Details zur Publikation
Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1007/s11269-026-04602-6
Volltext Shareable Link
Titel (primär) Near-real-time statistical analysis and visualization of streamflow from a deep-learning rainfall-runoff model
Autor Duong, T.D.; Tran, V.N.; Nguyen, V.T. ORCID logo
Quelle Water Resources Management
Erscheinungsjahr 2026
Department HDG
Band/Volume 40
Heft 5
Seite von art. 221
Sprache englisch
Topic T5 Future Landscapes
Daten-/Softwarelinks https://doi.org/10.5281/zenodo.15161047
https://doi.org/10.5281/zenodo.17006897
Supplements Supplement 1
Keywords Hydrological modeling; Machine learning; Streamflow; Deep learning; Near-real-time
Abstract Near-real-time (NRT) streamflow data are critical importance for timely water resources management. We developed an open-source tool, FlowStats, for NRT streamflow analysis and visualization in Germany, based on NRT meteorological data from the German Weather Service and simulated streamflow from a long short-term memory neural network (LSTM). The LSTM model achieved very good overall performance, median NSE of 0.80 for the test period across 1,479 catchments. FlowStats provides options for deriving various streamflow statistics, from normal and abnormal streamflow detection to drought and flood analyses. An example analysis from FlowStats revealed widespread below-normal to extreme low-flow conditions across Germany from March to May 2025, which weakened from June to September 2025. Drought analysis for September 2025 highlighted severe to extreme drought conditions in northwestern Germany, while flood classifications indicated that high-flow events occurred in southwestern Germany. FlowStats can be used for various hydrological assessments to support water resources management.
Duong, T.D., Tran, V.N., Nguyen, V.T. (2026):
Near-real-time statistical analysis and visualization of streamflow from a deep-learning rainfall-runoff model
Water Resour. Manag. 40 (5), art. 221
10.1007/s11269-026-04602-6