Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5605-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-5605-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
20 Dec 2022
Data description article |
| 20 Dec 2022
| 20 Dec 2022
WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting
Ibrahim Demir
Department of Civil and Environmental Engineering, University of
Iowa, Iowa City, 52246 Iowa, USA
Department of Electrical and Computer Engineering, University of
Iowa, Iowa City, 52246 Iowa, USA
Department of Civil and Environmental Engineering, University of
Iowa, Iowa City, 52246 Iowa, USA
Bekir Demiray
Interdisciplinary Graduate Program in Informatics, University of
Iowa, Iowa City, 52246 Iowa, USA
Muhammed Sit
Interdisciplinary Graduate Program in Informatics, University of
Iowa, Iowa City, 52246 Iowa, USA
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Total article views: 3,172 (including HTML, PDF, and XML)
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Total article views: 1,878 (including HTML, PDF, and XML)
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Cited
25 citations as recorded by crossref.
- Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. https://doi.org/10.3390/w17050676
- Agricultural Injury Severity Prediction Using Integrated Data-Driven Analysis: Global Versus Local Explainability Using SHAP O. Mermer et al. https://doi.org/10.3390/safety12010006
- Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction F. Gurbuz et al. https://doi.org/10.1016/j.jhydrol.2023.130504
- Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting L. Xu et al. https://doi.org/10.1016/j.cageo.2024.105536
- An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin J. Mount et al. https://doi.org/10.2166/hydro.2024.079
- Event-scale flood types and controlling mechanisms in tile-drained agricultural watersheds X. Chen et al. https://doi.org/10.1016/j.ejrh.2026.103711
- A novel robust mixture-of-experts model with causal priors for interpretable water quality diagnosis M. Wang et al. https://doi.org/10.1016/j.aei.2025.104267
- Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang https://doi.org/10.1016/j.ejrh.2026.103349
- Geo-WC: Custom web components for earth science organizations and agencies S. Kaynak et al. https://doi.org/10.1016/j.envsoft.2025.106328
- LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles B. McAfee et al. https://doi.org/10.5194/essd-17-3141-2025
- The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT L. Kadiyala et al. https://doi.org/10.3390/hydrology11090148
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
- A contemporary systematic review of cyberinfrastructure systems and applications for flood and drought data analytics and communication S. Yeşilköy et al. https://doi.org/10.1088/2515-7620/ad85c4
- A Multitask Transfer Learning Framework for LSTM-Based Streamflow Forecasting A. Alzhanov et al. https://doi.org/10.1109/ACCESS.2026.3705788
- A multi-task learning model for global soil moisture prediction based on adaptive weight allocation Y. li et al. https://doi.org/10.1038/s41598-025-01894-3
- TempNet – temporal super-resolution of radar rainfall products with residual CNNs M. Sit et al. https://doi.org/10.2166/hydro.2023.196
- EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis Z. Li et al. https://doi.org/10.1016/j.envsoft.2024.106292
- Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2 T. Dantzer & B. Kerkez https://doi.org/10.1016/j.envsoft.2024.106137
- Practical application of machine learning for organic matter and harmful algal blooms in freshwater systems: A review X. Nguyen et al. https://doi.org/10.1080/10643389.2023.2285691
- Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al. https://doi.org/10.3390/w15050947
- The future of coastal monitoring through satellite remote sensing S. Vitousek et al. https://doi.org/10.1017/cft.2022.4
- Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction X. Wang et al. https://doi.org/10.3390/w16081179
- Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. https://doi.org/10.2166/wst.2024.110
- Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAIHub M. Sit & I. Demir https://doi.org/10.3390/app13053185
- Long-term river flow forecasting: An integrated deep learning model with multi-scale feature extraction D. Wang et al. https://doi.org/10.1016/j.eswa.2025.127387
25 citations as recorded by crossref.
- Predicting Harmful Algal Blooms Using Explainable Deep Learning Models: A Comparative Study B. Demiray et al. https://doi.org/10.3390/w17050676
- Agricultural Injury Severity Prediction Using Integrated Data-Driven Analysis: Global Versus Local Explainability Using SHAP O. Mermer et al. https://doi.org/10.3390/safety12010006
- Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction F. Gurbuz et al. https://doi.org/10.1016/j.jhydrol.2023.130504
- Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting L. Xu et al. https://doi.org/10.1016/j.cageo.2024.105536
- An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin J. Mount et al. https://doi.org/10.2166/hydro.2024.079
- Event-scale flood types and controlling mechanisms in tile-drained agricultural watersheds X. Chen et al. https://doi.org/10.1016/j.ejrh.2026.103711
- A novel robust mixture-of-experts model with causal priors for interpretable water quality diagnosis M. Wang et al. https://doi.org/10.1016/j.aei.2025.104267
- Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang https://doi.org/10.1016/j.ejrh.2026.103349
- Geo-WC: Custom web components for earth science organizations and agencies S. Kaynak et al. https://doi.org/10.1016/j.envsoft.2025.106328
- LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles B. McAfee et al. https://doi.org/10.5194/essd-17-3141-2025
- The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT L. Kadiyala et al. https://doi.org/10.3390/hydrology11090148
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
- A contemporary systematic review of cyberinfrastructure systems and applications for flood and drought data analytics and communication S. Yeşilköy et al. https://doi.org/10.1088/2515-7620/ad85c4
- A Multitask Transfer Learning Framework for LSTM-Based Streamflow Forecasting A. Alzhanov et al. https://doi.org/10.1109/ACCESS.2026.3705788
- A multi-task learning model for global soil moisture prediction based on adaptive weight allocation Y. li et al. https://doi.org/10.1038/s41598-025-01894-3
- TempNet – temporal super-resolution of radar rainfall products with residual CNNs M. Sit et al. https://doi.org/10.2166/hydro.2023.196
- EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis Z. Li et al. https://doi.org/10.1016/j.envsoft.2024.106292
- Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2 T. Dantzer & B. Kerkez https://doi.org/10.1016/j.envsoft.2024.106137
- Practical application of machine learning for organic matter and harmful algal blooms in freshwater systems: A review X. Nguyen et al. https://doi.org/10.1080/10643389.2023.2285691
- Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al. https://doi.org/10.3390/w15050947
- The future of coastal monitoring through satellite remote sensing S. Vitousek et al. https://doi.org/10.1017/cft.2022.4
- Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction X. Wang et al. https://doi.org/10.3390/w16081179
- Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. https://doi.org/10.2166/wst.2024.110
- Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAIHub M. Sit & I. Demir https://doi.org/10.3390/app13053185
- Long-term river flow forecasting: An integrated deep learning model with multi-scale feature extraction D. Wang et al. https://doi.org/10.1016/j.eswa.2025.127387
Saved (final revised paper)
Latest update: 14 Jul 2026
Short summary
We provide a large benchmark dataset, WaterBench-Iowa, with valuable features for hydrological modeling. This dataset is designed to support cutting-edge deep learning studies for a more accurate streamflow forecast model. We also propose a modeling task for comparative model studies and provide sample models with codes and results as the benchmark for reference. This makes up for the lack of benchmarks in earth science research.
We provide a large benchmark dataset, WaterBench-Iowa, with valuable features for hydrological...
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