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Abstract Effective flood prediction supports developing proactive risk management strategies, but its application in ungauged basins faces tremendous challenges due to limited/no streamflow record. This study investigates the potential for integrating streamflow derived from synthetic aperture radar (SAR) data and U.S. National Water Model (NWM) reanalysis estimates to develop improved predictions of above-normal flow (ANF) over the coterminous US. Leveraging the SAR data from the Global Flood Detection System to estimate the antecedent conditions using principal component regression, we apply the spatial-temporal hierarchical model (STHM) using NWM outputs for improving ANF prediction. Our evaluation shows promising results with the integrated model, STHM-SAR, significantly improving NWE, especially in 60% of the sites in the coastal region. Spatial and temporal validations underscore the model’s robustness, with SAR data contributing to explained variance by 24% on average. This approach not only improves NWM prediction, but also uniquely combines existing remote sensing data with national-scale predictions, showcasing its potential to improve hydrological modeling, particularly in regions with limited stream gauges.
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Spatial-Temporal Augmented Adaptation via Cycle-Consistent AdversarialNetwork: An Application in Streamflow Prediction
Accurate prediction of water flow is of utmost importance, particularly for ensuring water supply and informing early actions for floods and droughts. Existing flow prediction methods rely on the input of weather drivers, which hinders their applicability to monitoring small headwater streams due to the limited spatial resolution of existing weather datasets. This paper introduces a new dataset with frequent imagery on streams for water monitoring tasks. Our objective is to automatically predict streamflow for each stream site using frequent images taken at a sub-hourly scale. To overcome the challenge of limited labels for certain stream sites, we employ knowledge transfer from well-observed sites to poorly-observed sites via domain adaptation. As each stream site involves highly variable time series data over long periods, we introduce a novel method STCGAN (Spatial-Temporal Cycle Generative Adversarial Network), which incorporates temporal context by conditioning on the sequence's time and learns overall trends of stream flow variation. It integrates the predictive modeling of streamflow with the cyclic generative process and enhances the prediction with data augmentation using generated synthetic samples. Our experiments demonstrate superior performance of the proposed method using data collected from the West Brook area located in western Massachusetts, US. The proposed method can be further extended to selectively combine information from multiple well-observed stream sites, leading to improved overall performance.
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- PAR ID:
- 10504015
- Publisher / Repository:
- SIAM
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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