.76

Hills

Conglomerate, sandstone, mud-stone and limestone

Foot slope LMO114 Typic Haplustults 2.12

Ridge Sandstone, mud-stone and limestone

Slope facet complex

LMO211 Ustoxic Dystropepts, Typic Ustropepts

1.46

Swale Collovium, allu-vium

Bottom side complex

LMO311 Aeric ropaquepts, Aeric Tropaqualfs Fluventic Ustropets

0.96

Low Mountain

Vale Alluvium, coll-ovium

Bottom side complex

LMO411 Fluventic Ustropepts, Aquic ustifluvents Aeric

1.96

Ridge Slope facet complex

HI111 Ultic Haplustalfs Typic Ustropepts

1.66 Hilland

Hogback

Sandstone and shale Sandstone and shale

Reversal HI211 Ultic Haplustalfs Typic Haplustalfs Lithic Ustorthents

3.47



47

Table 5-1 continued


Map unit

Dominant taxa

Area (%)

Front HI212 Paleustalfs Haplustalfs

3.73 Hogback Sandstone and shale Sandstone and shale

Talus HI213 Ultic Haplustalfs Typic Haplustalfs

4.17

Very high hills Slope facet complex

HI311 Lithic us-tortherms Vertic haplustolls

5.85

Slope facet complex

HI411 Typic us-torthents Typic ustropepts

0.53

Summit HI412 Typic haplustalfs Typic ustropepts

3.15

High hills

Sandstone, shale, andesite, mud-stone, granodiorite and siltstone

Incision HI413 Lithic argiustolls Lithic ustorthents

2.01

Moderate hills Sandstone, shale, mudstone and andesite

Slope facet complex

HI511 Typic ustropepts Typic haplustalfs

3.93

Low hills Shale, sandstone mudstone and andesite

Slope facet complex

HI611 Typic argiustolls Typic ustropepts

3.69

Very low hills Slope facet complex

HI711 Typic haplus-tults Ultic paleustalfs

1.39

Escarpment Sandstone, ande-site, siltstone, mudstone and shale

Slope facet complex

HI811 Ultic Haplustolls Ultic Haplustalfs Lithic storthents

1.55

Hilland


Bottom side complex

HI911 Ultic Haplustalfs Typic Eutropepts Aeric ropaquepts

3.17


PI111 Ulfic haplustalfs 0.81

Moderate hills Slope facet complex

PI211 Lithic argiustolls 0.41

Tread riser complex

PI311 Typic Haplustults Ultic Paleustalfs Ultic Haplustalfs

10.22

Piedmont

High glacis

Residual

Tread riser complex (un-dulating to rolling)

PI312 Typic Paleustults Typic Haplustalfs Typic andiustults

2.72



48

Table 5-1 continued


Map unit Dominant taxa

Area (%)

Piedmont

Middle glacis Tread riser complex

PI411 Typic Haplustalfs Ultic Haplustalfs Typic Ustropepts

0.41

Low glacis Residual alluvium, collovium

Tread riser complex

PI511 Typic Ustropepts Typic Haplustalfs Aeric Paleaquults

3.73


Bottom side complex

PI611 Aeric Tropaquepts Fluventic Ustropepts

2.01

Apical PI711 Typic haplustalfs Ultic haplustalfs

1.44

Fan Alluvium

Distal PI712 Fluventic ustropepts Ustic dystropepts

0.09

Valley Terrace Alluvium Bottom side complex

VA111 Typic haplustals 0.42


LVA111 Typic ustoxic

0.89

Hills Slope facet complex

LVA211 Typic ustropepts 5.94

Terrace complex Bottom side complex

LVA311 Fluventic Ustropepts Aquic Ustropepts Fluvaquentic Utropepts

10.22

Lateral valley

Depression

Alluvium, coll-ovium

Bottom side complex

LVA411 Fluventic Ustropepts Aquic Ustropets Fluvaquentic Utropepts

1.62

Trench Valley Terrace complex

Bottom side complex TVA111

Aeric Tropaquepts Aeric Tropaqualfs 3.63

High terrace

Alluvium, residual

Bottom side complex TVA211

Typic Ustifluvents Typic Haplustalfs Ultic paleustalfs *

Mountains The Mountains are categorized into 2 groups i.e. the High and Low Mountains. The Low Mountains are highly dissected with numerous incisions and are characterized with a dendritic drainage pattern. Overall the Mountains cover an area of 95 km2 representing 22% of the total area. Of the two Mountain categories, the low Mountains (Figure 5-2) occupy the largest area of 74.9 km2 (19.8%), while the high Mountains cover 18 km2 (4.6%) of the study area. The Low Mountains are located in the north east of the study area, while the High Mountains are located in the west. The main relief types in Mountain are ridges, hogbacks, swales and vales. 14 land-form units were interpreted and 36 map units delineated in the Mountain landscapes. The high-est altitude in the Mountain is 760 m above sea level. The soils on the high Mountains include Lithic Dystropepts, Typic Utropepts, Lithic Ustorthents, Ultic and Typic Haplustults (Ekkanit, 1998).



49

050

100150200250300350400450500550

0 2.5 5 7.5 10 12.5 15 17.5 20

Distance (km)

Alti

tude

(m)

Figure 5-2 Cross-sectional view (A-B) of the Low Mountains from West to North East

(See Figure 5-1 for location of cross section)

Hillands The Hillands occupy the largest part of the study area (38.4%) comprising a total area of 167.6 km2. The largest part of the Hilland landscape lies on the eastern part of the study area (Figure 5-3). The Hilland landscape was separated into two main categories i.e. the dissected and un-dissected Hilland. The lateral Valley separates the dissected Hilland on the extreme east from the un-dissected Hilland in the middle and north of the study area. The existence of numerous dissections in parts of the Hilland could be attributed to differences in parent material as well as to the intensity of surface processes particularly soil erosion. In the dissected Hillands, the dominant parent material is shale, which is much prone to erosion while in the un-dissected Hil-land; the dominant parent material is sandstone. There are five relief types identified in the Hil-land namely; ridges, hogbacks, hills, escarpments and vales. In these relief types, 15 landforms units were interpreted and 52 map units delineated. Gravels dominate the soils in the Hillands. They include Entisols, Inceptisols, Ultisols and Alfisols.

Low Mountains



50

050

100150200250300350400450500550

0 2.5 5 7.5 10 12.5 15

Distance (km)

Alti

tude

(m)

Figure 5-3 Cross-sectional view (C-D) of the Hilland from South to the East

(See figure 5-1 for location of cross section)

Piedmonts The Piedmont landscape largely occurs in the center of the study area, which is divided into two parts by the central river flowing from north to south (Figure 5-4). It starts from the lower slopes of high Mountains in the west and extends to the east up to the Hillands. The Piedmont landscape covers an area of 111 km2 representing 25.5% of the study area. The main relief types in the Piedmont include; - ridges, hills and glacis. In the Piedmont landscape, 11 landform units were identified and 51 map units delineated. The major soil types are Entisols, Inceptisols, Ultisols and Alfisols. Valleys The Valleys represent the lowest areas of the study area and mainly consist of terraces, depres-sions and due to cartographic limitations; some isolated ridges and hills are also included in the Valley landscape. The Valley landscape was categorized into three classes following the works of Ekkanit (1998). They include, main, lateral and trench Valley. There are rivers flowing in the Valley to the south of the study area (Figure 5-4). Overall, the Valley covers the smallest part (61.2 km2) representing 14% of the study area. The dominant soils are Aeric Tropaquepts, Fluventic Ustropepts, Typic Ustropepts, Fluvaquentic Eutropepts, Aquic Europets and Fluven-tic Dystropepts.



51

050

100150200250300350400450500550

0 2.5 5 7.5 10 12.5 15 17.5 20

Distance (km)

Alti

tude

(m)

Figure 5-4 Cross sectional view (E-F) of the Valley and Piedmont

(See Figure 5-1 for location of cross-section)

5.1.2. Variability of soil properties

Soil properties are key indicators of soil quality and are commonly assessed as an index to soil erosion (Lufafa, 2000). Because of their relevance to the soil erosion process, soil properties were characterized in regard to slope position in Lom Kao. The soil properties considered in this study include soil organic matter content (OM), soil pH, exchangeable calcium (Ca), mag-nesium (Mg), sodium (Na), cation exchange capacity (CEC), potassium (K) and the physical elements i.e. sand, silt and clay. To assess the variability of the soil properties, descriptive sta-tistics were applied. Statistical estimates, such as the arithmetic mean, range, standard deviation and co-efficient of variation, were calculated to characterize the variations in selected soil properties and the results are presented in the following sections. Soil chemical properties The variability of soil properties was evaluated according to the soil-landscape relation analy-sis. Details of the observed chemical and physical soil properties are shown in appendix 2. Soil properties in Lom Kao varied with landscape, slope position and soil depth. At landscape level, considerable variations in selected soil chemical properties were observed. The average soil organic matter content was highest (1.87%) in the Low Mountains and lowest (1.3%) in the Hillands (Figure 5-5). The highest mean amount of Ca, Mg, Na and K were recorded on High Mountains (Figure 5-6). On the other hand the Hillands registered the lowest average amount of CEC (10.23 cmolc/kg), soil pH (4.9), Ca (4.37 Me/100 g), and Mg (1.72 Me/100 g). The obser-vation of lesser amounts in the Hillands is indicative of relatively higher degradation status.

River



52

0

0.5

1

1.5

2

HM LM HI PI

Landscape

Per

cent

age

cont

ent

Figure 5-5 Variation of soil organic matter with landscape

02.5

57.510

12.515

17.520

22.525

HM LM HI PI

Landscape

Ave

rage

am

ount

(Me/

100g

)

Ca Mg Na K

Figure 5-6 Variations of Ca, Mg, Na and K with Landscape



53

0

5

10

15

20

25

30

35

HM LM HI PI

Landscape

Ave

rage

am

ount

(Cm

olc/

kg)

Figure 5-7 Variation of CEC with landscape

4.4

4.6

4.8

5

5.2

5.4

5.6

5.8

HM LM HI PI

Landscape

pH a

vera

ge a

mou

nt

Figure 5-8 Variation of soil pH with landscape

The calculated coefficients of variations (CV) are presented in Table 5-2. Generally, the highest CV was in the Hillands while the lowest are in the high mountains.



54

Table 5-2 Selected soil chemical properties and their CV at various landscapes

Coefficient of variation (%) Parameter High mountain Low mountain Hilland Piedmont

O.M 54.6 26.6 58.0 76.7 CEC 19 27.1 54.0 84.5 PH 10 12.4 9.9 17.0 Ca 36.3 51.8 108.3 75.1 Mg 23.8 45.5 35.2 89.8 Na 32.2 200.1 24.9 55.4 K 155.4 141.0 104.4 73.6 With regard to slope position, the foot slopes registered higher mean amounts of organic matter (2.2%), while the back slopes had the lowest (1.3%). Soil pH decreased with slope position, where higher amounts were observed at the summits (5.3) than on the back slopes (5.0) and foot slopes (4.9). CEC was highest on the back slopes (19.8 cmolc/kg), followed by the summits (19.3 cmolc/kg) and foot slopes (17.3 cmolc/kg). Ca and K were greater at summits with aver-age recorded values of 13.1 and 2.5 Me/100 g respectively, while Mg (6.5 Me/100 g) was greater on the back slopes. The lowest average amounts of Ca, K and Mg were registered on the foot slope, back slope and foot slope respectively. The average lowest values are 10.2 for Ca, 0.21 for K and 4.43 cmolc/kg. The highest average amount of Na (0.373 cmolc/kg) was ob-served on the foot slopes, followed by summits (0.33 cmolc/kg) and back slope (0.19). Table 5-3 shows the observed chemical properties at 3 slope positions. The student’s t-test analysis re-vealed no significant difference (P>0.05) in soil chemical properties with slope positions, ex-cept with sodium.

Table 5-3 Variation of soil chemical properties with slope position

Parameter Slope position Summit Back slope Foot slope O.M (%) 1.4 1.3 2.2 CEC (cmolc/kg) 19.3 19.8 17.3 PH 5.3 5 4.9 Ca Me/100 g 13.1 12 10.2 Mg Me/100 g 5.4 6.5 4.4 Na Me/100 g 0.33 0.19 0.37 K Me/100 g 2.5 0.21 0.35 The observed variations of soil chemical properties with soil depth positions are presented in Table 5-4. Generally, soil chemical properties were greater in the topsoil (0-20) than at the lower depth. With the exception of sodium and soil pH, other chemical properties were higher in the upper horizon (0-20 cm) than in the lower horizons. The average amount of soil organic matter was 2.13%. The highest value of organic matter registered in the topsoil was 4.08%, while the lowest was 0.49%. In the bottom horizon soil organic matter ranged between 2.21 and 0.36%. CEC decreased with soil depth with mean upper and lower values of 19.8 and 18.9 cmolc/kg respectively. The range of CEC in the topsoil was from 3.51 to 34.3 cmolc/kg, while in



55

the subsoil, CEC varied from 5.3 to 41.74 cmolc/kg. Soil pH had an average amount of 5.4 fit-ting the acidic rating according to Landon (1991). Soil pH in the topsoil varied from 4.1 to 6.6, while in the bottom soil, the variation was from 4 to 7.2. Calcium was 11.6 Me/100 g in average and it ranged from 1.77 to 25.79 Me/100 g in the topsoil. At the lower depth, calcium ranged from 1.03 to 44.71 Me/100 g. The range of magnesium in the top and bottom soil was 0.47 and 11.76 Me/100 g respectively. The average amount of magnesium was 5.1 Me/100 g. Sodium in the topsoil ranged from 0.08 to 3.69 Me/100 g. In the bottom soil, sodium varied from 0.09 to 1 Me/100 g. The average amount of sodium was 0.29 Me/100 g. The highest and lowest amount of potassium in the topsoil was 5.9 and 0.08 Me/100 g respectively, while in the bottom soil, potassium ranged from 0.08 to 6.7 Me/100 g.

Table 5-4 Variations of soil chemical properties with soil depth

pH CEC O.M Ca Mg Na K Soil depth (cmolc/kg) % ---Me/100 g---

0-20 5.4 19.8 2.13 12.0 5.1 0.29 0.86

20-50 5.4 18.9 1.2 11.2 5.0 0.33 0.74 Coefficients of variations were computed for the chemical properties at the different depth and classified according to Wilding et al., (1983) in which, CV<15 percent was classified as ho-mogenous (least), 15 to 35 percent as moderate and >35 percent as heterogeneous. Following this categorization, all parameters at the two soil depths were heterogeneous except pH, which fitted the homogenous category. The computed CV was 42.8% (O.M), 49.5% (CEC), 12.6% (pH), 74.9% (Ca), 75.1% (Mg), 53.1% (Na) and 192.5% (K). This statistically indicates higher variability of chemical properties at each soil depth. The observed amounts of soil chemical properties at different depth were subjected to a stu-dent’s t-test statistical analysis. With the exception of organic matter, which was greater at the topsoil, no statistically significant difference was detected by the analysis of variance, ANOVA (P>0.05). Soil physical properties Details of the observed soil physical properties are given in appendix 6. The results of varia-tions in soil physical properties at landscape level are shown in Figure 5-9. The highest average percentage content of sand (45%), silt (37%) and clay (52%) were observed on the Hilland, piedmont and Low Mountain respectively, while the lowest average percent content for sand (23%), silt (25%) and clay (23%) were registered on the low mountains, low mountains and Hilland respectively. The soils were classified as clay loam for the High Mountain, clay for the low mountain, loam for the Hilland and clay loam for the piedmont according to the USDA tex-tural classification system.



56

0

10

20

30

40

50

60

HM LM HL PD

Landsacpe

Per

cent

con

tent

Sand Silt Clay

Figure 5-9 Variation of sand, silt and clay with landscape

With respect to slope position and soil physical properties, sand increased with slope position, where greater amounts were observed at the summits (30%), while the foot slopes and back slopes registered 18 and 29% respectively. On the contrary, clay decreased with slope position, where higher amounts (51%) were observed at the foot slopes, followed by the back slopes (45%) and summits (41%). Silt was also higher at the foot slope (31%), than the back slope (27%) and summit (29%). All the soils at the foot slope, back slope and summits were classi-fied as clay in the USDA textural classification system. There was no significant difference ob-served (P>0.05) between soil properties and slope position.

0

10

20

30

40

50

60

FT BS ST

Slope position

Phy

sica

l pro

pert

ies

(%)

Sand Silt Clay

Figure 5-10 Variations of soil physical properties with slope position



57

There was no significant difference observed in soil physical properties in relation to soil depth (P>0.05). The variations of sand, silt and clay at the top were 10 to 82%, 12 to 57% and 7 to 62%, while at the lower depth, variations of 4 to 80%, 14 to 60% and 6 to 80 were observed for sand, silt and clay respectively. Sand and clay were generally higher at the lower depth, while silt was higher at the topsoil as shown in Figure 5-11. The mean values for sand, silt and clay at the top were 31, 32 and 37% respectively. The soils were classified as clay loam following the USDA textural classification system. At the lower depth, the values were 31% for sand, 31% for silt and 38% for clay. The soils also fitted the clay loam textural categorization of the USDA system. The coefficients of variation (CV) were high at both depth, thus the distribution were classified as heterogeneous according to Wilding et al., (1983). The CV’s were compara-tively greater at the bottom soil than at the top. The CV at the top horizon was 69.7% for sand, 33.7 for silt and 47.2% for clay, while at the lower horizon; the CV was 75.2% for sand, 39.9% for silt and 54.6 for clay.



58

05

1015202530354045

0-20 20-50

Soil depth (cm)

Phy

sica

l soi

l pro

pert

ies

(%)

Sand Silt Clay

Figure 5-11 Variation of soil physical properties with soil depth

There was higher spatial variability in soil properties manifested through the high CV values and the heterogeneous distribution, with the exception of soil pH. These variations suggest dif-ferential sensitivity of these properties to change under erosion. The variations arise from com-plex interactions between topography, climate as well as soil use. Diversities in these factors regularly cause redistribution of soil particles from one location to the other through erosion and deposition processes. The variations can also be explained by differences in land use. Ac-cording to Mainam (1998), soil properties that are directly affected by land use and soil man-agement vary significantly and this might be the cause since the soils in the study area are in-tensively cultivated. The observed differences in soil properties with landscape are attributed to systematic varia-tions in pedogenic processes controlled by factors, which differ in the respective landscapes. For example, the Hillands registered more sand than other landscapes because of differences in parent material. They are dominated by quartz, the main source of supply for sand. Similarly, the higher rate of erosion in the Hilland and mountains enables transportation of materials fre-quent redistribution of erodible soil properties to the piedmont and valley. The land use in re-spective landscapes is different and this was partly the cause for the observed variations. Recent studies on spatial variation of soil properties have highlighted that significant variation of soil properties arise from management practices such as tillage (Bourennane et al., 2003). The variation of soil properties with slope position is in line with the catena geo-pedological principle of soil distribution along transects. The observation of more organic matter and so-dium on the foot slope can be attributed to net deposition. Because of the down slope compo-nent that is greater at the summits and back slopes, these elements are transported downwards and accumulate at the foot slopes. The observed trend in distribution was also reported by Lu-fafa (2000) and (Oguz and Noyan, 2001). Lufafa (2000) reported that the processes leading to



59

the formation of organic matter i.e. weathering are more intense at the foot slopes. This is probably true for the study area due to higher moisture content at the foot slopes. The registra-tion of higher soil pH at the summits has been reported by other researchers (Webb et al., 2001). This may be attributed to increasing calcium in similar positions. Relationships between calcium and soil pH have been reported by many researches. According to Oguz and Noyan (2001), calcium is very effective in absorbing hydrogen ions in soils culminating into higher pH values. The increase in the exchangeable basic cations, cation exchange capacity and pH with soil depth can be related to the parent material. As the surface horizon become truncated due to ero-sion, the depth to the C-horizon decreases and the properties become greater at the lower depth, as they are relatively stable and being extracted from the parent source (Maiman 1998). The general presence of greater soil chemical properties in the topsoil was not unexpected and is consistent with the observations of Ekkanit (1998). This can be explained by the mineralisation process. The main source of supply of chemical properties is decomposition of plant material, which is more common in the topsoil. With regard to soil pH and Na, higher amounts were reg-istered at the lower soil depth probably due to leaching.

5.2. Soil loss estimation

5.2.1. Estimation Using RUSLE model

The spatial distribution of RUSLE predicted annual soil loss rates for the study area is shown in Figure 5-12. The estimated annual soil loss rates were classified into 5 severity classes i.e. no erosion (0-1 t ha-1 yr-1), slight (1-5 t ha-1 yr-1), moderate (5-10 t ha-1 yr-1), severe (10-20 t ha-1 yr-

1) and very severe (>20 t ha-1 yr-1). According to Morgan (1995), the appropriate measure of soil loss over which agriculturalist should be concerned is 10 t ha-1 yr-1. This threshold was adopted as the soil loss tolerance limit for Lom Kao, and was used as the critical value for separation of moderate and severe annual soil erosion categories. Subsequent categorical distinctions were made at an increasing rate of classification technique (Millward and Mersey, 1999). The RUSLE predicted soil loss rates are generally low. The annual soil erosion predictions for the study area ranged from 0.1 to 71.3 t ha-1 yr-1 (pixel values). The average annual soil loss estimate per pixel was 6 t ha-1 yr-1 and the CV was 115.1%. The CV is generally high probably due to the heterogeneous nature of the study area in terms of topography and land use. With respect to landscape, average annual soil rates of 24.3 t ha-1 yr-1 were predicted for High Moun-tains, 20.8 t ha-1 yr-1 for Low Mountains, 28.8 t ha-1 yr-1 for Hillands and 12.6 t ha-1 yr-1 for Piedmonts. No erosion was predicted for the valleys. It’s important to note that RUSLE pre-dicted annual soil loss rates for the Hillands are higher than for the High and Low Mountains.



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Figure 5-12 RUSLE soil erosion hazard map for Lom Kao

Area proportion was tabulated for each soil erosion hazard severity category. In terms of overall erosion distribution, the maximum proportion (33.9.%) of the study area falls within the slight soil loss hazard severity category, 19.4% was categorized as moderate, 14.7% as severe, and 5.8% was classified as very severe (Figure 5-13). The results therefore indicate that the largest proportion of the study area (79.5%) is within the acceptable soil loss tolerance threshold of 10 t ha-1 yr-1 (Morgan, 1995). However, soil loss predictions based on parameterization of the slope length factor using the flow accumulation approach were higher. Depending upon cover (C) parameterization, average annual soil loss rates varied from 7.0 to 7.56 t ha-1 yr-1 (pixel value) using flow accumulation LS.



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Figure 5-13 Severity of soil erosion based on RUSLE model

Determining soil loss rates and associated land use types helps in understanding the efforts needed to save the physical quality of land and ultimately holds valuable information for devel-oping necessary conservation strategies. The RUSLE model enables estimation of actual and potential soil loss in an area. In the latter, computations are based on three factors i.e. R, LS and K while in the former the CP factor is also considered. Table 5-5 shows the categorization of RUSLE average annual predicted soil rates under land use types, as well as the potential soil loss rates in areas occupied by the respective land uses. Results show variations in predicted average annual soil loss under land use, with greatest soil loss rates (24.9 t ha –1 yr-1) predicted for the annual land use and lowest for grassland (0.90 t ha-1 yr-1). Predicted annual soil loss was 6.9 t ha-1 yr-1 for orchards, 4.9 t ha-1 yr-1 for tree plantations and 3.3 t ha-1 yr-1 for degraded for-est. No soil loss rates were predicted for rice since the fields are always levelled prior to plant-ing, thus the slope factor in rice fields is zero. The predicted soil loss rates in the respective land uses represent 7.2, 1.1, 0.3, 2.0, and 1.8% of the RUSLE potential soil loss rates in areas covered by annual crops, degraded forest, grassland, orchards, rice and tree plantations. When soil loss rates were modeled using LDD cover (C) factor, predicted values were expectedly higher than the predictions based on derived cover factor. However, in all cases, the trend of predictions based on derived C factor was similar to that of predictions based on LDD cover (C) factor, where maximum (30.5 t ha-1 yr-1) and minimum (1.0 t ha-1 yr-1) soil rates were pre-dicted for annual and grassland land use respectively. Soil loss predictions were 3.3 t ha-1 yr-1 for disturbed forest, 10.8 t ha-1 yr-1 for orchards and 4.9 t ha-1 yr-1 for tree plantation.



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Soil loss estimates based the slope length factor derived using the flow accumulation approach were exaggerated. For all land uses soil loss prediction tripled those based on the aspect LS. There was a significant difference in soil loss predictions based on aspect LS and flow accumu-lation LS (P<0.05), though both methods show a consistent pattern in terms of distribution.

Table 5-5 RUSLE Annual soil loss predictions

Predicted soil loss (t ha-1 yr-1)

Land use Area (%)

Potential soil loss (t ha-1 yr-1)

1 2 3 4 Annual 54.0 347.5 24.9 66.3 30.5 79.6 D. Forest 9.18 304.0 3.3 8.3 3.3 8.3 Grassland 2.40 284.3 0.9 2.6 1.0 2.9 Orchard 17.8 347.8 6.9 22.1 10.8 33.1 Rice 12.7 306.9 0.0 0.0 0.0 0.0 T. Plantation 0.66 277.6 4.9 14.3 4.9 14.3 *Others 3.13 0 0 0 0 0 CV (%) 132.2 130.4 98.3 98.3 1 = Predictions based on derived C and Aspect LS, 2= Predictions based on derived C and Flow accumulation LS, 3=Predictions based on LDD C and Aspect LS, 4=Predictions based on LDD C and Flow accumulation LS, CV represents coefficient of variation, Lowland village and water bodies.

With respect to the effect of slope gradient on predicted soil loss rates, four-slope gradient cate-gories were created as generalizations from FAO (1990) categorization. These are gentle slopes (0-5%), moderate slopes (5-15%), steep slopes (15-30%) and very steep slopes (>30%). Results of predicted soil loss rates under the slope gradients are presented in Figure 5-14. Results indi-cate that predicted soil loss rates rapidly increases with slope gradient, with higher rates (24.3 t ha-1 yr-1) of soil loss estimated for very steep slopes (above 30%) and lower rates estimated (4.7 t ha-1 yr-1) on the gentle slopes (0-5%). On moderate (5-15%) and steep slope gradient catego-ries (15-30%), soil loss estimations were 11.6 and 18.5 t ha-1 yr-1 respectively. The predictions based on flow accumulation were much higher on the very steep segments (66.2 t ha-1 yr-1) and lower on the gentle slopes (2.3 t ha-1 yr-1). There was a significant difference in soil loss predictions with slope gradient (P<0.05%).



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0102030405060708090

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Figure 5-14 RUSLE predicted soil loss rates under slope Gradient

(GS=Gentle slopes, MS=Moderate slopes, SS=Steep slopes, VS= very steep slopes)

The Pearson rank correlation coefficient was applied to test the relationship between soil losses and slope gradient using a random sample of 100 observations. After an exploratory visual analysis of the scatter plot, the data fitted best with the polynomial curve that gave a strong cor-relation coefficient of 0.98 as shown in Figure 5-15, which statistically further illustrates the generally accepted view that soil loss increases with slope gradient.

y = -0.0102x2 + 0.7766x + 0.6182R2 = 0.9823

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Figure 5-15 Correlation between soil loss and slope gradient

Slope shapes (form) are key elements in the spatial distribution of soil erosion. The effect of slope form on soil loss was examined in this study by subdividing the study area into straight, concave and convex slopes. The average predicted soil loss rates for the 3 slope forms are pre-sented in Table 5-6. Soil loss estimates did not show significant variations with slope form. The



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average annual soil loss estimates were 22.2 t ha-1 yr-1 for concave slopes, 22.2 t ha-1 yr-1 for convex slopes and 22.7 t ha-1 yr-1 for straight slopes.

Table 5-6 Soil loss predictions with slope form

Soil loss (t ha-1 yr-1) Slope form 1 2 3 4

Concave 22.2 54.1 27.0 64.6 Convex 22.2 54.0 27.1 64.3 Straight 22.7 57.9 27.8 69.9 CV (%) 1.4 4.0 1.6 4.7 1 = Predictions based on derived C and Aspect LS; 2= Predictions based on derived C and Flow accumulation LS; 3=Predictions based on LDD C and Aspect LS; 4=Predictions based on LDD C and Flow accumulation LS. CV represents coefficient of variation

5.2.2. Estimation using RMMF

The RMMF model was run twice. The first time involved running the model in its structure (Morgan, 2001). The results based on the original structure of the model gave very low values of soil loss. The RMMF model tends to underestimate soil erosion rates especially when ero-sion is transport limited. The second time, adjustments were made in calculating mean rain day by excluding rainstorms less than 12.5 mm and the corresponding days. Most of the analysis in the following sections is based on results obtained after the adjustments. For each pixel, two results were obtained using the RMMF model i.e. the total annual soil detachment rate and the total annual soil transport capacity rate. The lesser of the two values of soil transport capacity rate and soil detachment rate in the respective pixel was taken as the predicted annual soil loss for the RMMF model. The annual detachment, transport and soil loss rates are presented in Ta-ble 5-7, while Figure 5-16 shows the spatial variation of predicted annual soil loss rates by the RMMF model for Lom Kao. The results generally show that the limiting factor to erosion ex-cept in the annual land use is transport.

Table 5-7 Predicted annual soil detachment, transport capacity and soil loss by RMMF model

RMMF without adjustment -----t ha-1 yr------

RMMF with erosive rains only -----t ha-1 yr------

Average Average soil loss Average Average soil loss

Land use Area --%--

D TC 1 2 D TC 1 2 Annual 54.0 4.6 4.7 2.7 3.3 11.2 68.0 9.7 9.9 Disturbed forest 9.18 2.1 0 0 0 2.1 0.3 0.3 0.3 Grassland 2.40 6.7 0 0 0 6.5 0.7 0.7 0.8 Orchard 17.8 5.3 0 0 0 5.5 2.8 2.8 3.1 Rice 12.7 5.1 0 0 0 5.5 0.0 0.0 0.0 Tree plantations 0.66 2.8 0 0 0 3.1 1.8 1.7 1.7 *Others 3.13 - - - - CV (%) 38.6 280 212.2 187.2 157.7 252.1 152.4 104.7 1&2 = Predictions based on derived C and LDD C values respectively. *Lowland village and water bodies. D and T represent soil detachment and transport capacity respectively.



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The estimated soil loss rates by the RMMF model in both cases are generally very low. The model gave zero predictions for all land uses except annuals (4.0) due to the limited transport capacity. Thus, though sediments are detached, the ability for transportation is very minimal. When modeled with erosive rains, the rates varied from 0 as the lowest pixel to 20.2 t ha-1 yr-1

as the maximum soil loss rate using adjusted climatic data. The average annual soil prediction per pixel in this case was 2.1 t ha-1 yr-1. The CV was 118.5%. Though the CV is high, it was not unexpected due to the heterogeneity of the study area in terms of factor inputs. There were no major differences in soil loss predictions while using LDD cover factor values. This is due to the structure of the model that considers the minimum of transport and detachment as the an-nual soil loss rate. The LDD factor cover increased transport capacity but since transport was initially higher than detachment, its effect on soil loss estimates was in this case neutralized. RMMF average annual soil loss predictions per landscape were 4.6 t ha-1 yr-1 for High Moun-tain, 6.4 t ha-1 yr-1 for Low Mountains, 9.4 t ha-1 yr-1 for Hilland and 3.4 t ha-1 yr-1 for piedmont when considered with erosive rains. There were no soil loss estimations for the valley, and similarly like in RUSLE, higher soil loss rates were estimated under Hillands.



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Figure 5-16 RMMF Soil erosion hazard map (with erosive rain) for Lom Kao

In terms of spatial severity, the largest part of the study area (49.3%) fell in the no erosion cate-gory, 40.0% was slight, 9.7% was rated moderate and 1% was rated as severe. Only 2 pixels registered soil loss rates in the very severe class rating as depicted in Figure 5-17. The model thus estimated using Morgan (1995)’s threshold that only 1% of the study area has soil loss be-yond the acceptable soil loss limits.



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Figure 5-17 Severity of soil erosion based on RMMF model with erosive rain

The annual soil loss rates predicted by the RMMF model under various land uses are presented in Table 5-7 above. The RMMF yearly soil loss predictions for the land uses are generally low. The greatest average annual soil loss rates were predicted for annual crops (9.7 t ha-1yr-1), while the lowest (0.3 t ha-1yr-1) soil losses were estimated under disturbed forest, when determined with erosive rains adjustments. In the grassland, orchard, rice and tree plantations, the predicted rates are 0.7, 2.8, 0.0 and 1.8 t ha-1yr-1 respectively. No soil loss rates were predicted for all land uses except for annual crops (4.0 t ha-1yr-1) when the model was run without sorting the erosive rains. On slope gradient, the annual RMMF predicted soil loss rates rapidly increase with slope gradi-ent as depicted in Figure 5-18. As expected, the highest average annual soil loss rates (9.7 t ha-

1yr-1) were estimated for the very steep. This is approximately 4.2 times higher than the rates predicted for the gentle slope. The average annual estimated soil loss for the gentle slope were 2.3 t ha-1yr-1, while for the moderate and steep slope, the rate was 3.4 and 4.2 t ha-1yr-1 respec-tively. There was a significant difference in soil loss with slope gradient (P<0.05), with higher soil loss rates predicted for very steep slopes.



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Figure 5-18 RMMF predicted soil loss rates under slope Gradient

(GS=Gentle slopes, MS=Moderate slopes, SS=Steep slopes, VS= very steep slopes)

The RMMF average annual soil predictions under slope shape were 8.8 t ha-1yr-1 for straight slopes and 9.5 t ha-1yr-1 for both concave and convex slopes.

5.2.3. Comparison between RUSLE and RMMF soil loss predictions

A comparison of the RUSLE predictions of soil loss in Lom Kao and those of the RMMF model is shown in Table 5-8 using descriptive statistics. Predictions made by the RUSLE model were averagely higher than the estimates of RMMF model. The overall CV was higher with RMMF while RUSLE had slightly higher confidence levels than the RMMF model. To statisti-cally establish whether the difference in model predictions was statistically significant, a non-parametric Kruskal Wallis was performed. This was preferred because the data before and after log transformation was not normally distributed. The predictions of the two models were sig-nificantly different (P=0.00).

Table 5-8 Comparative summary statistics of RUSLE and RMMF models

Model Summary statistic RUSLE RMMF

Mean (t/ha/yr) 6.0 2.1 Standard deviation 6.9 2.4

Minimum 0 0 Maximum 71.3 20.2

Coefficient of variation (%) 115.1 118.5 Confidence limit1 0.006 0.002 Confidence limit2 0.006 0.002 Confidence limit3 0.009 0.003

Total size of pixels 3964001 3964001 1,2 and 3 represent 0.1, 0.05 and 0.01 levels of significance.



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Another interesting difference between the two models is that overall distribution of soil loss by the RUSLE model is relatively continuous but skewed to the left as shown in Figure 5-19, unlike in the RMMF model where there is a discontinuous pattern manifested in two peaks of the histogram (Figure 5-20). The coefficient of correlation between the predicted soil loss maps of the two models was calculated to get an indication of the systematic error between the esti-mations. A moderate correlation coefficient of 0.69 was obtained. A closer inspection of varia-tions between two model predictions was performed by overlaying the RUSLE map with the RMMF map in the pixel information window. It was revealed that highest variations in pre-dicted soil loss rates occurred in the Low and High Mountains compared to other landscapes. A comparison of predictions by the models per landscape is shown in Figure 5-21 for High Moun-tains, Figures 5-22 for Low Mountains, Figure 5-23 for Hilland and Figure 5-24 for piedmonts.

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Figure 5-19 Showing distribution of pixels following the RUSLE model



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Figure 5-20 Showing the distribution of pixels following the RMMF model

HM x F

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Figure 5-21 Histogram of pixel distribution in the High Mountain



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Figure 5-22 Histogram of Pixel distribution in the Low Mountain

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Figure 5-23 Histogram of pixel distribution in the Hilland



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Figure 5-24 Histogram of pixel distribution for Piedmont

Generally, the predicted soil loss rates were low and the results suggest that both the extent and magnitude of soil erosion in the study area are not major cause for concern. The predicted mag-nitudes are not threatening and they manifest the terrain and management aspects of the study area. As explained earlier, the largest area consists of Valleys and Piedmonts in which the down slope component is very low limiting the movement of particles downwards, while the cover factor is also reasonable in the area. When considered under landscapes, the results gave higher predictions of soil loss on the Hillands. This is because the Hillands are dominated by clay loamy which are relatively more erodible, whereas the High and Low mountains are underlain by the less erodible clay. In addition, Hillands are characterized by steeper slopes and the aver-age slope gradient in the Hillands is higher than in the High and Low Mountains, which exacer-bates soil loss in the Hillands. Crucially though, is that the mountains have dense forest cover. The results showed that soil loss predictions are greater for annual land use than in the or-chards, rice, planted trees, grassland and forest. The observation of greater rates of soil loss in annual crops has been demonstrated in several studies (Magunda et al., 1999; Renschler, 1996; Shrestha, 1997). The greater rates of soil losses are due to the relatively short vegetation of an-nual crops and the fact that the crops generate small amounts of residues after harvesting. This is coupled with the fact that in annual crops, there is repeated soil disturbance arising from till-age and weeding practices. On the other hand, the low soil loss rates predicted for forest and tree plantations can be attributed to strength of the cover factor in these land uses. More over, trees have a high sediment trapping efficiency, which traps soil particles on the flow path of erosion (Anton et al., 2002), thus significantly curtailing soil loss in these land uses.



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There was a consistent pattern with both models in predicting soil loss rates with slope gradi-ent. This trend was generally expected and the increment of soil loss with slope gradient is well documented (Morgan, 1995; Nearing et al., 1994). This is because of differences in shear stress at gentle slopes and steep slopes. According to Bagoora (1998), the shear strength at steep slopes is greater than that at gentle slopes since the down slope component is also strong due to the higher gradient. Geomorphically, this puts particle on steep slopes with relative ease of movement and once the forces of transportation i.e. rainfall set in, soil transportation occurs. To get a deeper understanding of this relationship, a parametric analysis between slope gradient and soil loss was performed using the Pearson Product Moment Correlation coefficient, which produced a strong relationship (r=0.93) as shown in Figure 5-15. The difference in soil loss predicted using different cover factor values (derived Cover factor and LDD cover factor) could be explained by the fact that the derivation of LDD cover factor for the selected land uses is based on fewer input parameters. The LDD cover factor values were computed basing on aggregated percentage ground cover; mulch and roughness being de-ducted from the maximum cover factor value of 1 (LDD, 2002). This technique of cover factor parameterization, though simple exaggerates the cover factor since it ignores other important cover sub factors in the erosion process. On the other hand, the derived cover factor values were based on RUSLE algorithms and considered ground surface cover, canopy cover, plant height, and surface roughness. Secondly the C values of LDD are more generalized and more regional than the derived cover factor that are more realistic and reflective of the study area conditions. Similarly the flow accumulation technique of LS parameterization gave unreasona-bly higher values of soil loss probably because the resolution of the DEM could not enable dis-tinction of enough flow paths.

5.3. Sensitivity of model parameters

Sensitivity analysis has been used in several studies (Kadupitiya, 2002b; Renschler, 1996) to evaluate the relative stability of models to parameter change. In this study, factor input parame-ters that are identical in both models namely, cover factor; slope gradient; rainfall amount and organic matter were considered. The initial parameter values were changed by magnitudes of 5, 10, 15, 20 and 50% and the models were rerun using changed values in one parameter at ago, while other parameters retained their original values. These values were selected to evaluate whether there could be a detectable trend in sensitivities of a given model with small, medium and large shifts in the original values.

5.3.1. Sensitivity of models to cover factor

It’s generally accepted that ground cover is the most important factor in the soil erosion proc-ess. In parameterization for the surface cover factor (C) for both RUSLE and RMMF models, surface cover together with canopy cover, surface roughness, prior land use are used to calcu-



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late soil loss ratios (SLR) which are multiplied together as sub factors to derive the C factor as explained in section 4.4. Owing to this importance, it was deemed necessary to evaluate sensi-tivity of this factor at three levels i.e. sensitivity of SLR to ground cover, sensitivity of soil loss to SLR and sensitivity of soil loss to cover (C) factor. The observed sensitivities in SLR due to changes in ground cover are depicted in Figure 5-25. The results indicate that grassland is more sensitive to changes in ground cover, while least sen-sitivities were observed in the annual land use. For example a change in ground cover by 50% resulted in change of SLR by 18.9% for annual land use, 135.2% for rice, 195.1 for orchards and 616.8% for grassland. These results illustrate the non-linearness of the derivate equations for SLR. However the observed variations in sensitivities seem to be more related to site condi-tions than land use as a factor.

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Figure 5-25 Sensitivity of Soil loss ratios to ground cover

The sensitivity of predicted soil loss rates and SLR was examined by determining the degree of dependence between the calculated SLR and predicted soil loss rates by performing a partial correlation analysis between the SLR parameters and predicted soil loss rates for each model. The partial correlation coefficients indicated that the RUSLE model was more sensitive to sur-face cover sub factor (r=0.65), while the RMMF model was more sensitive to canopy (r=0.72). The least sensitive sub factor for both models was surface roughness with correlation coeffi-cients of 0.085 and –0.126 for RUSLE and RMMF respectively. The high sensitivity of RUSLE to surface cover sub factor is consistent with the findings of Renard (1994). With regard to sensitivity of soil loss to changes in the overall surface cover (C) factor, interest-ing observations were made. The change in cover factor by a specific margin did not induce proportional and uniform changes in soil loss and showed varied responses at each pixel. With changes in C factor by 5, 10, 15, 20 and 50%, resultant changes in soil loss were observed up to



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100%. This scenario illustrates the complexity of the erosion process and shows that limiting factors to erosion are site specific and vary spatially. When sensitivities were considered by model type, the highest sensitivity in soil loss to cover factor change occurred under the RUSLE model. Average soil loss responses to cover factor change were largely proportional to percentage change in cover factor, while in the RMMF model, the percentage change in soil loss decreased with increasing magnitudes in changes of the original cover values (Table 5-9). The observed trend in the RMMF model can be explained by the two-tie structure of taking minimum values between detachment and transport capacity. Consequently, wherever changes in cover factor increased transport capacity more than detachment, the latter was taken.

Table 5-9 Sensitivity of RUSLE and RMMF models to cover factor change

RUSLE RMMF Change in cover factor Soil loss

Change (%) Min Max CV Soil loss

Change (%) Min Max CV

(%) 5 5.1 0 25 19 5.6 0 100 88 10 10.0 0 25 8.6 10.1 0 100 46 15 15.0 0 28 5.9 14.7 0 100 31 20 20.1 0 40 4.5 18.8 0 100 28 50 51.5 0 75 5.3 37.7 0 100 36

5.3.2. Sensitivity of models to slope gradient

Although the slope factor hardly changes practically, it was considered important in sensitivity analysis to understand the comparative behaviour of the models to this parameter as well as get-ting a relative insight of the critical parameters of each model. Slope gradient factor is used in RUSLE model to derive the slope length (SL) factor (section 4.2.2), while in the RMMF model, slope gradient is incorporated in calculating soil particle detachment as well as transport capac-ity (section 4.3.4 and 4.3.5). Table 5-10 show the sensitivity of soil loss to changes in slope gra-dient for RUSLE and RMMF models. The results reveal that the RUSLE model was more sensitive to small changes, than large shifts in slope gradient. The shift in slope gradient by 5% initiated 5% change in soil loss, while a shift in slope gradient by 50% resulted in 34.6% change in soil by the RUSLE model. In the RMMF model, no particular trend could be dis-cerned. The results also show higher variability reflected by larger CV levels in RMMF than in RUSLE.

Table 5-10 Sensitivity of RUSLE and RMMF model to slope gradient

RUSLE RMMF Change in slope gradient (%) Change in soil

loss (%) Min Max CV

(%) Change in soil

loss (%) Min Max CV

(%) 5 5.0 0 20 22.2 4.6 0 100 151.7 10 9.6 0 20 16.0 9.0 0 100 95.0 15 13.7 0 25 14.7 13.3 0 100 70.3 20 17.4 0 30 14.1 17.7 0 100 57.2 50 34.6 0 47 11.3 43.9 0 100 32.1



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One consistent pattern observed with sensitivity analysis was that the response of soil loss to shifts of input parameters was highly variable and diverse. Therefore the output change maps as a result of changes in the slope factor were crossed with classified slope gradient and cover maps to establish the most sensitive areas or the most influencing factors. It was generally ob-served that pixels with minimum cover were highly sensitive to changes in slope gradient com-pared to pixels with more cover. This was not unexpected owing to the fact that regardless of slope steepness, soil loss rates are highly insignificant so long as there is reasonable surface cover. In terms of slope gradient, the results revealed the low to moderate slopes were more sensitive to changes in slope gradient than steep and very steep slopes. This observation seems to indicate that the slope factor has a critical limit beyond which no further effect can be felt on predicted soil loss rates.

5.3.3. Sensitivity of models to rainfall amount

Rainfall amount is represented in the Erosivity factor of the RUSLE model that is presumed equal to soil loss when other factors are constant. In the RMMF model, rainfall amount is con-sidered while computing runoff and soil particle detachment. The effect of change of rainfall amount on predicted soil loss rates by RUSLE model is presented in Table 5-11. The RUSLE model showed an almost linear sensitivity to changes in rainfall amount. A 5% shift in rainfall amount affected soil loss by an average of 5.6 %, while a 50% change in rainfall amount initi-ated an average change in RUSLE predicted rates by 45.6%. This behavior is in line with the findings of Wischmeier and Smith (1978), who observed that when other factors other than rainfall are constant, soil losses are directly proportional to the rainfall parameter. The slight differences realized in proportionality are related to flat parts of the study area which are gener-ally unaffected by increments in rainfall. The RMMF model was more sensitive to rainfall amount than RUSLE. Average percentage changes in soil loss were 12.8, 22.6, 36.5 and 58.6, for a 5, 10, 20 and 50% respective shift in rainfall amount.

Table 5-11 Sensitivity of RUSLE and RMMF models to rainfall amount

RUSLE RMMF Change in rainfall

amount (%) Change in soil

loss (%) Min Max CV

(%) Change in soil

loss (%) Min Max CV

(%) 5 4.54 0 50 30.8 12.8 0 50.0 62.2 10 9.15 0 50 18.6 22.6 0 66.7 56.4 15 13.65 0 50 11.6 29.4 0 80.5 0 20 18.22 0 50 8.4 36.5 0 85.7 46.6 50 45.5 0 100 4.1 58.6 0 97.0 28.6

5.3.4. Sensitivity of models to soil erodibility

Soil erodibility (K) is useful in both RUSLE and RMMF models as a measure of soil resistance to detachment. Since the K factor is computed using six parameters i.e. silt, sand clay, structure,



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permeability and very fine sand, the sensitivity of K to these parameters was first evaluated us-ing partial correlation coefficients (R). The obtained results are presented in Table 5-12. The most sensitive parameter was silt content, while the least sensitive was permeability.

Table 5-12 Partial correlation coefficients (r) between K and input variables

Variable R P Very fine sand -0.12 0.79 Silt +0.72 0.07 Clay -0.50 0.25 Organic matter -0.47 0.29 Permeability +0.1 0.83 Of the variables used to derive K, soil organic matter was assumed to be the most important in soil erosion and it was the variable used for sensitivity on soil loss. Results of both models show that soil loss was less sensitive to organic matter content (Table 5-13). For example, a 5% change in organic matter resulted in an average soil loss change of 0.77% for RUSLE and 0.32% for RMMF, while a 50% shift in organic matter affected soil loss by 9.6% for RUSLE and 0.39% for RMMF.

Table 5-13 Sensitivity of RUSLE and RMMF to organic matter content

RUSLE RMMF Change in

OM Change in soil

loss (%) Min Max CV Change in soil

loss (%) Min Max CV

5 0.77 0 25.0 1.1 0.32 0 58.2 8.8 10 1.56 0 25.0 0.7 0.32 0 58.2 8.8 15 2.60 0 33.3 0.6 0.33 0 58.4 8.7 20 3.50 0 33.3 0.5 0.34 0 58.6 8.5 50 9.59 0 33.3 0.5 0.39 0 59.8 8.1

5.4. Model performance

The difference observed in soil loss predictions by RUSLE and the RMMF model can be ex-plained by differences in the structure and calibration of the models. Practically, the accuracy of the model can only be validated with another set of quantified soil loss data from runoff plots. This data was not available rendering it difficult to tell which model predicted accurately. Thus a qualitative comparative approach with other studies was undertaken in attempt to evalu-ate the accuracy of the models used in this study. Although not specifically addressing the quantitative spatial distribution of soil loss in Lom Kao, comparison of model predictions with the soil erosion susceptibility map of Ojeda (2000) and the LDD soil erosion map gave a valu-able insight on the qualitative accuracy of the two models. In spatial distribution terms, the RUSLE model corroborated more closely with the LDD and Ojeda (2000)’s susceptibility as-sessment especially in the Mountains and Hillands than the RMMF model. This qualitative



78

comparison, though not mathematically rigorous presents an independent means to corroborate the success of the model at predicting locations with consequential locations of soil loss poten-tial. The approach was also used by (Millward and Mersey, 1999) to qualitatively validate the applicability of RUSLE model for a catchment area in Mexico. On the basis of sensitivity analysis, the models were equally sensitive with the RMMF being more sensitive to slope steepness and rainfall amount, while RUSLE was more sensitive to surface cover factor and organic matter.



79

6. CONCLUSIONS AND RECOMMENDATIONS

6.1. Conclusions

Two models were applied to estimate soil loss in Lom Kao with largely the same data input for most parameters. Both models gave a consistent pattern of the landscapes and land uses experiencing higher soil loss rates i.e. the Hillands for landscape and annuals for the land use. Average annual pre-dicted soil loss rates are low with most areas having rates below 10 t ha-1 yr-1, though in specific pix-els, rates of up to 250 t ha-1 yr-1 were predicted. The RUSLE model performed better than RMMF. The study shows that Hillands are the most vulnerable to erosion. The study further demonstrates GIS as a valuable tool in qualitative and quantitative assessment of soil erosion modeling. GIS enabled data compilation and analysis in a dynamic manner. The deriva-tion of input parameters, particularly topographic inputs for the models permit replication. This is not consistently achievable when using manual techniques. GIS provided a useful environment for param-eterization, data compilation (collection) and analysis in a dynamic manner and within a short period. In addition, the raster approach in GIS allows estimation of erosion at micro (pixel) level. Despite being developed under different conditions (USA), the study demonstrates that both models can be adopted locally to identify erosion critical areas. The RUSLE model is based on modest data requirements, a common limitation in developing nations. At the same time, the data for RMMF model can be easily obtained either from field survey or from literature. The two models provide a means to isolate and describe areas that are vulnerable to soil erosion lending immediate application to conservation planning. Conservations planners at the regional LDD level in Thailand can run the models locally and implement Best Management Practices (BMPs). Deriving model parameters using various methods (techniques) enabled identification of limitations in certain methods. The derived C values gave seemingly more reasonable soil loss predictions. They are more reflective of the study area conditions than those of LDD. The models need validation in the field to select the best and most accurate method of parameterization.

6.2. Limitations of the study

Looking carefully at the soil and land use data, most values for input parameters particularly for the RMMF model were gathered from literature. Some data such as local rainfall interception of every crop, soil detachability, and moisture content at field capacity for the local soils was not readily avail-able and values were taken from literature. Gathering this data takes time, but continuous research to obtain actual values of these parameters for the local conditions would have provided a much better picture about the capability of the models.



80

6.3. Recommendations

The evaluation of validity of the modelled soil loss predictions were based on qualitative criteria due to absence of independent data from controlled erosion plots. There is need for a quantitative data to validate the results of erosion models using reliable results from runoff plots. These plots should be established in critical places particularly in the Hillands, Low mountains and piedmonts, and should capture data for the dominant land uses and cover types in Lom Kao, while taking care of the spatial and temporal variability. Apparently it’s the annual crops in the Hillands that need attention in terms of conservation. Care should be taken while applying the RMMF models as it can give unreasonably low values if the climatic data is not well analyzed. For example, in calculating mean rainy day, the number of rain days applied is the total annual rain days, regardless of the rainfall erosivity of every rainfall incident. This can lead to erroneous computation of the runoff energy. As Jong and Riezebos (1992) observed, not all rain days are erosive. Thus, there is need to carefully examine the climatic data and consider only erosive rains. The predicted soil loss rates by RUSLE and the RMMF model may be subject to errors due to inaccu-racies inherent in each data layers and the limitations of methods used to derive the component factor values. Processing of data for input into RUSLE and RMMF model required use of several algo-rithms, each of which may accentuate existing errors in data and propagate through the model result-ing in uncertainty in the estimated rates (Millward and Mersey, 1999). For example the RUSLE model requires multiplication of four input layers, contributing to an even greater potential error in the resul-tant soil loss rates. A study to quantify these errors using the analytical or Monte Carlo simulation approach could be more informative on the weaknesses of the models. This was not done in this study due to time limitation. The estimation of cover (C) factor can be improved by considering time intervals, especially for the surface cover sub-factor. This was not possible in the apparent study due to time limitation. This could be done with satellite images especially for the cloud free season to monitor changes throughout the year.


References

81

References

Anton, J.J., G. Gerald, and P. Cindy. 2002. Modelling land use changes and their impact on soil ero-sion and sediment supply to rivers. Earth surface processes and landforms 27:481-494.

Bagoora, F.D.K. 1998. Soil loss in Rukiga highlands in Eastern Kabale, Western Uganda. PhD, Mak-erere University, Kampala.

Bernie, E. 1999. Estimating Soil Erosion Using RUSLE (Revised Universal Soil Loss Equation) using Arc View [Online]. Available by Purdue University http://pasture.ecn.purdue.edu/~engelb/abe526/wshddelin/wshddelin.html (posted February 8, 1999; verified October 28, 2002).

Boonma, D., T. Cholada, O. Somchai, and S. Sangchan. 2001. Soil degradation through deforestation in Petchaban, Thailand, p. 249-260, In S. Samran, ed. Response to land degradation. Science Publishers, Inc, Enfield.

Bourennane, H., S. Blanes, C. Cornu, and D. King. 2003. Scale of spatial dependence between chemi-cal properties of topsoil and subsoil over a geologically contrasted area (Massif central, France). Geoderma:235-251.

Chisci, G., and R.P.C. Morgan. 1988. Modelling soil erosion by water: Why and how, In T. Dino, ed. Agriculture erosion assessment and modelling. EU.

Eiumnoh, A. 2001. Tools for identification, assessment , and monitoring land degradation, p. 249-260, In E. B. Bridges, et al., eds. Response to land degradation. Science Publishers, Inc, Enfield.

Ekkanit, H. 1998. Soil pattern analysis and the effect of soil variability on land use in the upper Pasak area Phetchabun Thailand. Msc, ITC, Enschede.

Elirehema, Y.S. 2001. Soil water erosion modelling in selected watersheds in Southern Spain. IFA, ITC, Enschede.

Evans, R. 1990. Water erosion in British farmer's fields-some causes, impacts, prediction. Edward Arnold, Kent.

FAO. 1990. Guidelines for soil description. Third ed. FAO, Rome.

Foster, G.R. 1982. Modelling the erosional process, p. 297-380, In Haan, ed. Hydrological modelling of small watersheds, 5 ed. ASAE monograph.

Hagos, D.W. 1998. Assessment of the effect of present land use on soil degradation, a case study in Lom Kao Area, central Thailand. Msc, ITC, Enschede.

Harmsen, K. 1996. Assessment of current erosion damage Land Druck, Liebefeld.

Hudson, N. 1995. Soil conservation B T Batsford, London.

Jack, L. 2002. Quantifying recent erosion and sediment delivery using probabilistic sampling: A case study. Earth surface processes and landforms 27:559-572.


References

82

Jaroslav, M.H., R.S. Marcel, J.B. Grešáka, and S.B. Geografický. 1996. Modelling spatial and tempo-ral changes of soil water erosion. Geografický casopis 48:255-269.

Jong, D., and H. Riezebos. 1992. Assessment of erosion risk using multi-temporal remote sensing data and an empirical erosion model, University of Utrecht, The Netherlands.

Kadupitiya, H.K. 2002a. Empirical evaluation and comparative study of use of erosion modelling in small catchments in Naivasha, Kenya. Msc, ITC, Enschede.

Kadupitiya, H.K. 2002b. Empirical evaluation and comparative study of use of erosion modelling in small catchments in Naivasha, Kenya. MSc, ITC, Enschede.

Keneth, G., G. Foster, A. Glenn, and P. Jeffery. 1991. Revised Universal Soil Loss Equation. Journal of soil and water conservation Volume 46(1).

Landon, J.R. 1991. Booker Tropical Soil Manual Booker, Agric.international, London.

LDD. 2000. Soil loss map of Thailand. Land development Department, Ministry of agriculture and cooperatives, Bangkok, Thailand.

Lufafa, A. 2000. Validation of pedo-transfer functions for soil erosion prediction and management in a banana based micro-catchment. MSc, Makerere University, Kampala.

Magunda, M., M. Tenywa, M. Majaliwa, and F. Musiitwa. 1999. Soil loss and runoff from agricul-tural land use systems in the Sango Bay micro-catchment of the Lake Victoria. Soil Science Society of East Africa:227-230.

Mainam, F. 1999. Modelling soil erodibility in the semi arid zone of Cameroon. PhD, ITC, Enschede.

Meijerink, A.M.J., and A.M.V. Lieshout. 1996. Comparison of approaches for erosion modelling us-ing flow accumulation with GIS. HydroGIS 235:437-444.

Millward, A., and J. Mersey. 1999. Adapting the RUSLE to model soil erosion potential in a moun-tainous tropical watershed. Catena:38.

Mkhonta, M.M. 2000. Use of remote sensing and Geographic Information System (GIS) in the as-sessment of soil erosion in the Gwayimane and Mahhuku catchment areas with special atten-tion on soil erodibility (K-Factor). Msc, ITC, Enschede.

Mohamed, M.H., S.P. Aggarwal, and P.S. Ranjith. 2002. Application of remote sensing and GIS on soil erosion assessment at Bata river basin, India [Online]. Available by GIS development http://www.gisdevelopment.net/application/geology/geomorphology/geogmf001.htm (verified 26-6-2002).

Mongkolsawat, C., P. Thirangoon, and S. Sriwongsa. 1994. Soil erosion mapping with Universal Soil Loss Equation and GIS [Online]. Available by GISdevelopment http://www.gisdevelopment.net/aars/acrs/1994/ts3/ts3001.shtml (verified 10-6-2002).

Moore, I.D., and G.J. Burch. 1986. Modelling erosion and deposition: Topographic effects. American society of agricultural engineers 000-2351:1624-1640.

Morgan, R.P. 1995. Soil erosion and conservation. Second Edition ed. Longman Group, Cranfield.

Morgan, R.P.C. 2001. A simple approach to soil loss prediction: a revised Morgan-Morgan-Finney model. Catena 44:305-322.


References

83

Nadeem, H. 1999. High spatial resolution soil erosion modelling using remote sensing and GIS [Online]. Available by Department of Geography, Kings college London http://www.kcl.ac.uk/kis/schools/hums/geog/phdhashe.htm (verified 10-6-2002).

Nanna, S. 1996. A geo-information theoretical approach to inductive erosion modelling based on ter-rain mapping units. PhD, Wageningen Agricultural University, Wageningen.

Nearing, M.A., L.J. Lane, and V.L. Lopes. 1994. Modelling soil erosion, p. 340, In R. Lal, ed. Soil erosion research methods, Second ed. St. Lucie Press, Ankeny.

Oguz, I., and F. Noyan. 2001. Soil properties and soil erodibility changes along a slope [Online]. Available by ISD http://www.toprak.org.tr/isd/isd_21.htm (verified 15 November, 2001).

Ojeda, G.E. 2000. Assessing Soil Erosion Susceptibility using simple field tests and GIS techniques. IFA, ITC.

Olderman, L.R., and G.W.J. Vanlynden. 2001. Lonking methodologies for assessing land resources, their problems and possible solutions at small scales, p. 225-236, In S. Samran, ed. Response to land degradation. Science Publishers, Inc, Enfield.

Petter, P. 1992. GIS and Remote Sensing for Soil Erosion Studies in Semi-arid Environments. PhD, University of Lund, Lund.

Renard, K.G., G.R. Foster, G.A. Weesies, and J.P. Porter. 1991. RUSLE Revised Soil Loss Equation. Journal of soil and water conservation 46(1):30-33.

Renard, K.G., J.M. Laflen, G.R. Foster, and D.K. McCool. 1994. The Revised Universal Soil Loss Equation, p. 340, In R. Lal, ed. Soil erosion research methods, Second ed. St. Lucie Press, Ankeny.

Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder. 1997. Predicting Soil Ero-sion by Water: A guide to conservation planning with the Revised Universal Soil Loss Equa-tion (RUSLE). Agriculture Handbook 703 USDA, Washington.

Renschler, C. 1996. Soilerosion risk mapping by means of geographical information systems (GIS) and hydrologic modelling. MSc, Technical University of Braunschweig, Braunschweig.

Roo, A.P.J. 1993. Modelling surface runoff and soil erosion in catchments using Geograpgical Infor-mation Systems. PhD, Utrecht University, Utrecht.

Roo, A.P.J.D., C.G. Wesseling, N.H.D.T. Cremers, and R.J.E. Offermans. 1994. Lisem: A physically-based hydrological and soil erosion model incorporated in a GIS [Online]. Available by EGIS Foundation http://wwwsgi.ursus.maine.edu/gisweb/spatdb/egis/eg94023.html (verified 1/6/2002).

Rossiter, D.G., and T. Hengl. 2001. Creating geometrically correct photo-interpretations, photomosa-ics, and base maps for a project GIS, ITC lecture notes.

Samran, S. 2001. Thailand's response to land degradation: The need to control soil erosion, p. 314-324, In S. Samran, ed. Response to land degradation. Science Publishers, Inc, Enfield.

Shigeo, O., S.G. S, M. Nobuyuki, U. Satoshi, M. Nasir, and S. Muhammad. 1998. Estimation of Soil Erosion using USLE and Landsat TM in Pakistan. National Institute of Agro-environment Sciences.


References

84

Shrestha, D.P. 1997. Assessment of soil erosion in the Nepalese Himalaya: A case study in Likhu Khola Valley, Middle Mountain region. Land husbandry 2:59-80.

Shrestha, D.P. 2000. Aspects of erosion and sedimentation in the Nepalese Himalaya: Highland-Lowland relations. PhD thesis, Ghent University, Ghent.

Shrestha, D.P. 2002. Creating digital elevation models, ITC Lecture notes ITC.

Shrestha, R., E. Apisit, and B. Somchai. 1996. Soil erosion assessment and its policy implications: A case study of RS and GIS applications in Uthai Thani, Thailand [Online]. Available by GIS-development http://www.gisdevelopment.net/aars/acrs/1996/ss/ss1004pf.thm (verified 8/7/2002).

Sohan, W., and S. Lal. 2001. Extraction of parameters and modeling soil erosion using GIS in a grid environment, center for remote imaging, sensing and processing.

Soil_Survey_Staff. 1998. Keys to soil taxonomy. Eigth ed. USDA, Washington DC.

Srikhajon, M., A. Somrang, P. Pramojanee, S. Pradubvith, and C. Anecksamphant. 1994. Application of the Universal Soil Loss Equation for Thailand. Fifth Asean Conference, Bangkok, Thai-land, 10-23 June 1994.

Webb, K.T., C. Wang, T. Astatkie, and D.R. Langille. 2001. Spatial and temporal trends in soil prop-erties at a soil quality benchmark site in Central Nova Scotia. Canadian journal of soil science 80:567-575.

Wessels, K.J., H.M.V.D. Berg, and D.J. Pretorius. 2001. Spatial natural resource monitoring in Mpu-malanga Province of South Africa, p. 237-246, In S. Samran, ed. Response to land degrada-tion. Science Publishers, Inc, Enfield.

Wilding, L.P., N.E. Sinlek, and G.H. Hall. 1983. Pedogenesis and Soil Taxonomy. Concepts and in-teraction. Elsevier, Amsterdam, Netherlands:303.

Wishmeier, W.H., and D.D. Smith. 1978. Predicting rainfall erosion losses: a guide to conservation planning USDA Agricultural handbook.

Woldu, H.D. 1998. assessment of the effect of present Land use on soil degradation, A case study of Lom Kao area, Central Thailand. MSc, ITC, Enschede.

Wolfgang, A., M. Michael, M. Sandro, and R. Giuliano. 2002. Integrating GIS, remote sensing, ground truthing and modelling approaches for regional erosion classification of semi arid catchments in south africa and Swaziland. [Online].

Zinck, J.A. 1988. Soil Survey courses ITC, Enschede.


Appendices

85

Appendices

Appendix 1: FAO (1994) chart for estimating surface cover.


Appendices

86

Appendix 2 Soil properties

OM CEC PH Ca Mg Na K Sample 0-20 20-50 0-20 20-50 0-20 20-50 0-20 20-50 0-20 20-50 0-20 20-50 0-20 20-50

1 2.94 2.14 15.66 15.5 5.5 5.5 8.93 9.81 3.62 2.72 0.35 0.32 2.6 2.6

2 0.96 0.5 34 41.74 5.6 7.1 25.8 44.7 11.76 11.47 0.5 0.53 5.9 6.7

3 1.1 0.57 6.78 6.43 5.2 4.8 2.74 2.3 1.88 1.85 0.35 0.37 0.9 0.8

4 0.67 0.36 7.17 6.28 4.7 4 2.37 1.03 1.93 1.24 0.32 0.32 0.23 0.08

5 2.1 2.21 9.77 9.99 4.1 4.1 2.37 2.01 1.4 1.12 0.35 1 0.16 0.12

6 1.59 0.41 30.86 32.38 5.4 5.2 23.4 22.7 10.82 15.11 0.17 0.35 0.74 0.1

7 2.31 1.98 23.07 23.42 5.2 5.2 17.1 17.3 7.11 6.64 0.31 0.35 0.74 0.54

8 2.91 1.75 19.43 16.62 5 4.2 8.11 3.29 5.71 3.86 0.09 0.09 0.21 0.1

9 1.74 0.86 17.93 16.42 4.6 4.2 6.9 3.96 5.47 4.47 0.09 0.1 0.13 0.09

10 3.37 1.4 20.22 17.32 6 4.8 13.9 8.77 5.69 4.24 0.09 0.09 0.43 0.12

11 1.35 1.26 25.91 19.69 6 5.4 22.6 12.5 6.59 6.87 0.09 0.1 0.13 0.1

12 4.08 1.07 34.3 34.5 6.1 6 19.1 16.4 10 13 0.4 0.3 0.4 0.2

13 2.09 0.95 9.3 5.3 6.1 5.9 5.9 3.2 1.5 1 0.3 0.2 0.6 0.2

14 2.83 1.44 18.8 17.8 4.9 6.1 11 11.9 2.6 2.3 0.5 0.6 0.3 0.1

15 2.48 2 22.1 18.1 6.6 5.7 14.9 10.1 2.5 2.5 0.4 0.2 0.3 0.2

16 1.6 0.56 12.8 11.1 4.7 7.2 6.5 8.4 1.8 2.3 0.4 0.4 0.2 0.1

17 3.69 2.09 28.2 29.5 5.6 5.6 13.2 11.5 5.9 4.9 0.3 0.3 0.6 0.4

0-20 and 20-50 are depths in cm. Sand Silt Clay

Sample 0-20 20-50 0-20 20-50 0-20 20-50 119.8 24.8 32.1 28.6 48 46.6 215.7 11.6 34.1 44.2 50 44.2 358.2 65 25.1 20.9 17 14.1 466.8 73 19.8 15.4 13 11.6 532.6 27.2 30 35.3 37 37.5 620.5 31.1 35 38 45 30.9 718.8 17.4 37.4 39.1 44 43.5 810.5 7.9 27.6 21 62 71.1 924.5 21.8 24.3 24 51 54.2

109.7 4.2 28.2 16.3 62 79.5 1111.1 10.9 31.2 24.2 58 64.9 1220.1 23.1 39 37 41 39.9 1320.4 18.5 42.6 39.1 37 42.4 1437.3 37 45.6 41.7 17 21.3 1533.7 25.2 21.3 31.3 45 43.5 1617.1 17.7 56.7 59.5 26 22.8 1763.7 65.6 23 19.3 13 15.1 1881.5 80.4 11.5 14.1 7 5.5

0-20 and 20-50 are depths in cm


Appendices

87

Data obtained from soils division OB LDSP Depth O.M VC C M F VF SI C Texture B1-04 MTN 0-20 4.08 2.3 2.6 3.6 6.7 4.9 3.9 40.9 C C-013 PD 0-18 3.69 5.9 3.3 3.1 5.6 2.5 42.6 37 CL B1-07 PD 0-10 1.60 0.6 2.4 7.6 17.1 9.6 45.6 17.1 L C-23 PD 0-15 2.48 1.2 0.8 3.4 15.5 12.8 21.3 62 C B1-027 HL 0-15 2.83 0.2 0.3 2.5 8.4 5.7 56.7 26.2 SL C-19 LMTN 0-8 2.09 0.7 5.2 21.6 26.6 9.6 23 13.3 SL C1-04 HMTN 0-12 0.50 0.2 5.4 37.5 34.8 3.6 11.5 7.0 LS


Appendices

88

APPENDIX 3 SOIL PROFILE DESCRIPTION

Soil profile number OB14 A) Information on soil profile site Date of examination 3/10/02……………. Authors BAMUTAZE, GEBREKIRSTOS AND MELKAM Status Mini-pit description Location 0730240 , 1884597 (UTM,)… Elevation 304 metres above sea level Landscape High Mountain (HM) Geopedological unit HM 113………………………. Position Summit Slope 40%……………… Micro-topography No micro relief……………. Vegetation No vegetation……… Land use maize and mixed orchard trees B) Information on soil profile Classification USDA Soil Taxonomy Udertic Paleustolls-Mollisols Parent material Sandstone and shale Drainage class Well drained Internal drainage rarely saturated……………… External drainage Rapid runoff……………… Rock outcrops Nil Surface stoniness Nil Evidence of erosion Water erosion (sheet) Water table Not observed with C) Soil profile description Horizon Depth (cm) Description Ap 0 - 29 Dark brown (7.5YR 3/2) when moist, clay; moderate coarse subangu-

lar blocky; friable when moist; very sticky and very plastic; pH 6.0 AB 19 - 54 Brown (7.5YR 4/2) when moist, clay; moderate coarse sub angular

blocky; firm when moist ; very sticky and very plastic ; abundant cu-tans ; pH 5.5

Bt 54 - 83 Brown (7.5YR 4/3) when moist, clay ; moderate coarse sub angular blocky ; firm when moist ; very sticky and very plastic ; pH 6.0


Appendices

89

Profile number OB16 Information on soil profile site Date of examination 3/10/02 Location 0743375 ,1870580 (UTM) Elevation (m) 300 Authors BAMUTAZE, GEBREKIRSTOS AND MELKAM Status Mini pit Micro-Topography No micro relief Position Back slope Slope 30% Landscape Hilland Geopedological unit HI 511 Vegetation No vegetation Land use Mungbean B) Information on soil profile Classification USDA Soil Taxonomy Udic paleustolls, Ultisols Parent material Sand stone, Shale, Slate, Andesite Drainage class Well drained Internal drainage never saturated External drainage Rapid runoff Rock outcrop Nil Surface stoniness Nil Erosion water erosion Water table not observed C) Soil profile description Horizon Depth (cm) Description AP 0-20 Dark brown (7.5YR3/3) when moist, Structure: weak sub

angular, sandy loam, firm when moist, sticky, plastic, with abundance rock fragment:, pH 5

B 20-60 Brown (7.5yr4/4) when moist, weak firm sub angular,

; Sandy loam, firm when moist, sticky, plastic abundance rock frag-ment, and cutans: common, and pH 5.5


Appendices

90

Profile number OB22 A) Information on soil profile site Date of examination 05/10/02 Author(s) BAMUTAZE, GEBREKIRSTOS AND MELKAM Location 731180,1875736(UTM) Elevation (m) 239 Status Mini pit Micro-Topography Low gilgai Position Foot slope Slope 12 % Landscape Piedmont Relief Middle glacis Landform Tread riser complex Geopedological unit PI 312 Vegetation No vegetation Land use Mungbean B) Information on soil profile Classification USDA Soil Taxonomy Typic Haplustalfs, Alfisols Soil climate Parent material Residual Drainage class well drained Soil drainage class well drained Internal drainage rarely saturated External drainage Slow runoff Rock outcrop Nil Surface stoniness Nil Erosion sheet & rill erosion Water table not observed C) Soil profile description Horizon Depth (cm) Description AP 0-24 Dark brown (7.5YR 3/4) moist, sandy loam, week

, coarse, sub angular blocky none sticky and plastic with none rock Fragment; pH=4.5

BA 24-50+ Dark brown (7.5YR 3/4) moist, sandy loam, moderate,

coarse, sub angular blocky, none sticky, plastic, and rock fragment; common mottles, and many cutans; pH=5.5.


Appendices

91

Profile number OB26 A) Information on soil profile site Date of examination 04/10/02 Author(s) BAMUTAZE, GEBREKIRSTOS AND MELKAM Location 747098,1887312 (UTM) Elevation (m) 432 Status Mini pit Micro-Topography Low Gilgai Position Upper slope Slope 25% Landscape Low mountain Relief Middle glacis Landform Tread riser complex Geopedological unit LM112 Vegetation No vegetation Land use Tamarind B) Information on soil profile Classification USDA Soil Taxonomy Udic Kanhaplustults, Ultisols Soil climate Parent material Hills Soil drainage classes well drained Internal drainage rarely saturated External drainage Moderately rapid run off Rock outcrop Nil Surface stoniness Nil Erosion water erosion (sheet & rill erosion) Water table not observed C) Soil profile description Horizon Depth (cm) Description Ap 0-25 Yellowish red (5YR 4/6) moist, clay, strong, medium, sub

angular structure, sticky, plastic; pH=6.0. BA 25-80+ Clay, strong, medium, sub angular blocky, very sticky, very

plastic, with common cutans; pH=5.4.


Appendices

92

Appendix 4: Sample soil profile

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