80 CHAPTER VII FULLY DISTRIBUTED RAINFALL-RUNOFF MODEL USING GIS 7.1GENERAL This chapter is discussed in six parts. Introduction to Runoff estimation using fully Distributed model is discussed in first part. SCS Curve Number method is described in second part. Delineation of Watershed boundary has been discussed in third part. In fourth part, preparation of Soil map and Hydrological Soil Group map has been presented. Fifth part contains the methodology of preparation of Land Use/ Land Cover map. Methodology adopted for estimating the daily run off developing a fully distributed model based on SCS CN method has been presented in the sixth part. 7.2 INTRODUCTION Runoff estimation is required for planning and execution of water resource projects. Several methods are available for estimation of runoff. Among them, the USDA Soil Conservation Service curve number (SCS-CN) method is the most popular and widely used. The advantages of this method are its simplicity, predictability, stability and its reliance on only one parameter namely the Curve Number (CN). The land Use / Land Cover classes can be integrated with the hydrologic soil groups of the sub basin in GIS. The main inputs required to the SCS-CN method are delineation of the watershed boundary, preparation of soil map, preparation of land use/land cover
81 thematic map and antecedent moisture condition to estimate daily runoff. Watersheds may be modeled by a lumped model using basin average input data and producing total basin stream flow. Such a model may produce reasonable result but because of the distributed nature of hydrological properties like soil type, slope and land use, the model can not be expected to accurately represent the watershed conditions. In order to consider the spatial heterogeneity of hydrological characteristics in terms of each grid element within a catchment, fully distributed models are to be developed. For connection of topography, the computer based methodology known as GIS is quite good enough to cover the link between the topographic, land use and other information related to geographical location. With the development of GIS, the hydrological catchment models have been more physically based and distributed considering the spatial heterogeneity. There are certain limitations to the present study. Firstly, precipitation is considered as uniform over each grid i.e., the point rainfall data has been applied uniformly for the entire grid area. Further, it is assumed that Land use/land cover details were assumed to remain same for the entire season i.e., one for Kharif season and another for Rabi season for each grid. The fourth class of Todini (1988) classification i.e., distributed differential or fully Distributed Model forms the basis for the present
82 study. There are certain advantages with fully distributed model which prompted its selection for this study. Firstly, the advantage of developing a fully distributed model for the basin is that it enables to divide any big catchment into a number of grids and accordingly runoff can be generated over each grid. Runoff can be routed from each pixel to the outlet of the whole basin for the purpose of calibration and validation. Secondly, once the model is developed, calibrated and validated, the same can be applied to any grid in that basin to calculate runoff, even if they are ungauged at sub catchment or sub basin level. In the development of hydrological models, grid sizes in vector analysis and pixel sizes in raster analysis were considered in many studies as computational elements. The purpose of the present study is to develop a fully distributed GIS based hydrologic model considering physiographic heterogeneity in terms of soil and land use to estimate the daily run-off from all the computational elements into which the watershed has been divided based on widely accepted SCS- CN method. The computational elements are subgrids with combination of Land Use/Land Cover and Hydrological Soil Group resulting from grid sizes of 1km x 1km after integrating the Land Use, Hydrological soil group and Thiessen Polygon network maps in GIS environment. Keeping these points in view, a Fully Distributed Rainfall Runoff Model has been developed based on the widely accepted, SCS-
83 CN method in GIS environment to compute daily run-off from all the computational elements into which the watershed has been divided. 7.3 SCS CURVE NUMBER METHOD The SCS-CN method is the most popular method for computing surface run off for rainfall event. This approach involves the use of simple empirical formula and readily available tables. It was reported that SCS-CN method proves to a viable method for estimating the effects of land treatment and land use changes on volumes of run-off under the wide range of climatic conditions. The SCS-CN method continues to be most satisfactory when used for different types of hydrologic problems that were designed to solve evaluating the effects of land use changes (The task committee, ASCE, 1985). The advantages of this method are its simplicity, predictability, stability and its reliance on only one parameter namely the Curve Number (CN). The land Use / Land Cover classes can be integrated with the hydrologic soil groups of the sub basin in GIS. The computed daily runoff values can be checked with the observed data. The main inputs required to the SCS-CN method are watershed boundary, soil map, land use/land cover thematic map and antecedent moisture condition to estimate daily runoff. 7.3.1 ANTECEDENT MOISTURE CONDITION (AMC) AMC has a significant effect on runoff. Considering this aspect, the Soil Conservation Service (SCS) developed three AMC conditions such as AMC I, AMC II and AMC III. Prior to estimating runoff for a
84 storm event, the curve numbers should be adjusted based on the season and total 5-day antecedent precipitation. Antecedent soil moisture conditions are defined in Table 7.1. The curve numbers for AMC II condition for hydrologic soil cover complexes and curve number adjustments for antecedent soil moisture conditions AMC I & AMC III are chosen from the information presented in Chapter 7, Soil Conservation Service, National Engineering Handbook, Section 4, Hydrology (USDA,1972). 7.4 DELINEATION OF WATERSHED BOUNDARY: The Survey of India topographic maps namely 56-I 3, I 6, I 7, I 8, I 10, I 11, I 12, I 14, I 15 & I 16 on a scale of 1:50,000 were collected from Survey of India, Uppal, Hyderabad. The collected topographic sheets were scanned and registered with tic points and rectified in Arc map of ArcGIS 9.2. Further, the rectified maps were projected and merged together as a single layer. The present study area which is a part of Middle Godavari sub basin was delineated up to Kadam Reservoir in GIS environment. 7.5 PREPARATION OF SOIL MAP AND HYDROLOGICAL SOIL GROUP MAP: The soil maps were collected from National Bureau of Soil Survey and Land Use, Nagpur which were prepared on a scale of 1:5,00,000. The collected soil maps were scanned and registered with tic points and rectified. Further, the rectified maps were projected. All individual projected maps were finally merged as a single layer. Later, the delineated study area map was overlaid on projected soil map and
85 finally, soil map pertaining to the study area was thus extracted in GIS environment. Boundaries of different soil textures were digitized in ARC/INFO and the polygons representing soil classes were assigned different colours for reorganization of hydrologic soil groups. The soils of the study area are classified into two Hydrologic Soil Groups based on their minimum infiltration rate (SCS, 1972). The Hydrologic Soil Group prepared from Soil map is presented in Fig 7.1. 7.6 PREPARATION OF LAND USE / LAND COVER THEMATIC MAP Spatial data in the form of satellite imageries for the preparation of Land Use/Land Cover details for the study area were procured from National Remote Sensing Agency (NRSA), Balanagar, Hyderabad. These satellite imageries for both Kharif and Rabi seasons for the year 2005 pertain to Indian Remote Sensing Satellite (IRS) - P6, Linear Imaging and Self Scanning Sensor -III with a resolution of 23.5m. The collected satellite imageries were georeferenced in ERDAS 8.7 then rectified and finally projected. The delineated watershed in vector form was overlaid on projected satellite imagery to get sub set of the study area. Normalized Difference Vegetation Index (NDVI) was employed as the basis for Land Use / Land Cover classification. This method of classification has been found to be suitable for the study area as the data used was pertaining to the past period i.e., years 2003, 2004 and 2005 and also the study area is considerably large comprising predominantly of vegetation.
86 Study area has been classified for Land Use / Land Cover into five classes viz., Water bodies, Crop land, Bare soil and Fallow land and Forest in ERDAS 8.7. Area under each class has been calculated from the attribute table. The classified thematic map was converted from raster to vector format in Arc GIS 9.1 for further analysis. 7.7 CALCULATION OF DAILY RUNOFF USING FULLY DISTRIBUTED MODEL: The process for calculation of daily runoff using fully distributed model consists of several steps. The first step is to delineate the watershed boundary which was carried out. Once the boundary of the watershed has been defined and mapped, Grid map for the study area with grid size of 1 km x 1 km has been carried out in GIS environment. Daily Rainfall data for a period of 11 years from 1996 to 2006 was collected from Bureau of Economics and Statistics, Khairatabad, Hyderabad. Thiessen Polygon network map has been prepared in GIS environment. Then preparation of Soil map and conversion of the soil types based on hydrologic soil groups is done. Later, preparation of Land use/land Cover map for the study area has been carried out. Thiessen network map, Land Use/ Land Cover map and Hydrologic Soil Group maps along with the Grid area map of the study area were overlaid to identify each unique land use-soil group as well as rain gauge information for each subgrid. The curve number (CN) is a function of land use and soil hydrologic group. CN values range between 0 and 100, with higher CN values associated with higher runoff potential. Traditionally, an area
87 weighed average curve number is used for the entire watershed to study the runoff of a watershed. The details of the spatial variation in the watershed are often lost. A fully distributed rainfall-runoff model with a fixed grid size of 1km is constructed in the present work. Appropriate curve number values for each grid element have been assigned based on standard SCS curve number tables (SCS, 1975) considering antecedent moisture conditions from the soil and land use information by using logical expression. Rainfall Runoff calculations are done for each subgrid. The quality of this model is improved by incorporating the spatial variation of watershed characteristics using Remote Sensing and GIS. The runoff curve numbers (AMC II) for hydrologic soil cover complexes and curve number adjustments for antecedent soil moisture conditions for Indian conditions are chosen from the information presented in the Handbook of Hydrology (1972). The CN values arrived at based on the combination of land use and Hydrologic soil group are meant for AMC-II condition. CN values for AMC-I and AMC-III conditions have been calculated using conversion equations 1 & 2 given below. Details of AMC I and AMC III which indicate dry and wet conditions and the rainfall limits for these conditions have been presented in Table.7.1. CN 1 CN 2.281 0.01281CN 2 (7.1) 2 CN CN 2 3 (7. 2) 0.427 0.00573CN 2
88 The recharge capacity S was calculated by substituting the value of weighted CN in the equation (3) 25400 S 254 (7.3) CN Daily rainfall data (P) collected for a period of 1996 to 2006 from Bureau of Statistics and Economics (BES), Hyderabad was used as input. Program in Micro Soft Excel was developed to calculate daily runoff for each grid using SCS-CN method. The daily direct runoff (Q) of each grid has been computed by where, 2 P 0.3S Q (7.4) P 0.7S Q = Runoff depth in mm, P = Rainfall in mm, S = Maximum recharge capacity of watershed after 5 days rainfall antecedent I a. = 0.3S (for Indian conditions), CN = Weighted curve Number The daily rainfall database from 1996 to 2006 of the watershed and the curve numbers corresponding to different land use and hydrological soil cover complex are given as inputs for each subgrid and daily runoff results have been obtained for each subgrid. The cumulative runoff from basin outlet is computed. The calculated daily runoff has been converted to monthly and yearly runoff for further analysis.
89 Table 7.1 Classification of rainfall abstraction AMC Group I II Characteristics of soil Lowest runoff potential. The average conditions Total 5-day antecedent rainfall in mm Growing season Less than 35.6 mm Dormant season Less than 12.7 mm 12.7-27.9 mm 35.6-53.3 mm III Highest runoff potential Over 27.9 mm Over 53.3 mm Table 7.2 Areas of Hydrological soil Groups Name of the Thiessen polygon area Soil group type C area in sq.km Soil group type D area in sq.km Total area in each sub area (sq.km) Boath 153.40 140.01 293.41 Bazarhathnoor 119.72 88.85 208.57 Ichoda 154.21 210.41 364.62 Indervelly 167.71 151.42 319.13 Kadam 221.24 27.86 249.1 Khanapur 193.05 34.27 227.32 Naradigonda 335.28 104.41 439.69 Utnoor 461.57 53.94 515.15 Total 1806.18 811.17 2617.35
90 Fig 7.1 Grid id Map for the study area Fig 7.2 Hydrological soil Group Map for the study area
91 Scanned toposheets Georeferenced and rectified a toposheets using Arcgis Delineated Study area Boundary Base Map IRS LISS III images rectify wrt Toposheets Location of RG Stations Soil Map HSG map LU/LC Map Ground truth verfication Thiessen polygon network map Grid Map Integrated map Data base file Export to MS EXCEL Formulatio n of Model based on SCS CN method Sub-Grid Wise Run off Values Fig 7.3 Flow chart showing the Methodology for developing the Fully Distributed Model to estimate Sub-Grid wise daily Runoff