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.

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.