Uncertain Benefits of Using Remotely Sensed Evapotranspiration for
Streamflow Estimation-Insights from a Randomized, Large-Sample
Experiment
Abstract
Remotely sensed evapotranspiration (ETRS) is
increasingly used for streamflow estimation. Earlier reports are
conflicting as to whether ETRS is useful in improving
streamflow estimation skills. We believe that it is because earlier
works used calibrated models and explored only small subspaces of the
complex relationship between model skills for streamflow (Q) and ET. To
shed some light on this complex relationship, we design a novel
randomized, large sample experiment to explore the full ET-Q skill
space, using seven catchments in Vietnam and four global
ETRS products. For each catchment and each
ETRS product, we employ 10,000 SWAT (Soil and Water
Assessment Tool) model runs whose parameters are randomly generated via
Latin Hypercube sampling. We then assess the full joint distribution of
streamflow and ET skills using all model simulations. Results show that
the relationship between ET and streamflow skills varies with regions,
ETRS products, and the selected performance indices.
This relationship even changes with different ranges of ET skills.
Parameter sensitivity analysis indicates that the most sensitive
parameters could have opposite contributions to ET and streamflow
skills. Conditional probability assessment reveals that with certain
ETRS products, the probabilities of having good
streamflow skills are high and increase with better ET skills, but for
other ETRS products, good model skills for streamflow
are only achievable with certain intermediate ranges of ET skills, not
the best ones. Overall, our study provides a useful approach for
evaluating the value of ETRS for streamflow estimation.