Use of Satellite Data, GIS and RUSLE for Estimation of Average Annual Soil Loss in Daltonganj Watershed of Jharkhand (India)

Tirkey, Anamika Shalini; Pandey, Ashish; Nathawat, Mahendra Singh

doi:10.18005/jrst0101004

Cited by 65 publications

(17 citation statements)

References 26 publications

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“…High runoff among the majority of sub-watersheds in drought years over larger parts of the study area have been noticed in the present study. SCS CN method has also been used by other researchers in different parts of India which shows results similar to present outcomes (Patil et al 2008;Tirkey et al 2013;Pandey & Stuti 2017;Pradhan & Joshi 2019). The quantitative estimates from the rainfall anomaly index revealed drought and non-drought years with drought in the major parts of the study area identified in 2001, 2005, 2010, 2015 and 2017 and the larger numbers of sub-watersheds occupied upper catchments with high total runoff volume.…”

Section: Discussionsupporting

confidence: 77%

“…The area taken up for the present study forms a part of South Koel river basin of Jharkhand which is drought prone area facing high runoff problem (Tirkey et al 2013;Pandey & Stuti 2017). In India majority of watersheds are not having rainfallrunoff past records and therefore SCS-CN method becomes a more appropriate method for estimation of surface runoff due to its simplistic approach.…”

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confidence: 99%

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PCA driven watershed prioritization based on runoff modeling and drought severity assessment in parts of Koel river basin, Jharkhand (India)

Chaudhary

Pandey

2021

Water Supply

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Global warming influencing regional climate is playing a significant role in triggering the recurrent drought. The current study demonstrates a PCA (Principal Component Analysis) driven watershed prioritization in a part of Koel river basin by runoff computation during monsoon season along with assessment of Vegetation Health Index (VHI) derived from MODIS satellite data during the period from 2000 to 2017. Koel river catchment area of 7,261 sq km was divided into 82 sub-watersheds based on drainage networks derived from Survey of India (SOI) topographical map on scale 1: 50,000. High resolution satellite image of Sentinel-2 was used to prepare land use land cover map. Soil conservation service curve number method (SCS CN) was used to estimate runoff. Result obtained from runoff estimation of 82 sub watersheds shows high runoff (50 to 60% of rainfall) with 290,000 m3 total runoff volume in the upper and middle parts of the catchment dominated by agricultural/fallow and barren lands whereas low runoff was estimated (20 to 30%) with 29,467 m3 in the lower catchments where a large area is covered with forests. The value of satellite based VHI ranges between 23 to 53 with major parts of the area exhibiting values less than 30 reflecting poor vegetation health. Most of the sub-watersheds in parts of Ranchi, Lohardaga, Gumla and Khunti districts experienced high total runoff with poor vegetation health index reflecting more proneness to drought. Watershed prioritization was done based on correlation among four parameters viz., rainfall, drought zones, direct runoff and total runoff through PCA. Strong correlation between total runoff volume and drought areas was used for watershed prioritization which indicated 42 sub-watersheds (4,703 sq km) in the upper catchment required high prioritization. The outcomes of study would help proper planning of water resources and soil moisture management to overcome the recurrent drought conditions at watershed level.

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Section: Discussionsupporting

confidence: 77%

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confidence: 99%

PCA driven watershed prioritization based on runoff modeling and drought severity assessment in parts of Koel river basin, Jharkhand (India)

Chaudhary

Pandey

2021

Water Supply

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“…The integration of the RUSLE model with GIS enables detailed analysis of soil erosion because the mechanism has a spatially distributed character. GIS and remote sensing (RS) provide the model with spatial input data, whereas the RUSLE can be used to estimate watershed soil erosion [25,26].…”

Section: Introductionmentioning

confidence: 99%

Prioritization of Watershed Using Remote Sensing and Geographic Information System

et al. 2021

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Soil erosion is becoming a major concern at the watershed scale for the environment, natural resources, and sustainable resource management. Therefore, the estimation of soil loss through this phenomenon and the identification of critical soil erosion-prone areas are considered to be key tasks in the soil conservation programme for the design and implementation of best management practices for specific regions or areas. In the present study, revised universal soil loss equation (RUSLE) modelling is combined with remote sensing (RS) and geographical information system (GIS) techniques and used to predict soil erosion and the prioritization of watersheds in Nainital district Uttarakhand, India. For the estimation of soil loss, different factors, namely, rainfall-runoff erosivity (R) factor, soil erodability (K) factor, slope length steepness (LS) factor, cover management (C) factor, and the erosion control practices (P) factor were computed. The data on various other aspects such as land use/land cover (LU/LC), the digital elevation model (DEM), slope, contours, drainage network, soil texture, organic matter, and rainfall were integrated to prepare a database for the RUSLE equation by employing ENVI & QGIS software. The results showed that a major portion (70.26%) of Nainital district is covered with forest, followed by area under fallow and agricultural land. Annual average soil loss ranged between 20 to 80 t ha−1 yr−1 in the study area. Out of 50 watersheds in the study area, 7 watersheds were given top priority for conserving natural resources, while 11 watersheds, mostly in the east-central part of Nainital, were kept under the next priority category. Only 4 watersheds of the total were given lowest priority. Moreover, it was concluded that major portions of Nainital district were in a severely prone category of soil erosion, and therefore required immediate action plans to check soil erosion and evade the possibility of landslides.

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“…Throughout 1992–2015, these states have been facing a steady rate of degradation with a slight increase in the last decade due to anthropogenic activities such as deforestation and coal mining. Lack of effective mitigation plans unlike the western states, has promoted continuous desertification in these states (Kumar, Rani, Pandey, Majumdar, & Nathawat, 2010; Kumar, Sharma, Tara, & Muniyan, 2019; Mazumdar & Paul, 2017; Shalini Tirkey, Pandey, & Nathawat, 2013). The north‐eastern region, however, is showing a constant resistance toward desertification throughout 1992–2015.…”

Section: Discussionmentioning

confidence: 99%

Monitoring land sensitivity to desertification using the ESAI approach and evaluation of the key indicators: A spatio‐temporal study in India

Rajbanshi

Das

2021

Land Degrad Dev

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Desertification is one of the primary environmental problems that negatively affect agricultural production leading to poverty, hunger, and economic instability in a country like India. A holistic study is therefore extremely valuable rather than localized ones for addressing this issue and its' further consequences. In this study, the environmentally sensitive area index (ESAI) has been used to identify the areas vulnerable to desertification from 1992 to 2015 in India. In addition to this, the random forest (RF) model was used to identify the drivers with major influence over the phenomenon. The result revealed that the desertification process has extended from the west and spread over the southern region in the Country. Among the states of India, Rajasthan and Ladakh possess the highest mean ESAI values (1.5–1.7), where 87.61% and 83.83% of land, respectively, are critically degraded due to desertification. According to the Mann‐Kendall mutation point detection test, the years of 1996, 2009, and 2014 were identified as the abrupt change of desertification processes over the Country. The most severe form of degradation is observed in the period between 1997 and 2009, where the highly sensitive (Critical‐3) area increased up to 37.44%. Based on variable importance for the RF‐model, drought resistance has the highest importance among all the other inputs that is, 46% followed by erosion protection with 20%. Our study also points out that 33.76% of the land of the Country is still classed under a very high critical zone and without any immediate action; it may result in irreversible loss.

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Use of Satellite Data, GIS and RUSLE for Estimation of Average Annual Soil Loss in Daltonganj Watershed of Jharkhand (India)

Cited by 65 publications

References 26 publications

PCA driven watershed prioritization based on runoff modeling and drought severity assessment in parts of Koel river basin, Jharkhand (India)

PCA driven watershed prioritization based on runoff modeling and drought severity assessment in parts of Koel river basin, Jharkhand (India)

Prioritization of Watershed Using Remote Sensing and Geographic Information System

Monitoring land sensitivity to desertification using the ESAI approach and evaluation of the key indicators: A spatio‐temporal study in India

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