GRUN: Global Runoff Reconstruction (GRUN_v1)

Ghiggi, Gionata; Humphrey, Vincent; Gudmundsson, Lukas

doi:10.3929/ethz-b-000324386

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“…To establish the quality of the STREAM model in runoff simulation, monthly runoff data obtained from the Global Runoff Reconstruction (GRUN_v1, https://doi.org/10.3929/ethz-b-000324386, Ghiggi et al, 2019b) have been used for comparison. The GRUN dataset (Ghiggi et al, 2019a) is a global monthly runoff dataset derived through the use of a machinelearning algorithm trained with in situ river discharge observations of relatively small catchments (< 2500 km 2 ) and gridded precipitation and temperature derived from the Global Soil Wetness Project Phase 3 (GSWP3) dataset (Kim et al, 2017).…”

Section: Runoff Verification Datamentioning

confidence: 99%

Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation

et al. 2022

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This paper presents an innovative approach, STREAM -SaTellite-based Runoff Evaluation And Mapping -to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). 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. The two are then added together to obtain river discharge estimates.The method is tested over the Mississippi River basin for the period 2003-2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite 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). Despite the model simplicity, relatively high-performance scores are 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. 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 observa-tions only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land.

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Section: Runoff Verification Datamentioning

confidence: 99%