A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reli...

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Veröffentlicht in:Water resources research Jg. 59; H. 2
Hauptverfasser: Roy, Abhinanda, Kasiviswanathan, K. S., Patidar, Sandhya, Adeloye, Adebayo J., Soundharajan, Bankaru‐Swamy, Ojha, Chandra Shekhar P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington John Wiley & Sons, Inc 01.02.2023
Schlagworte:
Accuracy
Algorithms
Climate change
Environmental impact
Error correction
Error correction & detection
Flood forecasting
Flood warning systems
Floods
Forecast accuracy
Frameworks
HBV model
hybrid model
India
Lead time
Machine learning
Mathematical models
Model accuracy
model validation
Modelling
Nepal
particle filter
Physics
random forest
River basins
Rivers
Stream discharge
Stream flow
streamflow forecast
Streamflow forecasting
Uncertainty
uncertainty quantification
Warning systems
water
watersheds
Nepal
India
ISSN:0043-1397, 1944-7973
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Zusammenfassung:Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation technique Dynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposed Overall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty
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ISSN:0043-1397
1944-7973
DOI:10.1029/2022WR033318