Surface Runoff Estimation using Remote Sensing & GIS based Curve Number Method Ishtiyaq Ahmad, Dr. M. K. Verma

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1 Surface Runoff Estimation using Remote Sensing & GIS based Curve Number Method Ishtiyaq Ahmad, Dr. M. K. Verma Department of Civil Engineering, National Institute of Technology, Raipur, India Abstract One of the important components of hydrologic cycle is runoff and influenced by various factors including precipitation and watershed characteristics. Numbers of mathematical models are available to quantify runoff. National Resources Conservation Service (NRSC) has developed a Geographic Information System (GIS) based method known as Soil Conservation Services-Curve Number method (SCS-CN) for computing the runoff depth based on the rainfall depth. This method has universal acceptance as it is simple, predictable and stable method for computing runoff depth. This method is based on one parameter i.e. curve number, which is basically a coefficient that reduces the rainfall to runoff. In the present study SCS-CN method has been applied to estimate the runoff depth in Kharun River basin, a subbasin of Sheonath river in Chhattisgarh state. Various layers has been prepared namely base map, soil map, land use map and other map of the study area using GIS and remote sensing data. Based on the rainfall data of 21 raingauge stations in and around the study area, daily runoff depth has been estimated. Keywords Curve Number, Hydrologic Soil Group, Geographic Information System, Remote Sensing, Runoff I. INTRODUCTION This study aims to compute the runoff depth using Soil Conservation Service-Curve Number (SCS-CN) method using Remote Sensing and Geographic Information System (GIS). The SCS-CN is a quantitative description of land use / land cover / soil complex characteristics of a watershed. This model is a widely used hydrological model for estimating runoff using runoff and curve number (CN). The CN is an index that represents the watershed runoff potential. In the present study GIS based SCS-CN method is used for estimating the runoff depth in the Kharun River Sub-Basin of Sheonath river Basin of Chhattisgarh State of India. The present study reveals that the remote sensing and GIS based SCS-CN can be effectively used to estimate the runoff from the river basins of similar geo-hydrological characteristics. II. KHARUN RIVER SUB-BASIN Kharun River sub-basin a major tributary to Sheonath river in Chhattisgarh State was considered for this study. The study area extends between latitudes 20º32'9.6'' N and 21º39'25''N, and longitudes 81º12'54'' E and 81º58'26'' E. As per GIS total area of Kharun river subbasin is about sq.km. It comprises of Balod (area= sq.km.), Dhamtari (area= sq.km.), Raipur (area= sq.km.), Bemetara (area= sq.km.)and Durg (area= sq.km.) Districts of Chhattisgarh State. There are about 21 rain gauge stations recording the rainfall data in the study area. Some of the data needed for the study were available from various sources and some of them were procured. The Study area map is shown in Figure 1. The following paragraph gives brief information on the data sources. The Indian Remote Sensing satellite with Linear Imaging Self Scanning sensors (IRS LISS III) satellite data of scale 1:50000 were collected from Bhuvan portal of Indian Space and Research Organization (ISRO), to use land use/ land cover of the study area. Daily rainfall data for all the 21 rain gauge stations from Water Resources Department Chhattisgarh were used. The soil data from National Bureau of Soil Survey & Land Use Planning (NBSS & LUP). III. SCS-CN METHOD Curve Number is basically a coefficient which reduces the rainfall amount. Soil Conservation Services (SCS) CN method is based on two concepts. 73

2 Fig 1. Location Map of Study Area The first concept is that the ratio of actual amount of runoff to maximum potential runoff is equal to the ratio of actual infiltration to the potential maximum retention. This proportionality concept is expressed as ( )/ =/( ) (1) Where P = precipitation in millimeters (when P Q); Q = runoff in millimeters; S = potential maximum retention in millimeters; I a = Initial Abstraction The second concept is that the amount of initial abstraction is some fraction of the potential maximum retention and thus expressed as: Solving equation (1) and (2) we have =( )^2/( + ) (3) For Indian condition I a =0.3S Above equation is used in the estimation of daily runoff from the storm rainfall. Hydrologic Soil Group As per National Engineering Handbook (NEH) developed by USDA, soils are classified in four groups A, B, C and D based upon the infiltration and other characteristics. = (2) Where S = 25400/CN- 254 Group A: Soils in this group have low runoff potential and high infiltration rate when thoroughly wet. Water is transmitted freely through the soil; Group B: Soils in this group have moderately low runoff potential and moderate 74

3 infiltration rate when thoroughly wet. Water transmission through the soil is moderate; Group C: Soils in this group have moderately high runoff potential and low infiltration rate, when thoroughly wet. Water transmission is somewhat restricted through the soil; Group C: Soils in this group have high runoff potential and low very low infiltration rate, when thoroughly wet. Water transmission is restricted through the soil. Antecedent Moisture Condition AMC indicates the moisture content of soil at the beginning of the rainfall event. The AMC is an attempt to account for the variation in curve number in an area under consideration from time to time. Three levels of AMC were documented by SCS AMC I, AMC II & AMC III. The limits of these three AMC classes are based on rainfall magnitude of previous five days and season (dormant season and growing season). AMC for determination of curve number is given in Table 1. Fig 2.Flowchat for computing runoff AMC Table 1.AMC for determination of CN value Total Rain in Previous 5 days Dormant Season Growing Season I Less than 13 mm Less than 36 mm II 13 to 28 mm 36 to 53 mm III More than 28 mm More than 53 mm A conclusion section must be included and should indicate clearly the advantages, limitations, and possible applications of the paper. Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions. IV. METHODOLOGY The methodology adopted in assessing the runoff potential of the study area is explained in the following steps. The same is shown with the help of flowchart given in Figure Preparation of Land use/land cover information of the study area using the satellite imageries in GIS. Land use / Land cover map of the study area is shown in Figure Soil information of the study area obtained is used for making appropriate hydrological soil classification A, B, C & D as shown in Figure Theissen polygons are established for each identified rain gauge station. The weightage of each rain gauge stations are given in Table2. Table 2.Raingauge Stations Weight S.No. Raingauge Station Area_sqkm Weightage 1 Balod Banbarod Bhalukona Chandi Dhamtari Durg Gangrel Gunderdehi Gurur Khapri Kharun_Amdi Kondapar Newara Oteband Patan Patharidih Pindrawan Raipur Simga Sond Thanod Total

4 For each Theissen cell, area weighted CN (AMC II) and also CN (AMC I) and CN (AMC III) were determined. CN for AMC II is given in Table 3. Table 3.Curve Number for HSG under AMC II Conditions Land Use Hydrologic Soil Group A B C D Agriculture Land Buid Up Tree cover Forest Wasteland Water bodies CN for AMC I iscalculated as: = /( ) CN for AMC I iscalculated as: = /( ) Fig 4. Hydrologic Soil Group Map SCS runoff CN for hydrologic soil cover complex under AMC II condition for the study area is given in Table 2. Area weighted composite curve number for various conditions of land use and hydrologic soil conditions are computed as follows: CN=(CN A )+(CN A )+ +(CN " A " )/A Where A 1, A 2, A 3,..., A n represent areas of polygon having CN values CN 1, CN 2, CN 3,..,CNn respectively and A is the total area. Composite curve number for different AMC conditions computed is tabulated in Table 4. Table 4.Raingauge Stations Weight AMC Condition Composite CN AMC I AMC II AMC III Using equation (3) with rainfall data, corresponding runoff series is derived Fig 3. Land Use map of Study Area 76

5 Table 5.Sample of Daily Rainfall Runoff Computation of Study Area Day Month Year Daily rainfall (mm) 5-day cumulative rainfall Season AMC Condition Composite Curve number (CN) Surface retention (S) Daily runoff (mm) D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III D AMC III G AMC I G AMC I G AMC I G AMC I G AMC I G AMC I G AMC I G AMC I G AMC I G AMC I G AMC I G AMC II G AMC II G AMC III G AMC III

6 V. CONCLUSION GIS based curve number method along with daily rainfall data were used for computing the daily runoff. Antecedent moisture condition plays an important role in the estimation of runoff as it provides the information on moisture content of the land surface for previous the five days rainfall data. Land use layer and soil layer of the study area were prepared and merged in GIS to identify the suitable curve number. Weighted or composite curve number for the study area were calculated and found to be 74.66, and for AMC I, AMC II and AMC III conditions respectively. Now using equation (3) the daily runoff depth were computed for the year The sample of runoff computation is shown in Table 5. From this daily runoff event, monthly and yearly runoff can be computed. For those rainfall events whose intensity is less than 0.3 times the surface retention, runoff is taken as zero.monthly runoff generated in Kharun river sub-basin is shown in Table 6. Table 6.Monthly runoff depth for the year 2013 Month Rainfall (mm) Runoff (mm) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov 0 0 Dec 0 0 Runoff being the important component in planning and management of watershed, its proper quantification is necessary. With the availability of remote sensing data in public domain and GIS, its precise quantification is possible. In The present study reveals that the GIS based SCS-CN method proves to be suitable tool for runoff computation, which helps in proper planning of watershed and its management ACKNOWLEDGEMENTS The authors acknowledge the support provides by Chhattisgarh Council of Science & Technology Raipur, Chhattisgarh & State Data Centre, Water Resources Department Chhattisgarh Raipur, NBLSS & LUP Nagpur. REFERENCES [1] Ahmad I, Verma V, VermaM. K Application of Curve Number Method for Estimation of Runoff. Potential in GIS Environment International Proceedings of Chemical, Biological and Environmental Engineering, Vol. 80: [2] Chapter 7, Hydrologic Soils Groups, National Engineering Handbook, National Resources Conservation Services, USDA, May [3] Jena SK, Tiwari KN, Pandey Ashish, Mishra SK RS and Geographical Information System-Based Evaluation of Distributed and Composite Curve Number Techniques Journal of Hydrologic Engineering, ASCE, Vol. 17, No. 11, November 1, 2012: [4] Jena SK, Tiwari KN, Pandey Ashish, Mishra SK Runoff Estimation by Distributed Curve Number Technique using Remot Sesnsing and GIS Journal of Indian Water Resources Society, Vol. 30, No. 1, January, 2010: [5] Ministry of Agriculture, Govt. of India, Handbook of Hydrology, New Delhi [6] Nagarajan N, Poongothai Spatial Mapping of Runoff from a Watershed Using SCS-CN Method with Remote Sensing and GIS. Journal of Hydrologic Engineering, ASCE, Vol. 17, No. 11, November 1, 2012: [7] Reza Kabiri, 2014, Simulation of Runoff using SCS-Cn Method using GIS System, Case Study: Klang Watershed in Malaysia. Research Journal of Environmental Science, 8: [8] Seth SM, Kumar Bhism, Thomas T, Jaiswal RK Rainfall- Runoff Modelling for Water Availability Study in Ken River Basin Using SCS-CN Model and Remote Sensing Approach Technical Reports, National Institute of Hydrology, Roorkee, No. CS/AR-12/ [9] SherifMM, Mohamed MM, Sheety Amapr, Almulla M Rainfall-Runoff Modeling of Three Wadis Journal of Hydrologic Engineering, ASCE, Vol. 16, No. 1, January 1, 2011: [10] Subramanya K, Engineering Hydrology. Fourth Edition McGraw-Hill Education (India) Private Limited, New Delhi. [11] Xiao Bo, Wang Qing Hai, Fan Jun, Han Feng Peng, Quan Hou Application of the SCS-CN Model to Runoff Estimation in a Small Watershed with High Spatial Heterogeneity Pedosphere, 21 (6), 2011: