Identification of Time-varying Parameters of a Monthly Budyko Function
and its Implications
Abstract
The Budyko framework, which can describe a simple but useful
partitioning of precipitation under supply and energy limits, is used
widely to estimate evapotranspiration (ET). Previous studies have
investigated time-variant Budyko functions on annual or interannual
scales but seldom on the intra-annual scale. This study used a monthly
two-parameter ( κ and y0) Budyko function and three
schemes that considered single observations (ET or streamflow (Q)) and
dual observations (ET and Q) to assimilate the time-varying parameters
using the ensemble Kalman filter method. The study considered the
contiguous USA (CONUS) using the Model Parameter Estimation Experiment
dataset. The time-varying parameters were explained on the basis of time
series analysis and correlation with meteorological data. Three
conclusions were as follows. (1) The identified time-varying parameters
( κ and y0) of the Budyko function could effectively
simulate ET. (2) The assimilation using only ET observations could
identify a plausible set for parameter κ but was inadequate for
y0 . (3) Most time-varying parameters exhibited a
12-month period, and the trend and change points detected for Midwest
CONUS were related to anthropogenic influences such as extraction and
use of groundwater. The findings show that changing environment can be
detected by using the proposed time-varying parameters of the Budyko
function.