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 STATE

M. V. Raju Assistant Professor, Department of Civil Engineering, Vignan’s University, Vadlamudi, Guntur, Andhra Pradesh,

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., , Telangana, India

Hepsibah Palivela Research Project Associate, HVM INDIA, , 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 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 . 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 . 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 enters Telangana in Makthal taluk. The Tungahadra flows through the taluks of and Alampur. Mahabubnagar district is bounded on north by Rangareddy and district, on the east by Nalgonda and . On south by river Krishna and Tungabadra on west by Raichur and 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.

http://iaeme.com/Home/journal/IJCIET 1135 [email protected] Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State 3.1.2. Database Creation Satellite imageries are geo referenced using the ground control points with SOI toposheets as a reference and further merged to obtain a fused, high resolution (5.8m of PAN) and colored (R, G, B bands of LISS-III), output in EASI/PACE Image processing software. The study area is then delineated and subsetted from the fused data based on the latitude and longitude values and a final hard copy output is prepared for the generation of thematic maps using visual interpretation technique. These thematic maps (raster data) are converted to vector format by scanning with an A0 flatbed desk jet scanner and digitized using AutoCAD software for generation of digital thematic maps using Arc/Info and ARCVIEW GIS software. The GIS digital database consists of thematic maps of land use/land cover, drainage, soil, slope, using SOI toposheets and fused IRS - ID PAN and LISS-III satellite imagery. The methodology adopted for carrying out the present study is given as a flow chart Fig. 1.

3.1.3. Spatial database In the present study, the following layers are generated from toposheet and satellite data using visual interpretation technique. Base map, drainage map, and slope map are prepared using drainage pattern, contours and spot heights from SOI toposheet. The thematic maps generation system used in this study is the system pioneered by United States Geological Survey (USGS) and is modified by NRSA according to Indian conditions and includes Land Use/ Land Cover (LU/LC), soil. The thematic maps thus prepared are converted to digital mode using scanning and automated digitization process using AUTOCAD software. These maps are prepared to a certain scale and show the attributes of entities by different symbols or colouring. This digitized data is then exported to ARC/INFO and Arc View GIS to create digital database for subsequent data analysis (ESRI, 1992 and Mark, 1994). The various thematic layers generated using remote sensing data namely, land use/land cover, slope, drainage and other collateral data in a GIS framework. This could be further analyzed in GIS domain using logical conditions to derive groundwater. This concept of integrated remote sensing and GIS has proved to be an efficient tool in various groundwater studies (Krishnamurthy et al, 1996, Saraf, 1998 and Khan, 2002).

3.1.4. Attribute database The attribute database includes the collateral data collected during fieldwork and from government organizations. The attribute data collected for the present study include the groundwater level data, demographic data etc. that are further integrated with the spatial data in GIS domain (Aronson, 1987).

http://iaeme.com/Home/journal/IJCIET 1136 [email protected] M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela

Figure 2 Flow chart showing the methodology adopted for the present study

3.2. Runoff estimation The Soil Conservation Service (SCS) model developed by United States Department of Agriculture (USDA) computes direct runoff through an empirical equation that requires the rainfall and a watershed coefficient as inputs. The watershed coefficient is called the curve number (CN), which represents the runoff potential of the land cover soil complex. This model involves relationship between land cover, hydrologic soil class and curve number. The method is based on an assumption of proportionality between retention and runoff in the form.Normally the SCS model computes direct runoff with the help of following relationship (Hand book of Hydrology, 1972) .S = (24500/CN) – 254 (1)

http://iaeme.com/Home/journal/IJCIET 1137 [email protected] Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State Q = ((P - 0.3S)2)/(P + 0.7S) (2) Where, CN = (∑ (Ci X Ai )/A (3) Where, CN = weighted curve number. CNi = curve number from 1 to any no. N. Ai = area with curve number CNi A = the total area of the watershed. where CN is the runoff curve number of hydrologic soil cover complex, which is a function of soil type, land cover and antecedent moisture condition (AMC); Q, actual direct runoff, mm; P, total storm rainfall, mm; and S, the potential maximum retention of water by the soil, mm. The basin has been divided into eight smaller sub-basins in order to fulfill the requirement of SCS model. Thiessen polygon method was followed to get weighted average rainfall in the eight sub-basins. Visual interpretation technique was followed to prepare the land use/land cover map of Krishna / Musi river basin using IRS LISS-II data on 1:50 000 scale. The whole catchment area falls under shallow and medium black soils, which fall under group-C of hydrologic soil group. The drainage map, Thiessen polygon map and the final interpreted map showing land use/land cover of the basin were digitized and stored as thematic maps in vector format. These maps were then converted into raster maps. The spatial information and the area statistics were extracted from integration of different layers (maps) thus generated.

Table 1 Runoff Curve Numbers for (AMC II) for the Indian Conditions Hydrologic Hydrologic Soil Group Sl. No. Land Use Treatment/ Practice Condition A B C D Straight Row ------76 86 90 93 Poor 70 79 84 88 Contoured Good 65 75 82 86 Poor 66 74 80 82 1 Cultivated Contoured and terraced Good 62 71 77 81 Poor 67 75 81 83 Bunded Good 59 69 76 79 Paddy (rice) ------95 95 95 95 With under stony cover ------39 53 67 71 2 Orchards Without under stony cover ------41 55 69 73 Dense ------26 40 58 61 3 Forest Open 28 44 60 64 Shrubs 33 47 64 67 Poor 68 79 86 89 4 Pasture ------Fair 49 69 79 84 Good 39 61 74 80 5 Wasted Land ------71 80 85 88 6 Hard Surface ------77 86 91 93

4. RESULTS AND DISCUSSIONS

4.1. Base map A topographic map is a representation of the shape, size, position and relation of the physical features of an area. The base map is prepared using SOI toposheet on 1:50,000 scale and updated with the help of satellite imagery. It consists of various features like the road network,

http://iaeme.com/Home/journal/IJCIET 1138 [email protected] M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela settlements, water bodies, canals, railway track, vegetation etc. delineated from the toposheet. The map thus drawn is scanned and digitized to get a digital output. The information content of this map is used as a baseline data to finalize the physical features of other thematic maps

4.2. Drainage pattern The drainage map prepared from the toposheet forms the base map for the preparation of thematic maps related to surface and groundwater. All the rivers, tributaries and small stream channels shown on the toposheets are extracted to prepare the drainage map. Care is taken that the boundaries of rivers/ water bodies appearing on land use /land cover map or base map are perfectly matched with those on the toposheet. All the drainage lines are examined very closely and final drainage map is prepared. The dendritic Drainage pattern is observed in the study area.

4.3. Slope The slope map of the study area is prepared from the contours and spot heights the altitude varies from 500m in the west to 400m in the east with maximum elevation of 697m in the SE region. Slope classes 1, 2, 3, 4, 6 and 7 are observed in the study area. Nearly level slope class covers 88% of the total study area where the distance between the successive contours is wide (DMIS, 1998). 10% of the study area is under gently sloping class, 2% under moderate sloping, 0.35% under moderately steep sloping, 0.06% under steep sloping and 0.08%very steep sloping.

http://iaeme.com/Home/journal/IJCIET 1139 [email protected] Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State 4.4. Land Use/land Cover (LU/LC) Present land use/land cover map showing the spatial distribution of various categories and their areal extent is vital for the present study. The spatial distributions of various land uses are interpreted based on fused data of IRS–1D PAN and LISS-III data. The different land use, land cover categorize such as agriculture, water bodies, forest, wastelands, built-upland, others have been identified and mapped (IMSD, 1995).From the satellite data, agriculture area (62%) could be clearly delineated into three categories namely, single crop, double crop, and plantations. Though single crop has been observed at various parts of the study area, double crop has been observed don banks of the river Musi and major streams.

Plantationsare observed at some places of the study area. About 2.7%of study area is occupied by water bodies underwater bodies category tanks with water and without water classes which was observed in this, some of which include Rawiral cheruvu, Ibrabimpatan cheruvu, Timmapur chervu, Sandamma chervu, Mailamma cheruvu and Mucheruvu. About 10% of the study area is occupied by forest under this category scrub forest, forest plantation class has been observed. About 23% of study area is under wastelands in this category which is further categorized into land with scrub, land without scrub, fallow lands and barren sheet area. About 1.5 % Built-up land category villages, towns are observed. About 0.8 % of study area is under others category in this mining area, major industry, classes has observed as shown in figure 2. Within the LU/LC categories, infiltration rates of water are less at built-up land and barren sheet rock areas and therefore groundwater potentials at these places are low, while areas near water bodies have high and moderate groundwater potentials.

Lu/Lc Area (Sq.kms) Tanks with water 02.60 Dry Tank 17.79 Major Industry 00.06 Single Crop 347.26 Double Crop 107.21 Fallow Land 02.04 Plantation 00.89 Forest Plantation 00.93 Scrub Forest 79.92 Barren Sheet area 20.90 Land with Scrub 141.46 Land without Scrub 07.85 Mining Area 00.07 Village 01.80 Other Settlements 06.14

http://iaeme.com/Home/journal/IJCIET 1140 [email protected] M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela

4.5. Soil A clear and intimate knowledge of the kind of soils and the extent of their distribution are essential prerequisites in developing rational land-use plans for agriculture, forestry, irrigation, drainage, etc. Soil resource inventory provides an insight into the potentialities and limitations of the mapped area for its effective exploitation. It is important that we prepare an inventory of this resource so that we can develop optimum land use and conservation plans (Sehgal, 1996).The specific objectives of soil mapping are identification, characterization and classification of the soils of the area, which serve as a crucial input for preparing an integrated plan for sustainable development of the area. Soil surveys provide desired information on nature, location, extent and physic-chemical characteristics along with their spatial distribution. The soil map for the present study area is prepared using the satellite imagery and the soil map provided and prepared by the National Bureau of Soil Survey and Land use Planning (ICAR), Nagpur in co-operation with Department of Agriculture, Andhra Pradesh, 2000. Nineteen different soil types were identified in the study area based on the particle size and available water capacity. The soil types identified in the study area can be broadly grouped into clayey calcareous soil, clayey soil, gravelly clayey soil, gravelly loamy soil loamy soil and rock out crops on undulating lands. These soils within each soil type are further subdivided into soils with very low available water capacity (VLAWC), low available water capacity (LAWC), very high available water capacity (VHAWC), high available water capacity (HAWC) and medium available water capacity (MAWC). The area distribution of these soil types is given in figure 4.

4.5.1. Gravelly loamy soils Occupy major part of the study area with VHAWC, HAWC, LAWC, VLAWC, while gravelly clayey soils with LAWC occupy the smallest part of the study area. Gravelly loamy soils are moderately deep, well drained with low to very low available water capacity found on gently sloping lands which are slightly to moderately eroded. 4.5.2. Clayey calcareous Soils which are moderately deep, well drained with HAWC are observed on nearly level valleys, slightly eroded and associated with deep, well drained, loamy, stratified Soils. Clayey soils with MAWC observed in the study area were found to be moderately deep to very deep, well drained soils observed on nearly level and very gently sloping lands and with salinity patches. Similarly Gravelly clayey soils, classified with LAWC observed, are deep,

http://iaeme.com/Home/journal/IJCIET 1141 [email protected] Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State moderately well drained found on nearly level to gently sloping valleys and moderately eroded. While loamy soils with VLAWC are very deep, they are moderately well drained on moderately eroded very gently sloping plains and valleys. Rock outcrops are observed on undulating lands as well as hills and ridges and are associated with moderately shallow to shallow, excessively and well drained, gravelly loamy and gravelly clayey soils with moderate to severe erosion.

4.6. Analysis of Runoff In the present study, an attempt has been made to estimate the runoff volume due to the rainfall occurred in catchment area. Thus, the total volume of water flowing through Krishna River at the proposed dam site during the monsoon period, i.e., June 15 to October 15 resulting from daily rainfall in the upstream catchment area has been estimated using the SCS curve number method Hyderabad basin was divided into eight sub-basins such that the area of each sub-basin does not exceed 100 km2. Weights were assigned according to area of Thiess polygons falling in each sub-basin and the weighted average daily rainfall was calculated for the eight sub- basins. Five land use/land cover classes could be identified based on the colour, tone, texture, shape, size and association of the objects in the imagery. The land use map is given in Figure3.0 and spatial distribution of land use in each sub-basin is given in Table 2.0.Composite curve number value for each sub-basin was calculated by multiplying weights according to the area occupied by each land use class and the corresponding curve number. Thus, a single weighted average curve number was calculated for each sub-basins for MC I, II and III and the same is given in Table 2. The direct runoff was then computed using appropriate equation on the basis of AMC condition. The direct surface runoff from observed data have also been calculated and compared with the computed data for the year 2006 to 2009. The correlation between the observed and computed monthly direct surface runoff have been estimated and given in Table 4.0. In general good correlation has been found between observed and computed runoff.

Table 2 Spatial distribution of land use / land cover in study area Land use Sub-basin-wise Area, ha Class # 1 # 2 # 3 # 4 # 5 #6 # 7 # 8 Total Area Wasteland 2.845 3.276 1.073 1.100 6.148 4.568 .773 6 6.577 29.360 Shrub 8.474 6.105 14.127 9.108 14.002 4.545 4.160 18.689 79.210 Open forest 8.482 5.647 15.473 5.405 17.677 0.000 0.000 19.106 71.790 Good crop 63.740 38.353 35.189 26.935 50.279 19.145 7.133 23.494 264.268 Poor crop 11.710 13.674 22.289 6.971 6.726 0.000 0.000 1.122 62.492 Total area 95.251 67.055 88.151 49.519 94.832 28.258 15.066 68.988 507.12

Table 3 Weighted average runoff curve number Sub-basins in the CN for Hydrologic Soil Group – C and Moisture Condition Study area AMC –I AMC- II AMC-III Sub-basin - 1 64 81 92 Sub-basin - 2 65 81 92 Sub-basin - 3 63 79 91 Sub-basin - 4 64 80 91 Sub-basin - 5 63 79 91 Sub-basin - 6 66 82 92 Sub-basin - 7 66 81 92 Sub-basin - 8 60 77 89

http://iaeme.com/Home/journal/IJCIET 1142 [email protected] M. V. Raju, SS. Asadi, M. Satish Kumar and Hepsibah Palivela

Table 4 Correlation between estimated and observed runoff Period Correlation coefficient for 2006 2007 2008 2009 June 15-30 0.202 0.993 * * July 01-31 0.992 0.943 0.952 0.924 August 01-31 0.968 0.950 0.852 0.971 September 01-30 0.856 0.749 0.973 0.918 October 01-15 * * 0.938 * Seasonal 0.936 0.926 0.939 0.941 Note: * The value divided by zero. Hence, R cannot be calculated

5. CONCLUSIONS The conventional hydrological data are inadequate for purpose of design and operation of water resources systems. In such cases remote sensing data are of great use for the estimation of relevant hydrological data. Remote sensing data can serve as model input for the determination of river catchment characteristics, such as land use/land cover, geomorphology, slope, drainage etc. GIS offers the potential to increase the degree of definition of spatial sub-units, in number and in descriptive detail. The conclusions that may be drawn are: 1. The combination of remote sensing and SCS model makes the runoff estimate more accurate and fast; 2. Geographical information system arises as an efficient tool for the preparation of most of the input data required by the SCS curve number model; 3. The runoff estimated using SCS curve number model are comparable with the runoff measured by the conventional method; and 4. The analysis can be extended further to assess the impact of land use changes after construction of the proposed dam on the rainfall-runoff relationship REFERENCES

[1] Anand Kumar and Sanjay Tomar (2002) Application of Remote Sensing and GIS For Groundwater Assessment. Development Alternatives Newsletter 12. [2] Aronson, P., (1987) Attribute Handling For Geographical Information System, Proceedings of AUTOCARTO 8, Falls Church, VA: ASPRS, 346-355. [3] Bhattacharya (1973) Elements of Geological Map Reading and Interpretation, Orient Longman Limited, Calcutta. Census of India (2001) Series-2, Part XII-A & B, District Census Handbook, Ranga Reddy, published by the Government of Andhra Pradesh. [4] DMIS (1998) Technical Guidelines for Disaster Management Information System (DMIS), Maharashtra Remote Sensing Centre (MRSAC), [5] ESRI (1992) Understanding GIS: The ARC/INFO Method, Environmental Systems Research Institute (ESRI), Redlands, C.A. [6] Integrated Mission for Sustainable Development (IMSD) Technical guidelines (1995) Andhra Pradesh State Remote Sensing Application Centre (APSRAC), Government of Andhra Pradesh. [7] E. Farg (2016) Evaluation of water distribution under pivot irrigation systems using remote sensing imagery in eastern Nile delta, The Egyptian Journal of Remote Sensing and Space Science, Volume 20, Supplement 1, Pages S13 –S19 [8] Krishnamurthy j, venkatesa kumar n, jayraman v, and manivelm (2002) An Approach to demarcate ground water potential zones through Remote Sensing and GIS, International Journal of Remote Sensing. 17 (10), 1867-1884.

http://iaeme.com/Home/journal/IJCIET 1143 [email protected] Estimation of Runoff For Agricultural Utilization Using Geoinformatics: A Model Study From Telangana State [9] Manual for preparation of thematic maps (2002) Industrial Pollution Prevention Project – II (IPPP-II), Consultancy services for GIS, Central Pollution Control Board (CPCB) [10] Mohamed A.E. (2016) Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district, Karnataka, India, The Egyptian Journal of Remote Sensing and Space Science, Volume 19, Issue 19, June 2016, Pages 125- 141 [11] Mary Hoffman, Dictionary of Geology, Special Indian edition, GOYL Saab Publishers and Distributors, Delhi, 1992. [12] Suyash B.Kamble, I.D.Burase, Avinash R.Kharat and Amol A.Nannikar, Development of Pedal Operated Unit For Agricultural Use. International Journal of Mechanical Engineering and Technology, 7(4), 2016, pp. 267–280. [13] Evaluation and mapping of groundwater prospects zone using Remote sensing and Geographical information system, SS. Asadi, Padmaja Vuppala,K. Santosh Kumar and M. Anji Reddy, Jour. of Geophysics, January-April-July & October 2009,Vol. XXX No.1- 4, pp 63 to 71

http://iaeme.com/Home/journal/IJCIET 1144 [email protected]