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View article: From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction
From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction Open
Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) have achieved significant success in hydrological modeling. However, the recent breakthroughs of foundation models like ChatGPT and the Segment Anything Model (SAM) in …
View article: From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction
From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction Open
Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) have achieved significant success in hydrological modeling. However, the recent successes of foundation models like ChatGPT and Segment Anything Model (SAM) in natural …
View article: Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning Open
To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as baseflow fraction - but are hindered by their limited ability to learn from data. He…
View article: High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning
High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning Open
The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel version…
View article: Machine Learning for a Heterogeneous Water Modeling Framework
Machine Learning for a Heterogeneous Water Modeling Framework Open
This technical note describes recent efforts to integrate machine learning (ML) models, specifically long short‐term memory (LSTM) networks and differentiable parameter learning conceptual hydrological models (δ conceptual models), into th…
View article: Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors
Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors Open
Manning's roughness coefficient, n , is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for…
View article: Can Attention Models Surpass LSTM in Hydrology?
Can Attention Models Surpass LSTM in Hydrology? Open
Accurate modeling of various hydrological variables is important for water resource management, flood forecasting, and pest control. Deep learning models, especially Long Short-Term Memory (LSTM) models based on Recurrent Neural Network (R…
View article: Enhanced Continental Runoff Prediction through Differentiable Muskingum-Cunge Routing (δMC-CONUS-hydroDL2)
Enhanced Continental Runoff Prediction through Differentiable Muskingum-Cunge Routing (δMC-CONUS-hydroDL2) Open
Recent advancements in flow routing models have enabled learning from big data using differentiable modeling techniques. However, their application remains constrained to smaller basins due to limitations in computational memory and hydrof…
View article: Differentiable modeling for global water resources under global change
Differentiable modeling for global water resources under global change Open
Process-based modeling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills …
View article: Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning
Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning Open
Recently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermi…
View article: Comment on hess-2023-237
Comment on hess-2023-237 Open
Abstract. Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial inf…
View article: dMC-Juniata-hydroDL River Graph Dataset
dMC-Juniata-hydroDL River Graph Dataset Open
This release is the data for the dMC-Juniata-hydroDL2 project (https://zenodo.org/doi/10.5281/zenodo.10183448). There is a readme inside of the .zip file which contains instructions for how to use this dataset.
View article: dMC-Juniata-hydroDL River Graph Dataset
dMC-Juniata-hydroDL River Graph Dataset Open
This release is the data for the dMC-Juniata-hydroDL2 project (https://zenodo.org/doi/10.5281/zenodo.10183448). There is a readme inside of the .zip file which contains instructions for how to use this dataset.
View article: Comment on hess-2023-252
Comment on hess-2023-252 Open
Abstract. Several studies have demonstrated the ability of Long Short-Term Memory (LSTM) machine learning based modeling to outperform traditional spatially-lumped process-based modeling approaches for streamflow prediction. However, due m…
View article: Comment on hess-2023-233
Comment on hess-2023-233 Open
Abstract. In the past decades, the world has experienced rapid urbanization and observed the appearances of large amount urbanizing watersheds with enhanced flooding, which has a constant changing land use/cover(LUC) types as the most sign…
View article: Comment on egusphere-2023-666
Comment on egusphere-2023-666 Open
Abstract. Several studies have demonstrated the ability of Long Short-Term Memory (LSTM) machine learning based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due m…
View article: Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning
Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning Open
Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that inter…
View article: Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing
Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing Open
Differentiable modeling has been introduced recently as a method to learn relationships from a combination of data and structural priors. This method uses end-to-end gradient tracking inside a process-based model to tune internal states an…
View article: Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations
Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Open
Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. This study seeks to test the feasibility of deep-network-based models to predict S…
View article: Differentiable modeling to unify machine learning and physical models and advance Geosciences
Differentiable modeling to unify machine learning and physical models and advance Geosciences Open
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier betwee…
View article: Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing
Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing Open
Recently, runoff simulations in small, headwater basins have been improved by methodological advances such as deep learning (DL). Hydrologic routing modules are typically needed to simulate flows in stem rivers downstream of large, heterog…