Estimation of surface runoff using SCS–CN and GIS approach in the Karpara watershed, Maharashtra, India

Introduction

The watershed is an ideal unit for analysing the development and management of natural resources, where different entities such as soil, water, geomorphology, and land use interact harmoniously, allowing for a holistic approach to problem-solving. The three basic resources of soil, vegetation, and water must be managed collectively and interestedly for sustainable watershed management. The productive interaction between these natural resources is determined by various factors such as the physiography of the land, slope and nature of soil cover, land use, land cover, hydrology, geomorphology, climate, socio-economic and legal aspects, and the hydrological characteristics of the land area. (Suhas P Wani and Kaushal K Garg, 2009) [26] . India receives an average annual rainfall of 119.4 cm and is home to about 4% of the world's fresh water. Despite occupying only 3.29 million km 2 of land, which is 2.4% of the world's land area, India supports over 15% of the world's population. With 1/6 th of the world's population, 1/50 th of the world's land, and 1/25 th of the world's water resources, India has a significant impact globally. Additionally, India has a livestock population of 500 million, accounting for approximately 2% of the world's total livestock. Cattle, in particular, play a crucial role in Indian agriculture. The country's total utilizable water resources are estimated at 1086 km 3 (Hutti, B., & Nijagunappa, R. 2011) [12] . Maharashtra is one of the major states in the country in terms of natural resources like rainfall, soil but it may soon become state facing increasing water crisis with perennial water shortage.

The agricultural land under irrigation is divided into two main categories: groundwater irrigated irrigated and flow/canal irrigated. Groundwater irrigation covers 28.75 lakh hectares (71%) of the total area, while flow or canal irrigation covers 11.83 lakh hectares (29%). A large portion of the groundwater, 85%, is utilized for irrigation, with 10% being used by industries and only 5% allocated for domestic consumption. Additionally, approximately 80% of the rural population relies on groundwater for their drinking water needs (CGWB, 2013) [6] . In Marathwada region, Parbhani is one of the district where groundwater occurrence and movement in the area is influenced by its rock formation. Parbhani district is underlain by basaltic lava flows and alluvium only. The average annual rainfall is about 900 mm which is unevenly distributed all over district. About 84% of the annual rainfall in the district is received during the South-West monsoon season for the month of June to September (Rawat, A., Kumar, P., & Deoli, V. 2019) [19] . Runoff is the most important hydrologic variables used in most of the resource applications. Reliable prediction of quantity and rate of runoff from land surface into stream and river is difficult and time consuming to obtain for ungauged watershed. However, this information is needed in dealing with many watershed development and management problems. Conventional methods for prediction of river discharge require considerable hydrological and meteorological data. Collection of these data is expensive time consuming and difficult process (Ramana, G. V. 2014) [18] . In our country, the availability of accurate information on runoff is scarcely available and that too in a few selected sites where recording and automatic hydrological gauging stations are installed. Thus, there is an urgent need to generate information on basin runoff and sediment yield for acceleration of the watershed development and management programs. In India most of the agricultural watersheds are ungauged having no past record what so ever of rainfall-runoff processes. Non availability of continuous rainfall and runoff record in majority of Indian watershed has led to the development of techniques for estimation of surface runoff from ungauged basins (Patil, J. P., Sarangi, A., Singh, A. K., & Ahmad, T. 2008) [16] . Quickening of the watershed management programme for conservation and development of natural resources management has necessitated the runoff information runoff estimation from Remote Sensing data so as to provide quick result for decision makers, before any experimentation of quantification is taken up (Sandhya Rani, B. H. (2004) [24] . This is a less time-consuming method and also gives a reliable result. Advances in computational power and the growing availability of spatial data have made it possible to accurately predict the runoff. The possibility of rapidly combining data of different types in geographical information system (GIS) has lead to significant increase in its use in hydrological applications (Gupta, P. K., Panigrahy, S., & Parihar, J. S. 2011) [10] . Out of several methods for runoff estimation from ungauged watershed, the curve number (CN) is an index developed by the Natural Resource Conservation Service (NRCS) to represent the potential for storm water runoff within a drainage area. The curve number method (SCS 1972) [24] also known as hydrologic soil cover Complex method is a versatile and widely used procedure for runoff estimation. The CN for drainage basin is estimated using a combination of land use, soil permeability and the antecedent soil moisture condition (AMC). In the present study the runoff from SCS (soil conservation services) curve number method modified for Indian conditions is used to determine runoff volume with aid of remote sensing and GIS techniques (Ayushi Trivedi and Manoj Kumar Awasthi, 2021) [3] .

Materials and Methods

The methodology adopted in achieving the set of objectives, the location, its characteristics, features and other relevant components of the study. The adopted methodology of the present study is shown in Figure 2.

Description of Study Area Location

The Karpara watershed is part of Godawari-Purna sub basin, located between 76 0 44' to 76 0 64' E and

Data Collection

The distinct information and data required for the present study were procured from different sources and then imported into the GIS environment. The ensuing section gives a brief review of data collection and GIS software.  SRTM Digital Elevation Model was obtained from USGS Earth Explorer.  The Landsat-8 satellite imagery was procured from USGS Earth Explorer for the study area dated 19 December 2020.  Rainfall data was obtained from Indian Meteorological Data as a grid data from 1991 to 2020.  Soil texture map obtained from FAO world soil map of scale 1:50000.

Software used: Q GIS and Arc GIS

Delineation of Watershed

In GIS based digital delineation of watershed, one grid data matrices are required. These are elevation, flow direction, flow accumulation and stream link grids, of which the elevation grid is the primary input-data matrix, which leads to the generation of the remaining three secondary and date matrices. The elevation grid represents the topography while other represents the drainage of an area. The elevation grid is represented by means of a Digital Elevation Model (DEM), which is a representation of the surface terrain consisting of a rectangular mesh of elevation points laid out over the landscape (Arbind K. Verma 2017). A 30 m resolution Digital elevation Model was obtained from USGS earth explorer and then Q GIS 3.32 software used for further demarcation of drainage line network and generation of various thematic layers.

Surface Runoff Estimation

The integration of GIS and Soil Conservation Service-Curve Number Method is used to estimate the surface runoff. The Soil Conservation Service Curve Number (SCS-CN) method is widely used in determination of direct surface run-off in longterm (continuous) hydrologic simulation models (Mishra SK, 2011). In this approach, GIS is used to create a spatial database that represents hydrologic characteristics of the watershed. Base map, land use and soil coverage's of the study area are created using GIS. The appropriate area-weighted curve number for the study area is computed using overlaying tool of GIS. Then the daily rainfall database is incorporated in the analysis to estimate the direct runoff. The result obtained is useful for watershed management and irrigation scheduling of the study area. The details flow chart for estimation surface runoff as shown in fig 2.

Fig 2: Methodology for estimation of runoff

Thematic maps

In order to study runoff and site suitability different thematic maps such as Elevation map, Slope map, Soil map, Geomorphology and Land use/ Land cover map were prepared in Q GIS 3.32.

Drainage map

The potential availability of water for harvesting depends on drainage network of the study area. Drainage network was derived by using Arc GIS and the watershed delineation has been done. The DEM had undergone different operations fill sinks, flow direction, flow accumulation.

Slope map

Slope influences recharge and infiltration, hence the amount of runoff that is expected from the terrain. Suitability of different water harvesting options highly depends on the slope of a given area. Input DEM used for deriving slope map after the delineation of watershed.

Land use/ Land cover map

Land use/ Land cover is the main representative factor in identifying runoff potential zones. The land use land cover map was prepared in Q-GIS environment for the analysis. supervised classification of the map was done to obtain different land cover classes for each sub watershed which includes agriculture land, fallow land, scrub land, water body etc.

Soil map

Soil texture was an important factor in determining infiltration rate of soil that which decides the runoff potential of soil and the suitability of structures. Soil map obtained from FAO world soil map of scale 1:50000 were used in Q-GIS to get the soil map of the study area.

Hydrological soil group

Hydrologic soil group for Indian conditions are divided into four classes i.e. HSG-I (A), HSG-II (B), HSG-III (C) and HSG-IV (D). Different HGS classes were demarcated by assigning number to each class and thematic map was prepared using Q GIS (USDA, SCS 1972) [24] . [24] .

SCS-CN method

SCS-CN method developed by Soil Conservation Services (SCS) of USA in 1969 is a simple, predictable and stable conceptual method for estimation of direct runoff depth based on-storm rainfall depth. It is a versatile and widely used procedure for runoff estimation. The SCS approach involves the use of simple empirical formulas and readily available tables and curves, developed by the Soil Conservation Service (SCS, 1985). The SCS runoff expressed in unit depth spread over the watershed for Indian conditions is given by ……. 1

……..2

Where, Q direct flow volume expressed as a depth, P total rainfall S potential maximum soil retention, CN curve number value used to estimate potential maximum soil retention (S) The potential maximum soil retention estimation, the initial abstractions (IA) was calculated. Initial abstractions are water losses, e.g. plant interception ns, infiltration and surface storage which occur prior to runoff and are then subtracted from the total runoff (USDA-SCS 1985). The standard assumption is that IA = 0.2S

Antecedent moisture condition (AMC)

The antecedent moisture condition refers to the water content present in the soil at a given time. It is determined by total rainfall in 5-day period preceding a rainfall event (SCS, 1986) [25] . There are three antecedent soil-moisture conditions (I, II & III) according to different soil conditions and rainfall limits for dormant and growing seasons AMC-I: Indicates the lowest runoff potential because the soils are dry enough AMC-II: Indicates the average soil moisture condition AMC-III: Indicates highest runoff potential of the soil, which practically happens when areas of watershed are saturated from antecedent rains. [11] as follows For AMC I If the antecedent moisture condition is III (wet), the curve number adjusted using Hawkins et al., 1981 [11] For AMC III

Results and Discussion

SCS-CN method were used for surface runoff potential for of the Karpara watershed of the Godawari-Purna basin. The results of the study of sub-watersheds have been described in this chapter.

Delineation of Karpara Watershed and Sub-watersheds

The extraction of drainage networks and demarcation of the all sub-watersheds boundary were completed with Q GIS 3.32 coupled with tools using ASTER (DEM). The Karpara watershed was sub divided into 19 sub watersheds based on the drainage pattern and shown in fig. 5. The area of each sub watershed is shown in Table 3. Among the sub watershed the lowest area (58.5 ha) in Karpara watershed was found to be for sub-watershed no 5 and the largest area (2634.12 ha) was found to be of sub-watershed No 2. The total geographical area of Karpara watershed was measured as 26932.59 ha.

Land use / Land cover map

The land use and land cover of a particular area have a substantial impact on the drainage patterns of a watershed. The differences in land coverage can significantly influence the susceptibility of erosion in micro watersheds. Additionally, variations in vegetal cover can strongly affect soil moisture, infiltration, evapotranspiration, and the interception process of watersheds (Choudhary et al., 1996; Rashid and Romshoo 2012) [7, 20] . Impervious land, whether naturally occurring or due to human settlement, contributes to increased runoff by obstructing the infiltration process (Dams et al., 2013) [8] . It also plays a vital role in shaping the drainage pattern within the watershed (Fohrer et al., 2001) [9] . A higher percentage of plant cover and a large amount of root biomass are effective in slowing erosion rates (Badar et al., 2013) [4] . Furthermore, tree canopies and foliage from smaller plants intercept rainwater, aiding in erosion prevention (Mustard and Sunshine 1999; Romshoo et al., 2012) [14, 20] . The classification of land use and land cover (LULC) was carried out based on the understanding that identified classes in the study area strongly influence runoff and soil erosion. The resulting classes were agriculture, barren land, built up area and waterbody as shown in fig 6. There was unavailability of rainfall station within watershed area. Therefore Indian Metrological Department grid rainfall data have been used for runoff estimation. The amount of runoff represents 15 to 24% of the total annual rainfall. In present study, the methodology for the tenacity of the runoff utilizing GIS & SCS-CN approach could be applied in Karpara watershed. In SCS-CN method, antecedent moisture condition of the soil plays a very consequential role because the CN number varies accordingly to the soil and that is considered while estimating runoff. For a given watershed, CN adjustments have been calculated by Hawkins (1981) [11] curve number formula to each sub watershed. Sub watershed no12 has lowest curve number (75.92 and sub watershed no 19 has highest curve number (85.20). The average rainfall for Karpara watershed were observed in between 786 to 829 mm and average runoff depth ranges from 124 to 298 mm. In conclusion, soil conservation servicecurve number approach is efficiently proven as a better method, which consumes less time and facility to handle extensive data set as well as site selection to identify suitable location for conservation structures.

References

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2. Table 7: Yearly runoff depth (mm) for Karpara sub watersheds (1991-2020)
3. Table 8: Yearly runoff depth (mm) for Karpara sub watersheds (1991-2020)
4. Table 9: Yearly runoff depth (mm) for Karpara sub watersheds (1991-2020)
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