Erosion susceptibility zoning and prioritization of mini watersheds using Geomatics approach

Similar documents
CHAPTER V WATERSHED CHARACTERIZATION USING GIS

Morphometric Analysis for Evaluating Groundwater Potential Zones, In Kusangai Jor Watershed Area, Dist. Bolangir, Orissa.

International Journal of Research (IJR) Vol-1, Issue-10 November 2014 ISSN

MORPHOMETRIC ANALYSIS OF WATERSHEDS IN THE KUNIGAL AREA OF TUMKUR DISTRICT, SOUTH INDIA USING REMOTE SENSING AND GIS TECHNOLOGY

About the Author: E mail ID: Contact: proceedings. Page 1 of 8

Morphometric Analysis of Shaliganga Sub Catchment, Kashmir Valley, India Using Geographical Information System

Prioritization of sub-watersheds in semi arid region, Western Maharashtra, India using Geographical Information System

PRIORITIZATION BASED ON MORPHOMETRIC ANALYSIS OF DUDHGANGA CATCHMENT,KASHMIR VALLEY, INDIA. USING REMOTE SENSING & GEOGRAPHIC INFORMATION SYSTEM.

Morphometric Analysis of Siswan Drainage Basin, Punjab (India) using Geographical Information System

Prioritization of Sub Watersheds using Morphometric Analysis: A Remote Sensing and GIS Perspective

MORPHOMETRIC ANALYSIS OF ADYAR WATERSHED

Morphometric analysis of Kharlikani watershed in Odisha, India using spatial information technology Kishor Choudhari 1, Panigrahi B 2, Paul J.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 3, No 1, Copyright by the authors - Licensee IPA- Under Creative Commons license 3.

Application of Watershed Erosion Response Model in Planning Resource Conservation of Dehrang Catchment, District Raigad

Block Level Micro Watershed Prioritization Based on Morphometric and Runoff Parameters

Sub-watershed prioritization based on potential zones of Kuttiadi river basin, A Geo-Morphometric approach using GIS

FOREST RESEARCH INSTITUTE, DEHRADUN

Morphometric Estimation of Parameters of Uttar Mand River Basin, Satara District, Maharashtra, India.

Morphometric Analysis of Jiya Dhol River Basin

Morphometric Analysis for Hard Rock Terrain of Upper Ponnaiyar Watershed, Tamilnadu A GIS Approach

Evaluation of Morphometric parameters of drainage networks derived from Topographic Map and Digital Elevation Model using Remote Sensing and GIS

Assessing Vulnerability to Soil Erosion of a Watershed of Tons River Basin in Madhya Pradesh using Remote Sensing and GIS

16 th Esri India User Conference 2015

MORPHOMETRIC ANALYSIS OF LAKSHMANTIRTHA RIVER BASIN AROUND HUNSUR TALUK, MYSORE, KARNATAKA, (INDIA)

University Grants Commission, New Delhi Recognized Journal No ISSN: Print: ISSN: Online: X

Morphometric Analysis Of Bhogavati River Basin, Kolhapur District, Maharashtra, India.

ESTIMATION OF MORPHOMETRIC PARAMETERS AND RUNOFF USING RS & GIS TECHNIQUES

A STUDY ON MORPHOMETRIC PARAMETER OF A WATERSHED FOR SUSTAINABLE WATER CONSERVATION

Chapter IV MORPHOMETRIC ANALYSIS AND STREAM NETWORK CHARACTERISTICS IN GADAG DISTRICT

MORPHOLOGICAL PARAMETER ESTIMATION DERIVED FROM TOPOSHEETS AND ASTER DEM A STUDY ON WATERSHEDS OF DAKSHINA PINAKINI RIVER BASIN IN KARNATAKA, INDIA

Gis Based On Morphometric Analysis of Part of Manair River Basin in Karimnagar District, Telangana State.

Prioritization of Balatira Watershed by Morphometric and Landuse Landcover Analysis, Atpadi Taluka, Sangli District, Maharashtra

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 2, 2011

MORPHOMETRIC ANALYSIS OF RAJGARDH WATERSHED OF MADHYA PRADESH

Geographical Information System Based Morphometric Analysis of Halia Drainage Area, Nalgonda District, Andhra Pradesh, India

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT

MORPHOMETRY OF BUGGAVANKA WATERSHED IN KADAPA, ANDHRA PRADESH, INDIA USING SPATIAL INFORMATION TECHNOLOGY

CHAPTER 4 THE INFLUENCE OF RIVER BASIN MORPHOLOGY ON RIVER GROUNDWATER INTERACTION

Chapter 5. Morphometric Control on Food

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 1, 2011

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 4, 2011

Morphometric analysis of Maun watershed in Tehri-Garhwal district of Uttarakhand using GIS

GIS based quantitative morphometric analysis and its consequences: a case study from Shanur River Basin, Maharashtra India

A Case Study: Morphometric Characteristics of Sub-Watershed (P- 17) in Paras Region, Akola District, Maharashtra, India using Remote Sensing & GIS

Bonfring International Journal of Industrial Engineering and Management Science, Vol. 2, Special Issue 1, July

Morphometric Analysis of Sonbhadra Sub- Watershed of Tawa Reservoir Catchment Area of Hoshangabad District, Madhya Pradesh using GIS Techniques

Each basin is surrounded & defined by a drainage divide (high point from which water flows away) Channel initiation

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 3, No 3, 2013

CHAPTER VII FULLY DISTRIBUTED RAINFALL-RUNOFF MODEL USING GIS

VILLAGE INFORMATION SYSTEM (V.I.S) FOR WATERSHED MANAGEMENT IN THE NORTH AHMADNAGAR DISTRICT, MAHARASHTRA

Chapter 4 : Morphometric Analysis

Watershed Development Prioritization by Applying WERM Model and GIS Techniques in Takoli Watershed of District Tehri (Uttarakhand)

Civil Engineering Journal

Keywords: Morphometry, Upper river basin, Remote sensing GIS, spatial information technology

Drainage Morphometric Analysis of Watershed Basin of River Beas at Harike Pattan, Punjab-Using Remote Sensing and GIS Approach

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

Prioritizing erosion-prone area through morphometric analysis: an RS and GIS perspective

Simplify Equation to Calculate Elongation River Basin Proposed by Schumm (1956)

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: Issue 09, Volume 3 (September 2016)

International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August ISSN

WATERSHED CHARACHTERIZATION AND PRIORITIZATION OF TULASI SUBWATERSHED: A GEOSPATIAL APPROACH

MORPHOMETRIC ANALYSIS OF SUB-BASINS IN JAISAMAND CATCHMENT USING GEOGRAPHICAL INFORMATION SYSTEM

GIS Based Delineation of Micro-watershed and its Applications: Mahendergarh District, Haryana

Soil Erosion Calculation using Remote Sensing and GIS in Río Grande de Arecibo Watershed, Puerto Rico

A comparative study of the Morphometric Analysis of High land sub-watersheds of Meenachil and Pamba Rivers of Kerala, Western Ghats, South India

Morphometric Analysis and Runoff Estimation of Harangi Command Area

Ground Water Potential Mapping in Chinnar Watershed (Koneri Sub Watershed) Using Remote Sensing & GIS

Application of USLE Model & GIS in Estimation of Soil Erosion for Tandula Reservoir

Floodplain modeling. Ovidius University of Constanta (P4) Romania & Technological Educational Institute of Serres, Greece

MORPHOMETRIC ANALYSIS OF KOSI RIVER SUB WATERSHED IN RAMNAGAR, UTTARAKHAND USING GIS AND REMOTE SENSING TECHNIQUES

Review Using the Geographical Information System and Remote Sensing Techniques for Soil Erosion Assessment

ANALYSIS OF MORPHOMETRIC PARAMETERS OF A PAVANA RIVER BASIN, INDIA USING ASTER (DEM) DATA AND GIS

GROUNDWATER CONFIGURATION IN THE UPPER CATCHMENT OF MEGHADRIGEDDA RESERVOIR, VISAKHAPATNAM DISTRICT, ANDHRA PRADESH

CHAPTER 4 MORPHOMETRICAL FEATURES OF CHITRAVATHI BASIN

International Journal of Modern Trends in Engineering and Research e-issn No.: , Date: April, 2016

LAND SUITABILITY STUDY IN LAND DEGRADED AREA DUE TO MINING IN DHANBAD DISTRICT, JHARKHAND.

MORPHOMETRIC CHARACTERISATION OF GAGAR WATERSHED IN KUMAONREGIONOFUTT ARAKHAND FOR MANAGEMENT PLANNING: A GIS APPROACH

LANDSLIDE SUSCEPTIBILITY MAPPING USING INFO VALUE METHOD BASED ON GIS

Geomorphological Analysis of Aralamallige Watershed, Bangalore Using Remote Sensing and GIS Approach

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 ISSN

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 1, 2010

Abstract. TECHNOFAME- A Journal of Multidisciplinary Advance Research. Vol.2 No. 2, (2013) Received: Feb.2013; Accepted Oct.

INTERNATIONAL JOURNAL OF APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

ABSTRACT. 2. Location : 31 o 04'16'' to 32 o 31'27'' North Latitudes and 74 o 30'38'' to 76 o 08'45'' East Longitudes

Extraction of Drainage Pattern from ASTER and SRTM Data for a River Basin using GIS Tools

Identification of Groundwater Recharge Potential Zones for a Watershed Using Remote Sensing and GIS

WATERSHED PRIORITIZATION OF MICRO-WATERSHEDS THROUGH MORPHOMETRIC ANALYSIS: A REMOTE SENSING AND GIS PERSPECTIVE

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 3, No 1, 2012

Classification of Erosion Susceptibility

ABSTRACT. Watershed management has emerged as a new paradigm for planning,

Morphometric Analysis of Singki River Catchment using Remote Sensing & GIS: Papumpare, Arunachal Pradesh

The relationship between drainage density and soil erosion rate: a study of five watersheds in Ardebil Province, Iran

Geog Lecture 19

Comparison of Geomatics Approach and Mathematical Model in Assessment of Soil Erosion Prone Areas Kolli Hills, Namakkal District Tamilnadu, India

Remote Sensing and GIS Applications for Hilly Watersheds SUBASHISA DUTTA DEPARTMENT OF CIVIL ENGINEERING IIT GUWAHATI

Effect of land use/land cover changes on runoff in a river basin: a case study

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 1, 2010

Modeling Surface Runoff Path and Soil Erosion in Catchment Area of Hanp River of District Kabeerdham, CG, INDIA, Using GIS

Morphometric Analysis of Watershed using GIS and RS: A Review

Transcription:

Erosion susceptibility zoning and prioritization of mini watersheds using Geomatics approach Sunil Londhe 1, Nathawat.M.S 2, Subudhi.A.P. 3 1 Soil Engineer, Geomatics Solutions Development Group, (GSDG), Centre for Development of Advanced Computing, Pune, India 2 Professor and Head, Department of Remote Sensing, Birla Institute of Technology (BIT), Ranchi, India 3 Scientist, Indian Institute of Remote Sensing, Dept of Space, Govt. of India, Dehradun Sunil.londhe73@gmail.com ABSTRACT Sediment yield data is generally not available for smaller hydrologic units and it becomes difficult to identify the most vulnerable erosion zone that can be treated on a priority basis. The study area is 163407 ha lies between 23 13' 12"N 23 33' 1" N latitude and 85 10' 28" E 85 54' 16" E longitude having 3870 streams linked with 7 order of streams and divided in to 20 mini watersheds. The evaluation of morphometric and Universal Soil Loss Equation (USLE) parameters using remote sensing and GIS for prioritization of mini watersheds in Subranerakha Sub catchment, Jharkhand, India. Furthermore, attempt was also made to compare the ranks obtained from both parameters to ascertain the validity of ranks of USLE. The ranks of USLE were found more appropriate and are considered for the prioritization of mini watersheds. Five categories of priority viz., very high, high, medium, low and very low, were used to classify the mini watersheds. The prioritization results reveal that four mini watersheds viz. USR7, USR8, USR10 and USR5 fall under the category of very high priority. The area is grouped in to very severe, moderately severe, moderate, slight and very slight erosion susceptibility zones. GIS and remote sensing based evaluation of morphometric and USLE parameters for ranking them is an immense help in the prioritization of small hydrologic units. Further the technology is helpful in erosion susceptibility zone mapping based on ranks obtained from morphometric and USLE parameters for evaluation of conservation measures for ridge to valley treatment. Keywords: Remote Sensing, GIS, Watershed Prioritization, Erosion Susceptibility Zones 1. Introduction Most of the drainage channels that revive water vary rapidly every year during the monsoon season die out soon after the season. In the absence of systematic development there are floods at down streams in monsoon season where as water scarcity at upper portion of watersheds. Hence, proper planning at smaller hydrologic units like mini and micro watershed level is a prerequisite for development of the drainage channels. 511

Therefore it is recognized that a watershed based approach to restoration is necessary for healthy and productive watersheds. It also recognizes that we will not be able to restore all degraded areas at once even with the most aggressive proposed management. The prioritization process identifies the highest priority watershed(s) or erosion susceptibility zone in which to conduct management. The Micro watersheds are characterized using IRS LISS III in Chota Nagpur plateau based on the dominant Agricultural Hydrologic Response Units (AHRU) giving spatial information about scarcity of water so that respective water conservation practices can be improved to use the potential AHRU (Arun, et al. 2005). The rapid evolution in satellite remote sensing and Geographical Information System (GIS) has made possible the development of new techniques for facilitating the mapping of degraded / eroded lands (Skidmore et al. 1997). Sediment yield is one of the main criteria for assessing the vulnerability of a watershed to soil erosion. However, this criterion requires continuous monitoring of sediment samples at the watershed outlet. Such data is hardly available for small hydrologic units. Although the sediment yield from large basins can be obtained from such observations, it is not possible to ascertain the vulnerability to soil erosion of small watersheds within a basin. A soil conservation programme is an expensive and cumbersome process, carried out in steps starting from the most vulnerable (highest sediment producing) region. Therefore, there is a need to assign relative priorities to different regions within a catchment (Jain and Goel, 2002). The objectives of present study to prioritize mini watershteds to identify erosion susceptibility zones based on morphometric and USLE parameters using Remote Sensing and Geographical Information System (GIS). Furthermore a comparative analysis of prioritized ranks of morphometric and USLE parameters was also carried out to ascertain the validity of ranks obtained from USLE parameters. 2. Study area The Study area, twenty mini watersheds of Upper Subernrekha Sub catchment, located in Ranchi, Hazaribag and Khuti districts of Jharkhand State, India. The area is bounded by 23 13' 12" N 23 33' 1" N latitude and 85 10' 28" E 85 54' 16" E longitude and the total coverage of the area is 163407 ha. The area is drained by Subarnrekha river and the Granite and Gneiss are the dominant rock types of the area. The climate is mainly sub tropical dry and sub humid type with mean annual precipitation of about 1465 mm of which nearly 90 per cent is received during southwest monsoon period. The mean annual air temperature is 27 o C. May is the hottest and January is coldest month of this area. Broadly the forest of this region can be classified in to categories: Tropical Moist deciduous forest and Dry deciduous forest. Sal (Shoria robusta) is the dominant forest tree species widely found in this area. 3. Methodology 3.1 Data Generation Mini watershed boundries were delineated using Geometrically rectified IRS P6 LISS IV satellite data in conjunction with toposheets (AIS & LUS, 1990). The drainage pattern 512

derived from SOI toposheets and further updated from IRS P6 LISS IV satellite data in ArcMap environment. Morphometric analysis was performed on this updated drainage layer using the formulae given in table 1. The Streams were numbered using methodology given by Strahler (1964). The flow chart of methodology used is given in figure 1. Figure 1: Methodology erosion susceptibility zoning 3.2 Prioritization Using Morphometric Parameters The parameters like bifurcation ratio, drainage density, stream frequency, form factor, circulatory ratio, elongation ratio, drainage texture and length of overland flow were used in the study for prioritizing mini watersheds. The highest value of parameters among 20 mini watersheds are given rating of 1, next higher value is given a rating of 2 and so on. The lowest value is rated last in the serial numbers. On the contrary, as the shape parameters have an inverse relation with erodability so lower their value, more is the erodability. Thus the lowest value of the parameter was rated as rank 1 and the second lowest as rank 2 and so on. Aftert the rating has been done based on every single parameter and then rating values for every mini watershed are arranged to arrive at a compound value (CP). Based on average value of these parameters, the mini watershed having the highest rating value is assigned highest priority number of 1, next higher value is assigned priority number 2 and so on (Chaudhary and Sharma, 1998; Biswas et al. 1999). 513

Table 1: Formulae for computation of morphometric parameters* Parameter Formula Reference Stream Order Hicrarchial rank Strahler (1964) Mean Stream Length (Lsm) Lsm = Lu/Nu Stream Length Ratio (RL) RL = Lu/Lu l Horton (1945) Drainage Texture (Rt) Rt = Nu/P Length of Overland flow (Lg) Lg = 1/ D*2 Bifurcation Ratio (Rb) Rb = Nu/Nu+l Schumn Relief Ratio (Rh) Rh = H/Lb (1956) Elongation Ratio (Re) Re = (2/Lb)* (A/Pi) 0.5 Mean bifurcatin ratio (Rbm) Rbm = Average Rb of all orders Strahler (1957) Drainage Density (D) D = Lu/A Horton (1932) Stream Frequency (Fs) Fs = Nu/A Form Factor (Rf) Rf = A/Lb 2 Circularity Ratio (Rc) Rc = 4*Pi*A/P 2 Miller (1953) Basin length (Lb) Lb = 1.312*A 0.568 Nooka Ratnam Compactness Coefficient (Cc) Cc = 0.2821P/ A 0.5 et al. (2005) Shape factor (Bs) Bs = Lb 2 /A Texture ratio (T) T = N1/P *Where Lu = Total stream length of order u, Nu = Total no. of stream segments of order u, Lu 1 = Total stream length of its next lower order, P = Perimeter (km), Nu+1 = Number of segments of the next higher order, H = Total relief (Relative relief) of the basin in kilometer, A = Area of the Basin (km 2 ), Pi = Pi value i.e. 3.14, N1= Total no of first order streams. 3.3 Soil Loss Estimation Using USLE The quantification soil loss at each mini watershed was estimated in GIS using Universal Soil Loss Equation i.e. A=RxKxLxSxCxP where; A = average annual soil loss from sheet and rill erosion caused by rainfall and associated overland flow (tons/ha/year), R = climatic or rainfall erosivity, K = soil erodibility factor, L = slope length factor, S = slope steepness factor, C = cover factor and P = support practice factor. Information extracted from different sources were used to derive the spatial database for above factors and further integrated in GIS as follows. 12 R = Σ pi 2 /P(Fournier, 1960) i = 1 L = 158 2.92xS (Desmet and Govers 1996). LS = (L/22.13)m (0.065 + 0.045S + 0.0065S2) (Wischmeier and Smith 1978) 514

K=1.2917[(2.1x10 4M1.14(12 a)+3.25(b 2)+2.5(C 3)]/100 1978) (Wischmeier and Smith where; p= monthly precipitation, P= annual precipitation, M = (% silt + very fine sand)(100 % clay), a= percent organic matter, b= soil structure code used in soil classification, c= permeability class, m= exponent varying between 0.2 to 0.6 depending on the percent slope. The physical and chemical data required for estimation of K was generated from soil mapping on 1:25000 scale (Londhe and Nathwat 2010). The slope of the study area is derived using Survey of India Toposheets having contours at 20 m interval. Slope length in meters (L) is calculated from the slope steepness in percentage (S). The land use/land cover map derived form on screen visual interpretation of IRS P6 LISS IV satellite data was used in computation of C factor values in GIS environment. Based on the USLE lookup table, the C factor values were assigned for different land use/land cover types to generate the cover factor (C) map (Wischmeier and Smith, 1978; Singh et al., 1981). The support practice factor (P) for major conservation practices were computed from the slope and crop cover conditions (Singh et al., 1981). The computed raster layers of LS, R, K, C and P factor were integrated using the raster multiplication option in GIS for estimation of actual soil loss. The final raster layers of actual soil loss was reclassified into different soil loss classes. 3.4 Prioritization using USLE Parameters The quantum of actual soil loss was estimated at each mini watershed level based on the multiplication of mean value of each erosion class and area coverage of the respective erosion class. The total and average actual soil loss for each mini watershed was calculated to derive compound parameter value. The compound parameter for actual soil loss was calculated separately based on the total volume of soil loss under all classes divided by the total area of the respective mini watershed. This compound parameter value of mini watershed will be indicative of its severity of the erosion. Based on the compound parameter values the final rank of the mini watershed was calculated. Depending upon the quantum of soil loss, the mini watershed having highest compound parameter value was assigned rank 1, next highest value was assigned rank 2 and so on. 3.5 Comparative analysis and erosion susceptibility zones The comparative analysis of ranks of morphometric and actual soil loss was carried out to ascertain the validity of ranks obtained from USLE parameters to prioritize miniwatersheds. The highest priority rank mini watershed has been classified under highest erosion susceptibility zone. 4. Results and Discussion The results of the present investigation are divided into quantification, prioritization of mini watershed using USLE and morphometric parameters and their comparision and erosion susceptibility of the area. 515

4.1 Quantification of USLE Parameters 4.1.1 Topographic factor (LS) The topographic factors slope gradient and length of slope significantly influence soil erosion by surface water movement. The very gentle and gentle slopes are having topographic factor of 0.88 and 2.30 respectively. The moderately sloping area is having topographic factor of 2.87. Strongly sloping area is having topographic factor of 4.21. The high and very high topographic factors are noticed in the area of moderately steep to steeply sloping and very steeply sloping landforms. 4.1.2 Soil erodibility (K) and rainfall erosivity factor (R) The soil erodibility factor K ranges from 0.001 to 0.33 is noticed in the parts of different landforms in the study area. The area with lower slopes is having k value less than 0.19 where as areas with higher slopes is having 0.22 to 0.33. The rainfall erosivity factor was calculated using a historic rainfall data at Ranchi station. The total rainfall of the area is 1462 mm and the rainfall erosivity factor ( R) is 258.88. 4.1.3 Cover (C) Support practice (P) factor The generated land use/land cover map from onscreen visual interpretation of IRS P6 LISS IV satellite data has been used in assigning values for various land use/land cover. The C factor value of 0.40 is assigned for the double cropped area, 0.50 for single cropped area, 0.10 for forest, 0.60 for open forest and 0.25 for scrublands. Since the fallow lands and wastelands have less ground cover, the C factor values of 0.95 and 1.0 were assigned respectively. The P factor values for main conservation practices are computed from the slope and land use/land cover types. The type of land use/land cover, level of land management and degree of slope are considered in computation of P values. Level to nearly level plains with double cropped area have been marked with P factor of 0.5. The P factor value of 0.60 was assigned for single cropped area. The areas mostly under forest with strongly sloping to very steeply sloping have been characterized with P factor value of 0.70. The open forest and scrublands under poor management conditions were assigned with P factor values of 0.80 and 0.90 respectively. The fallow lands and wastelands with very poor management conditions were assigned with P factor values of l.0. 4.2 Quantification morphometric parameters 4.2.1 Stream Order The 3870 streams linked with 7 orders of streams (Table 2 and figure 2) naturally spread over 20 mini watersheds. Order of streams is closely governed by the slope conditions. Higher the order of streams, lower is the slope value. Lowest order of streams exhibit with highest slope conditions. The laws of lower the order higher the number of streams is implied throughout the basin. 516

4.2.2 Mean Stream Length (Lsm) Order length is indicative of chronological developments of the streams segments including interlude tectonic disturbance if any. Generally higher the order, longer the length of streams is noticed in nature. But out of 20 mini watersheds 5 has show the reverse trend. Mini watersheds USR3, USR8, USR11 and USR12 has mean stream length of stream order 3 is1.64, 2.16, 0.88 and 2.33 km and at stream order 4 is 1.26, 1.38, 0.38 and 0.79 respectively. Mini watershed USR10 stream order 1 is 0.65 km that is longer as compare to stream order 2 which is 0.57 km in length. This is indication of a anomalous development of the above mini watersheds. 4.2.3 Bifurcation Ratio (Rb) Figure 2: Stream ordering Low Rb value indicates the less structural disturbance and the drainage patterns have not been distorted whereas high Rb value indicates high structural complexity and low permeability of the terrain. In the present analysis, USR13, USR19, USR7, USR8, USR6 and USR20 have bifurcation ratio of 5.75, 5.70, 4.45, 4.15, 4.14 and 4.06 respectively. This high Bifurcation ratio is indicating strong structural control on the drainage development. 4.2.4 Drainage Density (D) and Stream Frequency (Fs) The significance of D as a factor determining the time of travel by water within the basin and suggested that it varies between 0.55 and 2.09 km/km 2. In general, low value of D is observed in regions underlain by highly resistant permeable material with vegetative cover and low relief. High drainage density is observed in the regions of weak and impermeable subsurface material and sparse vegetation and mountainous relief 517

(Langbein, 1947). The values for drainage density ranges from 0.827 (USR18) to 2.859 (USR10). Low drainage density generally results in the area of highly resistant or permeable sub soil material while high drainage density is result of weak or impermale sub soil material (Horton, 1932). Close correlation with the drainage density values of all the mini watersheds indicating the increase in stream population with respect to increase in drainage density. 4.2.5 Drainage Texture (Rt) The infiltration capacity as the single important factors which influences drainage texture and considered drainage texture which includes drainage density and stream frequency. The values of drainage texture ratio of the study area varies from 0.82 to 5.76. Five different drainage texture have been classified based on the drainage density (Less than 2 = very coarse, 2 4= coarse, 4 6= moderate and greater than 8= very fine drainage texture) (Smith, 1950). The micro watershed USR7 is having moderate texture. The micro watersheds USR9, USR11, USR13, USR14, USR17, USR18 and USR19 are having very coarse texture where as remaining micro watersheds are having coarse texture. 4.2.6 Form Factor (Rf) The value of form factor would always be less than 0.7854 (for a perfectly circular basin) (Rudraiah et al., 2008). Smaller the value of form factor, more elongated will be the basin. The basins with high form factors have high peak flows of shorter duration, whereas, elongated watershed with low form factors have lower peak flow of longer duration. In the present case form factor values ranges from 0.020 to 0.051 indicating them to be elongated in shape and suggesting flatter peak flow for longer duration. Flood flows of such elongated basins are easier to manage than those of the circular basin. 4.2.7 Elongation ratio (Re) The elongation ratio values of the mini watersheds vary from 0.61 to 1.22. The elongation ratio values generally exhibit variation from 0.6 to 1.0 over a wide variety of climatic and geologic types. In case of USR2, USR3, USR4, USR5, USR6, USR7, USR15, USR16 and USR18 the elongation ratio is greater than 1.0 indicating lower relief, whereas the other mini watersheds have the values ranges from 0.61 to 0.99 indicating high relief and steep slope. 4.2.8 Circulatory Ratio (Rc) Circulatory Ratio is helpful for assessment of flood hazard. Higher the Rc value, higher is the flood hazard at a peak time at the outlet point. Circularity ratios of mini watersheds under study range between 0.17 to 0.64. The micro watersheds USR12, USR14, USR3, USR4 and USR7 are having 0.64, 0.63, 0.57, 0.57 and 0.51 circulatory ratio respectively which is higher and due to which there is possibility of flood. 518

4.2.9 Relief Ratio (Rh) In the present study, relief ratio ranges from 0.005 (USR18) to 0.066 (USR9). The elevation difference between the highest and lowest points on the valley floor of a miniwatershed is its total relief, whereas the ratio of maximum relief to horizontal distance along the longest dimension of the basin parallel to the principal drainage line is Relief Ratio (Rh) (Schumn, 1956). It measures the overall steepness of a drainage basin and is an indicator of intensity of erosion processes operating on the slopes of the basin. The Rh normally increases with decreasing drainage area and size of a given drainage basin (Gottschalk, 1964). 4.2.10 Length of Overland Flow (Lg) It is the length of water over the ground before it gets concentrated into definite stream channels. This factor relates inversely to the average slope of the channel and is quite synonymous with the length of sheet flow to a large degree. The Lg values of the study area shows variation from 0.699 to 2.418. The values of Lg are higher in case of USR18, USR19, USR3 and USR14 indicating low relief whereas the values of Lg are low in case USR10, USR7, USR2, USR9 and USR5 indicating high relief. 4.3 Prioritization of Mini Watersheds 4.3.1 Morphometric parameters In the present study, morphometric analysis of the parameters, namely stream order, stream length, bifurcation ratio, relief ratio, drainage density, stream frequency, drainage texture, form factor, circulatory and elongation ratio, area, perimeter and length of the mini watersheds has been carried out using. Prioritization of watersheds has been done using compound values by morphometric analysis (Figure 3). Out of 20 mini watersheds under study, four mini watersheds viz. USR7, USR8 USR10 and USR6 are fall under the category of very high priority (Table 3). Keeping these parameters in view, the areas with very high priority having stream order 1, 2, 3, joining streams of higher order may be considered for construction of check dams so that the runoff could be minimized and which may also help in ground water recharge. The mini watersheds USR1, USR2, USR5, USR19 and USR3, USR4, USR11, USR20 are having high and medium priority respectively. High priority mini watersheds indicates the greater degree of erosion and these becomes potential candidates after very high priority for applying soil conservative measure. The soil and water conservation measures can also be applied to medium priority mini watersheds after high priority mini watersheds. The remaining miniwatersheds are under low and very low priority. 4.3.2 USLE parameters Based on the integration of LS, R, K, C and P factors of USLE in GIS, seven classes of estimated actual soil loss i.e. negligible (< 2 t/ha/yr), very slight (2 5 t/ha/yr), slight (5 10 t/ha/yr), moderate (10 15 t/ha/yr), moderately severe (15 20 t/ha/yr), severe (20 40 t/ha/yr), very severe (40 80 t/ha/yr) and extremely severe (>80 t/ha/yr) were identified 519

(Table 4 and Figure 4). The actual soil loss is very slight in case of mini watershed USR18 as are having nearly level to level slopes with less drainage density and stream frequency and are endowed with deep to very deep soils having moderately well to well drainage conditions and accounts for about 6.3 % of the total area. ight soil loss is noticed in the area with of very less drainage density covering mini watershed USR 17 where as moderate soil loss occurs with moderately deep soils and less to moderate drainage density in mini watersheds of USR 9, USR12, USR13, USR15 and USR16. The area under slight and moderate erosion class is about 5.3 and 1.5 % respectively. Rest of the mini watersheds with very severe soil loss are situated on higher slopes. This zone consists of shallow to very shallow soils, high to very high drainage density, stream frequency and covering an area of 69.1 %. Table 2: Results of Morphometric Analysis Mn V Tt Lu Re Rt Rbm D Fs Rf Rc Lg USR1 VII 392 378.2 0.99 2.71 2.962 2.236 2.317 0.014 0.17 0.894 USR2 VI 361 354.8 1.11 4.13 3.252 2.750 2.798 0.029 0.36 0.727 USR3 V 177 147.3 1.03 3.57 3.542 2.371 2.850 0.045 0.57 0.843 USR4 V 287 259.5 1.22 4.28 4.146 2.150 2.378 0.045 0.57 0.930 USR5 VI 266 264.1 1.01 3.14 3.144 2.554 2.572 0.025 0.32 0.783 USR6 V 353 289.8 1.12 4.07 4.140 2.225 2.710 0.030 0.38 0.899 USR7 IV 337 222.9 1.08 5.76 4.446 2.761 4.174 0.041 0.51 0.724 USR8 VI 293 255.1 0.93 2.86 3.342 2.442 2.805 0.018 0.22 0.819 USR9 IV 30 28.8 0.61 1.12 2.956 2.610 2.717 0.031 0.39 0.766 USR10 IV 108 82.5 0.73 2.25 3.431 2.859 3.742 0.025 0.31 0.699 USR11 IV 84 58.0 0.71 1.90 3.143 2.291 3.319 0.025 0.32 0.873 USR12 V 95 76.6 0.89 2.75 2.970 2.462 3.054 0.051 0.64 0.812 USR13 III 40 33.7 0.70 1.39 5.750 1.835 2.176 0.032 0.41 1.090 USR14 IV 46 44.6 0.85 1.57 3.458 1.781 1.836 0.050 0.63 1.123 USR15 VI 212 248.4 1.01 2.28 3.209 2.175 1.857 0.022 0.28 0.919 USR16 V 262 260.8 1.10 3.26 3.501 2.219 2.229 0.031 0.38 0.901 USR17 VI 131 146.3 0.97 1.58 2.750 1.702 1.525 0.026 0.32 1.175 USR18 IV 56 84.6 1.12 0.82 3.511 0.827 0.548 0.039 0.48 2.418 USR19 IV 168 164.2 0.94 1.75 5.702 1.655 1.693 0.020 0.25 1.209 USR20 IV 173 137.3 0.94 2.47 4.061 1.823 2.298 0.026 0.32 1.097 Mn = Mini Watershed, V = Stream Order, Tt = Total Number of Streams of all Orders 520

Figure 3: Prioritization of mini watersheds ( morphometric parameters) Table 3: Compound Values and priorities depending upon the morphometric ranks Sr No Mn* Rbm D Rt T Fs Lg Rf Rh Bs Re Rc Cc CP Pri ority 1 USR1 17 10 10 10 12 11 15 14 1 11 1 1 9.42 8 2 USR2 12 3 3 3 7 18 9 10 3 17 10 10 8.75 5 3 USR3 6 8 5 5 5 13 3 15 14 14 18 18 10.3 12 4 USR4 3 14 2 2 11 7 3 16 4 20 17 17 9.67 10 5 USR5 14 5 7 7 10 16 11 7 8 13 6 6 9.17 6 6 USR6 4 11 4 4 9 10 8 11 2 19 11 11 8.67 4 7 USR7 3 2 1 1 1 19 4 9 12 15 16 16 8.25 1 8 USR8 11 7 9 8 6 14 14 13 7 7 2 2 8.33 2 9 USR9 18 4 19 19 8 17 7 1 20 1 8 13 11.3 16 10 USR10 10 1 12 13 2 20 11 4 16 4 5 5 8.58 3 11 USR11 15 9 14 14 3 12 11 3 17 3 7 7 9.58 9 12 USR12 16 6 8 9 4 15 1 8 15 6 20 20 10.7 14 13 USR13 1 15 18 18 15 6 6 12 19 2 14 14 11.7 17 14 USR14 9 17 17 17 17 4 2 5 18 5 19 19 12.4 18 15 USR15 13 13 13 12 16 8 12 18 6 12 4 4 10.9 15 16 USR16 8 12 6 5 14 9 7 19 5 16 13 12 10.5 13 17 USR17 19 18 16 16 19 3 10 17 11 10 8 8 12.9 19 18 USR18 7 20 20 20 20 1 5 20 9 18 15 15 14.2 20 19 USR19 2 19 15 15 18 2 13 2 10 8 3 3 9.17 7 20 USR20 5 16 11 11 13 5 10 6 13 9 9 9 9.75 11 * Mn= Mini Watersheds 521

Table 4: Estimated quantum of actual soil loss (t/ha/yr)* Mn Negli gible USR1 126.9 (253.7) USR2 40.2 (80.4) USR3 15.6 (31.2) USR4 29.3 (58.6) USR5 71.6 (143.2) USR6 76.7 (153.4) USR7 19.6 (39.1) USR8 103.0 (206.1) USR9 313.9 (627.8) USR10 0.74 (1.5) USR11 6.4 (12.8) USR12 470.6 (941.2) USR13 402.6 (805.3) USR14 634.1 (1268.2) USR15 1879.3 (3758.5) USR16 4018.4 (8036.8) USR17 3527.5 (7055.1) USR18 2741.2 (5482.5) USR19 455.6 (911.2) USR20 316.7 (633.3) Very Slight ModerModerately Severe Very Extremely Soil Slight ate Severe Severe Severe loss 64.4 1043.4 5071.6 6932.8 147898.5342659.5 401417.6 53.6 (25.7) (139.1) (405.7) (396.2) (4930.0) (5711.0) (5017.7) 66.8 803.7 5442.2 6745.5 78157.3254081.1 402202.9 57.9 (26.7) (107.2) (435.4) (385.5) (2605.2) (4234.7) (5027.5) 5.4 680.0 5662.2 6550.6 50714.0131912.1 109503.2 49.1 (2.1) (90.7) (453.0) (374.3) (1690.5) (2198.5) (1368.8) 66.1 1675.4 6027.0 7968.9 66284.6280193.0 315516.8 56.2 (26.4) (223.4) (482.2) (455.4) (2209.5) (4669.9) (3944.0) 25.8 2388.6 6872.9 9506.6 112434.2146779.2 206558.9 46.9 (10.3) (318.5) (549.8) (543.2) (3747.8) (2446.3) (2582.0) 25.9 2577.5 3608.0 10852.4 145152.9151933.9 330093.0 49.9 (10.4) (343.7) (288.6) (620.1) (4838.4) (2532.2) (4126.2) 16.5 268.5 1123.3 13762.2 39136.2155040.9 258203.0 57.9 (6.6) (35.8) (89.9) (786.4) (1304.5) (2584.0) (3227.5) 19.6 2359.1 2344.2 13011.1 82477.9101114.6 362132.3 54.1 (7.8) (314.5) (187.5) (743.5) (2749.3) (1685.2) (4526.7) 200.0 335.2 522.5 544.1 2657.4 3000.0 11246.3 17.0 (80.0) (44.7) (41.8) (31.1) (88.6) (50.0) (140.6) 3.5 58.1 500.4 49.7 6531.1 36156.6 160802.1 70.8 (1.4) (7.7) (40.0) (2.8) (217.7) (602.6) (2010.0) 3.9 231.9 1588.8 8.4 6389.1 33771.7 126567.0 66.6 (1.6) (30.9) (127.1) (0.5) (213.0) (562.9) (1582.1) 1768.3 1091.2 2482.1 4362.8 12481.4 11872.9 20418.7 17.7 (707.3) (145.5) (198.6) (249.3) (416.0) (197.9) (255.2) 751.9 583.8 1820.9 1856.1 4135.2 12422.8 4603.0 14.5 (300.8) (77.8) (145.7) (106.1) (137.8) (207.0) (57.5) 1253.5 813.0 712.5 463.2 7930.5 13370.3 4585.5 11.9 (501.4) (108.4) (57.0) (26.5) (264.4) (222.8) (57.3) 2555.5 22831.42159.2 3214.1 46483.1 71474.5 39510.9 16.7 (1022.2)(3044.2)(172.7) (183.7) (1549.4) (1191.2) (493.9) 289.4 399.1 2171.4 1325.0 38238.0 65537.8 74404.9 15.9 (115.8) (53.2) (173.7) (75.7) (1274.6) (1092.3) (930.1) 48.5 317.9 415.0 802.8 14643.5 43994.2 13759.0 9.0 (19.4) (42.4) (33.2) (45.9) (488.1) (733.2) (172.0) 8878.4 1366.4 1827.2 1424.5 13823.0 17280.0 2776.7 4.9 (3551.4)(182.2) (146.2) (81.4) (460.8) (288.0) (34.7) 15.6 1621.7 1146.2 3636.5 70662.7130623.3 316257.3 52.9 (6.2) (216.2) (91.7) (207.8) (2355.4) (2177.1) (3953.2) 49.4 1381.8 4492.3 2919.0 56243.3140010.3 156640.5 48.1 (19.8) (184.2) (359.4) (166.8) (1874.8) (2333.5) (1958.0) 522

Mn = Mini Watershed, *The values in parenthesis shows the area covered (hectares) under respective erosion class. 4.4 Comparative Analysis of Ranks Figure 4: Actual soil loss (USLE) The comparative analysis of prioritization ranks of morphometric and actual soil loss (USLE) parameters reveal that the ranks of actual soil loss are higher in the miniwatershedsof USR5, USR6, USR7 and USR8 when compared with ranks of morphometric parameters (Figure 5) where as mini watersheds USR 4, USR10 and USR11 are higher in priority as compared with USLE parameters. It indicates that soil erodibility, slope, cover and management factors are influencing the rate of erosion over the natural erosion conditions of drainage pattern. The cover and management factors were not considered in analysis of morphometric parameters. Conversely, the prioritization ranks of actual soil loss were marginally lower in the mini watersheds of USR2, USR7, USR10 and USR11. The slight lower trend was also observed in the actual soil loss ranks of moderate soil erosion of the mini watersheds. It shows that the cover and management factors are also influencing on the rate of erosion besides the drainage conditions. Similar studies also reported by Sharma et al. (1985). This signifies the importance of vegetation in protection of top soil against erosion. However, in the miniwatershed USR13, USR14, USR15, USR17 and USR18 the rank remains the same for both the parameters. The comparative analysis of prioritization ranks of morphometric and actual soil loss (USLE) reveals that the ranks obtained from USLE parameters are closely following the ranks obtained from morphometric parameters. 523

Figure 5: Correlation morphometric and USLE parameters 4.5 Erosion Susceptibility Zoning The comparative analysis of ranks obtained from morphometric, estimated actual soil loss was carried out to ascertain the validity of ranks obtained from estimated actual soil loss parameters. The ranks of actual soil loss were found more realistic to classify the miniwatersheds in terms of their erosion susceptibility. Hence, the ranks obtained from actual soil loss were considered in prioritization of mini watersheds namely very severe, moderately severe, moderate, slight and very slight erosion susceptibility zones (Figure 6). Figure 6: Erosion susceptibility zones 524

4.5.1 Very severe The mini watersheds USR1, USR2, USR3, USR4, USR5, USR6, USR7, USR8, USR10, USR11, USR19 and USR20 are classified under very severe erosion susceptibility zone. These mini watersheds consist of steep to very steep slopes, very high drainage density, stream frequency, texture ratio, lowest form factor and elongation ratio. Very severe soil erosion area is in associated with very shallow to moderately shallow soils and well to excessive drainage conditions. This need immediate attention to take up mechanical soil conservation measures gully control structures and grass waterways to protect the topsoil loss. 4.5.2 Moderately severe The mini watersheds USR9, USR12, USR13, USR15 and USR16 are grouped under moderately severe erosion susceptibility zone based on the quantum of estimated actual soil loss. They consist of gentle to steep slopes, moderate drainage density, stream frequency, texture ratio, lowest form factor, circulatory ratio and elongation ratio. The majority area of these zone coincides with shallow to deep soils. The moderately severe erosion susceptibility zone require combination of mechanical and agronomical measures to arrest the soil loss. 4.5.3 Moderate The mini watersheds USR14 is classified under moderate soil loss priority zone. This mini watersheds consist of moderate to gentle slopes, moderate drainage density, stream frequency, high texture ratio, lowest form factor, circulatory ratio and elongation ratio. The majority of the area is in association with shallow to deep soils. 4.5.4 Slight The mini watersheds USR17 is classified under slight soil loss priority zone. This miniwatershed associated with lower slopes, low drainage density, stream frequency, texture ratio, lowest form factor, circulatory ratio and elongation ratio with moderately shallow to deep soils. 4.5.5 Very Slight The mini watersheds USR18 is classified under very slight soil loss priority zone. This mini watersheds consists of lower slopes, very low drainage density, stream frequency, texture ratio, lowest form factor, circulatory ratio and elongation ratio. The very slight erosion susceptibility zone is found in the area of deep soils. The moderate, slight and very slight erosion susceptibility zone needs agronomical measures to protect the sheet and rill erosion. 5. Conclusions The analysis of ranks obtained from morphometric and USLE parameters shows good inter relationship. The erosion susceptibility zone map will be helpful in identification of priority zones for the evaluation and suggestion of soil conservation measures based on 525

the existing terrain conditions. GIS and remote sensing approach in prioritization of miniwatersheds and further erosion susceptibility zone mapping based on ranks obtained from morphometric and USLE parameters is found to be more appropriate. This approach will certainly help planners and decision makers in judicious allocation and utilization of available resources for treatment of small hydrologic units and effective checking of soil loss. Acknowledgements Authors acknowledge the cooperation and support provided by Director, Jharkhand Space Applications Center Ranchi and Programme Coordinator, GSDG, Center for Development of Advanced Computing, Pune, for providing facilities for carrying out the work. 6. References 1. AIS & LUS., (1990). Watershed atlas of India, Department of Agriculture and Cooperation, All India Soil and Land Use Survey, IARI Campus, New Delhi. 2. Arun, P.S., Jana R., and Nathawat, M.S., 2005, A Rule Base physiographic characterization of a drought prone watershed appling Remote Sensing and GIS, Photonirvachak, 33, pp 189 201. 3. Biswas, S., Sudhakar, S. and Desai, V.R., 1999, Prioritization of sub watersheds based on morphometric analysis of drainage basin a remote sensing and GIS approach, Photonirvachak, 27, pp 155 166. 4. Chaudhary, R.S. and Sharma, P.D., 1998, Erosion hazard assessment and treatment prioritization of Giri river catchment, North western Himalayas, Ind. J. Soil Cons., 26, pp 6 11. 5. Desmet, P. J. J., and Govers, G. A., 1996, GIS procedure for automatically calculating the USLE LS factor on topographically complex landscapes, Journal of Soil and Water Conservation, 51, pp 427 433. 6. Fournier, F., 1960, Climat et erosion (Paris:Press Universitaires de France). 7. Gottschalk, L.C., (1964). Reservoir sedimentation: in Handbook of Applied Hydrology, McGraw Hill Book Company, New York. 8. Horton, R.E., 1932, Drainage basin characteristics, Trans. Am. Geophys. Union, 13, pp 350 361. 526

9. Horton, R.E., 1945, Erosional development of streams and their drainage basins, hydrophysical approach to quantitative morphology, Geol. Soc. Am, 56, pp 275 370. 10. Jain, S. K. and Goel, M. K., 2002, Assessing the vulnerability to soil erosion of the Ukai Dam catchments using remote sensing and GIS, Hydrological Sciences, 47, pp 31 40. 11. Langbein, W.B., (1947).Topographic characteristics of drainage basins, U.S. Geol. Surv. Water Supply Paper, 986(C),157 159. 12. Londhe, S. L. and Nathwat, M. S., 2010, Large scale soil mapping techniques for granitic terrain using high resolution satellite data, Trends Soil Sci Plant Nutr J, 1, pp19 31. 13. Miller, V.C., 1953, A Quantitative geomorphic study of drainage basin characteristics in the Clinch Mountain area, Virginia and Tennessee, Proj. NR 389 402, Tech Rep 3, Columbia University, Department of Geology, ONR, New York. 14. Nooka Ratnam, N., Srivastava, Y. K., Venkateswara Rao, V., Amminedu, E and Murthy, K. S. R. 2005, Check dam positioning by prioritization micro watersheds using SYI model and morphometric analysis Remote Sensing and GIS perspective, Photonirvachak, 33, pp 25 38. 15. Rudraiah. M., Govindaiah, S and Srinivas Vittala, 2008, Morphometry using remote sensing and GIS techniques in the sub basins of kagna river basin, Gulburga district, Karnataka, India, Photonirvachak, 36, pp 351 360. 16. Schumn, S.A., 1956, Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey, Geol. Soc. Am. Bull., 67, pp 597 646. 17. Sharma, R., Sahai, B., and Karale, R. L, Identification of erosion prone areas in a part of Ukai catchment, 6th Asian conference on remote sensing, Hydrrabad, India, pp 121 126. 18. Singh, G., Rambabu, and Subash Chandra,1981, Soil loss prediction research in India, ICAR Bull. T 12/D 9, CSWCTRI, Dehradun, India. 19. Skidmore, A.K., Bijker, W., Schmidt, K. and Kumar, L., 1997, Use of Remote Sensing and GIS for sustainable land management, ITC J, ( 3/4), pp 302 315. 20. Smith, K.G., 1950, Standards for grading textures of erosional topography, Am. Jour. Sci,248, pp 655 668. 527

21. Strahler, A. N., (1957).Quantitative analysis of watershed geomorphology. Trans. Am. Geophys. Union, 38, pp 913 920. 22. Strahler, A.N, (1964). Quantitative geomorphology of drainage basins and channel networks: in Handbook of Applied Hydrology, McGraw Hill Book Company, New York. 23. Wischmeir, W. H. and Smith, D. D, 1978, Predicting rainfall erosion losses A guide to conservation Planning, Agricultural Handbook No. 537,USDA. 528