Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-149-2022
© 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-26-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jan 2022
Research article |
| 12 Jan 2022
| 12 Jan 2022
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
Álvaro Ossandón
CORRESPONDING AUTHOR
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO, USA
Departamento de Obras Civiles, Universidad Técnica Federico Santa María, Valparaíso, Chile
Manuela I. Brunner
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Institute of Earth and Environmental Sciences, University of Freiburg, Freiburg, Germany
Balaji Rajagopalan
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
William Kleiber
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
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Cited
13 citations as recorded by crossref.
- Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria J. Laimighofer et al. 10.5194/hess-26-4553-2022
- Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability J. Yu et al. 10.1016/j.jhydrol.2023.130057
- Nonstationary quantity-duration-frequency (QDF) relationships of lowflow in the source area of the Yellow River basin, China M. Ma et al. 10.1016/j.ejrh.2023.101450
- Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables F. Aranda et al. 10.3390/rs15235556
- Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework Á. Ossandón et al. 10.1029/2022WR033194
- A Bayesian hierarchical spatio-temporal model for summer extreme temperatures in Spain J. García et al. 10.1007/s00477-024-02754-8
- A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta Z. She et al. 10.1016/j.scitotenv.2024.172066
- Study of teleconnection between hydrological variables and climatological variables in a headwater basin of the Maipo River for forecast model application J. Montalva et al. 10.24850/j-tyca-16-4-3
- Spatial variability and moisture tracks of Indian monsoon rainfall and extremes S. Thota & B. Rajagopalan 10.1007/s00382-024-07373-1
- Quantifying the effect of climate variability on seasonal precipitation using Bayesian clustering approach in Kebir Rhumel Basin, Algeria L. Belkhiri & N. Krakauer 10.1007/s00477-023-02488-z
- Integrated GIS-based aquifer management system: A case study of Rajnandgaon District, Chhattisgarh, India A. Kumar et al. 10.1016/j.hazadv.2024.100586
- Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty 10.1029/2022WR033249
- Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty 10.1029/2022WR033249
12 citations as recorded by crossref.
- Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria J. Laimighofer et al. 10.5194/hess-26-4553-2022
- Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability J. Yu et al. 10.1016/j.jhydrol.2023.130057
- Nonstationary quantity-duration-frequency (QDF) relationships of lowflow in the source area of the Yellow River basin, China M. Ma et al. 10.1016/j.ejrh.2023.101450
- Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables F. Aranda et al. 10.3390/rs15235556
- Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework Á. Ossandón et al. 10.1029/2022WR033194
- A Bayesian hierarchical spatio-temporal model for summer extreme temperatures in Spain J. García et al. 10.1007/s00477-024-02754-8
- A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta Z. She et al. 10.1016/j.scitotenv.2024.172066
- Study of teleconnection between hydrological variables and climatological variables in a headwater basin of the Maipo River for forecast model application J. Montalva et al. 10.24850/j-tyca-16-4-3
- Spatial variability and moisture tracks of Indian monsoon rainfall and extremes S. Thota & B. Rajagopalan 10.1007/s00382-024-07373-1
- Quantifying the effect of climate variability on seasonal precipitation using Bayesian clustering approach in Kebir Rhumel Basin, Algeria L. Belkhiri & N. Krakauer 10.1007/s00477-023-02488-z
- Integrated GIS-based aquifer management system: A case study of Rajnandgaon District, Chhattisgarh, India A. Kumar et al. 10.1016/j.hazadv.2024.100586
- Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty 10.1029/2022WR033249
1 citations as recorded by crossref.
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Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Timely projections of seasonal streamflow extremes on a river network can be useful for flood...
