Erosion Mapping of a Watershed using USLE model in integration
with GIS and remote sensing : A case study of Kankai Mai Basin
Erosion Mapping of a Watershed using USLE model in integration
with GIS and remote sensing : A case study of Kankai Mai
BasinMid-Term Presentation
A thesis in partial fulfillment of the requirement for the
degree of Master of Science in Water Resources EngineeringInstitute
of Engineering, Pulchowk Campus
Presenter:Sunil Basnet (066 MSW 419)
Supervisor:Prof. Dr. Narendra Man Shakya
CONTENTSIntroductionNeed for researchStudy AreaLiterature
ReviewModel DescriptionMethodologyResultsConclusions and
Recommendations
IntroductionIncludes the processes of detachment, transportation
and depositionKinds of soil erosionSheet erosionRill erosionGully
erosionStream Channel erosion
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Need for researchFor conservation, development and utilization
of soil and water resourcesEntering of the sediment in the
irrigation and hydropower canalsPrediction of how much sediment
passes by the outlet of basinWhich part of the watershed what rate
of soil degradationField method of mapping time consuming and
tedious
26 40 40 N to 27 06 21 N87 41 00 E to 88 08 05 E
Kankai Mai WatershedChinaIndiaCatchment Area = 1147.33 Km2
Sub BasinCatchment Area
(km2)Jogmai151.03Mai644.17Puwa157.22Deumai279.53Kankai
Mai223.63
Literature ReviewEmpirical Model like USLE model, RUSLE model,
MUSLE modelPhysically based models like CREAMS model,ANSWERS
model,Morgan model etcConceptual model
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Model DescriptionUSLE ModelDerived and tested from real field
observationsDependent on six factorsA = RKLSCPR Rainfall Erosivity
factorK- Soil Erodibility factorL Slope Length factorS Slope
Steepness factorC- Crop management factorP Support control
factor
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Model DescriptionSediment Delivery Ratio (SDR)SDR = 0.627 SLP
0.403
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MethodologySoftware usedArc GIS 9.3Arc Hydro (Arc GIS
Extension)Erdas Imagine Visual Basic Environment in MS Excel
MethodologyAcquisition and processing of dataExtraction of
topographic dataMean catchment rainfall using Thiesson polygon
analysisSoil cover map by remote sensingDivision of the catchment
into no of gridsCalibration and validation parameters for each
monthCalculation of sediment yield at each sub catchment
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Methodology
Divide catchment into no of gridsGenerate remotely sensed soil
cover map R factorR = (F.I)F.I =pi2/PK factorUSDA Classification LS
factorA= R.K.LS.C.PC factorP factorY= SDRi . AiResidual Values=
(|observed - simulated|)If n>100Change values of and 12NY
Methodology
Calculate soil detachment rate at each grid running USLE model
after validationAdopt and for minimum values of RCalculate R2 value
for adopted and ( calibration period)If R2 >0.7Calculate R2
value for adopted and for validation period12If R2 >0.72YYNN
ResultsMonthJan0.0030.370Feb0.0030.291Mar0.0020.323Apr0.0030.377May0.0280.358Jun0.1590.499Jul0.5020.617Aug0.5310.408Sep0.3060.496Oct0.0800.592Nov0.0280.453Dec0.0120.230
Calibrated and validated values of parameters and
ResultsYearR219740.82Period of
Calibration19750.819760.8219770.7719780.8520010.73Period of
validation20020.8720030.75
Coefficient of determination (R2) for calibration and validation
period
Results
Results
R2=0.73R2=0.87R2=0.75
Results
Results
Conclusion and RecommendationsUSLE model with GIS and remote
sensing gives good estimation of soil loss rateAn attempt to
account for the distribution of rainfall erosivity factor within
every month is successful rather than using R as lump sum factor
for whole single yearR factor is the most promising factor for soil
erosionAverage sediment yield at the outlet of the basin is
obtained as 21.94 tons hectare/annumSediment yield values ranges
from 18.04 to 25.07 tons hectare/annumFew minutes interval
precipitation data gives more realistic and better
results.Projection of future erosion rates can be done
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THANK YOU
