Abstract
Key Points
Two scenarios of inaccurate rainfall inputs and a threshold of overall rainfall bias are identified for hydrological model adaptability
Hydrological model adaptability is further influenced by how event-based bias of individual rainstorms shapes the overall rainfall bias
Hydrological model adaptability is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage
Abstract
Numerous studies have evaluated the reliability and hydrologic utility of various rainfall datasets through hydrological modelling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty-eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall datasets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting (WRF) model simulations. Such rainfall datasets with varied errors are used to independently calibrate a widely used conceptual Xin’anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high-performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event-based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality.
Two scenarios of inaccurate rainfall inputs and a threshold of overall rainfall bias are identified for hydrological model adaptability
Hydrological model adaptability is further influenced by how event-based bias of individual rainstorms shapes the overall rainfall bias
Hydrological model adaptability is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage
Abstract
Numerous studies have evaluated the reliability and hydrologic utility of various rainfall datasets through hydrological modelling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty-eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall datasets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting (WRF) model simulations. Such rainfall datasets with varied errors are used to independently calibrate a widely used conceptual Xin’anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high-performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event-based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality.
| Original language | English |
|---|---|
| Article number | e2022WR032484 |
| Pages (from-to) | 1-20 |
| Number of pages | 20 |
| Journal | Water Resources Research |
| Volume | 59 |
| Issue number | 2 |
| Early online date | 20 Jan 2023 |
| DOIs | |
| Publication status | Published - 14 Feb 2023 |
Bibliographical note
Funding Information:J. Wang is grateful to the China Scholarship Council and the University of Bristol for providing the joint scholarship (No 201906380142) for her Ph.D. study at the University of Bristol. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol ‐ http://www.bris.ac.uk/acrc/ . Special acknowledgment goes to the data set developers from the NCEI/NOAA, Iowa Environmental Mesonet, NASA, CHRS, ECMWF, and USGS for producing and sharing the data sets employed in this study, and the WRF model developers. The authors are also grateful to Editor Peter Troch, Associate Editor, and three anonymous reviewers for their insightful and constructive comments that led to substantial improvements in the paper.
Funding Information:
J. Wang is grateful to the China Scholarship Council and the University of Bristol for providing the joint scholarship (No 201906380142) for her Ph.D. study at the University of Bristol. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/. Special acknowledgment goes to the data set developers from the NCEI/NOAA, Iowa Environmental Mesonet, NASA, CHRS, ECMWF, and USGS for producing and sharing the data sets employed in this study, and the WRF model developers. The authors are also grateful to Editor Peter Troch, Associate Editor, and three anonymous reviewers for their insightful and constructive comments that led to substantial improvements in the paper.
Publisher Copyright:
© 2023. The Authors.
Research Groups and Themes
- Water and Environmental Engineering
Keywords
- hydrological model adaptabilit
- rainfall input quality
- calibration compensation
- adaptable threshold
- event-based rainfall bias
- information content
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