![]() (Wisser et al., 2010), and climate forcings selected (e.g., Haddeland et al., 2012 Gudmundsson et al., 2012a,ī Prudhomme et al., 2014 Müller Schmied etĪlternatively, the observation-based approaches exploit machine-learning Strongly dependent on GHMs or LSMs used, analysis periods Vegetation, and land parameters, which is not always realistic, and are Water balance components rely on a massive parameterization of the soil, Of relevant variables of the hydrological cycle, including runoff and riverĭischarge at very high temporal and spatial resolution (up to hourly Mathematical description of the main hydrological processes (e.g., waterīalance models (WBMs), global hydrological models (GHMs e.g.,ĭöll et al., 2003), or, increasing in complexity, land surface models (LSMs e.g., Balsamo et al., 2009 Schellekens etĪl., 2017), are able to provide comprehensive information on a large number Solutions, i.e., model-based or observation-based approaches, for runoff and This precarious situation has led to growing interest in finding alternative Settlements and especially the loss of human lives that occurs regularly. Terrestrial water dynamics deserves greater attention due to huge damages to (Crochemore et al., 2020), where the knowledge of the Paradoxically, this latter issue is exacerbated in developing nations Monitoring capacity (Vörösmarty et al., 2001). Temporal coverage, substantial delay in data access, and a large decline in Moreover, river discharge observation networks sufferįrom many limitations, such as low station density and often incomplete ![]() Typically offer little information on the spatial distribution of runoff Traditional in situ observations of river discharge, even if generallyĬharacterized by high temporal resolution (up to sub-hourly time step), The accurate spatiotemporally continuous runoff and river discharge estimation at finer spatial or temporal resolution is still a big challenge for hydrologists. Time series over basin areas larger than 10 000 km 2 are sufficient, whereas observations up to a grid scale of a few kilometers and daily or sub-daily time steps are required for flood prediction. For water resources management and drought monitoring, monthly To accomplish these tasks, runoff and riverĭischarge data representing the aggregated signal of runoff (Fekete et al., 2012) should be available at adequate spatial and temporal Improving the understanding of the hydrological cycle, planning humanĪctivities related to water use, and preventing or mitigating the lossesĭue to extreme flood events. Spatial and temporal continuous river discharge monitoring is paramount for Observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite Obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Despite the model simplicity, relatively high-performance scores are The method is tested over the Mississippi River basin for the periodĢ003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). The two are then added together to obtain river discharge estimates. Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoffĮstimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs).
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