Articles | Volume 20, issue 8
https://doi.org/10.5194/nhess-20-2243-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-2243-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2020
Research article |
| 17 Aug 2020
| 17 Aug 2020
Bias correction of a gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo–Brahmaputra River basin
Institute of International Rivers and Eco-security, Yunnan University,
Kunming, China
Yunnan Key Laboratory of International Rivers and Transboundary
Eco-security, Kunming, China
Xuemei Fan
Institute of International Rivers and Eco-security, Yunnan University,
Kunming, China
Yungang Li
CORRESPONDING AUTHOR
Institute of International Rivers and Eco-security, Yunnan University,
Kunming, China
Yunnan Key Laboratory of International Rivers and Transboundary
Eco-security, Kunming, China
Institute of International Rivers and Eco-security, Yunnan University,
Kunming, China
Yunnan Key Laboratory of International Rivers and Transboundary
Eco-security, Kunming, China
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Cited
18 citations as recorded by crossref.
- Machine learning-based precipitation dataset for the Yarlung Zangbo River Basin: Generation, evaluation, and environmental factor analysis H. Zha et al.
- Comparison of bias-corrected multisatellite precipitation products by deep learning framework X. Le et al.
- Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission scenario in Woybo River catchment of Ethiopia T. Ukumo et al.
- Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China X. Li et al.
- A Review of APHRODITE Performance in Asia: Current Status and Research Concerns M. Tan et al.
- Development of Multi-Source Weighted-Ensemble Precipitation: Influence of bias correction based on recurrent convolutional neural networks Y. Kao et al.
- Modelling extreme precipitation projections under the effects of climate change: case study of the Caspian Sea S. Moradian et al.
- Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model G. Seo & J. Ahn
- Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al.
- Integrating machine learning and zoning-based techniques for bias correction in gridded precipitation data to improve hydrological estimation in the data-scarce region T. Meema et al.
- Quantile-based Bayesian Model Averaging approach towards merging of precipitation products K. Yumnam et al.
- Enhanced precipitation estimation in a Himalayan river basin through the fusion of multi-source datasets using various machine learning techniques H. Tiwari et al.
- Preface: Advances in extreme value analysis and application to natural hazards Y. Hamdi et al.
- Performance Comparison of Bias-Corrected Satellite Precipitation Products by Various Deep Learning Schemes X. Le et al.
- Improving satellite and reanalysis precipitation estimates in a Himalayan River Basin: a comparative study of bias correction methods with focus on extremes and ensemble method performance H. Tiwari & R. Garg
- An Integrated Approach for the Climate Change Impact Assessment on the Water Resources in the Sangu River Basin, Bangladesh, under Coupled-Model Inter-Comparison Project Phase 5 M. Hasan et al.
- Spatiotemporal patterns of precipitation based on the Bayesian maximum entropy method in a typical catchment of the Heihe River watershed, northwest China H. Wang et al.
- Performance evaluation of raw and bias-corrected ERA5 precipitation data with respect to extreme precipitation analysis: case study in Upper Jhelum Basin, South Asia R. Ansari & G. Grossi
18 citations as recorded by crossref.
- Machine learning-based precipitation dataset for the Yarlung Zangbo River Basin: Generation, evaluation, and environmental factor analysis H. Zha et al.
- Comparison of bias-corrected multisatellite precipitation products by deep learning framework X. Le et al.
- Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission scenario in Woybo River catchment of Ethiopia T. Ukumo et al.
- Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China X. Li et al.
- A Review of APHRODITE Performance in Asia: Current Status and Research Concerns M. Tan et al.
- Development of Multi-Source Weighted-Ensemble Precipitation: Influence of bias correction based on recurrent convolutional neural networks Y. Kao et al.
- Modelling extreme precipitation projections under the effects of climate change: case study of the Caspian Sea S. Moradian et al.
- Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model G. Seo & J. Ahn
- Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al.
- Integrating machine learning and zoning-based techniques for bias correction in gridded precipitation data to improve hydrological estimation in the data-scarce region T. Meema et al.
- Quantile-based Bayesian Model Averaging approach towards merging of precipitation products K. Yumnam et al.
- Enhanced precipitation estimation in a Himalayan river basin through the fusion of multi-source datasets using various machine learning techniques H. Tiwari et al.
- Preface: Advances in extreme value analysis and application to natural hazards Y. Hamdi et al.
- Performance Comparison of Bias-Corrected Satellite Precipitation Products by Various Deep Learning Schemes X. Le et al.
- Improving satellite and reanalysis precipitation estimates in a Himalayan River Basin: a comparative study of bias correction methods with focus on extremes and ensemble method performance H. Tiwari & R. Garg
- An Integrated Approach for the Climate Change Impact Assessment on the Water Resources in the Sangu River Basin, Bangladesh, under Coupled-Model Inter-Comparison Project Phase 5 M. Hasan et al.
- Spatiotemporal patterns of precipitation based on the Bayesian maximum entropy method in a typical catchment of the Heihe River watershed, northwest China H. Wang et al.
- Performance evaluation of raw and bias-corrected ERA5 precipitation data with respect to extreme precipitation analysis: case study in Upper Jhelum Basin, South Asia R. Ansari & G. Grossi
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
In this study, we corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo–Brahmaputra River Basin using both linear and nonlinear methods, and their influences on resulting extreme precipitation indices were assessed. Results showed that all methods were able to correct mean precipitation, but their ability to correct wet-day frequency and coefficient of variation were markedly different.
In this study, we corrected Asian Precipitation Highly Resolved Observational Data Integration...
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