Study region: This study covers 16 major river basins across Vietnam, encompassing diverse topographies and climatic zones. These basins represent key regions for national water resource planning, agricultural development, and ecosystem conservation. Study focus: This study presents the quantification results of Hydrological EcoSystem Services (HESS) for 16 major river basins in Vietnam, using integrated earth observation datasets with water and energy balance models such as the Regional Hydrological Extremes for Agriculture System (RHEAS) by NASA-JPL and the Water Productivity (WaPOR) by FAO. Eight HESS indicators, such as total runoff, rootzone water storage, carbon sequestration, and microclimate cooling were evaluated for the hydrological years, representing wet, average, and dry climatic conditions (2005, 2010, 2019 and 2022). A synthesized score was introduced to benchmark sustainability level of these basins throughout the period. New hydrological insights for the region: The results reveal distinct exhibit a diverse distribution of HESS across basins, interrelationship as well as trade-offs. This study illustrates how remote sensing data and spatial algorithms can be applied to determine various aspects of HESS across different landscapes and ecosystems. Basins in the central regions exhibited stronger ecosystem performance, while those in the more urbanized northern and southern regions showed comparatively lower levels. With quantified HESS and benchmarked sustainability score, the natural capital assets of Vietnam are herewith revealed, and this system can also be applied to other countries. The findings underscore the value of integrating earth observation and ecohydrological modelling to support HESS monitoring, the design of Nature-based Solutions (NbS), and sustainable water resource planning in data-scarce regions.

In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km2 Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R² = 0.71, Nash-Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R2 = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex.