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<p>Deforestation and urban expansion substantially alter hydrological processes by accelerating runoff, reducing groundwater recharge, and increasing flood risks. The current study assessed runoff dynamics in the Wardha River sub-basin from 2010 to 2020 using the NRCS-CN method, with land-use/land-cover (LULC) and rainfall inputs processed on the Google Earth Engine (GEE) platform and validated against observed discharge. During this period, mean runoff increased by 11.34%, linked to a 23.34% decline in forest cover and a 148% rise in impervious surfaces. Elasticity analysis indicated that rainfall influenced runoff (elasticity &gt; 1), but land-use transitions had a stronger effect, with the 2010–2020 changes showing elasticity 2.07. A hypothetical-future scenario (HFS), simulating full forest-to-cropland and fallow-to-built-up conversion, projected an additional 10% rise in runoff with elasticity 1.91, underscoring the hydrological risks of continued land transformation. Model evaluation confirmed good performance, with R² &gt; 0.90 across scenarios and the HFS scenario showing the best fit (NSE = 0.504, PBIAS = − 29.4%, RMSE = 21.59&#xa0;mm). Despite scale mismatches between area-averaged runoff and point-based discharge, simulations based on 2010, 2020, and CN-adjusted datasets achieved moderate to acceptable accuracy. These findings highlight the dominant role of land-use change, particularly deforestation, in shaping runoff dynamics and flood risks in the Wardha River sub-basin, compared with the broader but less intense influence of rainfall variability.</p>

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Impact of deforestation on runoff dynamics in the Wardha River sub-basin: a decadal analysis and future projections

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Asheesh Sharma,
  • Neha K. M. Nasim,
  • Mandeep Poonia,
  • Ankush O. Rai,
  • Sayali A. Jawalkar,
  • Reinhard Hinkelmann

摘要

Deforestation and urban expansion substantially alter hydrological processes by accelerating runoff, reducing groundwater recharge, and increasing flood risks. The current study assessed runoff dynamics in the Wardha River sub-basin from 2010 to 2020 using the NRCS-CN method, with land-use/land-cover (LULC) and rainfall inputs processed on the Google Earth Engine (GEE) platform and validated against observed discharge. During this period, mean runoff increased by 11.34%, linked to a 23.34% decline in forest cover and a 148% rise in impervious surfaces. Elasticity analysis indicated that rainfall influenced runoff (elasticity > 1), but land-use transitions had a stronger effect, with the 2010–2020 changes showing elasticity 2.07. A hypothetical-future scenario (HFS), simulating full forest-to-cropland and fallow-to-built-up conversion, projected an additional 10% rise in runoff with elasticity 1.91, underscoring the hydrological risks of continued land transformation. Model evaluation confirmed good performance, with R² > 0.90 across scenarios and the HFS scenario showing the best fit (NSE = 0.504, PBIAS = − 29.4%, RMSE = 21.59 mm). Despite scale mismatches between area-averaged runoff and point-based discharge, simulations based on 2010, 2020, and CN-adjusted datasets achieved moderate to acceptable accuracy. These findings highlight the dominant role of land-use change, particularly deforestation, in shaping runoff dynamics and flood risks in the Wardha River sub-basin, compared with the broader but less intense influence of rainfall variability.