Rainfall-Runoff Modelling Using SCS-CN and Geospatial Techniques in the Mubuku Catchment, Uganda by IRJET Journal

Rainfall-Runoff Modelling Using SCS-CN and Geospatial Techniques in the Mubuku Catchment, Uganda

Derick Samuel1 , K . Deepa2

1Department of Artificial Intelligence and Data Science, Loyola Institute of Technology, Chennai 600123, India. 2Department of Civil Engineering, Chennai Institute of Technology, Chennai ***

Abstract – Climatechangeisamajorfactorwhichimpacts on rainfall extremes and drought extremes all over the world. InthisresearchthestudyareaMubukuRiverbasin, facessuchextremityofrainfalleveryyearresultinginhuge volumeofsedimenttransportationtotheriver Thus,asan initialstep,therainfallrunoffquantityhasbeenthoroughly carriedoutaccordingthenatureofthetopography,soiland rainfall intensity. Techniques such as SCS method and geospatial tools are applied in determination of runoff quantityofthestudyarea.Fiveyearsofrainfall data from 2020to2024wasusedandrespectiverunoffquantityhas beencomputed. Thehighestrunoffcomputedof45568m3 for the year 2024 and the lowest runoff of 28672 m3 computedfortheyear2020.Itwasalsoobservedagradual increase of runoff throughout the five years in the study area.

Key Words: SCS method, Curve Number, Rainfall, Runoff, GIS, hydrologic soil

1. INTRODUCTION

The climate is a major determinant of water conditions throughitsinfluenceonthehydrologicalcycle(Vörösmarty etal.,2000). Changesinclimatepatternscanimpactwater systemsthroughshiftsintemperature,rainfall,andextreme weatherevents(IntergovernmentalPanelonClimateChange [IPCC],2021). Changesinclimatepatternslikeincreasesin global temperatures (Stocker et al., 2013), shifts in precipitation regimes (IPCC, 2021), and more frequent/severeextremeweatherevents(IPCC,2021)can directlyimpactsurfaceandgroundwatersystems.Altered rainfallandfloodingpatternsmodifywatershedhydrology andinfluencerunoffvolume/quality(Barnettetal.,2005). Rainfall-runoff is a mathematical model which directly relateswiththebasincharacteristicssuchasslope,soiltype, stream density and size of the basin (K Deepa, M Krishnaveni, 2012). Rainfall extremes result in flooding whichfurtherresultsinpollutionofsurfaceandsubsurface water contaminations, spreading of water borne diseases and transportation of sediments to surface water bodies, further resulting in reduction of storage capacity of the reservoir. On the other hand drought extremes result in reductioninsoilmoisturecondition,declineofwatertable andreductionofagricultureofacountry.Thus,itisessential tomonitoranyregionsorbasinorwatershedtheextremes ofthosehazards,inordertomanagetheenvironmentina

sustainable way. There by the economy of a region or countrycanbesaved.

2. LITERATURE REVIEW

Water resources management is a critical aspect of sustainable development, particularly in regions where agricultureanddomesticwatersupplyareheavilyrelianton rainfall (Govindaraju, et al, 2024) Assessing water availabilitybyevaluatingtherelationshipbetweenrainfall and runoff is therefore of paramount importance for effective watershed management, flood prediction and control of soil erosion. Rainfall-Runoff (RR) modelling is thereforeneededtopredicttheresponsesofawatershedto precipitation events. There are so many methods of modellingthatareusedinhydrologysuchasSCS-CN,SWAT, SHE,WaSIMandVICModelsamongothers (Yoshe,2025), (Solomon Eniyew, et al, 2024). In this study, the Soil ConservationService-CurveNumber(SCS-CN)methodhas beenemployedbecauseofitssimpleandrequiresminimum input data. The method is based on the concept of curve numbers (CN), which represent the runoff potential of a watershed based on Soil Type, Land Use and Land Cover (LULC),AntecedentMoistureConditions(AMC) (Chen,J., Hill, A. and Urbano, L. 2010). In the domain of flood management, this methodological approach significantly enhances forecasting, the establishment of early warning systems, and the design of flood control infrastructure. Additionally, it facilitates the identification of areas susceptible to flooding. Furthermore, the methodology contributestowaterharvestingandthemanagementofland use to promote sustainable agricultural practices. It is predominantlyemployedtoevaluatetheimpactsofclimatic variationsonwaterresources.Thismethodwasformulated bytheUnitedStatesDepartmentofAgriculture(USDA)and is recognized as one of the most extensively utilized techniques for estimating direct runoff resulting from precipitation(USDA,1972),(USDA,1985)andwasoriginally modelledforconditionsprevailingintheUS.Sincethen,it hasbeenadaptedtoconditionsinotherpartsoftheworld (Govindaraju, et al, 2024). Notwithstanding the advancements made by certain regional centers in developingsupplementarycriteria,thefundamentalconcept remains widely applied globally The method is well establishedbecauseofitsstabilityandabilitytoincorporate variousfactorscontributingtorunoff,includingbothspatial and non-spatial data sets such as daily and monthly

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precipitation. Its ability to be integrated with GIS, RS and latelyAItoolsmakethemethodmorereliable.WeuseGIS, RS and AI because field measurements are laborious and time consuming. Spatial data has made it practical to accuratelycalculaterunoff(AyushiTrivedi,2021).However, advancementsingeospatialtechnologyhelptoincludethese variables for evaluating runoff from bigger watersheds. (Yoshe, 2025) Since conventional methods are time consumingandverydemanding,combiningGISandRSaids spatialanalysis(Deepa,Ketal,2017).Severalstudieshave obtained CN and runoff with RS and GIS. Researchers concludethattheseapproachesareflexible,popular,faster and reliable (Yoshe, 2025) (Abhijit M.Zende et al, 2014) notes that with increase in the availability of finer spatial resolutioninformationforGISandRSdataonvegetation,it’s possible to use the method for large areas with enhanced accuracy.Thekeyadvantagesofthismethodisitsseamless integration with GIS and RS techniques and this has revolutionized the accuracy and efficiency of the model (Shashi Poonam, 2022). Integration of GIS and RS in RR modelling have become indispensable in hydrological studiesduetotheirabilitytomanage,analysesandvisualize spatial data. GIS facilitates the extraction of critical watershed parameters such as slope, land cover, and soil type,whichareessentialforrunoffestimation.Inthecontext ofthismethod,GISenablesthespatialmappingofCNvalues, thus improving the precision of runoff estimations. RS providessatelliteimagery ofthe earth’s environment that helpstoestimaterunoffandpotentialfloods.GISisalsoused asanefficienttooltoprepareinputdataobtainedfromRS.It can offer data for inaccessible locations with huge spatial and temporal variability (Solomon Eniyew, et al, 2024), (Yoshe, 2025). Studies have shown that SCS method is particularly effective in the data scarce regions, making it suitableforapplicationsindevelopingnationslikeUganda. Ugandanmodelshavetargetedurbansettingsanddeveloped river watersheds, leaving a gap in rural watersheds. Elkassar, G. et al, posits that alterations in land use accompanying urban expansion lead to increased surface runoffandaconcomitantriseinfloodrisk.Ifinadequately managed,surfacerunoffmayinflictdamageonpropertyand infrastructurewhilealsocontributingtothesiltingofwater resources(Elkassar,G. etal2017) MubukuRiverbasinis locatedinSouth-westernUganda,ischaracterisedbyhigh rainfall intensity, steep slopes, and diverse land use practices.Insuchabasin,SCS-CNmethodcanbeparticularly useful given its complex topography and varying land use patterns.Thesefactorsmakeitpronetoissuessuchassoil erosion, flooding, and water resource degradation. This region has endured floods due to natural and human interventions.Stepstoreversethesechallengessuchastree plantingtoboostsoilandwatermanagementeffortsarenot successfulasanticipated.

3. STUDY AREA

ThestudywillfocusontheMubukuRiverbasininUganda, situatedwithintheRwenzoriMountainsregion.Thisbasin encompasses a defined geographical area, including its tributaries and associated water resources within its catchmentarea(Bua,B.;Ojirot,2014).Thespatialextent ofthestudywillbebasedonthecoordinatesoftheMubuku Riverbasin,whichgenerallyrangebetweenapproximately 0.311°N to 0.375°N latitude and 29.783°E to 29.953°E longitude.Thesecoordinatesoutlinetheboundariesofthe basin and include its tributaries and water resources as showninfigure1.

Figure 1 Mubuku River Basin Uganda

TheMubukuRiverbasininsouthwesternUgandaprovides essentialwaterresourcesthatlocalcommunitiesdependon (Elkassar,G. et al 2017).The basin supports livelihood activitiessuchasagriculture,livestockrearing,anddomestic waterusage(MWE,2018).TheMubukuriverbasinareais about 256 km2. The temperature data indicates average warmingof1.3°Cbetween1980-2020.Thestudyareahas threerainfallstationslocatedinaroundsuchasFortportal, Kasesee,andMubukurespectively.Theslopetopographyof thestudyarea isin theformofmildslopesdistributed all over the study area. The basin was divided into two subbasinsbasedontherivernetwork.

4. METHODOLOGY

Themethodologyadoptedinthisresearchhasbeendepicted infigure2.Thestep-by-stepprocedurehasbeenexplained asbelow.

Step 1 – Thematic maps of landuse type, Landuse conservation practice, hydrologic soil group, hydrologic conditionwaspreparedinArcGISsoftware.Therainfalldata procuredfromthreeraingaugestationsunderwentspatial interpolationviathesplinemethod.Monthlyrainfallrecords spanning five years (2020-2024) were gathered from the Uganda Meteorological rainfall data center. The Thiessen

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

polygon method was employed to ascertain the average distribution of rainfall across the entire study area, as depicted in Figure 3. The satellite imagery utilized for the landuseandcovermapofthestudyareawassourcedfrom thewebsite:www.EarthExplorer.usgs.gov

Landsat8satelliteimagerywasacquiredinTIFFformatand subsequentlyreclassifiedutilizingsupervisedclassification methodologieswithintheArcGISenvironment.Fiveclasses oflandusepatternhavebeenclassifiednamely,builtupland, waterbodies,smallcropgrowingregion,pastureland,fallow row crop growing etc. The extracted land use map of the studyareahasbeenpresentedinfigure5.

Hydrologic Soil Group Map

The spatial distribution of soil layers comprises 13% clay and87%sand,exhibitingeitherloamysandorsandyloam textures.Furthermore,thesaturatedhydraulicconductivity within the least transmissive layer, extending from the surface to 35 cm, ranges from approximately 32 micrometers per second to 38 micrometers per second. Figure 4illustratesthepresenceof hydrologicsoilgroups withinthestudyarea.

Landuse map was prepared based on the classification of landusetype,landtreatment,andhydrologicalconditionsin the study area catchment (Bhura CS etal, 2015) and (Wanielista, M.P. and Yousef, Y.A. 1993) . Finally, the

Figure 2 Methodology flow chart of Rainfall-Runoff

Figure 3: Thiesson Polygon for Rainfall Distribution

Figure 4. Hydrologic Soil Group Map

Landuse Map

Figure 5. Land use map of the study area

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soil-covermapwasobtainedbysuperimposingtheland-use and the hydrologic soil group map which is presented in figure6.Superimposingwasachievedbyassigningweights for soil groups such as 3 for group A, 2 for group B, 1 for groupCandD.Also,forlanduseclass,theweightsassigned are 3 for fallow row crop and water bodies, 2 for small growing crop, 1 for pasture land and built-up land respectively.

Figure 6. Super imposed area of Land use and soil cover map of the study area

Step 2 - The SCS runoff is articulated as the ratio of cumulative infiltration of rainwater (F) to the watershed storage(S),whichequatestotheratioofactualdirectrunoff to effective rainfall (total rainfall - initial abstraction) (K Subramanya, 1972) and (Pandey, A. and Sahu, A.K, 2009). The general equation for the Soil Conservation Servicemodelofcurvenumberiscomputedasfollows.

F = Q

S P–I (1)

ThedimensionsofF,S,QandIaresameandalsothevalueof initial loss (Ia) is considered as 0.2S. The quantity of infiltratedrainwaterafterrunoffbeginscanbeexpressedas:

F=(P-I)-Q ….(2)

Thus,

Q=(�� 0.2S)2 (P+0.8S) ….(3)

Soilmapsandlandusedatasetswereemployedtogenerate the Curve Number (CN). The value of S for specific watershed characteristics or land areas considered in the runoffcomputationisafunctionofthecurvenumber(CN). TherelationshipbetweenCNandSisdelineatedinequation 4.

CN= 2540

25.4+S ….(4)

Where,Sistheretentioncapacityofthesoil(cm).

5. RESULTS AND DISCUSSION

TheCNvaluescorrespondingtoeachsoilhydrologicgroup, hydrologic condition, and associated land use classes are presented in Table 1. The weighted curve number for the areawasdeterminedusingequation(5),yieldingaWeighted CN value of 79.20. Consequently, the retention capacity S was calculated as 5.53 using equation (4). The results presentedinTable2indicateapersistentincreaseinrunoff depth within the study area. Runoff depth is directly proportionaltothehydrologicsoilconditionsandlanduse patterns (S. K. Mishra, 2000). The volume of runoff is computed with respect to superimposed area coverage of landusepatternandhydrologicsoilcondition.

Table -1: ComputationofCurveNumber

TheWeightedCurveNumber(CNw)

Table 2.ResultsofRunoffdepthforthestudyarea

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6. CONCLUSIONS

Theresultsofrainfall-runoffprocessusingSCSmethodsays thatthestudyareaisinfluencedbyvariousfactors,suchas rainfalldistribution,abstraction,slope,andhydrologicalsoil types. TheapplicationofGIStoolplayedvitalinextractionof rainfalldistributionspatially, landuseclasscoveragearea andsoilgrouparea.Thecontinuousincreaseofrunoffdepth in the study area warns the water managers and decision makers to give more attention in water resources management in aspect of soil erosion, water conservation techniquesandlandmanagement.

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