S
Sushil Kumar Himanshu
Researcher at Texas A&M University System
Publications - 46
Citations - 697
Sushil Kumar Himanshu is an academic researcher from Texas A&M University System. The author has contributed to research in topics: Biology & Environmental science. The author has an hindex of 11, co-authored 24 publications receiving 385 citations. Previous affiliations of Sushil Kumar Himanshu include Texas A&M University & Asian Institute of Technology.
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Physically based soil erosion and sediment yield models revisited
TL;DR: In this paper, the authors reviewed 50 physically based soil erosion and sediment yield models with respect to these factors including shortcomings and strengths, and proposed a guideline for selection of an appropriate model to the reader for a given application or case study.
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Evaluation of best management practices for sediment and nutrient loss control using SWAT model
TL;DR: In this paper, the authors evaluated and recommended the BMPs in an agriculture-based Marol watershed (5092 km2) of India, using a hydrologic model, Soil and Water Assessment Tool (SWAT).
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Application of SWAT in an Indian river basin for modeling runoff, sediment and water balance
TL;DR: In this paper, the authors integrated remote sensing derived products, gridded precipitation and temperature data, and the Soil and Water Assessment Tool (SWAT) within a geographic information system modeling environment to evaluate the hydrology, sediment yield and water balance for a medium-sized Ken basin of Central India.
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Evaluation of the SWAT model for water balance study of a mountainous snowfed river basin of Nepal
TL;DR: In this article, a semi-distributed hydrologic model Soil and Water Assessment Tool (SWAT) has been employed for the Karnali River basin, Nepal to test its applicability for hydrological simulation.
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Ensemble modelling framework for groundwater level prediction in urban areas of India.
TL;DR: Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels, which can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling.