Satellite based Budyko framework reveals the human imprint on long-term surface water partitioning across India

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 Tixeront-Fu’s equation, is used to represent the catchment’s long-term water partitioning behaviour. Tixeront-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 520 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: correlation between monthly time series of P and PET, long-term temperature, long-term precipitation, and the synchronicity of P and PET. Human influences quantified in terms of population density and the extent of cultivated area were found to be important factors influencing sub-regional scale hydrological processes. These anthropogenic factors displayed predictive importance similar to vegetation and topographic factors. Our results indicate the dominant influence of climate and its seasonality on long-term water partitioning across Indian landmasses with landscape features including human influences playing a secondary but important role.