INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 4, No 3, 2014

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1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 4, No 3, 2014 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Analysis of surface runoff from Yerala River Basin using SCS-CN and GIS Abhijit M. Zende 1, Nagarajan R 1, Atal K.R Center for Studies in Resource Engineering, Indian Institute of Technology, Bombay 2- Pune institute of Computer Technology, Pune zenabhi31@gmail.com ABSTRACT Watershed Management plays vital role in water resources engineering. It is necessary to plan and conserve the available resources. Remote Sensing (RS) and Geographic Information System (GIS) techniques can be effectively used to manage spatial and non spatial database that represent the hydrologic characteristics of the watershed. The present study area is of Yerala River Basin, located in upper Krishna basin, Western Maharashtra, India. The daily rainfall data of 10 rain gauge stations ( ) was collected and used to predict the daily runoff from the watershed using Soil Conservation Service Curve Number (SCS CN) and GIS. For the duration , minimum and maximum of (a) the yearly average rainfall are mm and mm respectively and (b) the yearly average runoff are mm 3 and mm 3 respectively. The developed rainfall runoff model has been used to understand the characteristics of the watershed and its runoff. Keywords: GIS, RS, Rainfall-runoff, SCS CN method, River basin 1. Introduction Water is one of the most vital requirements for economic and social development. Consistently increasing population in the Indian subcontinent directly results into increasing demand of water for domestic, agricultural and industrial use. However, the quantum of rainfall and surface water availability has remained the same. This has resulted into reduction of ground water level and ground water quality due to its over-exploitation. Therefore, sustainable, alternate and decentralized approach is required to develop watershed at micro level (of area < 5 km 2 ) to reduce the runoff, to recharge the ground water and its quality. Reduction of surface runoff can be achieved by constructing suitable structures or by making changes in land management. For Micro-watershed-development-approach, we require detailed understanding and analysis of various rainfall-runoff model related parameters such as land use, hydraulic properties of the soil, soil moisture, slope, rainfall intensity and lithology (Cosh et al 2004, D Ramakrishnan 2009). A watershed is the area of land where all of the water that is under it or drains off of it goes into the same place. Each watershed has different characteristics with regard to size, shape, slope, drainage, vegetation, geology, soil, geomorphology, climate and land use. Watershed management includes proper usage of water (from all resources), estimation of runoff etc. required for planning, developing, managing resources and scheduling uses of water. Runoff is one of the important hydrologic variables used in the watershed management. More time & efforts are required for accurate estimation of runoff in gauged-watershed. Therefore SCS- CN & GIS techniques are advantageous for watershed management (R. Amutha 2009). 1.1 Objective Submitted on January 2014 published on February

2 To generate a Rainfall-Runoff-Model (RR Model) by incorporating spatial variation of various physiographic characteristics of the study area such as geomorphology, geology, structures, land use/land cover, soil and drainage pattern integrated with the help of RS data and GIS techniques. 2. Materials and methods 2.1 Study area Yerala River Basin The Yerala River is a major tributary of Krishna River. It originates from the hilly regions of Manjarewadi, Maharashtra-India. It flows through rain shadow region of Satara and Sangli districts, which is confluence to Krishna River at Wasagade near Sangli. The main tributaries of Yerala River are Nani Nadi, Kapur Nala, Mahadev Odha, Darjai Odha, Sonhira Odha, and Chand Nadi. The study area is bounded by Latitude 16º 55 to 17º 28 N and Longitude 74º 20 to 74º 40 E. It covers total area of 3035 km² (Figure 1). The watershed experiences tropical monsoon climate with normal temperature, humidity and evaporation throughout the year. The monsoon season in the watershed is June to September. During July and August it rains more and significant runoff takes place. The rainfall stations are Vaduj, Vita, Kadegaon, and Tasgaon (within basin) and Koregaon, Dahiwadi, Karad, Palus, Miraj, Islampur (close to the basin boundary). In general, the annual rainfall is about mm. 3. Methodology Figure 1: Study area: Yerala river basin In this study, Survey of India topographical sheet no. 47 K 5, 6, 7, 8, 10, 11, 12 and 47 L - 9 on the scale 1:50,000 were used to delineate the watershed boundary, drainage (Fig. 2) and contour. Remote sensing data of IRS P6 - LISS 3 sensor on a scale of 1:50,000 for delineating land use/land cover map (Fig. 3), and soil map. Hydrologic soil group map (Fig. 4) was prepared according to soil characteristics and type of land use/land cover for the 509

3 estimation of runoff from river basin. Daily rainfall data from 10 rain-gauge stations for the year of 1998 to 2011 (14 years) data were used to calculate the runoff using SCS-CN method. 3.1 SCS curve number method The most commonly used empirical method is the Soil Conservation Service Curve Number (SCS-CN) method to estimate the direct runoff from a watershed (USDA, 1972). The SCS- CN method explaining the water balance equation can be expressed as below (Mishra and Singh 2003): where, P is the total precipitation (mm); Q the direct runoff (mm), F the cumulative infiltration (mm), Ia is the initial abstraction (mm); S the potential maximum retention (mm) and the initial abstraction coefficient (0.3). and includes surface storage, interception, and infiltration prior to runoff in the watershed and empirical relation was developed for the term Ia and it is given by, Which is valid for P Ia. Otherwise, Q = 0. For a constant value of Ia (O.3S), S can be determined from the P-Q data. In practice S is derived from a mapping equation expresses in terms of the curve number (CN): S = (5) The CN (dimensionless number ranging from 0 to 100) is determined from a table, based on land-cover, HSG and AMC. HSG is expressed in terms of four groups (A, B, C and D), according to the soil after prolonged wetting. AMC is expressed in three levels (1, 2 and 3), according to rainfall limits dormant and growing seasons. Although SCS method is originally designed for use in watershed of 15 km 2, it has been modified for application to larger watersheds by weighing curve numbers with respect to watersheds/landcover area. In this study, the curve numbers are weighted with respect to the micro-watershed are weighed with respect to the micro-watershed area (generally < 5 km 2 ) using the following equation: (6) CNw = 510

4 where, CNw is the weighted curve number; CNi is the curve number from 1 to any number N; Ai is the area with curve number CNi ; and A the total area of the micro-watershed. The SCS curve number is a function of the ability of soils to allow infiltration of water with respect to land use/land cover and antecedent soil moisture condition (AMC). According to U.S soil conservation service soils are divided into four hydrologic soil groups such as group A, B, C & D with respect to rate of runoff potential and final infiltration rate. Figure 2: Drainage pattern map of Yerala river basin Figure 3: Landuse/Landcover map 511

5 Figure 4: Hydrologic soil group map 3.2 HSG and Antecedent Soil Moisture Condition (AMC) HSG is expressed as four groups, according to the soil s minimum infiltration rate, which is obtained for a bare soil after prolonged wetting (Table 1). Antecedent soil moisture condition had a significant effect on runoff considering and this aspect the soil conservation service (SCS) had developed three antecedent soil moisture conditions such as AMC 1, AMC 2 & AMC 3. Prior to estimating runoff for a storm event, the curve numbers was adjusted based on the season and total 5 day antecedent precipitation. AMC is expressed as three levels, according to rainfall limits for dormant and growing seasons (Table 2). Although originally designed for use on river basin of 3331 km 2 (3,33,100 Hec), it has been modified by some users for application to larger watersheds, principally by land-cover based area-weighting of curve numbers (Rawls et al., 1981; Still and Shih, 1984, 1985, 1991). Table 1: USDA-SCS Soil classification Hydrologic Soil Group Group A Group B Type of Soil Runoff potential Final infiltration rate mm/hr Deep, well drained sands and gravels Low >7.5 Moderately deep, well drained with moderately fine to coarse textures Distribution (%) 4.73 Moderate Remarks High rate of water transmission Moderate rate of water transmission 512

6 Group C Group D Clay loams, shallow sandy loam, soils with moderately fine to fine textures Clay soils that swell significantly when wet, heavy plastic and soils with a permanent high water table Moderate High < Moderate rate of water transmission Moderate rate of water transmission AMC group 1 Table 2: Classification of Antecedent soil moisture classes (AMC II) Soil characteristics Soils are dry not to wilting point, satisfactory cultivation has taken place Total 5 day antecedent rainfall in mm Dormant Growing season Season Less than 13 Less than 36 2 Average condition Heavy rainfalls or light rainfall and low 3 temperatures have occurred within the last 5 days; stared soil Over 28 Over Area weighted curve number The different layers of soil, HSG and land use/land cover were overlaid one by one and the new PAT (polygon attribute table) was obtained using Arc GIS 9.3. The result obtained from this PAT was used to compute the total area weighted curve number of the study area to calculate the AMC 2 refer Table 3. Table 3: Weighted curve number for Yerala river basin (For AMC 2) S Soil Area in % % area * Land use No Type km 2 CN area CN B Agricultural C D B Settlement C D Degraded C Forest D B Fallow Land C D Open Scrub D Land 6 Water bodies Weighted Curve Number (WCN) AMC 1 = AMC 2 = AMC 3 =

7 3.4 Estimation of rain fall runoff The daily rainfall database of Yerala basin from 1998 to 2011 (for 14 years) and the area weighted curve number were inputs to the SCS formula and the results are obtained from the daily runoff values and monthly and annual runoff values are obtained. The detailed yearly (Monsoon period) rainfall and calculated runoff values for the 14 years are given below in Table 4. Average rainfall and average runoff of the period ( ) shows increasing trend of the Yerala river basin shows in Fig.5. Table 4: Yearly runoff from Yerala river basin Year Rainfall Runoff Runoff Rainfall Runoff Runoff (mm) (mm) (mm 3 Year ) (mm) (mm) (mm 3 ) Results and discussion Figure 5: Average rainfall vs average runoff In Yerala river basin drought like situation prevails every year due to low average annual rainfall, high runoff and evapo-transpiration. In the hard rock areas like Yerala watershed, discontinuities (fractures/joints) play a vital role in groundwater recharge movement and discharge. The basin constitutes different land use/ land cover of about 15.9% of the area is occupied by agricultural land, 5.61% area covers forest land, 70.89% area of fallow land and remaining 8.36% of the area is occupied by others such as water body, hills, settlement and tanks. In general, among the different land cover types the fallow land and open scrub land plays the major role for the direct surface runoff. Also the hydrologic soil type plays vital role while estimating the runoff potential which represents the soil characteristics, type, and its infiltration capacity. In the study area hydrologic soil type of B, C and D were 514

8 delineated with reference to soil atlas map, soil series of Maharashtra, remote sensing data and other secondary data. The study obtained that C type of HSG predominantly covered throughout the area which is mainly comprised of agricultural and crop land and then followed by B and D type. By intersecting the land use and hydrologic soil type the curve number was assigned according to US SCS and derived the antecedent moisture conditions values are AMC 1, AMC 2 and AMC 3. The annual runoff calculated in both mm and mm 3 and the study area is predominated by southwest monsoon. The average annual rainfall has decreased from the year of 1998 to 2003 and suddenly increased between the years of 2004 to 2007 and gradually decreases and increases from the year 1998 to The trend line for the average rainfall is in the straight line form indicates that rainfall has increased from the year 1998 to 2011 even through to irregular climatic season in the recent years. The average annual runoff fluctuated more throughout the computed years. The rainfall runoff result of the trend line shows that there is high runoff taking place comparatively and predicted trend line for the future runoff is further increasing. This may be a reason of moderate rainfall and normal temperature existing in this area in recent years. It is evident that moderately more runoff in this area and further it can be controlled by converting fallow land into agricultural land since it occupies 70.89% of the total land area. 5. Conclusion It may be inferred that estimation of runoff by SCS CN method integrated with GIS can be used in watershed management effectively. The results of the study show that from the annually runoff values in the river basin can be studied for reliable accuracy along with the spatial variation of soil type and land use type. By assessing the variation in annual runoff, water irrigation can be done to the associated agricultural land and other utility purposes. After synchronizing the available flow in the basin a real world model can be arrived in the efficient water management of the watershed. Acknowledgment The authors wish to record their support received from Indian Institute of Technology, Bombay, India, and Pune Institute of Computer Technology, Pune. The authors wish to record deep sense of gratitude to Meteorological Department of Government of India and other Government Departments for providing data for this research work. 6. References 1. Amutha R., P. Porchelvan., (2009), Estimation of surface runoff in Malatter Subwatershed using SCS-CN method, Journal of Indian Society Remote Sensing (June), 37, pp Cosh M. H., Jackson T. J., Bindlish R. and Prueger J. H., (2004), Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates; Remote Sensing of Environment 92, pp Mishra S. K. and Singh V. P., (2003), Soil Conservation Service Curve Number (SCS-CN) Methodology, Kluwer Academic Publishers, Dordrecht, The Netherlands. 4. Ramakrishnan D., A. Bandyopadhyay, K. N. Kusuma., (2009), SCS-CN and GIS based approach for identifying potential water harvesting sites in the Kali watershed, Mahi River Basin, India, Journal of Earth system sciences, 118(4), pp

9 5. Rawls, W. J., Shalaby, A., and McCuen, R. H., (1981), Evaluation of methods for determining urban runoff curve numbers, Transactions of ASAE, 24, pp Still D.A., and Shih S.F., (1985), Using Landsat to classify land use for assessing the basin-wide runoff index Water Resources Bulletin, 21, pp Still, D.A., and Shih, S.F., (1984), Using Landsat data to estimate runoff. ASAE Summer Meeting, Pap. No , St. Joseph, MI. 8. Still, D.A., and Shih, S.F., (1991), Satellite data and geographic information system in runoff curve number prediction. In: Proceeding of the International Conference on Computer Application in Water Resources, Taipei, Taiwan, R.O.C., pp 1014-/ United States Department of Agriculture (USDA), (1972), National Engineering Handbook, Section 4, hydrology. Soil Conservation Service, US, Government Printing Office, (Washington, DC). 516