Watershed prioritisation for soil conservation — a GIS approach

Sharada, D.; Kumar, Manish; Venkataratnam, L.; Rao, T. Ch. Malleswar

doi:10.1080/10106049309354396

Cited by 24 publications

(10 citation statements)

References 1 publication

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“…Pandey et al (2007) divided Karso Watershed of Hazaribagh, Jharkhand State, India into 200×200 m grid cells and average annual sediment yields were estimated for each cell of the watershed to identify the critically prone areas of watershed. Recent studies (Pandey et al 2007;Yoshino and Ishioka 2005;Chowdary et al 2004;Pandey et al 2004;Sharma et al 2001;Khan et al 2001;Saxena et al 2000;Sidhu et al 1998;Sharda et al 1993;Prasad et al 1992) revealed that RS and GIS techniques are of great use in characterization and prioritization of watershed areas.…”

Section: Introductionmentioning

confidence: 95%

Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

Dabral

Baithuri

Pandey

2008

Water Resour Manage

303

137

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In the present study, soil erosion assessment of Dikrong river basin of Arunachal Pradesh (India) was carried out. The river basin was divided into 200×200 m grid cells. The Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data and the USLE was used to predict the spatial distribution of the average annual soil loss on grid basis. The average rainfall erositivity factor (R) for Dikrong river basin was found to be 1,894.6 MJ mm ha −1 h −1 year −1 . The soil erodibility factor (K) with a magnitude of 0.055 t ha h ha −1 MJ −1 mm −1 is the highest, with 0.039 t ha h ha −1 MJ −1 mm −1 is the least for the watershed. The highest and lowest value of slope length factor (LS) is 53.5 and 5.39 respectively for the watershed. The highest and lowest values of crop management factor (C) were found out to be 0.004 and 1.0 respectively for the watershed. The highest and lowest value of conservation factor (P) were found to be 1 and 0.28 respectively for the watershed. The average annual soil loss of the Dikrong river basin is 51 t ha −1 year −1 . About 25.61% of the watershed area is found out to be under slight erosion class. Areas covered by moderate, high, very high, severe and very severe erosion potential zones are 26.51%, 17.87%, 13.74%, 2.39% and 13.88% respectively. Therefore, these areas need immediate attention from soil conservation point of view.

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Section: Introductionmentioning

confidence: 95%

Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

Dabral

Baithuri

Pandey

2008

Water Resour Manage

303

137

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“…Several researchers have adopted these techniques depending upon the purpose and the information availability. Recent studies (Yoshino and Ishioka, 2005;Pandey et al, 2004;Sharma et al, 2001;Khan et al, 2001;Saxena et al, 2000;Sidhu, 1998;Sharda et al, 1993;Prasad et al, 1992) revealed that RS and GIS techniques are of great use in characterization and prioritization of watershed areas.…”

mentioning

confidence: 96%

Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing

Pandey

Chowdary

Mal

2006

Water Resour Manage

321

168

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In the present study, Karso watershed of Hazaribagh, Jharkhand State, India was divided into 200 × 200 grid cells and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion prone areas of watershed for prioritization purpose. Average annual sediment yield data on grid basis was estimated using Universal Soil Loss Equation (USLE). In general, a major limitation in the use of hydrological models has been their inability to handle the large amounts of input data that describe the heterogeneity of the natural system. Remote sensing (RS) technology provides the vital spatial and temporal information on some of these parameters. A recent and emerging technology represented by Geographic Information System (GIS) was used as the tool to generate, manipulate and spatially organize disparate data for sediment yield modeling. Thus, the Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data to the erosion model, while the USLE was used to predict the spatial distribution of the sediment yield on grid basis. The deviation of estimated sediment yield from the observed values in the range of 1.37 to 13.85 percent indicates accurate estimation of sediment yield from the watershed.

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“…Several researchers have adopted these techniques depending upon the purpose and the information availability. Recent studies (Sharda et al 1993, Sidhu et al 1998, Saxena et al 2000, Khan et al 2001, Sharma et al 2001, Pandey et al 2004, Yoshino and Ishioka 2005 revealed that RS and GIS techniques are of great use in characterization and prioritization of watershed areas. Morphological parameters, both magnitude and spatial distribution of potential soil erosion in the catchment, are determined.…”

Section: Introductionmentioning

confidence: 98%

An integrated approach for prioritization of reservoir catchment using remote sensing and geographic information system techniques

Vemu

Udayabhaskar

2010

Geocarto International

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This study is aimed at evolving a watershed prioritization of reservoir catchment based on vegetation, morphological and topographical parameters, and average annual soil loss using geographic information system (GIS) and remote sensing techniques. A large multipurpose river valley project, Upper Indravati reservoir, situated in the state of Orissa, India, has been chosen for the present work. Watershed prioritization is useful to soil conservationist and decision makers. This study integrates the watershed erosion response model (WERM) and universal soil loss equation (USLE) with a geographic information system (GIS) to estimate the erosion risk assessment parameters of the catchment. The total catchment is divided into 15 sub-watersheds. Various erosion risk parameters are determined for all the sub-watersheds separately. Average annual soil loss is also estimated for the sub-watersheds using USLE. The integrated effect of all these parameters is evaluated to recommend the priority rating of the watersheds for soil conservation planning.

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Watershed prioritisation for soil conservation — a GIS approach

Cited by 24 publications

References 1 publication

Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing

An integrated approach for prioritization of reservoir catchment using remote sensing and geographic information system techniques

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