RUNOFF ESTIMATION IN AN UNGAUGED WATERSHED USING RS AND GISDr. M.V.S.S.Giridhar1 and Dr. G.K.Viswanadh2

1Asst. Prof., Centre for Water Resources, Institute of Science andTechnology, J N T U, Hyderabad

2 Professor and Head, Dept. of Civil Engineering, J.N.T.U.C.E.H, JNTU,Hyderabad - 500 085, India

AbstractThe rainfall-runoff process in a catchment is a complex

and complicated phenomenon governed by large number ofknown and unknown physiographic factors that vary both in spaceand time. Runoff estimation in ungauged catchment is a seriouschallenge for the hydrologist especially in developingcountries where most of the watersheds are ungauged. There arenumerous approaches available ranging from lumped tophysically based distributed models runoff estimation.However, these approaches are lacking the addressing thespatial variability of the parameters involved in rainfallrunoff process. Geographical Information System (GIS) andRemote Sensing (RS) technology can augment the conventionalmethods to a great extent in rainfall runoff studiesespecially for the ungauged watershed which lacks in data. Inthe present study an attempt has been made to investigate theimportance of spatial variability of different parametersinvolved in runoff estimation using SCS-CN method. This methodincludes several important properties of the watershed namelysoils permeability, land use and antecedent moisture conditionwhich are taken into consideration. The data on land use hasbeen prepared from IRS LISS-III imageries for Palleru subbasin (K-11) and ERDAS 8.7 and Arc GIS9.1 software were usedfor data generation, storage, manipulation and integration toestimate the daily runoff for the year 2000 and 2002. Rainfallreceived in the year 2000 and 2002 for the basin is 919.80mmand 555.80 mm respectively, runoff has been estimated for theyear 2000 and 2002 as 452.50 mm and 187.20 mm respectivelyusing SCS-CN approach. Percentage of runoff estimated usingSCS-CN for the year 2000 and 2002 is 49.2% and 33.6%respectively.

1.0 Introduction

Hydro logical models are divided in two groups mainlyStochastic and deterministic model. In the deterministic modelsystem is spatially arranged as a single point in spacewithout dimensions. The application of conceptual hydrologicalmodels in ungauged watersheds or watersheds with limited datato generate runoff records for planning and design purposes isintriguing. In such applications, the hydrological models arecalibrated to gauged watersheds of a homogeneous region, andregional equations explaining the variation of the modelparameters with physiographic factors are developed or theaverage model parameter values are used in the application ofthe model in ungauged watersheds of the region. However, theuncertainty of the calibrated model parameters is high enoughto simulate the hydrologic response of ungauged watershedswith reduced efficiency.

The SCS-CN method has recently been used quite widelysince the tabulated curve number values provide a relativelyeasy way of moving from a GIS data set on soils andvegetation to a rainfall-runoff model (Berod et al., 1999).Moore and Clarke (1981) showed that a variety ofdistributions can be easily incorporated into this type ofmodel structure and they derive analytical equations for theresponses of different distributions.

Hosking and Clarke (1990) was extended Moore and Clarkework, to show how the model can be used to derive arelationship between the frequencies of storm rainfall andflow peak magnitudes in an analytical form.

Moore (1985) examines the case where the stores/losewater to deep drainage and evapotranspiration, while Moore andClarke (1983) link the model to predicting sediment productionas well as discharges. A recent review of PDM concepts andequations has been provided by Clarke (1998). The modelcontinues to be used and developed. Recent work at the UKInstitute of Hydrology has shown the model used for long runsto derive flood frequencies (Lamb 1999), and also in a moredistributed application with radar rainfall and snowmeltinputs for flood forecasting.

Runoff estimation is required for planning and executionof water resource projects. Several methods are available forestimation of runoff. Among them, the USDA Soil ConservationService curve number (SCS-CN) method is the most popular andwidely used. The advantages of this method are its simplicity,

predictability, stability and its reliance on only oneparameter namely the Curve Number (CN). The land Use / LandCover classes can be integrated with the hydrologic soilgroups of the sub basin in GIS and the weighted CN can beestimated. These estimated weighted CN for the entire area canbe used to compute runoff. The computed runoff values can bechecked with the observed data. The main inputs required tothe SCS-CN method are delineation of the watershed boundary,preparation of soil map, preparation of landuse/land coverthematic map and antecedent moisture condition to estimatedaily runoff. The objective of the present paper is tocalculate daily runoff for K-11 sub basin of river Krishna,Andhra Pradesh, India using remote sensing and GIS for theyears 2000 and 2002.

2.0 Study Area The Krishna basin is situated between Eastlongitudes 730 21' to 810 09' and North latitudes 130 07' to19025' in the Deccan plateau covering large areas in the Statesof Maharashtra, Karnataka and Andhra Pradesh. The Krishnabasin extends over an area of 2,58,948 sq.km., which is nearly8% of the total geographical area of the country. Krishnacatchment was divided in to 12 sub basins in the report ofKrishna Water Dispute Tribunal. In the present study K-11 subbasin i.e Palleru basin of River Krishna has been consideredto analyze meteorological drought at each rain gauge sub area.The Palleru sub basin lies between latitudes 160 39’ and 170 50’

North and longitudes 790 17’ and 800 09’ East. The Palleru subbasin has a catchment area of 3,263km2, which constitutes 1.3%of the total basin area. The catchment area of the Palleru subbasin entirely lies in the State of Andhra Pradesh. In thepresent study Palleru sub basin has considered up to Pallerureservoir only. Ten rain gauge stations (Palakurthy,Kodagandla, Torrur, Tungaturthy, Aravapally, Maripeda,Noothankal, Atmakur, Kushmanchi and Mothey) were identified inand around the study area.

3.0 MethodologyThe Survey of India topographic maps namely 56 O7, O10,

O11, O12, O15, O16, 56-P13, 65-C4 and 65-D1 on a scale of1:50,000 were collected. The collected topographic sheets werescanned and registered with tic points and rectified in Arc

map of ArcGIS 9.1. Further, the rectified maps were projectedand merged together as a single layer. The present study areaof Palleru sub basin along with sub areas based on Thiessennetwork was delineated in GIS environment using

The soil maps were collected from National Bureau of SoilSurvey and Land Use, Nagpur which were prepared on a scale of1:5,00,000. The collected soil maps were scanned andregistered with tic points and rectified. Further, therectified maps were projected. All individual projected mapswere finally merged as a single layer. And later, thedelineated study area map of Palleru sub basin was overlaid onprojected soil map and finally, soil map pertaining to thestudy area was thus extracted in GIS environment. Boundariesof different soil textures were digitized in ARC/INFO and thepolygons representing soil classes were assigned differentcolours for reorganization of hydrologic soil groups.

3.2 Preparation of Land Use / Land Cover Thematic MapSpatial data in the form of satellite imageries for the

preparation of Land Use/Land Cover details at sub basin levelwere procured from National Remote Sensing Agency (NRSA).These satellite imageries for both Kharif and Rabi seasons fortwo years pertain to Indian Remote Sensing Satellite (IRS) -1C& 1D, Linear Imaging and Self Scanning Sensor (LISS –III) witha resolution of 23.5m. The collected satellite images weregeoreferenced in ERDAS 8.7 then rectified and finallyprojected. The delineated watershed in vector form wasoverlaid on projected satellite imagery to get sub set of thestudy area. Thiessen polygon coverage which was alreadyprepared in GIS in vector form was overlaid on sub set imageof the study area in ERDAS 8.7 to get delineation for all tensub areas. Normalized Difference Vegetation Index (NDVI) wasemployed as the basis for Land Use / Land Coverclassification. This method of classification has been foundto be suitable for the study area i.e., Palleru sub basin asthe data used was pertaining to the past period i.e., years2000 and 2002 and also the study area is considerably largecomprising predominantly of vegetation.

Study area has been classified for Land Use / Land Coverinto four classes viz., Water bodies, Crop land, Bare soil andFallow land with bushes in each sub area based on NDVI valuesgenerated in ERDAS 8.7. Area under each class has been

calculated from the attribute table. The classified thematicmap was converted from raster to vector format in Arc GIS 9.1for further analysis. The Land Use / Land Cover thematic mapand soil map were intersected in command tools of ARC/INFO.The areas of different Land use class and soil combinationswere obtained in the attributes selection menu by usinglogical expression and accordingly different CN values wereassigned. Then weighted CN for each sub area in the study areawas worked out. The calculated weighted curve number is usedfor the calculation of recharge capacity of each sub basin.The CN values for AMC-II condition were converted into CNvalues for AMC-I and AMC-III conditions. The calculated dailyrunoff has been converted to monthly and yearly runoff forfurther analysis for the years 2000 and 2002.

4.0 Results and DiscussionRainfall analysis indicated a declining trend in the

basin represented by the equation y = -0.5852x +69.307. Trend line fitted for yearly observed runoff reflecteda declining trend given by the equation y = -0.6986x + 35.999.The trend lines of both yearly rainfall and yearly runoffshowed decreasing trend over Palleru sub basin for the years2000 and 2002. The regression model is developed betweenrainfall and run off is y = 0.0101x2 + 0.208x - 0.0486 withR2as 0.9396 in broader sense. Comparison between variation ofdaily rainfall and daily runoff for each sub area was carriedout for the years 2000 to 2002. Daily values of rainfall anddaily runoff were converted into monthly values which weregiven in Table. Graphical representations of these monthlyvalues were shown in Figs from 7.28 to 7.37. On 4th and 6th ofJune 2000, rainfall recorded were 90 mm and 80 mmrespectively. The corresponding runoff calculated on thesedates was found to be very high i.e 78 mm and 68 mmrespectively, as the field already reached AMC condition IIIby that time. Similarly, on 23rd, 24th and 26th of August 2000,rainfall recorded was 60 mm, 127 mm and 54 mm respectively andthe corresponding runoff calculated was found to be high i.e.,49 mm, 67 mm and 43 mm respectively due to the same reasonmentioned above.

Correlation coefficient between monthly rainfall and themonthly computed runoff from SCS-CN method for each sub areawere calculated and given in Table 7.36. Considering all the

sub areas, as a whole, monthly correlation coefficientsexhibited 0.863 i.e., a good fit between rainfall and runoff.

Average runoff estimated from SCS-CN method fordifferent sub areas as 40, 34, 38, 37, 37, 33, 33, 27, 26 and36 percentage of rainfall for Palakurthy, Kodagandla, Torrur,Maripeda, Tungaturthy, Noothankal, Aravapally, Atmakur, Motheyand Kushmanchi respectively. Average runoff for the entirePalleru sub basin was estimated to be 36% of rainfall.

The rainfall run off relationship for four months is established. Thepercentage of run off is varying from 5% to 28% with minimum in Sept. tomaximum in Aug. The average percentage of run off is estimated as 21 %. Theregression model is developed between rainfall and run off is Y=0.00048 x20.139*+0.3342 in broader sense. it could be concluded that model can beapplied for estimating run off and evaluating. on struvtures the darerwatershed area.

5.0 ConclusionsRainfall analysis indicated a declining trend in the

basin represented by the equation y = -0.5852x +69.307. Trend line fitted for yearly observed runoff reflecteda declining trend given by the equation y = -0.6986x + 35.999.The trend lines of both yearly rainfall and yearly runoffshowed decreasing trend over Palleru sub basin for the years2000 and 2002. The regression model is developed betweenrainfall and run off is y = 0.0101x2 + 0.208x - 0.0486 withR2as 0.9396. All the hydrological parameters which arespatially and temporally variable were found to be moreaccurately estimated through RS and GIS. The average runoffwas estimated for the Palleru sub basin as 49.2% and 33.6 % ofrainfall from SCS-CN method for the years 2000 and 2002.

6.0 References1. Berod D. D., Singh V. P. and Musy A. (1999) “A

geomorphologic kinematic-wave (GKW) model for estimationof floods from small alpine watersheds”. HydrologicalProcesses 13:1391-1416.

2. Moore R. J. and Clarke R. T. (1981), A distributionfunction approach to rainfall-runoff modeling. WaterResour. Res., 17(5):1367-1382.

3. Hosking J. R. M. and Clarke R. T. (1990) “Rainfall-runoffrelations derived from the probability theory ofstorage”. Water Resources Research 26: 1455-1463.

4. Moore R. J. (1985) “The probability-distributed principleand runoff production at point and basin scales”.Hydrological Sciences Journal 30: 273-297.

5. Moore R. J. and Clarke R. T. (1983), “A distributionfunction approach to modelling basin sediment yield”.Journal of Hydrology 65: 239-257.

6. Lamb R. (1999), “Calibration of a conceptual rainfall-runoff model for flood frequency estimation by continuoussimulation”. Water Resources Research 35, 3103-3114.

7. Yu B. (1998), “Theoretical justification of SCS methodfor runoff estimation”, Journal of Irrigation andDrainage Engineering, ASCE 124: 306-309.

0

10

20

30

40

50

60

70

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

June2000

0

510

15

20

2530

35

40

1 4 7 10 13 16 19 22 25 28 31

July 2000

0

10

20

30

40

50

60

70

1 4 7 10 13 16 19 22 25 28 31

August 2000

0

5

10

15

20

25

1 4 7 10 13 16 19 22 25 28

September 2000Fig.1 Comparison of monthly Rainfall and runoff for the year

2000

0

5

10

15

20

25

30

1 4 7 10 13 16 19 22 25 28

June2002

02468101214161820

1 4 7 10 13 16 19 22 25 28 31

July 2002

051015202530354045

1 4 7 10 13 16 19 22 25 28 31

August 2002

012345678910

1 4 7 10 13 16 19 22 25 28

September 2002Fig 2.Comparison of monthly Rainfall and runoff for the year

2002Year   JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

2000

Rainfall 0.00

3.57

0.00

11.42

31.25

280.78

180.37

343.15

55.84 8.90

1.95

2.57

Runoff 0.000.00

0.00 2.42 6.16

142.49 88.56

201.38

11.45 0.13

0.00

0.00

2002

Rainfall

12.30

0.00

2.92 0.20

24.64 76.16 80.62

196.47

24.65

137.78

0.00

0.00

Runoff 0.510.00

0.00 0.00 0.30 16.36 11.72 84.87 3.90 69.47

0.00

0.00

Table. 1. Comparison of monthly Rainfall and Runoff in mm for the year 2000 and 2002

y = 0.010x2+ 0.208x -0.048R² = 0.939

010203040506070

0 20 40 60 80

Runoff in mm

Rainfall in m m

Fig.3.Correlation coefficient between monthly Rainfall and runoff the year 2000 and 2002

Month Palkurthy Kodakandal Torrur MaripedaTungaturthy Noothankal Arvapally Atmakur Mothey Kushmanchi

Basin Average

1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2

Jan-00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Feb-00 0.0 0.0 0.0 0.0 0.0 0.0 20.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.5 0.0 3.6 0.0Mar-00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Apr-00 14.0 0.0 24.8 0.1 68.8 24.1 6.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.4 2.4May-00 27.4 0.0 36.7 0.0 40.0 4.1 77.2 7.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 131.2 49.7 31.3 6.2

Jun-00209.

9 84.1376.

7164.

0352.

5193.

3353.

6210.

8292.

4176.

4282.

0137.

5275.

2144.

3179.

868.

7121.

131.7 364.6

180.8

280.8

142.5

Jul-00171.

1 85.6243.

1122.

0320.

4190.

7205.

8104.

1103.

4 51.8145.

7 60.0122.

0 33.7 88.615.

2112.

630.3 291.0

135.0

180.4 88.6

Aug-00316.

5150.

0358.

4153.

0498.

0240.

0345.

0191.

2428.

8190.

0421.

2179.

0241.

6107.

0194.

289.

7226.

298.3 401.6

175.0

343.2

201.4

Sep-00 98.8 20.0142.

6 43.5 14.2 0.0 80.4 6.2 57.0 2.7 32.0 0.0 19.8 0.0 98.442.

1 0.0 0.0 15.2 0.0 55.8 11.5Oct-00 0.0 0.0 0.0 0.0 30.2 1.3 11.0 0.0 0.0 0.0 20.0 0.0 0.0 0.0 0.0 0.0 3.2 0.0 24.6 0.0 8.9 0.1Nov-00 0.0 0.0 19.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0Dec-00 3.5 0.0 3.5 0.0 3.5 0.0 3.5 0.0 0.0 0.0 11.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.6 0.0Jan-02 0.0 0.0 30.0 1.3 5.2 0.0 19.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.2 0.0 55.2 3.9 12.3 0.5Feb-02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Mar-02 0.0 0.0 0.0 0.0 10.2 0.0 11.0 0.0 0.0 0.0 0.0 0.0 8.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.9 0.0Apr-02 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0May-02 65.8 1.3 30.5 0.0 40.8 1.8 17.3 0.0 15.0 0.0 8.4 0.0 12.0 0.0 29.2 0.0 7.2 0.0 20.2 0.0 24.6 0.3

Jun-02 74.4 8.9 44.6 0.1 51.6 0.7106.

6 26.9 76.0 15.3169.

8 81.6 56.0 13.8 68.8 4.7 25.4 0.0 88.4 11.8 76.2 16.4

Jul-02 88.2 8.0 40.9 1.6119.

2 18.5 67.0 1.5105.

0 19.3135.

2 38.1 90.0 23.6 15.0 0.0 70.4 4.0 75.3 2.7 80.6 11.7

Aug-02234.

6130.

7225.

8114.

5279.

8147.

9215.

6 91.6294.

0137.

7172.

4 53.8151.

9 40.0 65.2 0.4106.

439.0 219.0 93.1

196.5 84.9

Sep-02 8.0 0.0 18.2 0.0 50.8 9.6 8.6 0.0 0.0 0.0 23.6 0.2 23.7 1.6 17.6 0.0 35.0 1.9 61.0 25.7 24.7 3.9

Oct-02103.

5 49.3156.

6 71.5 92.4 30.7160.

9 91.7186.

0112.

6117.

8 49.3225.

2144.

8149.

471.

7 84.426.2 101.6 46.8

137.8 69.5

Nov-02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Dec-02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Average

% of Runoff   0.38   0.38   0.44   0.43   0.45   0.39   0.42  

0.33  

0.29   0.39   0.44

 Correlation Coefficient   0.92   0.97   0.97   0.95   0.95   0.97   0.92  

0.88  

0.91

 

0.97   0.96