DROUGHT MONITORING USING RAINFALL DATA … MONITORING USING RAINFALL DATA AND SPATIAL SOIL MOISTURE MODELING Thesis submitted to the Graduate School, Faculty of Geography, Gadjah Mada

DROUGHT MONITORING USING RAINFALL DATA AND SPATIAL SOIL MOISTURE MODELING

Thesis submitted to the Graduate School, Faculty of Geography, Gadjah Mada University in partial fulfillment of the requirements for the degree of

Master of Science in Geo-information for Spatial Planning and Risk Management.

By:

Budi Hadi Narendra 19537/PS/MGISPRM/06

17511

Supervisors:

1. Dr. H.A. Sudibyakto, M.S. 2. Prof. Dr. V.G.(Victor) Jetten

GADJAH MADA UNIVERSITY

INTERNASIONAL INSTITUTE FOR GEO-INFORMATION

SCIENCE AND EARTH OBSERVATION

2008

U G M

THESIS


By:

Budi Hadi Narendra 19537/PS/MGISPRM/06

17511

Has been approved in Yogyakarta

On: February 11st, 2008

By Team of Supervisors:

Chairman: External Examiner:

Dr. Junun Sartohadi, M.Sc. Dr. Pramono Hadi, M.Sc.

Supervisor 1: Supervisor 2:

Dr. H.A. Sudibyakto, M.S. Prof.Dr. V.G.(Victor) Jetten

Certified by:

Program Director of Geo-Information for Spatial Planning and Risk Management, Graduate School Faculty of Geography, Gadjah Mada University

Dr. H.A. Sudibyakto, M.S.

Disclaimer

This document describes work undertaken as part of a program of study at the Double Degree International Program of Geo-Information for Spatial Planning and Risk Management, a Joint Program of ITC the Netherlands and UGM, Indonesia. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

Budi Hadi Narendra

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Abstract Drought is one of slow onset natural hazards that has the greatest impact and affect in many sectors, include agricultural. To cope or manage the drought, people must be familiar with drought characteristics happen in the area. The characteristics should provide information about drought vulnerability showed in a map, spatial and temporal aspect of the drought, as well as water deficit volume during drought events.

This research tries to explore drought characteristics in agriculture area of Gesing sub watershed based on meteorological and soil characteristics. The first approach is using rainfall data and Standardized Precipitation Index (SPI), and the second is defined by soil moisture drought modeling using PC Raster. Finally, the correlation between these methods was analyzed to know the differences.

The research result reveals that annual rainfall characteristics can describe drought occurrence at that year. The drought years classified using rainfall anomaly by MGA are significantly correlated with droughts based on SPI 12-month time scale in December, as well as SPI 1-month time scale has a high and significant correlation with monthly rainfall deficiency.

Soil moisture modeling generated using PCRaster can describe drought characteristics based on soil moisture deficit with flexible time scale. PCRaster output provides information about when, where, and how much water deficit occur in each time step. In daily time scale, soil moisture is closely linked with rainfall for time lag of one day. In monthly time scale, Drought information provided by SPI is less suitable in assessing agricultural drought compared modeling in PCRaster. Using SPI, a drought can be identified by showing negative value of SPI one month time scale.

Keywords: drought, Standardized Precipitation Index (SPI), soil moisture modeling

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Acknowledgements Firstly, I will start by saying Alhamdulillaahi rabbil ´aalamiin to Allah SWT for the Will, Guidance, and Permission such that I can finish my MSc study.

My sincere thanks are for my supervisors Dr. Sudibyakto, M.S. and Prof. Dr. Victor Jetten for their valuable support, kind advises comments and particularly their enormous encouragements. I would like to thank all the UGM Geography Faculty and ITC members who directly or indirectly helped me in the successful completion of my degree, essentially for Geo-Information for SPRM management. I am also thankful to the Meteorological and Geophysical Agency (BMG) of Jawa Tengah and Water Resource Agency (BPSDA) Probolo for providing me the rainfall and meteorological data.

I am appreciative of my employer, Forestry Department for providing me to pursue higher studies. Thanks to the Bappenas (National Development Planning Agency) for the scholarship program since in EAP course until finishing the study. Also for the NEC (Netherlands Education Centre) in Jakarta who gives STUNED fellowship therefore I have opportunity to study and stay abroad as well as get great experiences in some Europe countries.

Warm thanks go to my entire classmates who gave me corporation and a lot of happiness during the class, exam, “wiskul”, and holiday in UGM and ITC. Their support, help, love, care and friendship were valuable and unforgettable. I would like to give my special thanks to Arif, Maya, and Estu as my fieldmates during thesis fieldworks in Purworejo. As nice friends, they had been very helpful, cooperative and very fruitful discussion during fieldwork. I will always remember the hospitality provided by Arif’s family during stay at their house. Gratefully thank to Mas Rahman, Mone, Nugroho, Rino, Mas Safrudin, and Rudi for their help in software and modeling, as well as to Ebta, Pak Hosen, Wulan, Muktaf, Defi, Dody, Bu Lily, Firda, Utia, Anna for their warm and friendly discussing during the entire study. Not only to my friends during the study but also I thankful to all of my friends and collages in Yogyakarta and Enschede who has accompany me during spend my time in sport, traveling, and holiday. You all have made my live pleasurable and unforgettable.

Finally, I would like to express my heartfelt gratitude to my family, my wife and beautiful daughter, as well as my parents, sisters and brothers for their eternal encouragement which led to successful completion my study. I cannot express my thankfulness to them in words, I can say that it’s only because of their love, support and blessings that I gained the strength to complete this study.

Budi Hadi Narendra Yogyakarta, Indonesia January, 2008

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List of Content Abstract ………………………….…………………………………………………………… i Acknowledgement ………...……………………………………………………………… ii List of Figures ………………………………………………………….……………………. iv List of Tables ………….……………………….………………………………………..... v List of Appendices …..…………...………………………………………………………. vi

1. Introduction ..................................................................................... 11.1. Background...................................................................................... 11.2. Problem Statement ........................................................................... 11.3. Research Objectives ........................................................................ 21.4. Research Questions …....................................................................... 21.5. Overview of Research Methodology ………………………………………………. 21.6. Structure of the Thesis ………………………………………………………………… 32. Literature Review ............................................................................ 42.1. Drought Definitions ........................................................................... 42.2. Drought Monitoring and Drought Characteristics .…….………………………. 52.3. Standardized Precipitation Index (SPI) .……..……………………………………. 62.4. Soil Moisture Drought ……………………………………………………………………. 73. Methodology ………………………………………………………………………………… 103.1. Study area …………………………………………………………………………………... 103.1.1. Geography …………………………………………………………………………………… 103.1.2. Climate ………………………………………………………….…………………………….. 113.1.3. Soil ……………………………………………………………………………………………... 123.1.4. Agriculture Area ………………………………………………………………………….… 123.2. Rainfall Analysis ……………………………………………………………………………. 133.3. SPI Calculation ……………………………………………………………………………... 143.4. Soil Moisture Drought Modeling ……………………………………………………... 133.4.1. The Use of PC Raster …………………………………….…………………………….… 143.4.2. Determining Potential Evapotranspiration …..……………….…………………... 153.4.3. Soil and Crop Characteristics …..…….…………………….………………………... 203.4.4. Preparing PC Raster Inputs …………………………….………………………………. 223.5. Correlation Analysis ……….……………………………………………………………… 234. Result and Discussion ………….………………………………………………………... 244.1. Rainfall Characteristics …..…………..………………….…………………………….. 244.2. SPI Analysis ………………………………………………….……………………………... 294.3. Soil Moisture Drought Modeling …………………………………………………….… 364.4. Correlation of Soil Moisture drought and SPI .….…………………………….. 425. Conclusion and recommendation ……………………..……………………………… 50 References …………………………………………………………………………………… 51 Appendices …...……………………………………….…….……………………………... 54

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List of Figures

1-1 Conceptual framework of research ………………………………………….…… 32-1 Sequence of drought occurrence and impacts …………….…………….….. 53-1 Whole area of Gesing sub watershed …………………………………….…..…. 103-2 Rainfall and meteorological stations surround study area ………………… 113-3 Average monthly rainfall from 3 rainfall station for 27 year record …….. 113-4 Soil type map of study area ………………………………………………………… 123-5 Crop type on agriculture area ……………………………………………………… 133-6 Soil sampling points …………………………………………………………………… 204-1 Trend line of annual rainfall …..……………………………………………………… 244-2 Average monthly rainfall 1980 – 2006 from each station …………….….. 254-3 Trend line of dry month start ….…………………………………………………... 274-4 Trend line of dry month number averaged from 3 stations …..………….. 284-5 Trend line of rainy season start ….……………………………..................... 294-6 Different drought frequencies showed by different SPI time scales ….. 314-7 SPI 12 on December and average annual rainfall …………………….….… 324-8 SPI 6 on April and 6-month moving average rainfall ……………….……. 344-9 Rainfall zone map ………………………………………………………………….…... 364-10 Crop type map ……………………………………………………………………….….. 374-11 DEM map ……………………………………………………………………………….…. 374-12 Ksat map …………………………………………………………………………….……. 384-13 Porosity map ……………………………………………………………………….……. 384-14 Field capacity map ……………………………………………………………….……. 394-15 Wilting point map ………………………………………………………………….…… 394-16 Correlogram of soil moisture 2002 ………………………………………........... 404-17 Correlogram of soil moisture 2006 ………………………….…………........... 414-18 Lag cross-correlation between rainfall and soil moisture ……………....... 414-19 Drought occur in paddy field ………………………………………………………… 424-20 PCRaster output maps compared with SPI maps ……………………….….. 48

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List of Tables

2-1 Classification of SPI values ……………………………………………………………… 62-2 Water content characteristics of various soil texture classes ……..…….…. 83-1 General soil description in Bogowonto watershed ………………….............. 124-1 Drought years based on MGA classification …………………………………….… 254-2 Statistic descriptive of monthly rainfall of the 27 year record ……............. 264-3 Anova of dry month number …………………………………………………..…….… 274-4 Correlation of dry month start, dry month number and annual rainfall …... 284-5 Drought category for each SPI time scales …………………………................ 294-6 Annual rainfall characterized by SPI 12 and MGA classification …………... 334-7 SPI 1 and 3-month time scales for 2002 and 2006 ………………….............. 354-8 Coefficient correlation of drought based on MGA class with monthly

rainfall, SPI 1, and SPI 3 …………………………………………………................. 354-9 Meteorological (non rainfall) data for 2002 and 2006 ……………………….. 354-10 Drought occurrences based on rainfall, SPI, and soil moisture deficit …... 434-11 Correlation of soil moisture deficit with SPI ………………………................. 44

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List of Appendices

Appendix 1 : Start of dry period and average dry month number ……………..… 54Appendix 2 : Correlation between dry month number and annual rainfall…..… 54Appendix 3 : Start of rainy season in each station ………………………………….… 55Appendix 4 : Monthly rainfall, normality, and SPI values in 2002 and 2006 for

each station …………………………………………………………………..… 56Appendix 5 : Result of soil texture analysis ……………………………………………... 57Appendix 6 : Soil characteristics each sampling point …………………………….… 58Appendix 7 : PCRaster script for the generation of soil moisture deficit ………… 61Appendix 8 : Correlation of soil moisture and soil characteristics ………………… 65


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1. Introduction

1.1. Background Drought differs from other natural hazards, because drought is a slow onset natural hazard but it can have the greatest impact and affect large number of people. Drought must be considered as a relative, rather than an absolute condition. It can occur in both high and low rainfall areas. Commonly the main cause of a drought is the lack of precipitation for extensive period of time. Through hydrological process, this precipitation lack will make lower in soil moisture, ground water, and streamflow. As slow onset event, drought effects may accumulate over time and may remain for several years. The impact of drought results from the shortage of water or the unbalance between supply and demand for water as one of environmental aspect, until agricultural, economic and social aspect.

Gesing sub watershed is a part of Bogowonto watershed located in central Java. This area often faces drought disaster; even the same areas received enough total rainfall in a year. Almost every year, mass media reports drought occur in this area causing crop failure and water scarcity for domestic purposes. Increasing drought preparedness is important to minimize drought disaster effects. As the slow onset disaster, drought allows a warning time between the first indications, usually several months, to the point where the population will be affected. To cope or manage the drought, people must be familiar with drought characteristics happen in the area therefore it is important to investigate the drought. The drought characteristics should provide information about drought vulnerability showed in a map, spatial and temporal aspect of the drought, as well as water deficit volume during drought events.

1.2. Problem Statement In the lower part of Gesing sub watershed, agriculture is a primary economic sector. The water deficit is often the most limiting factor for crop production. Both long term and short term drought has severe impacts on agriculture. Even though most area in the watershed receives enough rainfall in a year, but in dry season some areas start to become drought.

In drought mitigation actions and programs, it is important to understand the drought characteristics through drought analysis. It consists of reliable information as a main factor in the decision making process. A drought analysis based only on rainfall data is often done because in many areas the rainfall data are more available than other meteorological or remote sensing data. Unfortunately this analysis does not directly provide soil moisture deficit during a drought which is very important for agricultural plant growth and yield. Spatial soil moisture modeling is expected to provide the drought characteristics more efficient and applicable in agricultural sector. The information is essential for a broad group of users within the geo-informatics society who are interested in monitoring, mitigation and management of drought.


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1.3. Research Objectives The general objective of the research is to explore drought characteristics in agriculture area of Gesing sub watershed based on meteorological and soil characteristics. Specific objectives of the research are the followings: - To analyze drought characteristics using rainfall data and Standardized

Precipitation Index (SPI) - To analyze drought characteristics by defining soil moisture drought model using

PC Raster - To analyze the correlation between drought characteristics performed by SPI and

soil moisture drought model

1.4. Research Questions 1. How far rainfall data and SPI can explain drought characteristics? 2. How do we transform a spatial soil moisture model to drought characteristics? 3. How is the correlation between drought characterized by SPI and soil moisture

model?

1.5. Overview of Research Methodology The following figure shows briefly summarized schematic work flow of the various steps that are undertaken to achieve the research objectives.


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Temperature,

relative humidity,windspeed, sun

duration,radiation

Initial soil moisture,porosity, saturated

hydraulicconductivity, organic

matter, texture

Crop factor:type,

rotation,coefitient

Percolation,wilting point,soilmoisture

Actual soilwater content

Infiltration

Calibration

Soil moisturedrought map

Daily rainfall

Potentialevapotranspiration

Actualevapotranspiration

Soil moisture

Spatial Soilmoisture

Monthly rainfall

Time seriesanalysis SPI analysis

Meteorologicaldrought mapCorrelation

Figure 1-1: Conceptual framework of research

1.6. Structure of the Thesis This thesis contains six chapters. The first chapter highlights the background, objectives and research questions. Chapter two provides with a literature review of the concepts of drought, monitoring, and uses of SPI and soil moisture model. The third chapter details the study area and methodology considered in order to achieve the research objective. Chapter four presents the results obtained and discusses them. The last chapter draws conclusion of this study and gives recommendation for further research.


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2. Literature Review

2.1. Drought Definition According to Wilhite & Glanz (1985), drought can be explained as conceptual (encyclopedia type) or operational definitions. The latter define drought characteristics using a variable based on the discipline of interest, for instance precipitation in meteorological drought, streamflow in hydrological drought, or soil moisture in agricultural drought.

Originally, drought is caused by lack of precipitation mentioned as meteorological drought. After that, drought transmits through hydrological system into soil moisture drought. In an agriculture area, this drought can decrease agricultural products and in a forest area it will increase the trigger of forest fire. Further more, the drought can develop into groundwater and streamflow drought, both known as hydrological drought. In this step, there is not enough surface water, even groundwater to supply drinking water, irrigation, industrial, or hydropower. Other authors also make drought terms based on the consequences such as socio-economical drought (Wilhite and Glantz, 1985; Tallaksen and Van Lanen, 2004).

Meteorological drought is usually defined by a precipitation deficiency threshold over a predetermined period of time. The threshold chosen, such as 75 percent of normal precipitation, and duration period, for example, six months, will vary by location according to user needs or applications.

Agricultural drought is defined more commonly by the availability of soil water to support crop and forage growth than by the departure of normal precipitation over some specified period of time. There is no direct relationship between precipitation and infiltration into the soil. Infiltration rates vary, depend on antecedent moisture conditions, slope, soil type and the intensity of the precipitation event. Soil characteristics also differ: some soils have a high water-holding capacity while others do not. The latter are more prone to agricultural drought.

Hydrological drought is even further removed from the precipitation deficiency since it is normally defined by the departure of surface and subsurface water supplies from some average condition at various points in time. Similar to agricultural drought, there is no direct relationship between precipitation amount and the status of surface and subsurface water supplies in lakes, reservoirs, aquifers and streams because these hydrological system components are used for multiple and competing purposes, such as irrigation, recreation, tourism, flood control, transportation, hydroelectric power production, domestic water supply, protection of endangered species and environmental and ecosystem management and preservation. There is also a considerable time lag between departures of precipitation and the point at which these deficiencies become evident in surface and subsurface components of the hydrologic system.


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Socio-economic drought differs markedly from the other types of drought because it reflects the relationship between the supply and demand for some commodities or economic good, such as water, livestock forage or hydroelectric power, which depend on precipitation. Supply varies annually as a function of precipitation or water availability. Demand also fluctuates and is often associated with a positive trend as a result of increasing population, development or other factors.

Figure 2-1: Sequence of drought occurrence and impacts (NDMC, 2007)

2.2. Drought Monitoring and Drought Characteristics Drought monitoring is done in order to identify climate and water supply trends. It can detect and predict the occurrence and severity of drought. This information is very important to reduce the drought impact if it can be distributed in right time and format, as well as followed preparedness plans. An effective drought monitoring must integrate precipitation characteristics and other climatic parameters with water information such as stream flow, groundwater levels, reservoir and lake levels, and soil moisture into a comprehensive assessment of current and future drought. There are three distinguishing features in a drought occurrence: intensity, duration and spatial coverage. Intensity refers to the amount of the precipitation shortfall. It is generally measured by the departure from normal of a climatic parameter such as precipitation, an indicator such as the reservoir level or an index such as SPI (World Meteorological Organization, 2006).

Different types of drought require different drought indicators. In the agricultural drought monitoring, the most suitable indicators needed are the factors that are


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responsive to soil moisture status as a result of soil water balance process. Soil moisture deficit has critical relation with crop water requirements and it will be important in assessing the impact of drought on crops.

Another essential characteristic of drought is its duration. Droughts can develop quickly in some climatic regimes, but usually require a minimum of two to three months to become established. The magnitude of drought impacts is closely related to the timing of the onset of the precipitation shortage, its intensity and the duration of the event. There are many tools to identify drought characteristics. The choice depends on hydroclimatology of the region, the type of drought, the vulnerability of society, the purpose of the study and the available data. The lack of a standard definition, making this choice is subjective (Hisdal et al., 2004).

2.3. Standardized Precipitation Index (SPI) The Standardized Precipitation Index (SPI) is a tool developed in 1993 by Tom McKee, Nolan Doesken and John Kleist in Colorado Climate Centre with the main purpose to defining and monitoring drought. Compared with PDSI (Palmer drought severity index), SPI is a more simple tool because it just based on rainfall data and less calculation effort. Basically the SPI is the number of standard deviations that the monthly rainfall data would deviate from the long-term mean. Firstly, a transformation is applied to make rainfall data follow a normal distribution (McKee et al., 1993).

Hayes et al. (1999) used the SPI to monitor the 1996 drought in the United States of America. They show how the SPI usefully can detect the start of the drought, its spatial extension and temporal progression. They also show that the onset of the drought could have been detected one month in advance of the Palmer Drought Severity Index (PSDI).

The SPI can be computed for different time scales, can provide early warning of drought and help assess drought severity, and is less complex than the Palmer index.

Table 2-1: Classification of SPI values (McKee et al., 1993)

SPI Values

2.0 and more extremely wet

1.5 to 1.99 very wet

1.0 to 1.49 moderately wet

-.99 to .99 near normal

-1.0 to -1.49 moderately dry

-1.5 to -1.99 severely dry

-2 and less extremely dry SPI was formulated to calculate rainfall deficit in multiple time scales. The time


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scale shows drought impact caused by different water sources. Drought caused by soil moisture deficit was a respond of rainfall shortage in relatively short time scale, while groundwater, streamflow, and reservoir storage reflect longer rainfall anomalies. For several purposes, McKee design SPI for 3, 6, 12, 24, and 48-month time scales and then classified the drought class as shown in Table 2-1. The droughts occur if SPI reaches -1.0 value or less. Every drought event can be calculated for the duration, intensity, and magnitude (NDMC, 2007).

There was a study focused on three analyses: relationship between NDVI and SPI at different time scales, response of NDVI to SPI during different time periods within a growing season, and regional characteristics of the NDVI SPI relationship. The result shows that the 3-month SPI time scale has the highest correlation to the NDVI, because the 3-month SPI is the best way for determining drought severity and duration (Ji and Peters, 2003).

2.4. Soil Moisture Drought Deficit of precipitation and high evapotranspiration are the important factors causing soil moisture drought which usually occur next step after meteorological drought. Water deficit actually is not only caused by insufficient water input into hydrological system but also on the rate of water losses through evapotranspiration, discharge from the area, or by various human activities.

Evapotranspiration is also an important factor in drought because it causes water loss almost at the same time and place with precipitation occurrence. The potential evapotranspiration can be determined by interaction of meteorological factors such as temperature, humidity, wind speed, radiation and plant types. The actual evapotranspiration depends on catchment’s characteristics such as land use, soil, and water table (Tallaksen & van Lanen, 2004).

How much water can be held by soil depends on several factors. The most important are soil texture, structure, and organic matter. In the soil itself, water is held around soil particles and organic matter, also in soil pores with different potential. When soil is saturated with water, it has no more water potential and there is available water free. With time, some of the water from saturated soil will drain to the underlying layers of soil (Bureau Land Management, 2003). Representative values for various soil textural classes are presented in Table 2-2.


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Table 2-2: Water content characteristics of soil texture classes (USDA, 1955)

Water per 30 cm of soil depth Field capacity Permanent

wilting point Plant-available water capacity

% by weight

cm/30 cm

% by weight

cm/30 cm

% by weight

cm/30 cm

Medium sand Fine sand Sandy loam Fine sandy loam Loam Silt loam Clay loam Clay

6.8 8.5

11.3 14.7 18.1 19.8 21.5 22.6

3.0 3.7 5.0 6.5 8.0 8.7 9.5

10.0

1.7 2.3 3.4 4.5 6.8 7.9

10.2 14.7

0.7 1.0 1.5 2.0 3.0 3.5 4.5 6.5

5.1 6.2 7.9

10.2 11.3 11.9 11.3 7.9

2.3 2.7 3.5 4.5 5.0 5.2 5.0 3.5

Aggregation is closely related to biological activity and organic matter content in the soil. Organic matter acts as the “glue” to hold the framework of soil particles and pores together, and can build a stronger internal and superficial structure in the soil profile to a condition allowing easy entry of water and its storage in plant-available form. Organic matter in the form of mulch and leaf litter can also be a significant protection against surface sealing by raindrops.

Under favorable conditions, the cloud droplets fall to the surface as precipitation (P). Over land areas, where P is greater than ET and the excess, called runoff (R) occur. Under certain circumstances a part of the excess water infiltrates to the deeper soil layers. Infiltration (I) is not easily determined, so for practical purposes it is better to consider a column which extends from the surface to a depth where significant vertical exchanges are already absent. In general, the form of the water balance,

including also the net change in soil moisture content (ΔS) is given by: ΔS = P - Roff

+ Ron - ET - percolation

For the calculation of the available soil moisture content, expressed in precipitation mm, the simplified form of the water balance equation was used. Because the study area is flat we can assume the runon and runoff are negligible. After the simplification for the calculation a reduced form of the equation was used. The upper one meter layer soil moisture content (SMC) in the next time unit (SMCi) will be expressed as a function of the previous soil water content:

SMCi = SMCi-1 + P – ET In both cases, the time unit is one month. The maximum water a soil can contain is its porosity: all pores are full. Then quickly water drains through gravity until the soil reaches a moisture content called field capacity. This happens in few days so that water is not considered available to plants called as the wilting point (WP). After that he soil is too dry for plants to survive and only in an over can we extract more water. Thus the "plant available water" is the maximum value of available water content of the examined soil layer (AWC) can be expressed as AWC = FC – WP.


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The two methods of soil moisture computation differ only in estimation of the ET term, but both concerning the potential evapotranspiration and also in the way of derivation of real evapotranspiration as a function of the potential one, as well (Sze, et al., 2005). The single-layer water balance model is a commonly used tool in New Zealand for soil moisture assessments, irrigation management, and pasture production studies. The modeling objective is generally to estimate the water status of particular soils, without the need for (or with a minimum of) field measurements, particularly during periods when growth-limiting water stress is likely. A further important application is the derivation of moisture deficit parameters for drought incidence and severity studies, such as comparing drought occurrence and risk between growing seasons, or between regions of the country (Porteous et al., 1994).


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3. Methodology

3.1. Study Area 3.1.1. Geography The study area is located in Gesing sub watershed as a part of Bogowonto watershed located in south part of Central Java Province. In the north part, Gesing sub watershed is bordered by Mongo sub watershed, the south part by Ngasinan sub watershed, the west part by Bogowonto Hilir sub watershed, and the east part by Kulonprogo district. The total area of Gesing watershed is 49.63 km2. The sub districts (kecamatan) included in Gesing sub watershed are Bagelen, Bener, Kaligesing, Loano, and Purworejo.

Based on PSBA – UGM (2004) and Balai PSDA Bogowonto, Gesing sub watershed is included in drought vulnerable area. The study area is focused on agricultural land, mainly located in lower part (downstream) of Gesing sub Watershed.

N

Pacekelan

Plipir

Ganggeng

Brenggong

Semawung

Cangkrep Kidul

Kali Gono

Tlogoguwo

Dono RejoHulosobo

Kali Harjo

Ngaran

PandanRejo

Gunung Wangi

Kedung Gubah

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road

Projection: UTMDatum: WGS 1984Zone: 49S

Figure 3-1: Whole area of Gesing sub watershed

study area


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N

#

#

#

$

Kaligesing(100)

Kedungputri(86)

Cengkawak (27.5)

Keradenan(50)

Gesing

river

75.0

62.5

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87.5

50.0

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# Rainfall station$ Meteorological station

Figure 3-2: Rainfall and meteorological stations surround study area

3.1.2. Climate For 27 year record (1980-2006), the rainy season in 3 stations (Cengkawak, Kaligesing, and Kedungputri) are usually started in October until April, and the dry season is in May until September (see Figure 3-3). Annual rainfall has a variation from 1146 mm until 3855 mm. Based on temperature calculation the maximum temperature is 31.1°C and the minimum is 23.1°C. Relative humidity is between 80 – 90 %.

050

100150200250300350400450

Janu

ary

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ary

March

April

MayJu

ne July

Augus

t

Septem

ber

Octobe

r

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ber

Decem

ber

rain

fall

(mm

)

Figure 3-3: Average monthly rainfall from 3 rainfall stations for 27 year record


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3.1.3. Soil Based on soil type distribution, Bogowonto watershed can be divided into 3 areas, as shown in Table 3-1. Table 3-1: General soil description in Bogowonto watershed

Soil type % area Productivity Use Alluvial 31.9 Low - high Agriculture, settlement Regosol 5.03 Low – high Agriculture, plantation Latosol 63.07 Medium - high Agriculture

Source: PSBA – UGM, 2004 Specific soil type in study area is revealed in figure 3-4. In this study, the soil physical properties as used in the PCRaster water balance model are obtained directly from soil samples.

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RoadRiver

Grey Alluvial Dark Brown Latosol Yellowish Red Latosol

Figure 3-4: Soil type map of study area

3.1.4. Agriculture Area Agriculture area is dominated by paddy field with terrace system. There are 3 types of cropping pattern: paddy followed by paddy with area 324.7 hectare, paddy followed by groundnut with area 53.3 hectare, and paddy followed by tobacco with area 241.6 hectare as shown in Figure 3-5. The first crop type is always paddy started usually in November depends on rainy season start, and then followed by the second crop in March.


13

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Plipir

Semawung

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Kemanukan

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Figure 3-5: Crop type on agriculture area

3.2. Rainfall Analysis Daily rainfall data for 27 year (1980-2006) were obtained from Meteorological and Geophysical Agency (MGA) and Water Resource Agency (BPSDA) Probolo. The data consist of daily rainfall from 3 stations located as shown in Figure 3-2. In these stations, rainfall data were collected using manual rain gauge (ombrometer).

From daily rainfall, the data were tabulated into monthly data and then started to analyze. The rainfall analysis included rainfall difference among 3 stations, variability and frequency of dry months, changing of dry period start and duration, as well as start of rainy season. The data were prepared in Excel program whereas the statistical analysis was done by SPSS program.

Dry month are classified refer to Oldeman classification who classified agroclimate for agricultural crop based on average number of wet month (P>200 mm/month), and average number of dry months (P<100 mm/month).

3.3. SPI Calculation Mathematically, SPI is calculated based on equation:

( )σ−

= mi XXSPI

Where, Xi is monthly rainfall record of the station; Xm is rainfall mean; and σ is the


14

standard deviation. Monthly rainfall data from 1980 to 2006 in 3 rainfall stations are used as an input to SPI program. The SPI program is downloaded from http://www.drought.unl.edu/monitor/spi/program/spiprogram.htm#program Input data are arranged in three column format as shown below

yyyy mm pppp yyyy mm pppp yyyy mm pppp yyyy mm pppp Etc.

Where: yyyy = year mm = month pppp = precipitation (It should not be decimals) For all rainfall stations, data are arranged as shown above. Input files are created and one by one and SPI values are computed for each station on 1, 3, 6, 12-month time scales. Database is created for SPI results from 1980-2006.

3.4. Soil Moisture Drought Modeling 3.4.1. The Use of PC Raster The soil drought monitoring model is created in PCRaster software (Dept. of Geo-Sciences of the Utrecht University, 2007), a freeware GIS and spatial modeling language for environmental and hydrological models, which enables easy adaptation of a model to specific needs. It uses the open-source GDAL software for conversion between virtually all formats. Many models are available in PCRaster code, from large scale hydrological models to plant growth, wind and water erosion, land slides, groundwater movement etc. These codes are freely available from the authors and through the PCRaster website and mailing list.

Data needed in this modeling: - Daily meteorological data: rainfall, temperature, relative humidity, windspeed,

and sunshine duration (needed to calculate radiation). - Soil type map, soil texture, and organic matter content, saturated hydraulic

conductivity, porosity, soil depth, initial soil moisture content, wilting point, field capacity.

- Data of agricultural plant included distribution and area coverage by each plant type, rotation timing to determine growth stage, crop factors.

This section will create a simple 1 layer daily water balance model on agriculture area driven by rainfall and potential evapotranspiration. The soil moisture equation used in this model, assuming no flow along the surface, (Jetten, 2007) as shown below:

θt = θt -1 + (P - ETa - Perc) Δt/Δz ……………………………….… (1)


15

Where: θ = soil moisture content (fraction) for layer with a given depth Δz (e.g. root zone

depth in mm) P = rainfall (mm/day) ETa = actual evapotranspiration (mm/day) Perc = percolation (mm/day), i.e. drainage of water towards deeper layers Δt = timestep (daily) The actual evapotranspiration ETa is estimated from the actual soil evaporation (Ea) and the plant transpiration (Ta): ETa = (1-Ct) Ea +Ct Ta = (1-Ct)· f1·ETp + Ct· f2·k ·ETp ..….….……. (2) Where: ETp = potential evapotranspiration (mm/day) Ct = vegetation ground cover at time t (fraction) f1 = a factor for the decrease in soil evaporation related to soil moisture f2 = a factor describing the decrease of transpiration by plants in relation to soil

moisture k = crop factor, to account for differences in transpiration for various types of

crops or plant species. The equations for f1 and f2 are respectively: f1 = θt /θs ……………….…………………….….………………………………………. (3) f2 = 1/(1+θ50/θt)

a ………..…………....................……………………………….. (4) where: a = transpiration factor coefficient θs = soil porosity (fraction) θ50 = soil moisture content at which the transpiration is reduced to 50% (assumed

to be at 2/3 between porosity and wilting point) Percolation is also related to the moisture content, where it is assumed that the potential difference between the root zone (dψ) and the subsoil (dz) equals 1: Perc = K*dψ/dz = K*1.0 …………...........………....………………………….... (5) K = Ksat (θ/θs)b ................................................................................ (6) Where Ksat is the saturated hydraulic conductivity (mm/time) and the coefficient b depends on the type of soil.

Spatial processes in the model are limited from runoff process because the agriculture area is almost flat and the farmers always make terrace with high boundary around it. Therefore additional infiltration Ia is 0. 3.4.2. Determining Potential Evapotranspiration Potential evapotranspiration was predicted with reference evapotranspiration calculated using FAO Penman-Monteith using daily radiation, windspeed, relative humidity and air temperature data (Allen, et al., 1998).


16

………………..………………….. (7) Where:

ETo = reference evapotranspiration [mm day-1], Rn = net radiation at the crop surface [MJ m-2 day-1], G = soil heat flux density [MJ m-2 day-1], T = mean daily air temperature at 2 m height [°C], u2 = wind speed at 2 m height [m s-1], es = saturation vapour pressure [kPa], ea = actual vapour pressure [kPa], es-ea = saturation vapour pressure deficit [kPa], Δ = slope vapour pressure curve [kPa °C-1], γ = psychrometric constant [kPa °C-1].

Because of unavailable data on the actual incoming global radiation, which is measured with a pyranometer, the radiation is predicted using sunshine duration data measured with a Campbell-Stokes sunshine recorder and extraterrestrial radiation for daily periods (Ra). Net radiation (Rn)

The net radiation (Rn) is the difference between the incoming net shortwave radiation (Rns) and the outgoing net longwave radiation (Rnl): Rn = Rns - Rnl ………………………………………..…...…………….…………………. (8)

where Rns = net solar or shortwave radiation [MJ m-2 day-1], Rnl = net outgoing longwave radiation [MJ m-2 day-1] Net solar or net shortwave radiation (Rns)

The net shortwave radiation resulting from the balance between incoming and reflected solar radiation is given by: Rns = (1-α)Rs ……………………………………………………………………………… (9) where α = albedo or canopy reflection coefficient, which is 0.23 for the hypothetical

grass reference crop Rs = the incoming solar radiation [MJ m-2 day-1]. Solar radiation (Rs)

If the solar radiation, Rs, is not measured, it can be calculated with the Angstrom formula which relates solar radiation to extraterrestrial radiation and relative sunshine duration:

……………………………………….…………………………… (10)


17

where Rs = solar or shortwave radiation [MJ m-2 day-1], n = actual duration of sunshine [hour], N = maximum possible duration of sunshine or daylight hours [hour], n/N = relative sunshine duration [-], Ra = extraterrestrial radiation [MJ m-2 day-1], as = regression constant, expressing the fraction of extraterrestrial radiation

reaching the earth on overcast days (n = 0), as+bs = fraction of extraterrestrial radiation reaching the earth on clear days (n=N). If actual solar radiation data are not available and no calibration has been carried out for improved as and bs parameters, the values as = 0.25 and bs = 0.50 are recommended. Extraterrestrial radiation for daily periods (Ra)

The extraterrestrial radiation, Ra, for each day of the year and for different latitudes can be estimated from the solar constant, the solar declination and the time of the year by:

………………….… (11) where Ra = extraterrestrial radiation [MJ m-2 day-1] Gsc = solar constant = 0.0820 MJ m-2 min-1

dr = inverse relative distance Earth-Sun ω s = sunset hour angle φ = latitude [rad] positive for the northern hemisphere and negative for the

southern hemisphere δ = solar declination [rad] The conversion from decimal degrees to radians is given by: [Radians] = π/180 [decimaldegrees]

……………………………………..……...……………… (12)

………………………………………………….………… (13) Where: J is the number of the day in the year between 1 (1 January) and 365 or 366 (31 December). The sunset hour angle, ωs, is given by:

…...…… ………..………………………(14)


18

where

X = 1 - [tan(ϕ)]2 [tan(δ)]2 and X = 0.00001 if X ≤ 0 Net longwave radiation (Rnl)

The rate of longwave energy emission is proportional to the absolute temperature of the surface raised to the fourth power.

…………………… (15) where Rnl = net outgoing longwave radiation [MJ m-2 day-1] σ = Stefan-Boltzmann constant [4.903 10-9 MJ K-4 m-2 day-1], Tmax, K = maximum absolute temperature during the 24-hour period Tmin, K = minimum absolute temperature during the 24-hour period ea = actual vapour pressure [kPa], Rs/Rso = relative shortwave radiation (limited to ≤ 1.0) Rs = solar radiation [MJ m-2 day-1], Rso = clear-sky radiation [MJ m-2 day-1]. K = °C + 273.16 Clear-sky solar radiation (Rso)

The calculation of the clear-sky radiation, Rso, when n = N, is required for computing net longwave radiation. Rso = (0.75 + 2 l0-5z)Ra …………………………….……………………..…… (16) Where: z = station elevation above sea level [m]. Ra = extraterrestrial radiation [MJ m-2 day-1] Soil heat flux (G)

Soil heat flux is small compared to Rn, particularly when the surface is covered by vegetation and calculation time steps are 24 hours or longer. G for day and ten-day periods, as the magnitude is relatively small, it may be ignored and thus: Gday ≈ 0 …………………………………………………………………………….………… (17) Psychrometric constant (γ)

The psychrometric constant, γ, is given by:

……………………………...……………………….…….. (18) Where:


19

γ = psychometric constant [kPa °C-1], P = atmospheric pressure [kPa], λ = latent heat of vaporization, 2.45 [MJ kg-1], cp = specific heat at constant pressure, 1.013 10-3 [MJ kg-1 °C-1], ε = ratio molecular weight of water vapour/dry air = 0.622 Atmospheric pressure (P)

The atmospheric pressure, P, is the pressure exerted by the weight of the earth's atmosphere.

……………………………………………………………….. (19) where P = atmospheric pressure [kPa], z = elevation above sea level [m] Slope of saturation vapor pressure curve (Δ ) For the calculation of evapotranspiration, the slope of the relationship between saturation vapor pressure and temperature, Δ, is required.

……………………………………………..... (20) where Δ = slope of saturation vapor pressure curve [kPa °C-1], T = air temperature [°C] exp[..] 2.7183 (base of natural logarithm) raised to the power [..]. Mean saturation vapor pressure (es)

………………………...…………..……..…………….. (21) As saturation vapor pressure is related to air temperature, it can be calculated from the air temperature. The relationship is expressed by:

……………………………………………… (22) Where: e°(T) = saturation vapor pressure at the air temperature T [kPa] T = air temperature [°C] exp[..] 2.7183 (base of natural logarithm) raised to the power [..].


20

Actual vapor pressure (ea) derived from relative humidity data

………………………..………………..… (23) 3.4.3. Soil and Crop Characteristics Soil characteristics were obtained from soil sampling on the field. The points of soil sampling were randomly determined on the agriculture area. The total of 84 sampling points is shown in the following map.

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Mix plantationSettlementAgriculture area

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# Soil sample point

Disturbed soil samples for organic matter and texture analysis were collected from 7 different places and analyzed in soil physical laboratory. Undisturbed soil samples were also collected from 84 points separated in four times collecting for once a week. The samples were collected in 3rd and 4th weeks of August, and 1st and 2nd weeks of September. The samples were collected using steel sample rings with a volume 76cm3. Each sample will be analyzed to get soil moisture content value, porosity, and saturated hydraulic conductivity (Ksat). The steel rings were inserted into the soil until around 5 cm under ground level. After digging around the ring, the soil together with the soil ring was carefully removed. Aluminum foil was placed on

Figure 3-6: Soil sampling points


21

both sides of the soil ring and the ring was labeled.

Soil samples together with the ring were weighed to get actual weight. After a period of 24 hours at 105°C, the oven-dry samples were weighed again to get the soil moisture content for each sample. After that, nylon filter was installed in the bottom of the ring and the samples were subsequently saturated for 24 hours in tray contained water so the soil can freely absorb the water. Each sample was weighed to get a water-saturated weight. Soil porosity measured using water evaporation method, was calculated based on measured bulk density as follows: Porosity = (Ws – Wd)/V x 100 % …………………………….…………....…… (24) Where: Ws = water-saturated soil weight (gr) Wd = dried soil weight (gr) V = soil volume (cm3) The saturated hydraulic conductivity of the soil sample was measured by the constant hydraulic head method, based on Darcy law. Firstly, upper part of the ring was sealed using a rubber to keep constant water height on the soil surface. Then dripping carefully water on the soil and maintained it on 2 cm height. Water flowing below the ring was collected using a container and measurement was begun after water was flowing constantly. The quantity of water infiltrated under saturated conditions for at least 2 hours is calculated. Ksat = Q/A x L/ΔH ………...……….………………………………………………… (25) Where Ksat = saturated hydraulic conductivity (mm/hr) Q = water volume (ml) A = the cross sectional area of the soil core (cm2) L = the length of soil sample ring (cm) ∆H = the difference of hydraulic head (cm) Other values such as wilting point and field capacity were estimated using pedotransfer function by soil water characteristic software, version 6.02.74. This program can estimate soil water tension, conductivity and water holding capability based on the soil texture, organic matter, gravel content, salinity, and compaction (Saxton and Walter, 2007).

Crop characteristics such as crop type, crop factor value, crop cover area, and crop rotation are needed in soil moisture modeling. Crop factor value can be derived from FAO evapotranspiration guide line (Allen, et al., 1998). The other values were directly generated from field observation and interview with the farmers during taking of soil samples. The interviews were done in order to get information about agriculture area, crop calendar, water deficit, and canopy covering.


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3.4.4. Preparing PC Raster Inputs PC Raster inputs consist of maps for spatial data and tables for time series and static data. The maps were prepared in Arc View and Ilwis program and after that exported to ASCII format. The ASCII format can be directly generated into PC raster format map (.map) using PC Raster program.

The data derived from point source such as Ksat and porosity and has enough point number can be interpolated using kriging. The selected model was spherical with certain nugget, sill, and range based on the optimal variogram. The process was done using Ilwis 3.3 and the result was converted into ASCII format. In the PC Raster program, the ASCII format was proceed into PCRaster format map, as input in the next steps.

The input using table format such as rainfall data, meteorological data, daily crop factor, or daily crop cover must be prepared in a text format table using extension .tbl or .tss. The processes were done in Excel spreadsheet and edited in Notepad program.

PCRaster script is an ASCII text file, consists of separate sections, each with a defined function in the model. The sections are binding, area map, timer, initial, and dynamic section. Each section starts with the section keyword of the section. The section keyword is usually followed by one or more statements that give the content of the section. Each statement is terminated by a semicolon (;) sign. Remarks about the contents of the script are typed after a # character. A statement in a section may contain keywords, names of variables, or numbers. Keywords are defined by the PCRaster Dynamic Modeling language and have a special meaning in the language.

The binding section is identified by the section keyword binding. It allows one to use a name for a variable in the script that is different from the file name of variable in the database. Both file names used as input files and stored in the database may be given in the binding section. Area map section consist one statement: the name of a map which is used as clone map in the model, followed by a semi colon. All maps that are generated during a model run have the location attributes of the clone map. Also, all maps that are used as input to the model must have location attributes which correspond with the map in the area map section. The timer section gives the time dimension of the model. It contains one statement, consisting of three values: start time, end time, and time slice. The iterative part of the model is run between the start time and the end time. The time slice defines the time between the consecutive time steps. The initial section prepares the set of input variables which are needed to run the dynamic section at time step 1. All variables needed as input for running the dynamic section for the first time must either be defined in the initial section or must be already present in the database. The dynamic section contains pcrcalc operations that are performed at each time step i. The operations are sequentially performed from top to bottom in the section. Each line gives a pcrcalc operation and is concluded with a semicolon (;) sign (Dept. of Geo-Sciences of the Utrecht University, 2007).

Before running the PCRaster script, firstly the model was calibrated using soil


23

moisture data collected from the field. The calibration was done to find a transpiration factor for f2 equation. The equation for f2 is a S-shaped function assuming that transpiration occurs at 100% of the potential evapotranspiration when the soil moisture content is between porosity and slightly dryer than field capacity, and it decreases to zero when the moisture content reaches wilting point. Normally this function is based on the matrix potential in the soil, but soil moisture retention curves are not available for the area and the S-shape curve is translated to moisture contents (Simunek et al., 2005). The calibration was done in a point version of the model in Excel program. The resulting calibration was used for the spatial model in PCRaster. 3.5. Correlation Analysis The correlation is done between SPI pattern and soil moisture drought map. Correlation analyses were done to obtain relation between drought characterized by SPI and soil moisture deficit. Analyze was also done to identify the availability of soil moisture model provides the quantitative explanation for SPI.


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4. Result and Discussion

4.1. Rainfall Characteristics Rainfall analysis was done annually and monthly. The rainfall analysis especially for agriculture and drought risk study should include information concerning the precipitation amount trends, start of dry period, number of dry month, and start of rainy season. Annual Rainfall

Rainfall data collected during 27 years for 3 stations show the average annual rainfall as 2495 mm. The lowest average was 1146 mm in 1997 recorded at Kedungputri station and the highest was as high as 3855 mm in 1998 recorded at Kaligesing station. Actually the average is not very low because Indonesia has annual rainfall as 2700 mm per year and mainly drought was caused by a low of soil capacity to store water (Arifin, 2007).

Trend analysis of precipitation shows a slightly increase of precipitation amounts in the last 27 years. Figure 4-1 shows an increasing of annual rainfall with linear regression equation y = 6.0297x + 2410.8 and R2 = 0.0125 but statistically this linear trend is not significant at the 5% level. A non significant trend will change if one or two points are added against the trend.

1000

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Ann

ual r

ainf

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Linear (Annualrainfall)

Figure 4-1: Trend line of Annual rainfall

Meteorological drought indicates the deficiency of rainfall compared to normal rainfall in a given region. According to the Meteorological and Geophysical Agency of Indonesia (MGA), meteorological drought is defined by percent of normal rainfall as a rainfall deficiency index. In this approach, rainfall characteristics are divided into 3 classes (Hadi, 2001). There are normal, above normal, and below normal. The rainfall is classified into normal if the ratio of rainfall amount to the long time average is between 85 % - 115 %. The amount is classified into above normal if the


25

ratio is greater than 115 %, and classified into below normal if the ratio less than 85 % and this amount have a tendency to be drought in certain area. MGA uses long period of average rainfall for 30 years as normal rainfall threshold. In this study, the threshold was set based on average precipitation value for 27 years. Based on the criteria, there are 5 years categorized as drought years i.e. 1980, 1982, 1991, 1994, and 1997.

Table 4-1: Drought years based on MGA classification

Year Annual Rainfall (mm) Normality (%) 1980 1988 80 1982 1755 70 1991 1994 80 1994 1852 74 1997 1519 61

Monthly rainfall

Monthly rainfall is a main input in SPI calculation. To generate these data, daily rainfall from 3 stations was proceed into Excel spreadsheet and resulted monthly rainfall for each station as well as the monthly average rainfall. Figure 4-2 shows that the general shape of the seasonal distribution from 3 stations is not significantly different. The average monthly rainfall rapidly increases during the October to the maximum in either December or January, and starting to decrease sharply in April.

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Kedungputri Kaligesing Cengkawak Figure 4-2: Average monthly rainfall 1980 – 2006 from each station

Statistical analysis shows that the biggest monthly average rainfall was in Kaligesing station and the smallest was in Cengkawak station. These facts also happen on rainfall cumulative for 27 year. The average, as well as total rainfall in each station has a positive correlation with station elevation. Kaligesing station which has the highest elevation showed the highest average and total rainfall, while Cengkawak station with the lowest elevation has the lowest average and total rainfall.


26

Table 4-2: Statistic descriptive of monthly rainfall of the 27 year record

Station Year Month Kedungputri Kaligesing Cengkawak

Mean 420 416 344 Max 627 728 753 January Min 190 146 169

Mean 389 403 300 Max 609 988 493 February Min 191 114 162

Mean 375 357 298 Max 702 554 581 March Min 51 85 36

Mean 244 236 178 Max 614 707 532 April Min 8 0 10

Mean 92 112 72 Max 355 549 225 May Min 0 0 0

Mean 83 84 73 Max 420 507 392 June Min 0 0 0

Mean 30 43 29 Max 225 248 279 July Min 0 0 0

Mean 27 28 24 Max 336 261 306 August Min 0 0 0

Mean 33 42 39 Max 286 342 298 September Min 0 0 0

Mean 142 152 160 Max 408 483 655 October Min 0 0 0

Mean 339 370 366 Max 681 918 820 November Min 0 0 3

Mean 429 411 370 Max 776 934 829 December Min 79 35 38

The next analysis indicates rainfall differences among three stations are not significant. The result of variance analysis only has significance at 0.075 levels. Event there are elevation differences as showed in Figure 3-2, but the differences are not extremely high. They all were laid on elevation 0 - 100 above sea level. Start and number of dry months A dry month is defined as the month which has monthly rainfall less than 100 mm.


27

Dry months almost occur in dry season, in this area is usually starting in May. Trend line of dry month start indicates that generally dry month start becomes slightly earlier. Pearson correlation coefficient shows -0.075, but the trend is insignificant with R2=0.0056 as showed in Figure 4-3.

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Figure 4-3: Trend line of dry period start

Based on dry month criteria, dry month numbers for each year are averaged from 3 stations as shown in Figure 4-4. Although the trend is insignificant, it can be seen that the start of the dry period is extremely variable from year to year. The dry period start and average dry month number for each station can be seen in Appendix 1. The longest dry period (8 months) happened in 1997 and the shortest was 1 month happened in 1989. All stations show 5 months for dry month number average.

Similar to amount of rainfall, the differences number of dry month among 3 stations are also insignificant as showed in Table 4-3. An analysis of variance was only resulting significant at 0.223 levels. It can be noted that all of stations have relative the same dry period duration.

Table 4-3: Anova of dry month number

Sum of Squares df Mean

Square F Sig.

Between Groups (Combined) 8.469 2 4.235 1.531 0.223 Deviation 0.302 1 0.302 0.109 0.742 Within Groups 215.778 78 2.766 Total 224.247 80

The trend line of dry month number indicates that generally dry month number slightly increase year to year. Similar to start of dry period, this trend is also insignificant so it can change by adding one or two points against the line.


28

0123456789

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Figure 4-4: Trend line of dry month number averaged from 3 stations

All of drought years based on MGA classification have dry number more than the average. We can also see that most of years with high dry month number start the dry period earlier than the average month. The correlation test showed in Appendix 2 indicates that there are significant correlations among the start of dry period, number of dry month, and annual rainfall amount in a year. Table 4-4 notices if dry period come late it will decrease the dry month number and increase annual rainfall.

Table 4-4: Correlation of dry period start, dry month number and annual rainfall

Dry period start

Dry month number

Annual rainfall

Pearson Correlation 1 -.559(**) .569(**)Dry period start Sig. (2-tailed) . .002 .002

Pearson Correlation -.559(**) 1 -.855(**)Dry month number Sig. (2-tailed) .002 . .000

Pearson Correlation .569(**) -.855(**) 1Annual rainfall Sig. (2-tailed) .002 .000 .

Dry month excess in a year will affect the growth period. In study area, the farmers usually plant a crop type for around 4 months and in a year they can plant at least twice. If the dry period occur for more than 4 months, the second period plantations will have opportunity to get the drought. Start of rainy season

In this study, a rainy season start is identified as a more than 100 mm rainfall amount in a month. It is based on Oldeman classification that the amount is enough for certain type of agricultural crops. During 27 years, almost every year the rainy season among 3 stations started in the same month, except in 1980, 1990, 1999, 2003, and 2005 there are differences of starting month among stations as shown in Appendix 3.


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Based on annual average, rainy season normally comes in October. The delay of rainy season is usually one or two months. Only in 1994, Kaligesing station showed extremely delay up to 3 months. The other years categorized by MGA as drought year also have late in rainy season start. There are only 3 years that rainy season come earlier: 1981, 1986, and 1992. The last rainfall data received from 3 stations in 2007 also indicates the late of rainy season, most likely in December.

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n

Figure 4-5: Trend line of rainy season start

4.2. SPI Analysis SPI is calculated in various time scales with different dry month category as shown in Table 4-5. Positive SPI values indicate the rainfall is greater than median rainfall and negative values indicate less than median rainfall. In dry condition monitoring, the drought part of the SPI range is divided into near normal conditions (0.99 < SPI <-0.99), moderately dry (-1.0 < SPI < -1.49), severely dry (-1.5 < SPI <-1.99) and extremely dry (SPI < -2.0). A drought event starts when SPI value reaches -1.0 and ends when SPI becomes positive again.

Table 4-5: Drought category for each SPI time scale

Number of months SPI value Category

SPI 1 SPI 3 SPI 6 SPI 12 -1.0 to -1.49 Moderately 27 26 20 31 -1.5 to -1.99 Severely 6 12 14 10 -2 and less Extremely 6 10 15 9

Duration (months) 1 - 2 1 – 6 1 – 7 1 – 7 SPI calculation shows that the lowest SPI 1-month time scale is -3.48 happen in March 1997, -2.88 for 3-month time scale in the same time, and -2.74 for 6-month time scale in May 1987.


30

Figure 4-6 describes the differences of drought frequencies and their duration as the result of different SPI time scales. In a shorter SPI time scale (e.g., 1-month), the dry month (SPI<0) and the wet month (SPI>0) periods are showed in a high temporal frequency, otherwise when the time scale increases, the frequencies are decreases. At the time scale of 12-month, from 18 times periods there are four important drought periods recognized: the 1982/1983, 1991/1992, 1994/1995, and 1997/1998 period. The average duration of the dry periods (SPI<0) changing noticeably as a function of the time scales. Average dry period of 1-month time scale is 3 months, while 12-months SPI time scale is 8 months as the longest average duration.


31

SPI 1

-3.5-3

-2.5-2

-1.5-1

-0.50

0.51

1.52

2.53

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

SPI 3

-3-2.5

-2-1.5

-1-0.5

00.5

11.5

22.5

1981

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2003

2004

2005

2006

SPI 6

-3-2.5

-2-1.5

-1-0.5

00.5

11.5

22.5

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

SPI 12

-3.5-3

-2.5-2

-1.5-1

-0.50

0.51

1.52

2.53

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Figure 4-6: Different drought frequencies showed by different SPI time scales


32

SPI on 12-month time scale in December fits with rainfall amount in a year. A Pearson correlation coefficient also indicates that drought years defined by MGA were significantly correlated with drought year defined using SPI 12 calculation in December. SPI 12 calculation indicates the extremely drought year only occurred in 1997. In that year, the lowest SPI 12 was -3.23 in Cengkawak, while -3.01 in Kedungputri and -2.35 in Kaligesing. In severely level of drought, the years experiencing are 1982, 1991, and 1994.

All of these drought years were mainly caused by El Niño. This phenomenon takes place when sea surface temperature in Pacific Ocean increases anomaly, affecting rainfall decreases drastically and lengthening the dry period. Boer et al. (1999) stated that sea surface temperature anomaly has a strong relation with rainfall anomaly. The effect can be seen in two forms, increasing or decreasing rainfall amount. If sea surface temperature anomaly increase 0,50°C, it is called weak El-Niño, 1,1-1,50°C called medium El-Niño, and >1,50°C called strong El-Niño. These kinds of El-Niño will affect to rainfall decrease. Oppositely, the negative anomaly <0.5 indicates there is La-Niña and affecting to rainfall increase. The drought in 1997 was recognized by many experts as the worst El Niño effect in 20th century.

500

1000

1500

2000

2500

3000

3500

4000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006Year

Rai

nfal

l

-3.50-3.00-2.50-2.00-1.50-1.00-0.500.000.501.001.502.00

SP

I

Average annual rainfall (mm)SPI 12 on December

Figure 4-7: SPI 12 on December and average annual rainfall

A threshold level of SPI 12 used -1.0 as the lowest level of a normal rainfall amount is compatible with drought occurrence commonly in Indonesia. It can be seen from Table 4-6 that the threshold also fits with category specified by MGA although MGA classification is not detail in determining drought severity level.

Drought threshold level


33

Table 4-6: Annual rainfall characterized by SPI 12 and MGA classification

SPI class MGA class Year Annual Rainfall Value Category Normality (%) Category

1980 1988 0.00 Near normal 80 Below normal 1981 2641 0.62 Near normal 106 Normal 1982 1755 -1.49 Moderately dry 70 Below normal 1983 2873 0.93 Near normal 115 Above normal 1984 2391 -0.44 Near normal 96 Normal 1985 2955 0.64 Near normal 118 Above normal 1986 2674 0.76 Near normal 107 Normal 1987 2433 -0.16 Near normal 98 Normal 1988 2253 -0.65 Near normal 90 Normal 1989 2807 0.70 Near normal 112 Normal 1990 2537 -0.50 Near normal 102 Normal 1991 1994 -1.11 Moderately dry 80 Below normal 1992 3141 1.16 Wet 126 Above normal 1993 2615 -0.04 Near normal 105 Normal 1994 1852 -1.21 Moderately dry 74 Below normal 1995 2641 0.31 Near normal 106 Normal 1996 2401 0.13 Near normal 96 Normal 1997 1519 -3.13 Extremely dry 61 Below normal 1998 3272 1.75 Wet 131 Above normal 1999 2419 0.43 Near normal 97 Normal 2000 3282 1.40 Wet 132 Above normal 2001 2609 0.77 Near normal 105 Normal 2002 2396 -0.51 Near normal 96 Normal 2003 2439 0.22 Near normal 98 Normal 2004 2411 -0.16 Near normal 97 Normal 2005 2409 -0.11 Near normal 97 Normal 2006 2664 -0.24 Near normal 107 Normal

Six-month SPI time scale in April was done especially to know the trend of rainfall amount during rainy season (seasonal rainfall), as a cumulative rainfall amount in following November, December, January, February, March, and April. Figure 4-8 notices that except dry years based on SPI 12-month time scale, the year 1986/1987 and 1992/1993 were also included as dry year periods. This 6-month SPI time scale is different from SPI 12-month time scale value. SPI 12 especially in December calculates rainfall amount in an entire year started from January until December.


34

250

300

350

400

450

500

1980

/1981

1982

/1983

1984

/1985

1986

/1987

1988

/1989

1990

/1991

1992

/1993

1994

/1995

1996

/1997

1998

/1999

2000

/2001

2002

/2003

2004

/2005

Rai

nfal

l

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

SP

I 6

6 months per moving average rainfall (mm)SPI 6 on April

Figure 4-8: SPI 6 on April and 6-month moving average rainfall

SPI 1-month and 3-month time scales are specially analyzed for each station in the year 2002 and 2006 because these year-periods will also be used in drought analysis based on soil moisture deficit. Table 4-7 indicates that the values of SPI 1 more fit to MGA monthly rainfall categories. As mentioned before, SPI divides rainfall amount into 7 index classes, while MGA only divides into 3 classes. It makes some months indicate normal category while SPI 1 values are negative, such as in January 2002, March 2002, and February 2006. Generally, SPI 3 values indicate less fit classes than the SPI 1. Some normal MGA months were indicated as negative SPI 3 values such as in January and December 2002, March and December 2006, as well as below normal months were indicated as positive SPI 3 values such as in June and July 2006. For each station, the detail of monthly rainfall, normality, and SPI values in 2002 and 2006 can be seen in Appendix 4.

Threshold level


35

Table 4-7: SPI 1 and 3-month time scales for 2002 and 2006

Period Monthly Rainfall (mm)

Normality(%)

MGA Rainfall Category SPI 1 SPI 3

Jan 380 97 Normal -0.1 -0.6 Feb 560 154 Above normal 1.9 0.0 Mar 296 86 Normal -0.3 0.7 Apr 207 94 Normal 0.2 0.7 May 27 29 Below normal -0.6 -0.4 Jun 12 15 Below normal -0.3 -0.5 Jul 0 0 Below normal -0.3 -0.9 Aug 0 0 Below normal -0.1 -0.6 Sep 0 0 Below normal -0.2 -0.9 Oct 9 6 Below normal -0.8 -0.8 Nov 252 70 Below normal -0.3 -0.6

2002

Dec 435 108 Normal 0.3 -0.5 Jan 440 112 Normal 0.5 0.1 Feb 313 86 Normal -0.5 0.7 Mar 331 96 Normal 0.0 -0.1 Apr 516 235 Above normal 1.6 1.1 May 197 214 Above normal 1.2 1.5 Jun 1 2 Below normal -0.8 1.3 Jul 2 7 Below normal -0.1 0.3 Aug 0 0 Below normal -0.1 -1.0 Sep 0 0 Below normal -0.2 -0.6 Oct 0 0 Below normal -1.0 -1.0 Nov 33 9 Below normal -2.3 -2.2

2006

Dec 499 124 Above normal 0.7 -1.1

The bivariate correlation is done to compute Pearson's correlation coefficient, with its significance levels. The correlation measures how MGA categories for each station are related to SPI values. These two variables can be perfectly related, but if the relationship is not linear, Pearson's correlation coefficient does not show an appropriate statistic value. Table 4-8 indicates that drought classified by MGA has high and significant correlations with both monthly rainfall and SPI 1 in each station. On the contrary, SPI 3 shows low correlations.

Table 4-8: Coefficient correlation of drought based on MGA class with monthly

rainfall, SPI 1, and SPI 3

Station Monthly rainfall SPI 1 SPI 3 Kedungputri Pearson Correlation .827(**) .789(**) .313 Sig. (2-tailed) .000 .000 .137Kaligesing Pearson Correlation .895(**) .756(**) .396 Sig. (2-tailed) .000 .000 .056Cengkawak Pearson Correlation .516(**) .606(**) .370 Sig. (2-tailed) .010 .002 .075

** Correlation is significant at the 0.01 level


36

4.3. Soil Moisture Drought Modeling Year of analysis

Due to discontinuous meteorological data, the drought analysis will be focused on available years, 2002 and 2006. Although annually these years are categorized as normal years by MGA classification and as near normal category based on SPI 12, but some months in these years have negative SPI values less than -1.0. The values probably indicate that there are drought occurrences in certain months. In the year 2002 had 2396 mm rainfall, 6 dry months started in May, and the rainy season was started in November. In the year 2006 had 2664 mm rainfall with 6 dry months started in June, and the rainy season start in December.

Inputs used in PCRaster

The inputs needed for soil moisture modeling consist of daily rainfall and meteorological data, soil characteristics, DEM map, as well as crop type characteristics. The meteorological data were collected from Kradenan station. The study area was divided into 3 rainfall zones according to Thiessen polygon method. This way was taken in order to limit the effect of rainfall amount recorded from each station to the each zone. Thiessen polygon was done in ArcView. The result map was converted in Ilwis to the ASCII file format using 10 m pixel size, as well as for other maps used as PCRaster inputs. Then, the ASCII file was made as input map in PCRaster program using asc2map command. The rainfall zone map indicates that 348 hectares area is affected by Cengkawak rainfall, 531 ha by Kaligesing and 181 ha by Kedungputri rainfall.

Figure 4-9: Rainfall zone map

Crop type map in Figure 4-10 was generated based on field observations, interview to the farmers, and to be cross-checked with landuse map. The result reveals that in the study area, there are 3 types of crop patterns which are usually done by the farmers every year. The first crop type is paddy, the same as for 3 areas. The farmers always start to plant paddy at the beginning of rainy season. After the paddy being harvested, they continue to plant different crop for different area. The first agriculture area, in 53 hectares they use groundnut as the second crop. In the second area (325 ha), other farmers choose to continue with paddy again, and in the last area (242 ha) the others prefer to plant tobacco.

Scale N 1 : 100000

=Cengkawak =Kaligesing =Kedungputri


37

Figure 4-10: Crop type map

The digital elevation model (DEM) was derived from elevation points extracted from a digital topographical map. The process was done in ArcView and ERMapper, and than exported to the ASCII format using Global Mapper. In PCRaster program, this format was generated using col2map command become DEM map with dot map (.map) extension. Figure 4-11 notices that in the study area the highest elevation is 124 meters and the lowest is 22 meters. The agriculture area is dominantly located in low elevation.

Figure 4-11: DEM map

Saturated hydraulic conductivity (Ksat) map was generated by using data of soil samples analysis. The analysis was done by using the constant hydraulic head method. This step resulted Ksat of 84 sample points. These values were processed into Ksat map using kriging, a point interpolation technique based on statistical method in the Ilwis 3.3. The Ksat and the other soil characteristics can be referred in Appendix 5.

The process was started with variogram computation, resulting spherical model with nugget 200, sill 750, and range 4000. These values were inputted to run the kriging process. Figure 4-12 shows that Ksat values are various between 15.5 until 62.5 mm/day. Ksat describes water movement within the soil matrix driven by matrix and gravitational potentials. The Ksat value depends on soil texture and

N

Scale 1 : 100000

=paddy-groundnut =paddy-paddy =paddy-tobacco

N Scale 1 : 100000


38

moisture content. In the study area, the highest Ksat value is mainly found in the lower part. This part, based on soil texture analysis, has highest sand content (up to 39%), much more than the other part. It makes water flow easily through soil having large pore size with good connectivity between them.

Figure 4-12: Ksat map

Preparation steps of porosity map were similar to Ksat map process. The source is also 84 points with each porosity value. In the kriging process, the value used in spherical model is: nugget 0.001, sill 0.006, and range 3500. The result shows that centre part of the area has the highest porosity and both upper and lower parts have low porosity. It is also related to soil texture. Texture class in the centre part is clay. Clay content in this part is up to 52%. Horgan (1996) mentioned that a typical bulk density of clay soil is between 1.1 and 1.3 g/cm3, and will give porosity value between 0.58 and 0.51, contrary to sandy soil, which has a typical bulk density between 1.5 and 1.7 g/cm3 and porosity between 0.43 and 0.36. Some soil samples in this study have value more than 0.6, higher than the value in the literature. It is possibly caused by the occurrence of organic matter in the sample. The organic matter dries out in the oven while drying the sample, which creates a weight loss that is seen as porosity.

The highest porosity areas have the lowest Ksat values. It is caused by structured nature of clay minerals, which able to hold a large volume of water per bulk material volume. Doe to ability to hold water tightly, the clayey soil releases water very slowly.

Figure 4-13: Porosity map

N Scale 1 : 100000

N Scale 1 : 100000

http://en.wikipedia.org/wiki/Clay_minerals�


39

Field capacity value shows water content of a saturated soil that has been allowed to freely drain. The values were estimated as a hydraulic tension of 33 kPa (.33 Bar) and depend on the soil texture (Saxton and Walter, 2007). Figure 4-14 shows that centre and north part of the area have the highest field capacity. The value reaches up to 0.432 or 43.2% volume. It is caused by clay texture class in this area. Based on soil texture analysis (see Appendix 6), average clay content in the centre part is 52%, and 48% in the north part. High clay content in the soil makes more water can be held and more available water can support plant growth.

Figure 4-14: Field capacity map

Wilting point is described as a soil water content threshold, below which plants are generally unable to extract it from the soil. The value is also more affected by soil texture. Similar to field capacity map, high wilting point values are also found in the centre and north part of area.

Figure 4-15: Wilting point map

Daily crop cover value was determined by measuring directly on the field and interviewing the farmers about crop age at certain crop cover condition for each crop type. Each crop type was identified what percentage of crop area covering soil surface in certain growth steps. The identification result shows that paddy in the late season stage has the largest crop cover value as 0.99. The groundnut is 0.85 and tobacco 0.80. Tobacco has the smallest crop cover because the farmers always plant it using wide space 60 x 60 cm between the crops.

N Scale 1 : 100000

N Scale 1 : 100000


40

The model calibration was done using soil moisture data collected from the field. The calibration indicates that the optimum evapotranspiration factor coefficient is 7.

Soil moisture

Soil moisture map animation in 2002 and 2006 were resulted by PCRaster script referred in Appendix 7. The animations indicate that the centre part of area has highest daily soil moisture during a year. As known before, this part has the highest clay content therefore it can absorb more water than other parts. Pearson correlation test (Appendix 8) shows that soil moisture is highly correlated with porosity and Ksat.

An auto-correlation analysis was done to observe daily soil moisture from two years calculation. The correlogram shows that soil moisture is highly auto-correlated. It is caused by soil moisture in current day depends on soil moisture in the previous day. The shape of sinusoidal in Figure 4-16 indicates the soil moisture is highly seasonal depending on number of daily rainfall and evapotranspiration.

Figure 4-16: Correlogram of soil moisture 2002


41

Figure 4-17: Correlogram of soil moisture 2006

A cross correlation analysis between rainfall and soil moisture indicates that soil moisture signs positive correlation with one day time lag. Pearson Correlation also shows positive correlation significantly. One day lag describes time needed by rainfall to reach optimal soil moisture. As Narasimhan and Srinivasan (2005) research result that the lag will increase similar to increase of water holding capacity. Cross Correlations: daily rainfall and soil moisture Cross Stand. Lag Corr. Err. -1 -.75 -.5 -.25 0 .25 .5 .75 1 ùòòòòôòòòòôòòòòôòòòòôòòòòôòòòòôòòòòôòòòòú

-5 .396 .037 .ó.*******

-4 .414 .037 .ó.*******

-3 .433 .037 .ó.********

-2 .453 .037 .ó.********

-1 .487 .037 .ó.*********

0 .574 .037 .ó.**********

1 .578 .037 .ó.***********

2 .575 .037 .ó.***********

3 .567 .037 .ó.**********

4 .555 .037 .ó.**********

5 .550 .037 .ó.********** Plot Symbols: Autocorrelations * Two Standard Error Limits.

Figure 4-18: Lag cross-correlation between rainfall and soil moisture


42

Soil moisture deficit

PCRaster map animations of soil moisture deficit on 2002 shows that drought starts to happen in May. In this month drought occurs in area mainly affected by Kedungputri rainfall and small part area affected by Kaligesing rainfall. During this month, rainfall just occurred twice recorded by Cengkawak and Kaligesing station. At that time, agriculture area was planted with paddy, type more sensitive to the water stress. In July, the condition still continued occurring almost in all area, and the most severe deficit was still in paddy field area. In the next months, there was no rain anymore and no agriculture activity. The dry period ended in the middle of November, characterized with raining in view days, especially in the area affected by Cengkawak rainfall. Soil moisture deficit was not occurring anymore in the middle of December.

In 2006, drought also started in May; firstly it occurred in area affected by Cengkawak rainfall and covered with paddy and groundnut crop type. All of areas became drought in the early of July. The most severe drought occurred in paddy and small part in tobacco area. The dry condition was continued until middle of December. During these months, there was no crop anymore in the field. Agriculture activities started in December as the start of rainy season.

Figure 4.19: Drought occur in paddy field

4.4. Correlation of Soil Moisture Drought and SPI In this study, soil moisture drought was defined as a drought caused by deficit of soil moisture content. The soil moisture at wilting point was made as the drought threshold, so the soil moisture deficit is a difference between actual soil moisture and soil moisture at wilting point. Based on this criterion, Table 4-10 illustrates the relation of drought defined by SPI and soil moisture deficit.

Using a value -1.0 as drought threshold level, SPI 1-month time scale only indicates


43

two drought occurrences, October and November 2006. Otherwise, SPI 3-month time scale indicates three drought occurrences. But some SPI 1 and SPI 3 values can indicate that there is soil moisture deficit by showing negative values as dry periods. By following this rule, there are 8 month soil moisture deficits in 2002 as well as 8 months in 2006. These months are also identified as negative value in SPI 1 and SPI 3.

Table 4-10: Drought occurrences based on rainfall, SPI, and soil moisture deficit

Year Month Rainfall (mm)

Rainfallnorma-lity (%)

SPI 1

SPI 3

SPI 6

SPI 12

Average soil moisture

deficit (mm)

2002 Jan 380 97 -0.06 -0.57 0.15 0.50 0 Feb 560 154 1.88 0.00 0.61 0.82 0 mar 296 86 -0.25 0.74 0.58 0.63 0 Apr 207 94 0.18 0.67 -0.18 0.37 0 May 27 29 -0.6 -0.44 -0.38 0.23 52 Jun 12 15 -0.33 -0.53 0 0.19 101 Jul* 0 0 -0.33 -0.91 0 0.00 140 Aug* 0 0 -0.05 -0.61 -0.69 0.00 167 Sep* 0 0 -0.23 -0.90 -0.71 -0.01 186 Oct* 9 6 -0.75 -0.79 -1.08 -0.99 200 Nov 252 70 -0.26 -0.59 -0.71 -1.01 110 Dec 435 108 0.32 -0.50 -0.65 -0.51 32006 Jan 440 18 0.48 0.10 -0.02 0.06 0 Feb 313 86 -0.48 0.74 -0.14 -0.23 0 Mar 331 96 0.03 -0.09 -0.17 -0.12 0 Apr 516 235 1.56 1.06 0.69 0.38 0 May 197 214 1.16 1.52 1.59 0.76 6 Jun 1 2 -0.82 1.33 0.97 0.54 38 Jul 2 7 -0.11 0.32 0.74 0.42 99 Aug* 0 0 -0.05 -0.95 0.81 0.43 139 Sep* 0 0 -0.23 -0.63 0.83 0.35 168 Oct* 0 0 -1.04 -1.04 -0.23 0.06 189 Nov* 33 9 -2.3 -2.23 -2.19 -0.11 190 Dec 499 124 0.66 -1.07 -1.16 -0.24 48

* No crop during this month

Using values in Table 4-10 above, correlation test was done to get correlation between soil moisture deficits with various SPI time scales. Pearson correlation coefficients indicate that SPI 1 has higher and significant correlation compared to the other time scales.


44

Table 4-11: Correlation of soil moisture deficit with SPI

SPI 1 SPI 3 SPI 6 SPI 12Soil moisture deficit Pearson Correlation .639(**) .619(**) .168 .150 Sig. (2-tailed) .001 .001 .432 .485

** Correlation is significant at the 0.01 level (2-tailed).

Based on PCRaster output map, in 2002 the drought started in May. The first area experiencing with drought was in the north part. This area was mainly planted with paddy. During this month, SPI 1 and SPI 3 also indicated dry month although the values are bigger than -1.0. It was designated that even the SPI value did not reach -1.0 but the rainfall amount was not enough for the soil to support crop water requirement. On the contrary, in the early 2002 and 2006 although some SPI values were negative but the water was enough to supply the crop. These could occur because the rainfall amount at these months was smaller than long term average, but these amounts were still enough to support crop requirement.

In June until November 2002, PCRaster patterns showed almost whole area experienced with drought whereas in this period, SPI 1 pattern just shows real droughts in October and November. The agricultural droughts occurred in May, June, and November during plantation period. In May, the drought happened mainly in paddy area while in June happened mainly in paddy and groundnut area. These droughts happened during last period of second crop rotation. In November, the drought occurred almost in whole crop area because there was still not enough water during the start of first crop rotation.

In 2006, the comparison between drought indicated by SPI and PCRaster pattern are more correlated than in 2002. In the first half year, SPI and PCRaster patterns just show small drought. In August until November, PCRaster patterns indicate the drought increase while SPI 1 and SPI 3 show the increase in October and November. The drought affect to almost crop area mainly indicated in July as the last of second plant rotation. In December, the drought still happened but in the narrower area.


45

SPI and PCRaster output maps

SPI 1-month 2002 SPI 3-month 2002 moisture deficit 2002 (mm)

January 2002

February 2002

March 2002

April 2002

May 2002

June 2002


46

July 2002

August 2002

September 2002

October 2002

November 2002

December 2002


47

SPI 1-month 2006 SPI 3-month 2006 moisture deficit 2006 (mm)

January 2006

February 2006

March 2006

April 2006

May 2006

June 2006


48

July 2006

August 2006

September 2006

October 2006

November 2006

December 2006

Figure 4-20: PCRaster output maps compared with SPI maps


49

From two-year simulations (in 2002 and 2006), the most severe drought occurred in north and centre parts of study area. Although this area received more rainfall amount, but soil characteristics and crop type made them get more severe drought. The wilting point map shows that these areas have higher wilting point values. It makes the area need more water to support crop available water. The crop type of north part area is paddy both in the first and the second rotation. At the same growth stage, paddy has the highest crop coefficient than groundnut and tobacco. Consequently, at the same soil water and climate condition paddy will evaporate more water, and will make it more vulnerable to the drought. It makes this area needs more water compared with those planted with groundnut or tobacco. As FAO (2004) stated, a rice system needs water for three main purposes: evapotranspiration, seepage and percolation, and land management practices. Almost 50% in that system is used for evapotranspiration.


50

5. Conclusions and Recommendation

Annual rainfall characteristics such as rainfall amount, start of dry period, number of dry month, and start of rainy season can describe drought occurrence at that year. The drought years classified using rainfall anomaly by MGA are significantly correlated with droughts based on SPI 12-month time scale in December, as well as SPI 1-month time scale has a high and significant correlation with monthly rainfall deficiency.

Soil moisture modeling generated using PCRaster can describe drought characteristics based on soil moisture deficit with flexible time scale. PCRaster outputs provide information about when, where, and how much water deficit occur in each time step.

In daily time scale, soil moisture is closely linked with rainfall for time lag of one day. In monthly time scale, drought information provided by SPI is less suitable in assessing agricultural drought compared with modeling in PCRaster. It can be understood because SPI just assess anomaly value of rainfall in a certain time scale, it does not consider with water quantity needed by each type of crops. Using SPI, a drought can be identified by showing negative value of SPI 1-month time scale.

Drought analysis from socio-economic point of view has not been analyzed in this study. Therefore for the further study, the relation between SPI on a short time scale and soil moisture modeling can be analyzed related to economic indicators such as crop production. In addition to identifying the drought affected area, the study could be more meaningful if effects of drought on crop production were assessed.


51

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54

Appendices

Appendix 1: Start of dry period and average dry month number

Station Year Kedungputri Kaligesing Cengkawak

Average Average dry month number

1980 May April May May 6 1981 June July June June 3 1982 May May May May 7 1983 June June June June 4 1984 April May May May 6 1985 May May May May 4 1986 May May April May 4 1987 May April April April 7 1988 April April April April 5 1989 September September September September 1 1990 June June April May 5 1991 May May May May 6 1992 June May May May 3 1993 June May May May 6 1994 May May May May 7 1995 May April April April 5 1996 May April April April 5 1997 March March March March 8 1998 August August August August 3 1999 June June June June 5 2000 May May June May 4 2001 June June May June 5 2002 May April May May 6 2003 April April April April 6 2004 April April April April 6 2005 May May May May 5 2006 June June June June 6

Average May 5 Appendix 2: Correlation between dry month number and annual rainfall

Annual rainfall Dry Month Number

Annual rainfall Pearson Correlation 1 -.841(**) Sig. (2-tailed) . .000Dry month number Pearson Correlation -.841(**) 1 Sig. (2-tailed) .000 .



55

Appendix 3: Start of rainy season in each station

Kedungputri Kaligesing Cengkawak Average delay from normal start (month)

1980* November October November 1 1981 September September September - 1982 December December December 2 1983 October October October 0 1984 November November November 1 1985 October October October 0 1986 September September September - 1987 November November November 1 1988 October October October 0 1989 October October October 0 1990* November December November 1 1991 November November November 1 1992 August August August - 1993 November November November 1 1994 November January(**) November 1 1995 October October October 0 1996 October October October 0 1997 December December December 2 1998 October October October 0 1999* October November November 1 2000 October October October 0 2001 October October October 0 2002 November November November 1 2003* November October October 0 2004 November November November 1 2005* October November October 0 2006 December December December 2

Average October October October (*) : one or two stations started in different month (**): started in the next year


56

Appendix 4: Monthly rainfall, normality, and SPI values in 2002 and 2006 for each station

Kedungputri Kaligesing Cengkawak Monthly

rainfall (mm) Normality

(%) SPI 1 SPI 3 Monthly rainfall (mm)

Normality (%) SPI 1 SPI 3 Monthly

rainfall (mm) Normality

(%) SPI 1 SPI 3

Jan 512 122 0.84 -1.06 432 104 0.22 -1.08 196 57 -1.31 0.65 Feb 464 119 0.71 -0.78 988 245 2.42 0.97 227 76 -0.79 -0.72 Mar 454 121 0.59 1.04 189 53 -1.3 1.65 246 83 -0.23 -1.21 Apr 397 162 0.93 1.08 60 25 -0.92 0.76 163 92 0.21 -0.54 May 33 36 -0.57 0.72 0 0 -1.04 -1.8 47 65 -0.1 -0.24 Jun 36 43 0.08 0.38 0 0 -0.23 -1.8 0 0 -0.23 -0.39 Jul 0 0 -0.23 -0.58 0 0 0.14 -1.45 0 0 0.14 -0.75 Agt 0 0 0.23 -0.21 0 0 0.43 -0.53 0 0 0.33 -0.65 Sep 0 0 -0.05 -0.65 0 0 0.14 -0.33 0 0 0.33 -0.43 Oct 26 18 -0.59 -0.63 0 0 -0.23 -0.33 0 0 -0.65 -0.65 Nov 200 59 -0.52 -0.73 265 72 -0.18 -0.51 290 79 -0.04 -0.46

2002

Dec 440 103 0.21 -0.78 558 136 0.74 -0.02 307 83 -0.12 -0.68 Jan 340 81 -0.65 -1.01 553 133 0.96 0.68 427 124 0.74 0.09 Feb 333 86 -0.42 0.05 352 87 -0.13 0.96 254 85 -0.44 0.62 Mar 294 78 -0.39 -0.92 356 100 0.13 0.41 342 115 0.45 0.42 Apr 614 251 1.63 0.85 707 299 1.84 1.19 226 127 0.58 0.29 May 159 173 0.92 1.27 294 262 1.31 1.77 137 190 0.97 0.74 Jun 4 5 -0.45 1.26 0 0 -0.23 1.57 0 0 -0.23 0.4 Jul 7 23 0.11 0.12 0 0 0.14 0.53 0 0 0.14 0.08 Agt 0 0 0.23 -0.54 0 0 0.43 -0.53 0 0 0.33 -0.65 Sep 0 0 -0.05 -0.32 0 0 0.14 -0.33 0 0 0.33 -0.43 Oct 0 0 -0.9 -0.9 0 0 -0.23 -0.33 0 0 -0.65 -0.65 Nov 35 10 -1.7 -2.28 0 0 -1.45 -1.79 64 17 -1.54 -1.7

2006

Dec 602 140 0.95 -0.9 540 131 0.68 -0.68 355 96 0.12 -1.32


57

Appendix 5: Result of soil texture analysis

Sample code Sand % Silt % Clay % Texture class Organic matter %

1 13.20 48.73 38.07 Silty clay loam 2.24 2 27.15 39.50 33.35 Clay loam 2.24 3 12.73 35.00 52.27 Clay 1.51 4 39.40 37.52 23.08 Loam 1.48 5 12.74 39.82 47.44 Silty clay 1.50 6 20.23 31.79 47.98 Clay 2.23 7 14.97 45.37 39.66 Silty clay 2.26


58

Appendix 6: Soil characteristics each sampling point

X_Coord Y_Coord Point Soil type Texture Crop type moisture pore Ksat 392223.75711 9142697.99930 1.01 aquertic udifluvent Silty clay loam Paddy-Groundnut 0.27 0.58 18.41392142.82072 9142613.01609 1.02 aquertic udifluvent Silty clay loam Paddy-Groundnut 0.26 0.61 40.51392005.22887 9142580.64154 1.03 aquertic udifluvent Silty clay loam Paddy-Groundnut 0.25 0.58 22.10392074.02480 9142722.28022 1.04 aquertic udifluvent Silty clay loam Paddy-Groundnut 0.19 0.44 76.42391539.84464 9141273.51888 1.05 aquertic udifluvent Silty clay loam Paddy-Paddy 0.19 0.45 67.22391750.27924 9142115.25731 1.06 aquertic udifluvent Silty clay loam Paddy-Paddy 0.20 0.46 81.02391527.70418 9141504.18758 1.07 aquertic udifluvent Silty clay loam Paddy-Paddy 0.25 0.57 24.86391742.18561 9141856.26087 1.08 aquertic udifluvent Silty clay loam Paddy-Paddy 0.26 0.61 42.36391721.95151 9141512.28122 1.09 aquertic udifluvent Silty clay loam Paddy-Paddy 0.21 0.48 49.73391604.59375 9141957.43135 1.10 aquertic udifluvent Silty clay loam Paddy-Paddy 0.18 0.43 83.78391535.79782 9141706.52855 1.11 aquertic udifluvent Silty clay loam Paddy-Paddy 0.19 0.45 75.50391806.93472 9142406.62830 1.12 aquertic udifluvent Silty clay loam Paddy-Paddy 0.23 0.54 36.84392150.91436 9142908.43391 2.01 aquertic udifluvent Clay loam Paddy-Tobacco 0.21 0.47 57.10392867.20139 9142936.76164 2.02 aquertic udifluvent Clay loam Paddy-Tobacco 0.22 0.51 48.79393417.56883 9143175.52399 2.03 aquertic udifluvent Clay loam Paddy-Tobacco 0.21 0.45 59.86392377.53625 9142815.35706 2.04 aquertic udifluvent Clay loam Paddy-Tobacco 0.19 0.42 66.84392956.23142 9143163.38353 2.05 aquertic udifluvent Clay loam Paddy-Tobacco 0.23 0.52 41.42393433.75611 9142956.99574 2.06 aquertic udifluvent Clay loam Paddy-Tobacco 0.19 0.43 78.26392624.39223 9142900.34027 2.07 aquertic udifluvent Clay loam Paddy-Tobacco 0.19 0.39 74.40393049.30827 9143029.83849 2.08 aquertic udifluvent Clay loam Paddy-Tobacco 0.17 0.40 91.15393579.44161 9142993.41711 2.09 aquertic udifluvent Clay loam Paddy-Tobacco 0.22 0.50 50.64392709.37544 9143078.40032 2.10 aquertic udifluvent Clay loam Paddy-Tobacco 0.20 0.46 62.62393138.33829 9143126.96215 2.11 aquertic udifluvent Clay loam Paddy-Tobacco 0.23 0.47 42.36393312.35153 9143110.77488 2.12 aquertic udifluvent Clay loam Paddy-Tobacco 0.19 0.44 69.98392511.08129 9142095.02321 3.01 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Groundnut 0.22 0.53 61.68392215.66347 9142414.72194 3.02 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Groundnut 0.24 0.53 26.71392442.28536 9142333.78556 3.03 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Groundnut 0.20 0.50 72.74


59

X_Coord Y_Coord Point Soil type Texture Crop type moisture pore Ksat 392741.74999 9142333.78556 3.04 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Groundnut 0.23 0.53 46.97392001.18205 9141326.12753 3.05 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.22 0.53 70.90392341.11487 9141605.35807 3.06 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.23 0.55 34.99392211.61665 9141257.33160 3.07 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.21 0.50 63.53391940.47976 9141803.65222 3.08 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.25 0.55 19.34391891.91792 9141504.18758 3.09 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.18 0.39 81.94392207.56983 9141848.16723 3.10 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.19 0.44 60.77392114.49299 9141564.88987 3.11 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.23 0.55 37.75391968.80749 9142123.35095 3.12 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.22 0.51 49.73392078.07161 9143313.11585 4.01 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.25 0.56 13.80392616.29859 9143640.90822 4.02 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.27 0.59 6.46 392980.51234 9144077.96471 4.03 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.27 0.61 4.61 392470.61309 9143373.81814 4.04 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.21 0.51 41.42392454.42582 9143790.64053 4.05 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.24 0.53 19.34393162.61921 9144280.30568 4.06 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.21 0.46 49.73392260.17849 9143515.45682 4.07 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.22 0.51 44.18392778.17137 9143806.82781 4.08 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.23 0.51 29.47392802.45228 9144276.25886 4.09 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.20 0.51 51.55392689.14134 9143430.47361 4.10 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.28 0.62 7.37 392681.04770 9143984.88786 4.11 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.21 0.46 44.18392956.23142 9144494.78711 4.12 vertic eutrudepts,oxyaquic eutrudepts Loam Paddy-Paddy 0.26 0.51 12.89393445.72013 9144229.75981 5.01 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.24 0.54 19.34393881.33264 9144765.89829 5.02 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.27 0.52 6.46 394347.10294 9144886.52944 5.03 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.22 0.47 33.14393743.94715 9144236.46154 5.04 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.27 0.61 7.37 393713.78937 9144916.68723 5.05 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.21 0.46 32.23394430.87458 9145151.24782 5.06 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.20 0.42 39.60393405.50974 9144722.33704 5.07 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.27 0.61 2.76 394035.47245 9144407.35568 5.08 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.27 0.58 24.58


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X_Coord Y_Coord Point Soil type Texture Crop type moisture pore Ksat 394055.57764 9144983.70454 5.09 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.26 0.59 11.04393656.82465 9144588.30242 5.10 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.23 0.44 23.21394169.50707 9144722.33704 5.11 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.23 0.49 28.54394648.68083 9144896.58204 5.12 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Paddy 0.22 0.48 31.30394495.37874 9143589.74451 6.01 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.23 0.54 29.47394918.42550 9144335.31207 6.02 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.27 0.62 23.02395856.66783 9144171.95738 6.03 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.27 0.58 26.71394143.53786 9144008.60269 6.04 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.28 0.61 15.65394776.01372 9144004.41411 6.05 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.29 0.55 11.04395609.54150 9144104.94007 6.06 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.27 0.61 17.50394545.64172 9144176.14597 6.07 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.16 0.32 49.73395354.03801 9144272.48335 6.08 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.16 0.42 51.55396309.03467 9144247.35186 6.09 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.21 0.53 31.30394574.96179 9143803.36218 6.10 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.17 0.39 42.36395240.94630 9143991.84836 6.11 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.20 0.53 40.51396639.93263 9144268.29477 6.12 vertic eutrudepts,oxyaquic eutrudepts Clay Paddy-Paddy 0.24 0.50 41.42393753.45484 9142528.03289 7.01 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.24 0.53 25.78393971.98309 9143126.96215 7.02 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.24 0.58 22.10393466.13066 9143612.58048 7.03 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.31 0.68 2.76 393838.43805 9142778.93569 7.04 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.24 0.50 28.18394004.35764 9143381.91178 7.05 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.25 0.53 20.26393215.22786 9143470.94180 7.06 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.23 0.58 31.30393785.82939 9142989.37030 7.07 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.23 0.50 26.71393899.14034 9143572.11229 7.08 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.26 0.61 11.98393486.36476 9143839.20237 7.09 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.25 0.63 12.89393834.39123 9143211.94536 7.10 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.27 0.60 8.28 394101.48131 9143677.32959 7.11 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.24 0.58 28.54393696.79937 9143972.74741 7.12 vertic eutrudepts,oxyaquic eutrudepts Silty clay Paddy-Tobacco 0.26 0.63 12.89


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Appendix 7: PCRaster script for the generation of soil moisture deficit (Jetten, 2007).

##################################################### # soil moisture model for Gesing area # # # # Description: single layer soil water balance # # FAO Penman-Monteith ET calculation from meteo data # # timestep: daily # # input: daily rainfall of 3 stations and daily meteorological, DEM, crop cover type, # # Soil properties # # # # Adopted from: # # NGIC project sub-component 1.4 Desertification # # soil moisture model for the BoumBougour Soum # # Version 0.9 beta # # Date: 070606 # # (c) V. Jetten, ITC, ICC Mongolia # ##################################################### binding ### Input ### RainfallZone = rainzone.map; # map with areas 1,2 and 3 for the 3 stations rain_tbl = rain2006.tss; # table with 3 columns for the 3 rainfall stations in mm/day meteo_tbl = meteo.tss; fc_tbl = fc.tss; Kcb_tbl = Kcb.tss; DEM = dem.map; Croptype = croptype.map; Ksat = ksat.map; # saturated hydraulic conductivity mm/day porosity = porosity.map; theta_i = fcap.map; # initial soil moisture content (fraction) theta_wp = wpoint.map; # gesing = maskgesing.map; paddy = paddy.map; gnut = gnut.map; tbacco = tbacco.map; ### Output ### SoilMoisture = moist; dz = soildepth.map; theta_rel = theta_r; def_area = dfar; wtrdef = wdef; theta_def = tdef; ### Constants ### Perc_param = 3; # power of percolation reduction function Trans_param = 6; # power of transpiration reduction function z = 50; #elevation #Stefan-Boltzmann constant


62

sigma = 4.903E-9; #latent heat of vaporization MJ/kg L = 2.45; #psychrometric constant (kPa/oC) gamma = 0.067; timer 1 365 1; tenday = 1,10+10..endtime; # save result every 10 days initial dt = 1; #timestep 1 day, not really needed but for clarity # create mask of the area, filled with 1. mask = gesing; # assumed rooting depth of plants in mm rootingdepth = 1000; # saturated hydraulic conductivity mm/day Ksat = Ksat*gesing; #porosity (-) porosity = porosity*gesing; #wilting point (-) theta_wp = theta_wp *gesing; # 50% inflection point on transpiration curve (-) theta50 = (theta_wp + porosity)/3; #reldepth: asumed that soil depth is related to slope; reldepth = 1 - sqrt(slope(DEM)); report dz = reldepth * rootingdepth*gesing; # initialize soil moisture theta and relative theta theta = theta_i*gesing; theta_rel = theta/porosity; # totals, initialize ETacum = 0; ETocum = 0; Pcum = 0; thetacum = 0; day = 0; PI = 3.141593; lat = -7.7/180*PI; # radians latitude of location albedo_soil = 0.23; albedo_plant = 0.20; dynamic ############################ ### meteoorological data input ### ############################ # rainfall for 3 stations P = timeinputscalar(rain_tbl, RainfallZone)*gesing; # mm/day # relative humidity RH = timeinputscalar(meteo_tbl, 1); # min, max, avg temperature Tmax = timeinputscalar(meteo_tbl, 2); Tmin = timeinputscalar(meteo_tbl, 3);


63

T = (Tmin+Tmax)/2.0; # average wind speed m/s U = timeinputscalar(meteo_tbl, 4); # daily sunshine duration dur = timeinputscalar(meteo_tbl, 5); # maximum daily sunshine duration during clear sky maxdur = timeinputscalar(meteo_tbl, 6); # extraterrestrial daily radiation arriving at the top of the canopy in J/s/m2 Ra = timeinputscalar(meteo_tbl, 7); # vegetation cover for each crop type fc = timeinputscalar(fc_tbl, Croptype); # FAO crop factor for 3 crop types Kcb = timeinputscalar(Kcb_tbl, Croptype); ############################## ### solar movement and daily radiation ### ############################## day = day + 1; # declination declin = -23.45 * PI/180 * cos(2*PI*(day+10)/365); # incoming shortwave radiation in J/m2/day Rs = (0.25+0.5*dur/maxdur)*Ra; # albedo as weighted sum albedo = albedo_plant*fc + albedo_soil*(1-fc); # saturated vapour pressure (kPa) e_s = 0.611*exp((17.32*T)/(237.3+T)); # actual vapour pressure (kPa) e_a = RH/100*e_s; # slope vapour pressure curve, kPa/oC delta = 4098/((237.3+T)**2) * e_s; # clear-sky radiation Rso = (0.75+2*z/100000)*Ra; # outgoing net longwave radiation Rl=sigma*((Tmax+273)**4+(Tmin+273)**4)/2*(0.34-0.14*e_a**0.5)*(1.35*Rs/Rso-0.35); #net solar or shortwave radiation Rns = (1-albedo)*Rs; #net radiation Rn = Rns - Rl; #################### ### evapotranspiration ### #################### report ETo=(0.408*delta*(Rn-0)+gamma*900/(T+273.16)*U*(e_s-_a))/(delta+gamma*(1+0.34*U)); # relative soil moisture content = theta/pore theta_rel = theta/porosity; # soil evaporation linear with relative soil moisture content (mm) report Ea = theta_rel * ETo*dt;


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TaFactor = 1/(1+(theta50/theta) ** Trans_param); # plant transpiration, gaussian shape function with ETp (mm) Ta = Kcb * TaFactor * ETo * dt; report Ta = if(theta lt theta_wp, 0, Ta); # actual evapotranspiration ETa (mm) sum with vegetation cover report ETa = ( 1 - fc) * Ea + fc * Ta; ################ ### infilration ### ################ # assumed all precipitation will be infiltrated doe to terrace system in agriculture areas Infil = P*gesing; ################## ### Percolation ### ################## # assume dH/dz = 1 percolation depends on unsaturated hydr conductivity report Perc = Ksat* (theta_rel ** Perc_param)* dt * gesing; #################### ### soil moisture ### #################### # update soil moisture (fraction) with all fluxes: fraction + mm/day * day / mm theta = theta + (Infil - ETa - Perc ) * dt/dz*gesing; # cannot be less than 0.01 and more than porosity report theta = max (0.01, min(theta, porosity)); report SoilMoisture = theta * dz *dt; #(mm) ################### ### water deficit ### ################### #which area facing water deficit report def_area = if(theta lt theta_wp, 1,0)*gesing; #how much deficit of water (mm/day) theta_deficit = (theta - theta_wp)*dz; report wtrdef = if(theta_deficit gt 0, 0, (theta_wp - theta)*dz);


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Appendix 8: Correlation of soil moisture and soil characteristics

Moisture Porosity Ksat Wilting point

Field capacity

Pearson Correlation 1 .889(**) -.841(**) .003 -.007

Moisture Sig. (2-tailed) . .000 .000 .981 .947 Pearson Correlation .889(**) 1 -.738(**) .027 .023

Porosity Sig. (2-tailed) .000 . .000 .810 .836 Pearson Correlation -.841(**) -.738(**) 1 -.010 .032

Ksat Sig. (2-tailed) .000 .000 . .931 .773 Pearson Correlation .003 .027 -.010 1 .982(**) Wilting

point Sig. (2-tailed) .981 .810 .931 . .000 Pearson Correlation -.007 .023 .032 .982(**) 1 Field

capacity Sig. (2-tailed) .947 .836 .773 .000 .


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DROUGHT MONITORING USING RAINFALL DATA … MONITORING USING RAINFALL DATA AND SPATIAL SOIL MOISTURE MODELING Thesis submitted to the Graduate School, Faculty of Geography, Gadjah Mada