Satellite-based rainfall datasets and autocalibration techniques’
effects on SWAT+ flow prediction
Abstract
Accurate flow prediction is a primary goal of hydrological modeling
studies, which can be affected by the use of varying rainfall datasets,
autocalibration methods, and performance indices. The combined effect of
three rainfall datasets — Fifth generation of European ReAnalysis
(ERA-5), Gridded meteorological data (gridMET), Global Precipitation
Measurement Integrated Multi-satellitE Retrievals (GPM IMERG) — and
three autocalibration techniques — Dynamically Dimensioned Search
(DDS), Generalized Likelihood Uncertainty Estimation (GLUE), Latin
Hypercube Sampling (LHS) — on SWAT+ river flow prediction was measured
using three evaluation metrics — Nash Sutcliffe Efficiency (NSE),
Kling Gupta Efficiency (KGE) and coefficient of determination (R
2) — for two watersheds in North Carolina (Cape
Fear, Tar Pamlico) using the Soil Water Assessment Tool Plus (SWAT+)
model. Five parameters in the SWAT+ model, cn2, revap_co, flo_min,
revap_min, and awc, were found to be significantly sensitive under all
combinations for both watersheds. Simulated flow varied more with the
change in rainfall than the calibration technique used. We discovered
that GPM IMERG gave the best results of the rainfall datasets, followed
by ERA-5 and gridMET. We observed that the NSE score is more sensitive
to different combinations of rainfall datasets and calibration
techniques than the KGE scores. SWAT+ underperformed in the prediction
of base flow for the groundwater-driven watershed. Overall, we recommend
using the GPM IMERG rainfall dataset with the GLUE optimization
technique and KGE performance index for optimal flow simulations. The
results from this study will help hydrological modelers choose an
optimal combination of rainfall dataset, autocalibration technique, and
performance index depending on watershed characteristics.