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.

Water system operations require subannual streamflow data—e.g., monthly or weekly—that are not readily achievable with conventional streamflow reconstructions from annual tree rings. This mismatch is particularly relevant to highly seasonal rivers such as Thailand’s Chao Phraya. Here, we combine tree ring width and oxygen isotope (δ18O) from Southeast Asia to produce 254-year, monthly-resolved reconstructions for all four major tributaries of the Chao Phraya. From the reconstructions, we derive subannual streamflow indices to examine past hydrological droughts and pluvials, and find coherence and heterogeneity in their histories. The monthly resolution reveals the spatiotemporal variability in wet season timing, caused by interactions between early summer typhoons, monsoon rains, catchment location, and topography. Monthly-resolved reconstructions, like the ones presented here, not only broaden our understanding of past hydroclimatic variability, but also provide data that are functional to water management and climate-risk analyses, a significant improvement over annual reconstructions.

Increased flood risks have been projected, but with large uncertainties, in the Kabul River Basin (Afghanistan and Pakistan). To place future changes in a long-term perspective, we produce a 382-year precipitation reconstruction for the basin using seven tree-ring chronologies of old-growth conifers from the Hindu Kush Mountains, a monsoon-shadow area. The reconstruction proves robust over rigorous cross-validations (R2 = 0.60, RE = 0.60, CE = 0.53). The full reconstruction (1637–2018) reveals a steady decline in the low end of the precipitation distribution, implying increasing drought risks. We show that droughts are getting more severe, shorter, and more frequent, interspersed with more frequent pluvials in the past century. Drought risks, compounded with projected flood intensification, pose significant threats for this transboundary river. Therefore, future water management needs to account for both flood and drought risks and be informed by long-term hydroclimatic variability.