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 humans may impact long-term
runoff. Often a parametric form of the curve, the Fu’s equation, is used
to represent the relationship between a catchment’s long-term water
partitioning behaviour and climate. Fu’s parameter ω, typically derived
from observed climate and runoff data, can further be related to
catchments’ physio-climatic characteristics for understanding the main
drivers of its water balance. We employ this approach to quantify the
impact of human interventions on surface water partitioning across
India. We explore the relationship between ω and a curated database of
33 physio-climatic and socio-economic characteristics for 534 regional
divisions of India using two related machine learning algorithms:
classification and regression trees (CART) and random forest (RF). Both
algorithms diagnose the hierarchy of representative vegetation, climate,
soil, land use land cover, topography and anthropogenic controls. RF
validates CART output while also providing a data-driven model to
estimate ω in assumed data-scarce regions, enabling us to assess the
value of this dataset for predictions in ungauged basins. The most
relevant characteristics controlling ω based on CART and RF analysis
were: long-term temperature, percentage of short rooted vegetation,
population density, and long-term precipitation. RFs were able to
correctly predict the classified ω for 63.9 % of assumed ungauged
regions. We found that population density’s influence on ω was
comparable to that of climate and vegetation, highlighting the role of
humans in controlling long-term surface water partitioning variability
across India.