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.

We analyzed streamflow records from more than 300 stations across Thailand, a tropical country in Southeast Asia. Temporal changes in runoff yield were assessed over the 1960-2015 period, highlighting a prominent downward trend over the last two decades. To identify potential drivers of these changes, gridded data products representing precipitation, temperature, evapotranspiration, and land cover were also assessed. We found that runoff yield is primarily driven by annual precipitation, which has experienced an unprecedented decline since 2010. Two sub-regions with particularly robust data coverage reflected a spatial contrast in hydrologic response: a more consistent response of runoff yield to precipitation is observed in the sub-region characterized by a high density of forest cover relative to the region characterized by high cropland cover. This feature underscores the need to take land use and irrigation practices into account when forecasting, and determining management strategies for, tropical river streamflow in a warming climate.