Open-access remote sensing products provide data for transboundary water management. This study presents a comprehensive overview of the applications, uncertainties and implications of these remote sensing data products in the context of transboundary water management. Focusing on different stages within the transboundary cooperation continuum, we delineate the potential role and application of remote sensing data at the various stages of this cooperation. Despite the uncertainties and capacity requirements for data acquisition, processing and interpretation, we argue that remote sensing broadens opportunities to monitor, assess, forecast, track or validate compliance in transboundary basins, thereby challenging traditional notions of water data exclusivity.@en

Remote sensing (RS) data are becoming an increasingly important source of information for water resource management as they provide spatially distributed data on water availability and use. However, in order to guide appropriate use of the data, it is important to understand the impact of the uncertainties of RS data on water resource studies. Previous studies have shown that the degree of closure of the water balance from remote sensing data is highly variable across basins and that different RS products vary in their levels of accuracy depending on climatological and geographical conditions. In this paper, we analyzed the water-balance-derived runoff from global RS products for 931 catchments across the globe. We compared time series of runoff estimated through a simplified water balance equation using three precipitation (CHIRPS, GPM, and TRMM), five evapotranspiration (MODIS, SSEBop, GLEAM, CMRSET, and SEBS), and three water storage change (GRACE-CSR, GRACE-JPL, and GRACE-GFZ) RS datasets with monthly in situ discharge data for the period 2003–2016. Results were analyzed through the lens of 10 quantifiable catchment characteristics in order to investigate correlations between catchment characteristics and the quality of RS-based water balance estimates of runoff and whether specific products performed better than others under certain conditions. The median Nash–Sutcliffe efficiency (NSE) for all gauges and all product combinations was −0.02, and only 44.9 % of the time series reached a positive NSE. A positive NSE could be obtained for 73.7 % of stations with at least one product combination, while the overall best-performing product combination was positive for 58.4 % of stations. This confirms previous findings that the best-performing products cannot be globally established. When investigating the results by catchment characteristic, all combinations tended to show similar correlations between catchment characteristics and the quality of estimated runoff, with the exception of combinations using MODIS evapotranspiration, for which the correlation was frequently reversed. The combinations with the GPM precipitation product generally performed worse than the CHIRPS and TRMM data. However, this can be attributed to the fact that the GPM data are available at higher latitudes compared to the other products, where performance is generally poorer. When removing high-latitude stations, this difference was eliminated, and GPM and TRMM showed similar performance. The results show the highest positive correlation between highly seasonal rainfall and runoff NSE. On the other hand, increasing snow cover, altitude, and latitude decreased the ability of the RS products to close the water balance. The catchment's dominant climate zone was also found to be correlated with time series performance, with the tropical areas providing the highest (median NSE = 0.11) and arid areas the lowest (median NSE = −0.09) NSE values. No correlation was found between catchment area and runoff NSE. The results highlight the importance of further studies on the uncertainties of the different data products and how these interact when combining them, as well as of new approaches to using the data rather than simple water-balance-type approaches. Efforts to improve specific satellite products can also be better targeted using the results of this study.@en

Limited availability of ground measurements in the vast majority of river basins world-wide increases the value of alternative data sources such as satellite observations in hydrological modelling. This study investigates the potential of using remotely sensed river water levels, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River basin in Zambia. A distributed process-based rainfall-runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. Next, three alternative ways of further restricting feasible model parameter sets using satellite altimetry time series from 18 different locations along the river were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash-Sutcliffe efficiency of ENS,Q = 0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95 = 0.61-0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q =-1.4 (ENS,Q,5/95 =-2.3-0.38) with respect to discharge was obtained. The direct use of altimetry-based river levels frequently led to overestimated flows and poorly identified feasible parameter sets (ENS,Q,5/95 =-2.9-0.10). Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an overestimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95 =-2.6-0.25). However, accounting for river geometry proved to be highly effective. This included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth and applying the Strickler-Manning equation to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well, with an optimum of ENS,Q = 0.60 (ENS,Q,5/95 =-0.31-0.50). It was further shown that more accurate river cross-section data improved the water-level simulations, modelled rating curve, and discharge simulations during intermediate and low flows at the basin outlet where detailed on-site cross-section information was available. Also, increasing the number of virtual stations used for parameter selection in the calibration period considerably improved the model performance in a spatial split-sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data-scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly gauged or ungauged basins. @en