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.

Study region: Hindon River Basin, North India. Study focus: Accurate estimation of water balance components is crucial for water management applications yet challenging due to errors in monthly gridded water balance data products. Error and uncertainty quantification is especially important in the absence of extensive in-situ data. This paper presents a prior uncertainty analysis for such situations consisting of two components: (i) quantification of prior uncertainties using metrics that quantify errors in individual products and variability and consistency between products, and (ii) reduction of prior uncertainties by eliminating unrealistic water balance estimates. New hydrological insights for the region: Grid-scale inter-product uncertainty or variability, computed as the coefficient of variation (CV, %) at various temporal scales, reveals discrepancies between water balance products due to a combination of factors, including methodological differences, inherent spatial variability, data sources, and resolution disparities. At the mean annual scale, P fluxes display a lower grid-scale inter-product uncertainty (5–9 %) than ET (20–55 %), while the ∆TWS from GRACE solutions show a moderate mean annual grid-scale inter-product uncertainty (15–19 %). Grid-scale inter-product uncertainties of ∆SMS for July – representing the onset of the monsoon season – are high (CV = 54–122 %), indicating that the uncertainty in estimates of this component may have a large impact on water balance analyses. P fluxes exhibited fewer spatio-temporal uncertainties (R² above 0.8) than ET fluxes (R² less than 0.75). The exclusion of the unreliable data sets resulted in (a) reducing uncertainties in input water balance components with triple collocation range shifting from 15–38 to 17–23 mm/month for ET and from 16–52 to 11–23 mm/month for P, (b) obtaining updated prior estimates of seasonal water balance. The updated priors of water balance variables per season suggest a net basin outflow (from −318 to −57 mm/season) during the monsoon (rainy) season and net basin inflow (from −38 to 330 mm/season) during the non-monsoon (dry) season, the latter related to surface-water imports from outside the basin. All GRACE data sets exhibit a regional long-term decreasing trend in total water storage (ranging from −31 to −61 mm/year), qualitatively confirming previously documented unsustainable groundwater depletion in the basin. Prior ranges and uncertainties for all water balance variables reported here can be used as input into a posterior analysis that uses in-situ data for locally calibrating (bias-correcting, noise-filtering) and further updating the prior estimates.

Strategic planning of water management at the river-basin scale requires (1) measurement and accounting of individual hydrological processes, (2) quantification of water resources, and (3) their optimal allocation. Scalable Water Balances from Earth Observations (SWEO) is an open-access parameterization enabling automated reporting of water footprints and Sustainable Development Goal (SDG) indicators. We present its systematic arrangement and input datasets, and demonstrate its accuracy by independent riverflow measurements. We also review some achievements in remote sensing for hydrology during the last 50 years in quantifying hydrological and water management processes, flows, fluxes and changes in storage from various independent sources; and append mathematical formulations.

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.

Water managers around the world face the increasingly challenging task to evaluate the impacts of technological measures and policy mechanisms from the local to the river basin scale. A toolset providing quantitative, actionable information on dependencies and trade-offs between upstream and downstream water users is currently lacking. Yet, any intervention needs to be assessed in terms of consequences for downstream water users. This study evaluates the potential of a tracer-like approach, implemented in the water allocation software WEAP, to quantitatively track return flows and their downstream reuse in the river basin context. The WEAP-Virtual Tracer (WEAP-VT) approach was successfully applied to one of Europe's driest river basins, the Segura River Basin in Spain. For each water demand site, the different original sources of water supply, dependency on upstream return flows, and downstream reuse of its return flow were assessed. Based on these results, agricultural, urban, and environmental water users were evaluated in terms of their suitability for water saving measures and their vulnerability to the reduction in upstream return flows. A scenario analysis simulating the improvement of local efficiency improvements shows that specific irrigation schemes and ecosystems become deprived of water. Hence, efficiency improvement in water-scarce basins should be considered with caution. The demonstrated ability to quantify key water reuse indicators for individual water users and at different aggregation levels makes WEAP-VT a valuable tool to support water resource management decisions.

Actual evapotranspiration (ET) is a major component of the water balance. While several international flux measurement programs have been executed in the tropical rain forest of the Amazon, those measurements represent the evaporative process at a few selected sites only. The aim of this study is to obtain the spatial distribution of ET, using remote sensing techniques, across the entire Amazon River Basin. Results from six global ET products based on remote sensing techniques (GLEAM, SEBS, ALEXI, CMRSET, MOD16, and SSEBop) were merged to obtain an ensemble prediction of the ET rates for the complex and in-accessible environment of the Amazon at a spatial resolution of 250 m. The study shows that the basin-wide average ET is 1316 mm/year with a standard deviation of 192 mm/year. This new ET-Amazon product was validated against seven different historic flux tower measurements. The energy balance closure of the in situ measurements varied between 86 and 116%. Only months with more than 70% completeness of in situ measurements were considered for validation. Different procedures for closure correction were included in the analyses. The correlation between measured and remotely sensed ET is good (R ² > 0.97 for consecutive periods of 2 to 12 months), and the bias correction is negligible for the energy balance residual method, which seemed most favorable. Monthly ET values have more uncertainty. The monthly RMSE values vary between 7.4 and 27.8 mm/month (the average RMSE is 22.2 mm/month), and the coefficient of determination (R ² ) varies between 0.48 and 0.87 (the average R ² is 0.53). The ET from the water balance is 1380 mm/year, being − 64 mm/year difference and 4.6% less than ET derived from the water balance. The evaporation from the Amazon basin inside Brazil is 5063 km ³ /year, followed by Peru with 1165 km ³ /year. ET-Amazon shows more spatial details and accuracy than alternative global ET products such as LandFlux-EVAL, Model Tree Ensemble (MTE), and WACMOS-ET. This justifies the development of new regional ET products.

European agriculture and water policies require accurate information on climate change impacts on available water resources. Water accounting, that is a standardized documentation of data on water resources, is a useful tool to provide this information. Pan-European data on climate impacts do not recognize local anthropogenic interventions in the water cycle. Most European river basins have a specific toolset that is understood and used by local experts and stakeholders. However, these local tools are not versatile. Thus, there is a need for a common approach that can be understood by multi-fold users to quantify impact indicators based on local data and that can be used to synthesize information at the European level. Then, policies can be designed with the confidence that underlying data are backed-up by local context and expert knowledge. This work presents a simplified water accounting framework that allows for a standardized examination of climate impacts on water resource availability and use across multiple basins. The framework is applied to five different river basins across Europe. Several indicators are extracted that explicitly describe green water fluxes versus blue water fluxes and impacts on agriculture. The examples show that a simplified water accounting framework can be used to synthesize basin-level information on climate change impacts which can support policymaking on climate adaptation, water resources and agriculture.

Target 6.4 of the recently adopted Sustainable Development Goals (SDGs) deals with the reduction of water scarcity. To monitor progress towards this target, two indicators are used: Indicator 6.4.1 measuring water use efficiency and 6.4.2 measuring the level of water stress (WS). This paper aims to identify whether the currently proposed indicator 6.4.2 considers the different elements that need to be accounted for in a WS indicator. WS indicators compare water use with water availability. We identify seven essential elements: 1) both gross and net water abstraction (or withdrawal) provide important information to understand WS; 2) WS indicators need to incorporate environmental flow requirements (EFR); 3) temporal and 4) spatial disaggregation is required in a WS assessment; 5) both renewable surface water and groundwater resources, including their interaction, need to be accounted for as renewable water availability; 6) alternative available water resources need to be accounted for as well, like fossil groundwater and desalinated water; 7) WS indicators need to account for water storage in reservoirs, water recycling and managed aquifer recharge. Indicator 6.4.2 considers many of these elements, but there is need for improvement. It is recommended that WS is measured based on net abstraction as well, in addition to currently only measuring WS based on gross abstraction. It does incorporate EFR. Temporal and spatial disaggregation is indeed defined as a goal in more advanced monitoring levels, in which it is also called for a differentiation between surface and groundwater resources. However, regarding element 6 and 7 there are some shortcomings for which we provide recommendations. In addition, indicator 6.4.2 is only one indicator, which monitors blue WS, but does not give information on green or green-blue water scarcity or on water quality. Within the SDG indicator framework, some of these topics are covered with other indicators.

Crop water productivity (CWP) has become a recognised indicator in assessing the state of Sustainable Development Goals (SDG) 6.4—to substantially increase water use efficiency. This indicator, while useful at a global scale, is not comprehensive at a local scale. To fill this gap, this research proposes a CWP framework, that takes advantage of the spatio-temporal availability of remote sensing, that identifies CWP goals and sub-indicators specific to the needs of the targeted domain. Three sub-indicators are considered; (i) a global water productivity score (GWPS), (ii) a local water productivity score (LWPS) and (iii) a land and water use productivity score (YWPS). The GWPS places local CWP in the global context and focuses on maximised CWP. The LWPS differentiates yield zones, normalising for potential product, and focuses on minimising water consumption. The YWPS focuses simultaneously on improving land and water productivity equally. The CWP framework was applied to potato in the West Bank, Palestine. Three management practices were compared under each sub-indicator. The case study showed that fields with high and low performance were different under each sub-indicator. The performance associated with different management practices was also different under each sub-indicator. For example, a winter rotation had a higher performance under the YWPS, the fall rotation had a higher performance under the LWPS and under the GWPS there was little difference. The results showed, that depending on the basin goal, not only do the sub-indicators required change, but also the management practices or approach required to reach those basin goals. This highlights the importance of providing a CWP framework with multiple sub-indicators, suitable to basin needs, to ensure that meeting the SDG 6.4 goal does not jeopardise local objectives.

Scarcity of information on the water productivity of different water, land, and other ecosystems in Africa, hampers the optimal allocation of the limited water resources. This study presents an innovative method to quantify the spatial variability of biomass production, crop yield, and economic water productivity, in a data scarce landscape of the Pangani Basin. For the first time, gross return from carbon credits and other ecosystem services are considered, in the concept of Economic Water Productivity. The analysis relied on the MODIS satellite data of 250 m and eight-day resolutions, and the SEBAL model, utilizing Monteith's framework for ecological production. In agriculture, irrigated sugarcane and rice achieved the highest water productivities in both biophysical and economic values. Rainfed and supplementary irrigated banana and maize productivities were significantly lower than the potential values, reflecting a wide spatial variability. In natural landscapes, forest and wetland showed the highest biomass production. However, the transition to economic productivity was low but showed the potential to increase significantly when non-market goods and services were considered. Spatially explicit information, from both biophysical and economic water productivity, provides a holistic outlook of the socio-environmental and the economic water values of a land-use activity, and can identify areas for improvement, and trade-offs in river basins.

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.

The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation-anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

The area under sugarcane is rapidly growing worldwide. The consequences of such growth on basin scale water consumption and competing water resources need to be understood. Conventional models for sugarcane evapotranspiration have shown limitations for different environmental conditions. To improve current estimations of sugarcane water consumption, hourly and daily transpiration of rainfed sugarcane in Kwazulu-Natal (South Africa) and daily transpiration for irrigated sugarcane in Mpumalanga (South Africa) were calculated by using the Penman-Monteith equation (T_PM) with a variable canopy resistance. Canopy resistance was calculated with the Jarvis-Stewart model from calibrated environmental stress functions. The classic FAO56 crop coefficient approach (T_FAO56) was also investigated and crop coefficient values, crop basal coefficient and water stress coefficient were derived. There were differences between derived crop coefficient values and FAO56 weather-adjusted values. Derived crop basal coefficient (K_cb) was 0.9 for rainfed and irrigated sugarcane, which was lower than FAO56 weather-adjusted values of 1.19 for rainfed and 1.15 for irrigated at mid-stage. The reduction of the crop basal coefficient with the water stress coefficient resulted in an underestimation of transpiration for rainfed sugarcane. This indicates that water uptake under stress conditions is a complex process, not easy to model as water can be extracted from considerable depths. Daily estimates obtained from T_PM outperformed those obtained from T_FAO56 when compared to Bowen ratio and Surface Renewal system field measurements. For rainfed sugarcane with water-stressed conditions the T_FAO56 RMSE was 1.55 mm day⁻¹ compared to 0.3 mm day⁻¹ for T_PM. For rainfed sugarcane with water-unstressed conditions the T_FAO56 RMSE was 0.5 mm day⁻¹ and the T_PM RMSE was 0.22 mm day⁻¹ for T_PM. For irrigated sugarcane the T_PM RMSE of 0.47 mm day⁻¹ was slightly lower than the T_PM RMSE of 0.49 mm day⁻¹, and T_PM showed better correlation with an R² of 0.85 compared to an R² of 0.64 for T_FAO56. This suggests that calibrated variables of the Jarvis-Stewart model for sugarcane proved to be suitable for both rainfed and irrigated sugarcane in South Africa. More research is needed to verify the validity of the calibrated stressed functions in other regions with high intensity of sugarcane plantations.