Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model
- Thuyloi University, Hanoi (Vietnam)
- University of Michigan, Ann Arbor, MI (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Helmholtz Centre for Environmental Research, Leipzig (Germany)
Near-real-time (NRT) streamflow data are critical importance for timely water resources management. Here, 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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 3030313
- Journal Information:
- Water Resources Management, Journal Name: Water Resources Management Journal Issue: 5 Vol. 40; ISSN 1573-1650; ISSN 0920-4741
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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