Articles | Volume 12, issue 9
https://doi.org/10.5194/gmd-12-4115-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4115-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Sep 2019
Development and technical paper |
| 23 Sep 2019
| 23 Sep 2019
MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement
Rui Wu
CORRESPONDING AUTHOR
Department of Computer Science, East Carolina University, Greenville, NC, USA
Lei Yang
Department of Computer Science & Engineering, University of Nevada – Reno, Reno, NV, USA
Chao Chen
Department of Geosciences, Boise State University, Boise, ID, USA
Sajjad Ahmad
Department of Civil and Environmental Engineering and Construction, University of Nevada – Las Vegas, Las Vegas, NV, USA
Sergiu M. Dascalu
Department of Computer Science & Engineering, University of Nevada – Reno, Reno, NV, USA
Frederick C. Harris Jr.
Department of Computer Science & Engineering, University of Nevada – Reno, Reno, NV, USA
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Cited
16 citations as recorded by crossref.
- Enhancing the WAS physical hydrological model and integrating machine learning and residual decomposition to improve runoff simulation performance in the cold region of the Taoerhe River Basin, Northeast China Y. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103810
- Density-Regulated Snow Depth–Snow Water Equivalent Scaling Under Thermodynamic and Accumulation Perturbations K. Rakhymbek et al. https://doi.org/10.3390/app16073476
- Learning from mistakes—Assessing the performance and uncertainty in process‐based models M. Feigl et al. https://doi.org/10.1002/hyp.14515
- Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. https://doi.org/10.1016/j.jhydrol.2023.129199
- A hybrid framework based on LSTM for predicting karst spring discharge using historical data W. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.130946
- A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. https://doi.org/10.1016/j.envsoft.2022.105326
- Advancing low-cost air quality monitor calibration with machine learning methods S. Sousan et al. https://doi.org/10.1016/j.envpol.2025.126191
- Post-processing of hydrological model simulations using the convolutional neural network and support vector regression S. Liu et al. https://doi.org/10.2166/nh.2022.004
- A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty https://doi.org/10.1016/j.envsoft.2021.105094
- Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling Y. Bhattarai et al. https://doi.org/10.1016/j.teadva.2024.200116
- Daily Simulation of the Rainfall–Runoff Relationship in the Sirba River Basin in West Africa: Insights from the HEC-HMS Model I. Souley Tangam et al. https://doi.org/10.3390/hydrology11030034
- Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model S. Fang et al. https://doi.org/10.1029/2023WR034557
- Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach T. Iwanaga et al. https://doi.org/10.1016/j.envsoft.2020.104885
- Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. https://doi.org/10.1016/j.advwatres.2021.103965
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. https://doi.org/10.3390/su16041376
- Coupled surface water-groundwater-crop model considering the impact of irrigation using different calibration targets Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134556
16 citations as recorded by crossref.
- Enhancing the WAS physical hydrological model and integrating machine learning and residual decomposition to improve runoff simulation performance in the cold region of the Taoerhe River Basin, Northeast China Y. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103810
- Density-Regulated Snow Depth–Snow Water Equivalent Scaling Under Thermodynamic and Accumulation Perturbations K. Rakhymbek et al. https://doi.org/10.3390/app16073476
- Learning from mistakes—Assessing the performance and uncertainty in process‐based models M. Feigl et al. https://doi.org/10.1002/hyp.14515
- Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. https://doi.org/10.1016/j.jhydrol.2023.129199
- A hybrid framework based on LSTM for predicting karst spring discharge using historical data W. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.130946
- A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. https://doi.org/10.1016/j.envsoft.2022.105326
- Advancing low-cost air quality monitor calibration with machine learning methods S. Sousan et al. https://doi.org/10.1016/j.envpol.2025.126191
- Post-processing of hydrological model simulations using the convolutional neural network and support vector regression S. Liu et al. https://doi.org/10.2166/nh.2022.004
- A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty https://doi.org/10.1016/j.envsoft.2021.105094
- Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling Y. Bhattarai et al. https://doi.org/10.1016/j.teadva.2024.200116
- Daily Simulation of the Rainfall–Runoff Relationship in the Sirba River Basin in West Africa: Insights from the HEC-HMS Model I. Souley Tangam et al. https://doi.org/10.3390/hydrology11030034
- Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model S. Fang et al. https://doi.org/10.1029/2023WR034557
- Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach T. Iwanaga et al. https://doi.org/10.1016/j.envsoft.2020.104885
- Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. https://doi.org/10.1016/j.advwatres.2021.103965
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. https://doi.org/10.3390/su16041376
- Coupled surface water-groundwater-crop model considering the impact of irrigation using different calibration targets Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134556
Saved (final revised paper)
Latest update: 13 Aug 2026
Short summary
The paper mainly has two contributions. First, a post-processor framework is proposed to improve hydrologic model accuracy. The key is to characterize possible connections between model inputs and errors. Based on results, it is also possible to replace the time-consuming model calibration step using our post-processor framework. Second, a window selection method is proposed to handle nonstationary data. A window size is chosen containing stable data using a measure named
DSproposed by us.
The paper mainly has two contributions. First, a post-processor framework is proposed to improve...
