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Estimation of Runoff for Agricultural Utilization Using Geoinformatics: a Model Study from Telangana State

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Estimation of Runoff for Agricultural Utilization Using Geoinformatics: a Model Study from Telangana State International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 4, April 2017, pp. 1132–1144 Article ID: IJCIET_08_04_127 Available online at http://iaeme.com/Home/issue/IJCIET?Volume=8&Issue=4 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 © IAEME Publication Scopus Indexed ESTIMATION OF RUNOFF FOR AGRICULTURAL UTILIZATION USING GEOINFORMATICS: A MODEL STUDY FROM TELANGANA STATE M. V. Raju Assistant Professor, Department of Civil Engineering, Vignan’s University, Vadlamudi, Guntur, Andhra Pradesh, India SS. Asadi Professor & Associate Dean - Academics, Department of Civil Engineering, K L University, Vaddeswaram, Guntur, Andhra Pradesh, India M. Satish Kumar Research Scholar, Institute of Science and Technology, Centre for Environment, J.N.T.U., Hyderabad, Telangana, India Hepsibah Palivela Research Project Associate, HVM INDIA, Vijayawada, Andhra Pradesh, India ABSTRACT The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff volume for a given rainfall event. The applicability of the SCS- CN method and the runoff generation mechanism were thoroughly analysed in a Mediterranean experimental watershed in Hyderabad. The region is characterized by a Mediterranean semi-arid climate. A detailed land cover and soil survey using remote sensing and GIS techniques, showed that the watershed is dominated by coarse soils with high hydraulic conductivities, whereas a smaller part is covered with medium textured soils and impervious surfaces. The analysis indicated that the SCS-CN method fails to pre direct runoff for the storm events studied, and that there is a strong correlation between the CN values obtained from measured runoff and the rainfall depth. The hypothesis that this correlation could be attributed to the existence of an impermeable part in a very permeable watershed was examined in depth, by developing a numerical simulation water flow model for predicting surface runoff generated from each of the three 15 soil types of the watershed. The results support the validity of this hypothesis for most of the events examined where the linear runoff formula provides better results than the SCS-CN method. The runoff coefficient of this formula can be taken equal to the percentage of the impervious area. However, the linear formula http://iaeme.com/Home/journal/IJCIET 1132 [email protected] M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela should be applied with caution in case of 20 extreme events with very high rainfall intensities. In this case, the medium textured soils may significantly contribute to the total runoff and the linear formula may significantly underestimate the runoff produced. Key words: Soil Conservation Service Curve Number, Run off, Land use/Land cover, Slope map. Cite this Article: M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela, Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State. International Journal of Civil Engineering and Technology, 8(4), 2017, pp. 1132–1144. http://iaeme.com/Home/issue/IJCIET?Volume=8&Issue=4 1. INTRODUCTION Runoff is one of the most important hydrologic variables used in most of the water resources applications. Reliable prediction of quantity and rate of runoff from land surface into streams and rivers is difficult and time consuming to obtain for ungauged watersheds. However, this information is needed in dealing with many watershed development and management problems. Conventional models for prediction of river discharge require considerable hydrological and meteorological data. Collection of these data is expensive, time consuming and a difficult process. Remote sensing technology can augment the conventional methods to a great extent in rainfall-runoff studies. The role of remote sensing in runoff calculation is generally to provide a source of input data or as an aid for estimating equation coefficients and model parameters. Experience has shown that satellite data can be interpreted to derive thematic information on land use, soil, vegetation, drainage, etc which, combined with conventionally measured climatic parameters (precipitation, temperature etc) and topographic parameters height, contour, slope, provide the necessary inputs to the rainfall-runoff models. The information extracted from remote sensing and other sources can be stored as a geo referenced data base in geographical information system (GIS). The system provides efficient tools for data input into data base, retrieval of selected data items for further processing and software modules which can analyze/ manipulate the retrieved data in order to generate desired information on specific form. The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff volume for a given rainfall event. This method was originally developed by the US Department of Agriculture, Soil Conservation Service and documented in detail in the National Engineering Handbook, Sect. 4: Hydrology (NEH-4) SCS, 1956, 1964, 1971, 1985, 1993). Due to its simplicity, it soon became one of the most popular techniques among the engineers and the practitioners, mainly for small catchment hydrology (Mishra and Singh, 2006).The main reasons 5 for its success is that it accounts for many of the factors affecting runoff generation including soil type, land use and treatment, surface condition, and antecedent moisture condition, incorporating them in a single CN parameter. Furthermore, it is the only methodology that features readily grasped and reasonably well documented environmental inputs and it is a well established method, widely accepted for use in the United States and other countries. On the other hand, the SCS-CN main weak points are the following: it does not consider the impact of rainfall intensity and its temporal distribution, it does not address the effects of spatial scale, it is highly sensitive to changes in values of its sole parameter; and it does not address clearly the effect of adjacent moisture condition (Hawkins, 1993; Ponce and Hawkins, 1996, Michel et al., 2005). http://iaeme.com/Home/journal/IJCIET 1133 [email protected] Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State 1.1. Study Area Description The study area consists of Hyderabad and Mahabub Nagar district of Telanagana State, India. The historic city of Hyderabad (Deccan) was founded in the year 1591 A.D. by Mohd. Qutub shah, the fifth king of Golconda, with its city center at charminar. Hyderabad city became the capital of enlarge state of Andhra Pradesh from 1st November 1956. It is situated at southern part of in the country and is the fifth largest city in India. The whole city is surrounded by hillocks on all sides and is built on undulated ground. Hyderabad city is situated on the bank as river Musi at 170 22’ E longitudes and is an average of 1734ft.Above mean sea level. Musi River is a part of history and heritage of Hyderabad city .The Hussian Sagar bund known as Tank bund is a mile in length connects the twin cities Hyderabad and Secunderabad. Hyderabad contains innumerable archaeological, historical, educational and recreational places of interest and is a tourist paradise. The Golkonda fort, Qutub Shahi tombs, the famous charminar, Macca Masijid and Falaknuma palaces are some monuments of historical importance. Hyderabad has been the focus of administration, business and educational pursuits for over 400 years since its inception as the state capital. More recently Hyderabad has emerged as the hub of economic and IT revolution. Hyderabad as the state headquarters since the Nizam’s rule, had occupied a central position both geographically and politically. In the study area very small part of SW corner will coming in the Mahabub Nagar district was named after Mir Mahbub Ali Khan the Nizam of Hyderabad. The area of the district is 18,432 Sq.Kms. The total population of the district according to the 1981 census count is 2,444,619 persons. The district may be physiographical divided into more or less two distinct regions, the plains region with low laying scattered hills and the extensive Amarabad – Farhabad plateau. Two important rivers Krishna and Tungabhadra flow through the district. The Krishna River enters Telangana in Makthal taluk. The Tungahadra flows through the taluks of Gadwal and Alampur. Mahabubnagar district is bounded on north by Rangareddy and Nalgonda district, on the east by Nalgonda and Guntur district. On south by river Krishna and Tungabadra on west by Raichur and Gulbarga district of Karnataka state 1.1.1. Location and Regional Setting The Study area is situated in a part of Hyderabad and Mahabub Nagar district of Telangana state between east longitude 780 30’& 78045’ and north latitude 170 0’ & 17015’ falling in SOI toposheet no. 56K/12. The Hyderabad district is situated on 17º20’ of the north latitude and 78º30’ of eastern longitude. The Hyderabad district occupies an area of 217 sq Km with density population of14, 497 per sq km. The total population of district is 38, 29,754 as per 2001 census. The Mahabub Nagar district is situated on 16º and 17º northern latitudes and 77º and 79º eastern longitudes. The Mahabub Nagar district occupies an area of 18,432 sq km and has a population of district is 2,444,619 according to 1981 census. http://iaeme.com/Home/journal/IJCIET 1134 [email protected] M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela Figure 1 The location map of the study area 2. OBJECTIVES OF THE STUDY 1. To prepare the Land use/Land cover, drainage, slope, soil maps of the study area using remote sensing and GIS techniques. 2. To create attribute data consisting of estimation of runoff from the analysis of Soil Conservation Service (SCS) model 3. METHODOLOGY 3.1. Methodology for Thematic mapping 3.1.1. Data collection Different data products required for the study include SOI toposheet (56K/12), fused data of IRS–1D PAN and LISS-III satellite imagery obtained from National Remote Sensing Agency (NRSA) and collateral data collected from government and non-government organizations, comprising of groundwater level data and demographic data.
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