Using the Budyko framework to evaluate 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 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.