Abstract

Soil erosion is one of the serious environmental threats in the Himalayas, primarily exacerbated by the steep slopes, active tectonics, deforestation, and land system changes. The Revised Universal Soil Loss Equation was employed to quantify soil erosion from the Vishav watershed in the Kashmir Himalaya, India. Topography and land use/land cover (LULC) are important driving factors for soil erosion. Most often, a Digital Elevation Model (DEM) is used in erosion models without any evaluation and testing which sometimes leads to erroneous estimates of soil erosion. For the best topographic characterization of the watershed, four publicly available DEMs with almost identical resolution (∼30 m), were evaluated. The DEMs were compared with GPS measurements to determine the most reliable among the tested DEMs for soil erosion estimation. Statistical evaluation of the DEMs with GPS data indicated that the CARTO DEM is better with root mean square error (RMSE) of 18.2 m than the other three tested DEMs viz., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS). Slope length and slope steepness factors were computed from the DEMs. Crop cover and management factors were generated from the satellite-derived LULC. Moreover, rainfall data of the nearest stations were used to compute rainfall erosivity and soil erodibility factor was derived from the soil texture data generated from 375 soil samples. The simulated erosion estimates from SRTM, ALOS, and CARTO DEMs showed similar spatial patterns contrary to the ASTER estimates which showed somewhat different patterns and magnitude. The mean erosion in the study area has almost doubled from 2.3 × 106 tons in 1981 to 4.6 × 106 tons in 2019 mainly driven by the anthropogenic LULC changes. The increased soil erosion is due to the degradation of forest cover, urbanization, steep slopes, and land system changes observed during the period. In absence of the observations, the simulated soil erosion was validated with the land degradation map of the watershed which showed a good correspondence. It is hoped that the results from this work would inform policymaking on soil and water conservation measures in the data-scarce mountainous Kashmir Himalaya.

Highlights

  • Soil erosion is a serious global concern mainly caused by land cover changes, landslides, the collapse of man-made terraces, steep slopes, and high-intensity rainfall (ICIMOD, 1994)

  • The Revised Universal Soil Loss Equation (RUSLE) was used in this study to quantify soil erosion from the data-scarce Vishav watershed in the Kashmir Himalaya

  • Since the topography is a major contributor to the transport of the soil from a watershed, it was thought necessary to choose the best of the publicly available digital topographic datasets for accurate topographic characterization of the watershed so that the errors associated with soil erosion estimates due to the imprecise representation of the topography are minimized

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Summary

Introduction

Soil erosion is a serious global concern mainly caused by land cover changes, landslides, the collapse of man-made terraces, steep slopes, and high-intensity rainfall (ICIMOD, 1994). Soil erosion has always been a serious concern in the fragile mountainous Himalayan region (Jain et al, 2001) with an increasing tendency in the future (Pal et al, 2021). The impact of the depleting forest and pasture cover on the steep slopes in tandem with the high seismicity have been the major factors driving the soil erosion and sedimentation in the Himalayan headwaters (Jain et al, 2001; Dar et al, 2014; Romshoo et al, 2016). Especially by water, is a serious problem in watersheds and erodes fertile soil from catchment areas and deposits sediments in rivers, lakes, and reservoirs (Pal et al, 2021). Apart from these, the erosion processes lead to the high loss of fertile soil leading to the dwindling of cultivable lands, deterioration of water quality, loss of flora and fauna in rivers and lakes by pollution, eutrophication, and turbidity (Lal, 1998; Time, 2004)

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