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Using the Budyko framework to evaluate the human imprint on long-term surface water partitioning across India
  • Anav Vora,
  • Riddhi Singh
Anav Vora
Indian Institute of Technology Bombay
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Riddhi Singh
Indian Institute of Technology Bombay
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Abstract

The Budyko curve, relating a catchment’s water and energy balance, provides a useful tool to analyse how physio-climatic and socio-economic characteristics may impact long-term runoff. Often a parametric form of the curve, the Fu’s equation, is used to represent the catchment’s long-term water partitioning behaviour. Fu’s parameter ω, typically derived from observed climate and runoff data, can further be related to catchments’ physio-climatic characteristics to enable understanding the main drivers of their water balance. At times, prior analyses have reported potentially conflicting controls of characteristics on ω. Based on the rationale that several hydrological processes act across varying spatio-temporal scales, we hypothesize that the impact of a physio-climatic factor on ω is driven by its broader regional setting. We test our hypothesis by developing relationships between ω and a curated database of 33 physio-climatic and socio-economic characteristics for 534 regional divisions of India. We employ two related data-space splitting algorithms: classification and regression trees (CART) and random forest (RF) to study the effects of potential controlling factors within their regional context. The algorithms diagnose a hierarchy of representative vegetation, climate, soil, land use land cover, topography and anthropogenic controls. The most important characteristics controlling ω were found to be: long-term temperature, percentage of short rooted vegetation, population density, and long-term precipitation. We show the significance of considering the regional context by highlighting contrasting effects of two factors: long-term temperature and the proportion of sand to silt content on ω. Anthropogenic activities were found to be decisive in governing the effect of long-term temperature, indicating their influence on hydrological processes across the Indian subcontinent.