06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

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Publication Type: search.filters.UBSTyp.ZeitschriftenartikelInstitute: search.filters.UBSInstitut.Geodätisches InstitutAuthor: search.filters.author.Elmi, OmidSubject: search.filters.subject.550

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    Remote sensing-based extension of GRDC discharge time series : a monthly product with uncertainty estimates
    (2024) Elmi, Omid; Tourian, Mohammad J.; Saemian, Peyman; Sneeuw, Nico
    The Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km 3 /month.
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    Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)
    (2025) Saemian, Peyman; Elmi, Omid; Stroud, Molly; Riggs, Ryan; Kitambo, Benjamin M.; Papa, Fabrice; Allen, George H.; Tourian, Mohammad J.
    River discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. However, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. Remote sensing, especially radar-based techniques, offers an effective means to this issue. This study introduces the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) data set, which utilizes multiple satellite altimetry missions and estimates discharge using the existing worldwide networks of national and international gauges. In SAEM, we have explored 47 000 gauges and estimated height-based discharge for 8730 of them, which is approximately 3 times the number of gauges of the largest existing remote-sensing-based data set. These gauges cover approximately 88 % of the total gauged discharge volume. The height-based discharge estimates in SAEM demonstrate a median Kling–Gupta efficiency (KGE) of 0.48, outperforming current global data sets. In addition to the river discharge time series, the SAEM data set comprises three more products, each contributing a unique facet to better usage of our data. (1) A catalog of virtual stations (VSs) is defined by certain predefined criteria. In addition to each station's coordinates, this catalog provides information on satellite altimetry missions, distance to the discharge gauge, and relevant quality flags. (2) The altimetric water level time series of those VSs are included, for which we ultimately obtained good-quality discharge data. These water level time series are sourced from both existing Level-3 water level time series and newly generated ones within this study. The Level-3 data are gathered from pre-existing data sets, including Hydroweb.Next (formerly Hydroweb), the Database of Hydrological Time Series of Inland Waters (DAHITI), the Global River Radar Altimetry Time Series (GRRATS), and HydroSat. (3) SAEM's third product is rating curves for the defined VSs, which map water level values into discharge values, derived using a nonparametric stochastic quantile mapping function approach. The SAEM data set can be used to improve hydrological models, inform water resource management, and address nonlinear water-related challenges under climate change. The SAEM data set is available from https://doi.org/10.18419/darus-4475 .