Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4593-2018
© Author(s) 2018. 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-22-4593-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Aug 2018
Research article |
| 30 Aug 2018
| 30 Aug 2018
How good are hydrological models for gap-filling streamflow data?
Yongqiang Zhang
CORRESPONDING AUTHOR
CSIRO Land and Water, GPO Box 1700, Acton 2601, Canberra, Australia
David Post
CSIRO Land and Water, GPO Box 1700, Acton 2601, Canberra, Australia
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Cited
30 citations as recorded by crossref.
- Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series C. Vega-Garcia et al. 10.3390/w11081691
- Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. 10.1029/2018WR023325
- The changing nature and projection of floods across Australia X. Gu et al. 10.1016/j.jhydrol.2020.124703
- Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation T. Baddoo et al. 10.3390/ijerph18168375
- Divergent negative spring vegetation and summer runoff patterns and their driving mechanisms in natural ecosystems of northern latitudes X. Zhou et al. 10.1016/j.jhydrol.2020.125848
- Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps L. Lopes Martins et al. 10.1080/02626667.2022.2145200
- Reconstruction of Missing Daily Streamflow Data using the MissForest Algorithm in Southern Baluchestan Basin, Iran J. Aryanmanesh et al. 10.61186/jwmr.15.2.49
- Physical and biogeochemical drivers of solute mobilization and flux through the critical zone after wildfire R. Sánchez et al. 10.3389/frwa.2023.1148298
- Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm P. Arriagada et al. 10.1016/j.jhydrol.2021.126454
- Unravelling the potential of global streamflow reanalysis in characterizing local flow regime T. Zhao et al. 10.1016/j.scitotenv.2022.156125
- Increased Grain Crop Production Intensifies the Water Crisis in Northern China X. Li et al. 10.1029/2023EF003608
- Stage and discharge prediction from documentary time-lapse imagery K. Chapman et al. 10.1371/journal.pwat.0000106
- Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data R. Wu et al. 10.1109/JSEN.2022.3166643
- Research Trends of Hydrological Drought: A Systematic Review H. Hasan et al. 10.3390/w11112252
- A preliminary assessment of environmental flow in the three rivers' source region, Qinghai Tibetan Plateau, China and suggestions R. Mahmood et al. 10.1016/j.ecoleng.2019.105709
- Diagnosing the impacts of landscape characteristics on hydrologic signatures in the Krycklan catchment in Sweden using a flexible hydrological model R. Guo et al. 10.1016/j.pce.2024.103565
- On the use of distributed hydrologic model for filling large gaps at different parts of the streamflow data E. Ergün & M. Demirel 10.1016/j.jestch.2022.101321
- Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings H. Sahour et al. 10.1007/s12665-021-10054-5
- Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities Y. Zhou et al. 10.1016/j.jhydrol.2023.130404
- Prediction at Ungauged Catchments through Parameter Optimization and Uncertainty Estimation to Quantify the Regional Water Balance of the Ethiopian Rift Valley Lake Basin T. Abraham et al. 10.3390/hydrology9080150
- Recent streamflow trends across permafrost basins of North America K. Bennett et al. 10.3389/frwa.2023.1099660
- Modeling Lake Titicaca's water balance: the dominant roles of precipitation and evaporation N. Lima-Quispe et al. 10.5194/hess-29-655-2025
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Improving Linear Interpolation of Missing Hydrological Data by Applying Integrated Autoregressive Models T. Niedzielski & M. Halicki 10.1007/s11269-023-03625-7
- Regional significance of historical trends and step changes in Australian streamflow G. Amirthanathan et al. 10.5194/hess-27-229-2023
- Bushfire‐Induced Water Balance Changes Detected by a Modified Paired Catchment Method Z. Xu et al. 10.1029/2021WR031013
- Conceptual Models and Calibration Performance—Investigating Catchment Bias A. Buzacott et al. 10.3390/w11112424
- Quantifying uncertainty in annual runoff due to missing data C. See et al. 10.7717/peerj.9531
- A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico M. Valladares-Castellanos et al. 10.1016/j.scitotenv.2024.175111
- Southern Hemisphere dominates recent decline in global water availability Y. Zhang et al. 10.1126/science.adh0716
30 citations as recorded by crossref.
- Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series C. Vega-Garcia et al. 10.3390/w11081691
- Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. 10.1029/2018WR023325
- The changing nature and projection of floods across Australia X. Gu et al. 10.1016/j.jhydrol.2020.124703
- Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation T. Baddoo et al. 10.3390/ijerph18168375
- Divergent negative spring vegetation and summer runoff patterns and their driving mechanisms in natural ecosystems of northern latitudes X. Zhou et al. 10.1016/j.jhydrol.2020.125848
- Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps L. Lopes Martins et al. 10.1080/02626667.2022.2145200
- Reconstruction of Missing Daily Streamflow Data using the MissForest Algorithm in Southern Baluchestan Basin, Iran J. Aryanmanesh et al. 10.61186/jwmr.15.2.49
- Physical and biogeochemical drivers of solute mobilization and flux through the critical zone after wildfire R. Sánchez et al. 10.3389/frwa.2023.1148298
- Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm P. Arriagada et al. 10.1016/j.jhydrol.2021.126454
- Unravelling the potential of global streamflow reanalysis in characterizing local flow regime T. Zhao et al. 10.1016/j.scitotenv.2022.156125
- Increased Grain Crop Production Intensifies the Water Crisis in Northern China X. Li et al. 10.1029/2023EF003608
- Stage and discharge prediction from documentary time-lapse imagery K. Chapman et al. 10.1371/journal.pwat.0000106
- Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data R. Wu et al. 10.1109/JSEN.2022.3166643
- Research Trends of Hydrological Drought: A Systematic Review H. Hasan et al. 10.3390/w11112252
- A preliminary assessment of environmental flow in the three rivers' source region, Qinghai Tibetan Plateau, China and suggestions R. Mahmood et al. 10.1016/j.ecoleng.2019.105709
- Diagnosing the impacts of landscape characteristics on hydrologic signatures in the Krycklan catchment in Sweden using a flexible hydrological model R. Guo et al. 10.1016/j.pce.2024.103565
- On the use of distributed hydrologic model for filling large gaps at different parts of the streamflow data E. Ergün & M. Demirel 10.1016/j.jestch.2022.101321
- Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings H. Sahour et al. 10.1007/s12665-021-10054-5
- Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities Y. Zhou et al. 10.1016/j.jhydrol.2023.130404
- Prediction at Ungauged Catchments through Parameter Optimization and Uncertainty Estimation to Quantify the Regional Water Balance of the Ethiopian Rift Valley Lake Basin T. Abraham et al. 10.3390/hydrology9080150
- Recent streamflow trends across permafrost basins of North America K. Bennett et al. 10.3389/frwa.2023.1099660
- Modeling Lake Titicaca's water balance: the dominant roles of precipitation and evaporation N. Lima-Quispe et al. 10.5194/hess-29-655-2025
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Improving Linear Interpolation of Missing Hydrological Data by Applying Integrated Autoregressive Models T. Niedzielski & M. Halicki 10.1007/s11269-023-03625-7
- Regional significance of historical trends and step changes in Australian streamflow G. Amirthanathan et al. 10.5194/hess-27-229-2023
- Bushfire‐Induced Water Balance Changes Detected by a Modified Paired Catchment Method Z. Xu et al. 10.1029/2021WR031013
- Conceptual Models and Calibration Performance—Investigating Catchment Bias A. Buzacott et al. 10.3390/w11112424
- Quantifying uncertainty in annual runoff due to missing data C. See et al. 10.7717/peerj.9531
- A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico M. Valladares-Castellanos et al. 10.1016/j.scitotenv.2024.175111
- Southern Hemisphere dominates recent decline in global water availability Y. Zhang et al. 10.1126/science.adh0716
Discussed (preprint)
Latest update: 06 Mar 2025
Short summary
It is a critical step to gap-fill streamflow data for most hydrological studies, such as streamflow trend, flood, and drought analysis and predictions. However, quantitative evaluation of the gap-filled data accuracy is not available. Here we conducted the first comprehensive study, and found that when the missing data rate is less than 10 %, the gap-filled streamflow data using hydrological models are reliable for annual streamflow and its trend analysis.
It is a critical step to gap-fill streamflow data for most hydrological studies, such as...
