International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. http://www.ripublication.comEstimation of Runoff Using SCS-CN Method and Arcgis for KarjanReservoir BasinHina PathanFormer PG Research ScholarGeeta S. JoshiAssociate ProfessorCivil Engineering Department, Faculty of Technology & Engineering,The Maharaja Sayajirao University of Baroda, Vadodara, PIN: 390 001, India.stream degradation, erosion, flooding and accompanyingproperty damage . Hydrological watershed modelling hasbecome a central tool for conceptualizing these flows of surfaceand subsurface water. Models can then be used to generatingdecision support tools for policy makers, regulators andresource managers   . The SCS – CN model is used inthis study to compute the runoff from the available dailyrainfall data in Karajan reservoir basin. The observed inflowsat Karajan reservoir have been also collected as a data. Theresults of runoff obtained from SCS – CN method is comparedwith the observed runoff/inflows at the reservoir site. Also, theLinear regress model is established for the rainfall – runoffcorrelation. The results obtained from runoff throughregression model is closely agreeing with the observed runoff.AbstractWater is one of the most important natural resources and a keyelement in the socio-economic development of a State andCountry. Water resources of the world in general and in Indiaare under heavy stress due to increased demand and limitationof available quantity. Proper water management is the onlyoption that ensures a squeezed gap between the demand andsupply. Rainfall is the major component of the hydrologic cycleand this is the primary source of runoff. Karjan reservoir basin,located between 21 23’ to 21 50’ North latitude and 73 23’to 73 54’ East Longitude in Narmada districts, in GujaratState, India has been used for the study. Estimation of directrainfall-runoff is always efficient but is not possible for unsampled location the basin. Use of remote sensing and GIStechnology can be useful to overcome the problem forestimating runoff. The method used in this study is SCS-CNModel. The Daily rainfall data of 5 Rain gauge stations iscollected and used for the daily runoff calculation using SCSCN model and GIS. The Linear Regression model is used forverification of runoff obtained from SCS-CN method. It isfound that the results obtained from runoff obtained from SCS– CN model demonstrate deviations from the observed runoff.Linear regression model is closely agrees with the observedrunoff from the basin in comparison to the SCS – CN model.Study AreaThe river Karjan originates from the Satpuda hills in Gujarat.Karjan dam is constructed on river Karjan. Concrete dam isconstructed, through that a small watershed basin is form onupstream of Karjan dam which is located at73º 23’ to 73º 54’East longitude and 21º 23’ to 21º 50’ North latitude. Thecatchment area of Karajn reservoir is 1358.8 Sq. km. Figure 1shows the study area map.Keywords: ArcGIS, SCS-CN, Rainfall, RunoffINTRODUCTIONSustainable water management of a river basin is required toensure a long-term stable and flexible water supply to meetcrop water demands as well as growing municipal andindustrial water demands . Water resources structures needappropriate planning to ensure the fulfilment of the goals ofwater management . Water resources management requiresa systems approach that includes not only all the hydrologicalcomponents, but also the links, relations, interactions,consequences, and implications among these components .Human modifications of the environment, including land coverchange, irrigation, and flow regulation, now occur on scalesthat significantly affect seasonal and yearly hydrologicvariations . As population density and developmentcontinue trending upward, storm water runoff from increasedimpervious surfaces presents challenges on local and globalscales [Ref 7]. Besides collecting contaminants from urbansurfaces (nutrients, road salt, heavy metals, pesticides andbacteria), changes in storm water flow patterns can causeFigure 1: Map of Karajan reservoir basin, IndiaMATERIALS AND METHODSThe adopted methodology of the present study is shown inFigure 2. The land use and land cover map is obtained fromSatellite image LISS III collected from Bhaskaracharya SpaceApplication And Geo-Informatics (BISAG). The data for Soiltypes (clayey and fine) and texture have been collected fromNational Bureau of Soil Survey And Land Use Planning2945
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. http://www.ripublication.com(NBSS&LUP, India). Digital Elevation model (DEM) is madeavailable from BISAG. The daily rainfall data is collected from1991 upto 2015 from State Water Data Centre (SWDC),Gujarat, India. The integration of GIS and Soil ConservationService - Curve Number Method is used to estimate the surfacerunoff. The Soil Conservation Service Curve Number (SCSCN) method is widely used in determination of surface run-offin long-term (continuous) hydrologic simulation models. TheDaily Rainfall data ofdifferent stationsappropriate area-weighted curve number for the study area iscomputed using overlaying tool of ArcGIS. Then the dailyrainfall database is incorporated in the analysis to estimate thedirect runoff. The linear regression model is also establishedfor rainfall – runoff correlation. The results obtained from SCS- CN model and linear regression model are compared withobserved runoff at the Karajan reservoir site.Satellite dataSoil textureDaily Runoff data atKarjan damHSG MapAveragerainfallusingThiessonpolygon inArcGISLU/LC Map, ted CNMapComparisonofSimulatedrunoff andObservedrunoffFigure 2: Flow chart showing methodologyThe average precipitation of the area is given byAssessment of average rainfall by Thiesson Polygon tool𝑃 (𝑃1𝐴1 𝑃2𝐴2 𝑃𝑛𝐴𝑛)/(𝐴1 𝐴2 . . . 𝐴𝑛)A.M. Thiesson (1911) suggested this method in whichweighing effect of area in the area in the form of polygon closetto the station has been considered. Thus, it tries to eliminate theerror due to non-uniform distribution of rain gauges. Figure 2and Figure 3 shows Thiesson polygon for the study area for 5raingauge station and 4 raingauge station respectively.Where, A1, A2 ., An Areas of the Thiesson polygonrepresenting the stations 1, 2, .,n.P1, P2, . , Pn Precipitations of corresponding stations. A Total area of the catchment.Fig 3 Thiesson polygon of study areafor 5 rain gauge station2946Fig 4 Thiesson polygon of study areafor 4 rain gauge station
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. http://www.ripublication.comSubstituting in equation (1), the equation becomesSurface runoff through SCS - CN modelHydrological Soil Group is developed from the type of soil inthe study area. Land use and land cover (LU/LC) map isintegrated to Hydrological soil group map. The curve numbermap is developed in GIS based on HSG group and Land useLand cover . The weighted curve number have beenobtained by area-weighting calculated from the land use-soilgroup polygons within the drainage basin boundaries.𝑄 (𝑃 0.2𝑆)2 (2)𝑃 0.8𝑆Which is valid for P 0.2S, otherwise Q 0S can be determined from the P - Q data. In practice, S isderived from a mapping equation expressed in terms of thecurve number (CN):𝑆 SCS-CN modelIn the early 1950s, the United States Department of Agriculture(USDA) Natural Resources Conservation Service (NRCS)(then named the Soil Conservation Service (SCS)) developed amethod for estimating runoff from rainfall. This method alsoreferred as the CN method . The SCS curve number methodis based on the water balance equation.25400𝐶𝑁 254 (3)Where CN function of watershed hydrologic landuse/land cover units hydrologic soil groups antecedentmoisture condition. CN values can be obtained for differentland uses and hydrologic condition from the standard Table CNvalues for AMC-I and II can be obtained using thefollowing equation (4) and equation (5).The SCS-CN method, expressed as equation (1) below.𝑄 (𝑃 𝐼𝑎)2𝑃 𝐼𝑎 𝑆The CN (dimensionless number ranging from 0 to 100) isdetermined from a Table 1 as shown below. Table 1 shows theHydrologic Soil Group based on land-cover, AMC condition.HSG is expressed in terms of four groups (A, B, C, D)according to the soil’s infiltration rate. AMC is expressed inthree levels (I, II and III), according to rainfall limits fordormant and growing seasons. Table 2 shows the classificationof the Antecedent moisture content. .(1)Ia 0.2Swhere, Q Accumulated storm runoff, m; P Accumulatedstorm rainfall, mm, S Potential maximum retention of waterby the soil, Ia Initial quantity of interception,depression and infiltration.Table 1: USDA-SCS Hydrologic Soil GroupSrnoHSGADeep well drained soilsBCDSoil TexturesRunoffPotentialSand, loamy sand, Lowsandy loamModerately deep, well drained with Silt loam or loamModeratemoderately fine to coarse textureModerately fine to fine textureSandy clay loamModerateSoil which swell significantly when Clay loam, silty clay Highwet, heavy plastic and soil with a loam, sandy clay, siltypermanent high-water tableclay, clayMinimumRate ofInfiltration(mm/hr)7.62- 11.43WaterTransmission3.81- 7.62Moderate rate (0.15-0.3in/hr)1.27- 3.810-1.27Low rate (0.05-0.15in/hr)Very low rate (0-0.05in/hr)High rate (0.3 in/hr)Table 2: Classification of Antecedent Moisture Conditions (AMC)SrnoSoil characteristicsTotal 5-day antecedent rainfall(mm)Dormant seasonGrowing seasonSoils are dry not to wilting point, Cultivation has 13 mm 36 mm taken place 13 mm 36 mmIAverage Condition13-28mm36-53mmII 28 mm 53 mmIII Heavy or light Rainfall and low temperatures have occurred within the last 5 days;saturated soilsCN value is obtained from Technical release TR-55. .2947
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. http://www.ripublication.comAntecedent Soil Moisture condition (AMC)𝐶𝑁𝑤 Antecedent Moisture Condition (AMC) refers to the watercontent present in the soil at a given time. It is a factor importantto determine CN value. SCS developed three antecedent soilmoisture conditions and labelled them as I, II, III, according tosoil conditions and rainfall limits for dormant and growingseasons. Classification of Antecedent Moisture Condition isshown in Table 4.2.𝐶𝑁(𝐴𝑀𝐶 𝐼𝐼𝐼) 4.2 CN(AMC I)10 0.058 CN(AMC II)23 𝐶𝑁(𝐴𝑀𝐶 𝐼𝐼)10 0.13 𝐶𝑁(𝐴𝑀𝐶 𝐼𝐼)𝐴, . .(6)where CNw is the weighted curve number; CNi is the curvenumber from 1 to number N; Ai is the area with curve numberCNi; and A the total area of the watershed.Hydrologic Soil Group (HSG) mapSoil properties influence the generation of runoff from rainfallin the methods of runoff estimation. Soil map prepared byNational Bureau of Soil Survey and Land Use Planning (NBSS& LUP) and soil report of the study area have been used forclassifying various soils into hydrologic soil groups. Soilclassification system developed by SCS-CN has been followedwhile classifying soils into different hydrologic soil groups.Since, standard table for CN values (ranges from 1 to 100),considering land use/cover and HSG are given for AMC-II[Ref.2], following conversion formulas are used to convert CNfrom AMC-II (average condition) to the AMC-I (dry condition)and AMC-III (wet condition) (SCS, 1972).CN(AMC I) 𝛴(𝐶𝑁𝑖 𝐴𝑖) .(4)In this classification system, soils are classified as A, B, C or Dhydrologic soil group depending on their properties. Category“A” has lowest runoff potential whereas category “D” hashighest runoff potential. Hydrologic soil group map of thestudy area having mainly 2 classes of soil as fine and loamyclayey are shown in Figure 4.2 Category “C” has fine soils andCategory “D” has clayey soils. .(5)where, (AMC - II) CN is the curve number for normalcondition, and AMC- I, CN is the curve number, for drycondition, and (AMC - III) CN is the curve number for wetconditions.Although, SCS method is originally designed for use inwatersheds of 15 km², and it has been modified for applicationto larger watersheds by weighing curve numbers with respectto Watershed/land cover area. The equation of weighted curvenumber is given below.RESULTSThe Hydrological Soil Group map and Curve number map forthe study area have been developed in GIS as shown in Figure5 and Figure 6 respectively.Fig 5: Hydrologic soil group mapFig 6: Curve Number (CN) map2948
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. http://www.ripublication.comPercentage area and Curve number can be used to find out areaweighted curve number by using Equation (6). Estimatedcomposite curve number for catchment area of Karjan is 88.95for AMC-III. Details of curve number estimation for catchmentarea of Karjan are shown in Table 3.Table 3: Details of weighted curve number of the study areaLand use typeSoil 31clayeybuilt upForestOthersWastelandsWaterbodies%Area HSGCN(AMC-II)CN(AMC-III)(%AREA*CN)/100Area WeightedCurve ble 4 and Figure 7 shows the annual rainfall and annual simulated runoff through SCS-CN model.Table 4: Annual Rainfall and SCS-CN model- simulated runoffYearRainfall (mm) SCS-CN model-simulatedRunoff (mm)YearRainfall (mm)SCS-CN model –simulated Runoff 25.9420031298.96393.312015865.97339.692949
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. 2012201320142015Rainfall (mm), Simulated runoff(mm)Annual observed rainfall and simulated runoffTime (year)Rainfall(mm)simulated Runoff(mm)Fig 7: Annual Rainfall and Simulated runoff by SCS-CN methodComparison of the result of SCS-CN model and regression model have been made and is as shown in Fig. 8.Simulated runoff (SCS-CNModel) (mm/year), Simulated runoff(Linear regression Model) (mm/year)Comarison of Linear regression model and SCS-CN model1800.00Observed runoffLinear regression modelSCS-CN ModelLinear (Observed runoff)Linear (Linear regression model)Linear (SCS-CN Model)1600.001400.001200.001000.00R² 0.8319800.00R² 800.001000.001200.001400.001600.001800.00Observed runoff (mm/year)Fig 8: Annual observed runoff Vs. simulated runoff (SCS-CN model) and simulated runoff (Linear regression)Fig 8 shows that the result of simulated annual runoff usingLinear regression is more matching with the observed annualrunoff in comparison to the simulated annual runoff using SCSCN model.comparison to the simulated annual runoff using SCS-CNmodel. It can be concluded that Linear regression model isfound more accurate in comparison to SCS-CN model. Thevalue of coefficient of determination (R²) for SCS-CN Modelis 0.65 and the value of coefficient of determination (R²) forLinear Regression Model is 0.83.As Linear regression model is found more matching incomparison to SCS-CN model.ACKNOWLEDGEMENTCONCLUSIONAuthors are thankful to the State Water Data Centre andBhaskaracharya Institute of Space Application and GeoInformatics, Gujarat for providing necessary data.The result of simulated annual runoff using Linear regressionis more matching with the observed annual runoff in2950
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 12 (2019) pp. 2945-2951 Research India Publications. http://www.ripublication.comREFERENCESC. Lalitha Muthu and M. Helen Santhi 2015,“Estimation of Surface Runoff Potential using SCS-CNMethod Integrated with GIS,” Indian Journal of Scienceand Technology, Vol 8 (28), PP 1-5.S. Satheeshkumar, S ,Venkateswaran, R, Kannan.,2017, “Rainfall–runoff estimation using SCS–CN andGIS approach in the Pappiredipatti watershed of theVaniyar sub basin, South India,” Modelling EarthSystems and Environment Vol. 3 (24).Sartor J. D., Boyd, J. B., Agardy, J., 1974, “WaterPollution Aspects of Street Surface Contaminants”,Water Pollution Control Federation, Vol. 46 (3), PP458467.Nhamo, Luxon, and Chilonda, Pius,. 2013, “Validationof the rainfall-runoff SCS-CN model in a catchment withlimited measured data in Zimbabwe,” InternationalJournal of Water Resources and EnvironmentalEngineering, Vol. 5 (6), PP 295-303.Elliot, Alexander, Trowsdale, S. A. 2007, “A Reviewof Models of Low Impact Urban Strom WaterDrainage”, Environmental Modelling and Software,Vol.22 (3), PP. 394-405.Sahu, Ram kumar, Mishra, S. K., Eldho, T. I., 2012, “Animproved AMC‐ Coupled Runoff Curve NumberModel”, Hydrological Processes, Vol.24 (20), PP. 28342839.Patil, J. P., Sarangi, A, Singh, A. K., Ahmad, T., 2008,“Evaluation of Modified CN Methods For WatershedRunoff Estimation Using a GIS-based Interface”, Biosystem Engineering, Vol. 100 (1), 137 – 146.Askar, M. Kh., 2014, “Rainfall-runoff Model using theSCS-CN Method and Geographic Information Systems:A Case Study of Gomal River Watershed,” Water andSociety II, Vol. 178, PP 159-170.Dhawale, Arun, W., 2013. “Runoff Estimation forDarewadi Watershed using RS and GIS”, InternationalJournal of Recent Technology and Engineering(IJRTE), Vol. 1(6), PP 46-50. USDA-SCS (1986). "Urban hydrology for smallwatersheds." U. S. Department of Agriculture, TechnicalRelease No. 55.2951
D Soil which swell significantly when wet, heavy plastic and soil with a permanent high-water table Clay loam, silty clay loam, sandy clay, silty clay, clay High 0-1.27 Very low rate (0-0.05in/hr) Table 2: Classification of Antecedent Moisture Conditions (AMC) Sr no Soil characteristics Total 5-day antecedent rainfall(mm)
v Agriculture Handbook 590 Ponds—Planning, Design, Construction Tables Table 1 Runoff curve numbers for urban areas 14 Table 2 Runoff curve numbers for agricultural lands 15 Table 3 Runoff curve numbers for other agricultural lands 16 Table 4 Runoff curve numbers for arid and semiarid rangelands 17 Table 5 Runoff depth, in inches 18 Table 6 I a values for runoff curve numbers 21
v Agriculture Handbook 590 Ponds—Planning, Design, Construction Tables Table 1 Runoff curve numbers for urban areas 14 Table 2 Runoff curve numbers for agricultural lands 15 Table 3 Runoff curve numbers for other agricultural lands 16 Table 4 Runoff curve numbers for arid and semiarid rangelands 17 Table 5 Runoff depth, in
relationship between urbanization and runoff quality and quantity. However, the PSWSMRP focused primarily on the impacts of runoff on wetlands themselves, and not on the effects of urbanization on runoff flowing to wetlands. Runoff can alter four major wetland components: hydrology, water quality, soils, and
water quality and threaten aquatic habitats. Any type of development can increase the amount of stormwater runoff, alter natural drainage patterns and increase the concen-tration and types of pollutants carried by runoff. Runoff is a concern for marinas in areas used for boat hull maintenance. The materi-als and compounds used to repair boats,
Packet: Water Cycle Leigh-Manuell - 3. 4. During a rainstorm, when soil becomes saturated, the amount of inﬁltration a. decreases and runoff decreases b. decreases and runoff increases c. increases and runoff decreases d. increases and runoff increases 5. A paved blacktop pa
A spreadsheet template for Three Point Estimation is available together with a Worked Example illustrating how the template is used in practice. Estimation Technique 2 - Base and Contingency Estimation Base and Contingency is an alternative estimation technique to Three Point Estimation. It is less
Introduction The EKF has been applied extensively to the ﬁeld of non-linear estimation. General applicationareasmaybe divided into state-estimation and machine learning. We further di-vide machine learning into parameter estimation and dual estimation. The framework for these areas are brieﬂy re-viewed next. State-estimation
2 Unit-III 1) Runoff: Runoff, sources and component, classification of streams, factors affecting runoff, Estimation Methods. Measurement of discharge of a stream by Area-slope and Area-velocity methods. 2) Hydrograph: Flood hydrographs and its components, Base flow & Base flow separation, S-Curve technique, unit hydrograph, synthetic hydrograph.
state-of-the-art bifactor-ESEM framework to the factor structure of the SCS supports the existence of a global self-compassion factor as well as the six specific dimensions, but does not support the use of two separate factors. Adaptations of the SCS include a short version, a youth version, a state version, and a measure of compassion for others.
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Start in upper left corner on right side. Join yarn A in first stitch, ch1. Row 1 3sc, skip 1 stitch *5sc, skip 1 stitch* rep * to * 29 times, 2sc, ch1, turn. (155 scs) Row 2 155sc, ch1, turn First row in chart 1: Row 3 Same as row 2. (155 scs) Row 4 Same as row 2, change to yarn B in last stitch, ch1, turn (155 scs)
Step 1: Unpack your 4200-SCS . The box contains: 4200-SCS Semiconductor Characterization System, with the 4210-CVU card integrated into the mainframe. How to lift the 4200-SCS: Lift from the bottom, not from the front bezel. Set it on a bench or install
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Raven Product Controller a ISO Product Controller I a ISO Product Controller II a CONSOLES Viper 4/Viper 4 a a a Envizio Pro Series a SCS 5000 a SCS 4400 a SCS 4600 a 3rd Party ISO Consoles* a *Subject to 3rd party ISO console capabilit
UofT SCS Data Analytics Boot Camp - Powered by Trilogy Education Services LLC 2 ABOUT THE ONLINE DATA ANALYTICS BOOT CAMP UofT SCS Data Analytics Boot Camp is a part-time program taking place over the course of 24 weeks. You will learn the same skills an
USING THE NRCS METHOD Step 1. Determine the drainage area. Step 2. Determine a weighted Curve Number and Tc Step 3. Select appropriate Rainfall amounts. (Depth, not intensity) Step 4. Determine peak discharge. Example #1 Using the SCS Method, determine the total amount of runoff volume produced from a 10 year storm event that is located in
Runoff—A guide to flood estimation (Pilgrim 1987). Figure 3.3 gives an example of an IFD curve. IFD data can be used to estimate peak rates of runoff for a specified return period. IFD curves, along with the necessary coefficients used to generate the curves, can be obtained for any location in Australia from the Bureau of Meteorology.
mathematical model was y 11.49x2-116.82x 647.69 with a coefficient of regression of 98.61%. The developed model performs at 70% of coefficient of accuracy. The peak runoff of the catchment was obtained between the months of July -October. The mathematical relationship exist between discharged and runoff with 92% of coefficient of regression.
nonlinear state estimation problem. For example, the aug-mented state approach turns joint estimation of an uncertain linear system with afne parameter dependencies into a bilinear state estimation problem. Following this path, it is typically difcult to provide convergence results . Joint parameter and state estimation schemes that do provide