Accurate maps of surface water are essential for many environmental applications. Surface water maps can be generated by combining measurements from multiple sources. Precise estimation of surface water using satellite imagery remains a challenging task due to the sensor limitations, complex land cover, topography, and atmospheric conditions. As a complementary dataset, in the case of hilly landscapes, a drainage network can be extracted from high-resolution digital elevation models. Additionally, Volunteered Geographic Information (VGI) initiatives, such as OpenStreetMap, can also be used to produce high-resolution surface water masks. In this study, we derive a high-resolution water mask using Landsat 8 imagery and OpenStreetMap as well as (potential) a drainage network using 30 m SRTM. Our approach to derive a surface water mask from Landsat 8 imagery comprises the use of a lower 15% percentile of Landsat 8 Top of Atmosphere (TOA) reflectance from 2013 to 2015. We introduce a new non-parametric unsupervised method based on the Canny edge filter and Otsu thresholding to detect water in flat areas. For hilly areas, the method is extended with an additional supervised classification step used to refine the water mask. We applied the method across the Murray-Darling basin, Australia. Differences between our new Landsat-based water mask and the OpenStreetMap water mask regarding positional differences along the rivers and overall coverage were analyzed. Our results show that about 50% of the OpenStreetMap linear water features can be confirmed using the water mask extracted from Landsat 8 imagery and the drainage network derived from SRTM. We also show that the observed distances between river features derived from OpenStreetMap and Landsat 8 are mostly smaller than 60 m. The differences between the new water mask and SRTM-based linear features and hilly areas are slightly larger (110 m). The overall agreement between OpenStreetMap and Landsat 8 water masks is about 30%.@en

Intercatchment groundwater flows (IGFs), defined as groundwater flows across topographic divides, can occur as regional groundwater flows that bypass headwater streams and only drain into the channel further downstream or directly to the sea. However, groundwater flows can also be diverted to adjacent river basins due to geological features (e.g., faults, dipping beds and highly permeable conduits). Even though intercatchment groundwater flows can be a significant part of the water balance, they are often not considered in hydrological studies. Yet, assuming this process to be negligible may introduce misrepresentation of the natural system in hydrological models, for example in regions with complex geological features. The presence of limestone formations in France and Belgium potentially further exacerbates the importance of intercatchment groundwater flows, and thus brings into question the validity of neglecting intercatchment groundwater flows in the Meuse basin. To isolate and quantify the potential relevance of net intercatchment groundwater flows in this study, we propose a three-step approach that relies on the comparison and analysis of (1) observed water balance data within the Budyko framework, (2) results from a suite of different conceptual hydrological models and (3) remote-sensing-based estimates of actual evaporation. The data of 58 catchments in the Meuse basin provide evidence of the likely presence of significant net intercatchment groundwater flows occurring mainly in small headwater catchments underlain by fractured aquifers. The data suggest that the relative importance of net intercatchment groundwater flows is reduced at the scale of the Meuse basin, as regional groundwater flows are mostly expected to be self-contained in large basins. The analysis further suggests that net intercatchment groundwater flow processes vary over the year and that at the scale of the headwaters, net intercatchment groundwater flows can make up a relatively large proportion of the water balance (on average 10 % of mean annual precipitation) and should be accounted for to prevent overestimating actual evaporation rates.@en

The original version of this Article contained an error in the Data Availability section where “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the TODO: upload and add Zenodo link here. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” now reads: “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the supplementary materials document below. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” The original Article has been corrected.@en

The spatiotemporal dynamics of water volumes stored in the unsaturated root zone are a key control on the response of terrestrial hydrological systems. Robust, catchment-scale root-zone soil moisture estimates are thus critical for reliable predictions of river flow, groundwater recharge, or evaporation. Satellites provide estimates of near-surface soil moisture that can be used to approximate the moisture content in the entire unsaturated root zone through the Soil Water Index (SWI). The characteristic time length (T, in days), as only parameter in the SWI approach, characterizes the temporal variability of soil moisture. The factors controlling T are typically assumed to be related to soil properties and climate; however, no clear link has so far been established. In this study, we hypothesize that optimal T values (Topt) are linked to the interplay of precipitation and evaporation during dry periods, thus to catchment-scale vegetation accessible water storage capacities in the unsaturated root zone. We identify Topt by matching modeled time series of root-zone soil moisture from a calibrated process-based hydrological model to SWI from several satellite-based near-surface soil moisture products in 16 contrasting catchments in the Meuse river basin. Topt values are strongly and positively correlated with vegetation accessible water volumes that can be stored in the root zone, here estimated for each study catchment both as model calibration parameter and from a water-balance approach. Differences in Topt across catchments are also explained by land cover (% agriculture), soil texture (% silt), and runoff signatures (flashiness index).@en

Irrigation requirements are mostly determined by estimating the atmospheric evaporative demand, in combination with precipitation data to estimate the irrigation need per field. However, in case of a high groundwater table, the contribution of capillary rise is often not taken into account. Nevertheless, this flux can contribute significantly to the actual evaporation. Ignoring this flux in irrigation practices might lead to over-irrigation and reduced yields. The significance of the groundwater flux in a furrow irrigated sugarcane plantation in Mozambique with a shallow groundwater table is presented here. Groundwater levels in a sugarcane plantation in Xinavane in Mozambique were recorded in several fields for a duration of six months. The groundwater recordings, potential evaporation estimates from satellite remote sensing, and field data were combined in the Vegetation-AtMosPhere-Soil water model (VAMPS) that was set up to understand the effect of groundwater contribution on the actual evaporation of a sugarcane field. With the hydrological field representations set up, we analyzed whether the current furrow irrigation requirement in the plantation, of 1350 mm/year for furrow irrigation, is efficient. The results show that groundwater contribution to the transpiration flux reduces the need for irrigation in the study area. As such, we conclude that the current irrigation requirement is leading to over-irrigation. The incorporation of the groundwater contribution is needed to provide adequate estimations for irrigation. A reduction in irrigation for these fields will lead to a higher water productivity in the study area.@en

Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t−3) was the most informative hydrological variable for landslide hazard assessment (AUC ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and following the cause–trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ∼20 % at the expense of a minor reduction in true alarms by ∼8 %.@en

Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.@en

Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. The moisture content of the root-zone regulates the partitioning between drainage and water fluxes and reliable estimates are, therefore, critical for predictions of runoff. Remotely-sensed soil moisture products are available globally, however, they are representative of the upper-most few centimeters of the soil. The Soil Water Index (SWI) features a single parameter, representing the characteristic time length T of temporal soil moisture variability, and enables to infer root-zone soil moisture from near-surface estimates. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established. In this study, we hypothesize that the optimal value for T can be linked to the seasonal signals of the interplay between precipitation (water supply) and evaporation (atmospheric water demand), and, thus, to catchment-scale vegetation-accessible water storage capacities in the unsaturated root-zone. We first identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of unsaturated root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T positively and strongly correlate to catchment-scale estimates of unsaturated root-zone capacities, estimated both as model calibration parameter and from a simple water-balance approach. This correlation provides evidence that the T value, used to infer root-zone soil moisture, reflects the vegetation-accessible water storage capacities in the unsaturated root-zone, that can be estimated from water-balance data.@en

Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. The moisture content of the root-zone regulates the partitioning between drainage and water fluxes and reliable estimates are, therefore, critical for predictions of runoff. Remotely-sensed soil moisture products are available globally, however, they are representative of the upper-most few centimeters of the soil. The Soil Water Index (SWI) features a single parameter, representing the characteristic time length T of temporal soil moisture variability, and enables to infer root-zone soil moisture from near-surface estimates. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established. In this study, we hypothesize that the optimal value for T can be linked to the seasonal signals of the interplay between precipitation (water supply) and evaporation (atmospheric water demand), and, thus, to catchment-scale vegetation-accessible water storage capacities in the unsaturated root-zone. We first identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of unsaturated root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T positively and strongly correlate to catchment-scale estimates of unsaturated root-zone capacities, estimated both as model calibration parameter and from a simple water-balance approach. This correlation provides evidence that the T value, used to infer root-zone soil moisture, reflects the vegetation-accessible water storage capacities in the unsaturated root-zone, that can be estimated from water-balance data.@en

Currently, the hydrological model used in the operational forecasting system of the river Meuse is lumped and does not account for the heterogeneity of the landscape, topography and vegetation. Previous studies have shown the importance of model structure distribution in different hydrological response units (HRUs) to improve model simulations. These HRUs take into account the different dominant runoff generation processes that occur in different parts of the landscape. The conceptualization of a runoff response with a very rapid time scale is essential to model the rapid runoff generated by very high intensity rainfall events. The parameterization of this rapid runoff response in the different sub-catchments of the Meuse is very sensitive due to the non-linearity of this threshold process and to the spatio-temporal variability of high-intensity rain events. In this study, we formulate several hypotheses on what controls the very quick runoff response in the Meuse basin and we try to use additional sources of data to test the a-priori assumptions that we made in the conceptualization of the HRUs in our hydrological model and to facilitate model parameterization. We hypothesize that by using appropriate runoff signatures, we may be able to assess the importance of the threshold response in the different catchments. The selection of specific storm events is useful to split the runoff in different time scales to improve the a-priori estimation of the very rapid runoff parameterization. Linking these differences to topographic and physiographic properties of the catchment like soil texture and land use may help us to explain the difference in observed spatial patterns. Especially the assessment of the fraction of roads and paved areas that cross the different hydrological response units may help to explain the observed spatial patterns. Additionally, we believe that deriving permanent and temporary wet areas using the Modified Normalized Difference Water Index (MNDWI) may guide us in strengthening or adapting the assumptions we made concerning the HRU classes. @en