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Accurate streamflow prediction is critical for ensuring water supply and detecting floods, while also providing essential hydrological inputs for other scientific models in fields such as climate and agriculture.Recently, deep learning models have been shown to achieve state-of-the-art regionalization performance by building a global hydrologic model. These models predict streamflow given catchment physical characteristics and weather forcing data.However, these models are only focused on gauged basins and cannot adapt to ungaugaed basins, i.e., basins without training data. Prediction in Ungauged Basins (PUB) is considered one of the most important challenges in hydrology, as most basins in the United States and around the world have no observations. In this work, we propose a meta-transfer learning approach by enhancing imperfect physics equations that facilitate model adaptation. Intuitively, physical equations can often be used to regularize deep learning models to achieve robust regionalization performance under gauged scenarios, but they can be inaccurate due to the simplified representation of physics. We correct such uncertainty in physical equation by residual approximation and let these corrected equations guide the model training process. We evaluated the proposed method for predicting daily streamflow on the catchment attributes and meteorology for large-sample studies (CAMELS) dataset. The experiment results on hydrological data over 19 years demonstrate the effectiveness of the proposed method in ungauged scenarios.
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Probabilistic Physics‐Guided Deep Neural Networks With Recurrence and Attention Mechanisms for Interpretable Daily Streamflow Simulation
Abstract As Deep Neural Networks (DNNs) are being increasingly employed to make important simulations in rainfall‐runoff contexts, the demand for interpretability is increasing in the hydrology community. Interpretability is not just a scientific question, but rather knowing where the models fall flat, how to fix them, and how to explain their outcomes to scientific communities so that everyone understands how the model arrives at specific simulations This paper addresses these challenges by deciphering interpretable probabilistic DNNs utilizing the Deep Autoregressive Recurrent (DeepAR) and Temporal Fusion Transformer (TFT) for daily streamflow simulation across the continental United States (CONUS). We benchmarked TFT and DeepAR against conceptual to physics‐based hydrologic models. In this setting, catchment physical attributes were incorporated into the training process to create physics‐guided TFT and DeepAR configurations. Our proposed physics‐guided configurations are also designed to aggregate the patterns across the entire data set, analyze the sensitivity of key catchment physical attributes and facilitate the interpretability of temporal dynamics in rainfall‐runoff generation mechanisms. To assess the uncertainty, the modeling configurations were coupled with a quantile regression by adding Gaussian noise with increasing standard deviation to the individual catchment attributes. Analysis suggested that the physics‐guided TFT was superior in predicting daily streamflow compared to the original TFT and DeepAR as well as benchmark hydrologic models. Predictive uncertainty intervals effectively bracketed most of the observational data by simultaneous simulation of various percentiles (e.g., 10th, 50th, and 90th). Interpretable physics‐guided TFT proved to be a strong candidate for CONUS daily streamflow simulations.
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- PAR ID:
- 10637476
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 61
- Issue:
- 9
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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