Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2387-2022
© 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/hess-26-2387-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 May 2022
Research article |
| 09 May 2022
| 09 May 2022
Impact of spatial distribution information of rainfall in runoff simulation using deep learning method
Yang Wang
CORRESPONDING AUTHOR
Geoinformatics Laboratory, School of Computing and Information,
University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA 15213, USA
Hassan A. Karimi
Geoinformatics Laboratory, School of Computing and Information,
University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA 15213, USA
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Cited
20 citations as recorded by crossref.
- A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. https://doi.org/10.1016/j.jhydrol.2023.129269
- Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
- Impact of hydroclimatic changes on the operation of water resources systems: a case study of the Cantareira Water Production System J. Tercini & A. Méllo Júnior https://doi.org/10.1590/2318-0331.292420230132
- Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model K. Pakoksung https://doi.org/10.3390/eng5010004
- Time-Variant Instantaneous Unit Hydrograph Based on Machine Learning Pretraining and Rainfall Spatiotemporal Patterns W. Dong et al. https://doi.org/10.3390/w17152216
- Urban Rainfall Runoff Prediction Using LSTM with DTW-Enhanced Clustering Y. Tian et al. https://doi.org/10.1061/JHYEFF.HEENG-6450
- Improving watershed-scale daily nutrient simulation using a process-model-informed graph attention network with multi-source data integration W. Wang et al. https://doi.org/10.1016/j.watres.2026.125532
- Exploring large language models for climate forecasting Y. Wang & H. Karimi https://doi.org/10.3934/aci.2025001
- A self-supervised deep learning method for retrieving historically similar extreme rainfall patterns T. Wan et al. https://doi.org/10.1016/j.envsoft.2026.107116
- Enhanced runoff simulation with improved evapotranspiration accounting for vegetation response to climate variability N. Chitsaz et al. https://doi.org/10.1016/j.jhydrol.2025.133988
- A study on the runoff prediction mechanism of “water-soil-heat” in cold alpine regions with complex spatial distribution Q. Yu et al. https://doi.org/10.1016/j.scitotenv.2024.178059
- Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique X. Le et al. https://doi.org/10.1016/j.ejrh.2025.102668
- Entropy-MIMR-LSTM framework for rain gauge network optimization in mountainous small watersheds: A case study of Fuhuxi Watershed, China Y. Cui et al. https://doi.org/10.1007/s11629-025-0174-3
- A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data W. Liu et al. https://doi.org/10.1016/j.eswa.2025.129974
- Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. https://doi.org/10.2166/hydro.2024.268
- Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. https://doi.org/10.3390/rs15051395
- LSTM-Based River Discharge Forecasting Using Spatially Gridded Input Data K. Rakhymbek et al. https://doi.org/10.3390/data10080122
- Diagnosing the Added Value of Remote Sensing and Gridded Precipitation for Daily Runoff Forecasting Under Strong Antecedent Runoff Control R. Song et al. https://doi.org/10.3390/su18147494
- Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. https://doi.org/10.5194/hess-28-2107-2024
- Advancing Water Resources Management Through Reservoir Release Optimization: A Study Case in Piracicaba River Basin in Brazil R. Perez et al. https://doi.org/10.3390/hydrology12100269
20 citations as recorded by crossref.
- A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. https://doi.org/10.1016/j.jhydrol.2023.129269
- Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
- Impact of hydroclimatic changes on the operation of water resources systems: a case study of the Cantareira Water Production System J. Tercini & A. Méllo Júnior https://doi.org/10.1590/2318-0331.292420230132
- Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model K. Pakoksung https://doi.org/10.3390/eng5010004
- Time-Variant Instantaneous Unit Hydrograph Based on Machine Learning Pretraining and Rainfall Spatiotemporal Patterns W. Dong et al. https://doi.org/10.3390/w17152216
- Urban Rainfall Runoff Prediction Using LSTM with DTW-Enhanced Clustering Y. Tian et al. https://doi.org/10.1061/JHYEFF.HEENG-6450
- Improving watershed-scale daily nutrient simulation using a process-model-informed graph attention network with multi-source data integration W. Wang et al. https://doi.org/10.1016/j.watres.2026.125532
- Exploring large language models for climate forecasting Y. Wang & H. Karimi https://doi.org/10.3934/aci.2025001
- A self-supervised deep learning method for retrieving historically similar extreme rainfall patterns T. Wan et al. https://doi.org/10.1016/j.envsoft.2026.107116
- Enhanced runoff simulation with improved evapotranspiration accounting for vegetation response to climate variability N. Chitsaz et al. https://doi.org/10.1016/j.jhydrol.2025.133988
- A study on the runoff prediction mechanism of “water-soil-heat” in cold alpine regions with complex spatial distribution Q. Yu et al. https://doi.org/10.1016/j.scitotenv.2024.178059
- Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique X. Le et al. https://doi.org/10.1016/j.ejrh.2025.102668
- Entropy-MIMR-LSTM framework for rain gauge network optimization in mountainous small watersheds: A case study of Fuhuxi Watershed, China Y. Cui et al. https://doi.org/10.1007/s11629-025-0174-3
- A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data W. Liu et al. https://doi.org/10.1016/j.eswa.2025.129974
- Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. https://doi.org/10.2166/hydro.2024.268
- Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. https://doi.org/10.3390/rs15051395
- LSTM-Based River Discharge Forecasting Using Spatially Gridded Input Data K. Rakhymbek et al. https://doi.org/10.3390/data10080122
- Diagnosing the Added Value of Remote Sensing and Gridded Precipitation for Daily Runoff Forecasting Under Strong Antecedent Runoff Control R. Song et al. https://doi.org/10.3390/su18147494
- Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. https://doi.org/10.5194/hess-28-2107-2024
- Advancing Water Resources Management Through Reservoir Release Optimization: A Study Case in Piracicaba River Basin in Brazil R. Perez et al. https://doi.org/10.3390/hydrology12100269
Saved (final revised paper)
Latest update: 04 Aug 2026
Short summary
We found that rainfall data with spatial information can improve the model's performance, especially when simulating the future multi-day discharges. We did not observe that regional LSTM as a regional model achieved better results than LSTM as individual model. This conclusion applies to both one-day and multi-day simulations. However, we found that using spatially distributed rainfall data can reduce the difference between individual LSTM and regional LSTM.
We found that rainfall data with spatial information can improve the model's performance,...
