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- Author or Editor: Fan Wang x
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Abstract
Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.
Significance Statement
Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.
Abstract
Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.
Significance Statement
Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.
Abstract
The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.
Abstract
The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.
Abstract
In this study, the Providing Regional Climates for Impacts Studies (PRECIS) and the Regional Climate Model (RegCM) system as well as the Variable Infiltration Capacity (VIC) macroscale hydrologic model were integrated into a general framework to investigate impacts of future climates on the hydrologic regime of the Athabasca River basin. Regional climate models (RCMs) including PRECIS and RegCM were used to develop ensemble high-resolution climate projections for 1979–2099. RCMs were driven by the boundary conditions from the Hadley Centre Global Environment Model, version 2 with Earth system configurations (HadGEM2-ES); the Second Generation Canadian Earth System Model (CanESM2); and the Geophysical Fluid Dynamics Laboratory Earth System Model with MOM (GFDL-ESM2M) under the representative concentration pathways (RCPs). The ensemble climate simulations were validated through comparison with observations for 1984–2003. The RCMs project increases in temperature, precipitation, and wind speed under RCPs across most of the Athabasca River basin. Meanwhile, VIC was calibrated using the University of Arizona Shuffled Complex Evolution method (SCE-UA). The performance of the VIC model in replicating the characteristics of the observed streamflow was validated for 1994–2003. Changes in runoff and streamflow under RCPs were then simulated by the validated VIC model. The validation results demonstrate that the ensemble-RCM-driven VIC model can effectively reproduce historical climatological and hydrological patterns in the Athabasca River basin. The ensemble-RCM-driven VIC model shows that monthly streamflow is projected to increase in the 2050s and 2080s under RCPs, with notably higher flows expected in the spring for the 2080s. This will have substantial impacts on water balance on the Athabasca River basin, thus affecting the surrounding industry and ecosystems. The developed framework can be applied to other regions for exploration of hydrologic impacts under climate change.
Abstract
In this study, the Providing Regional Climates for Impacts Studies (PRECIS) and the Regional Climate Model (RegCM) system as well as the Variable Infiltration Capacity (VIC) macroscale hydrologic model were integrated into a general framework to investigate impacts of future climates on the hydrologic regime of the Athabasca River basin. Regional climate models (RCMs) including PRECIS and RegCM were used to develop ensemble high-resolution climate projections for 1979–2099. RCMs were driven by the boundary conditions from the Hadley Centre Global Environment Model, version 2 with Earth system configurations (HadGEM2-ES); the Second Generation Canadian Earth System Model (CanESM2); and the Geophysical Fluid Dynamics Laboratory Earth System Model with MOM (GFDL-ESM2M) under the representative concentration pathways (RCPs). The ensemble climate simulations were validated through comparison with observations for 1984–2003. The RCMs project increases in temperature, precipitation, and wind speed under RCPs across most of the Athabasca River basin. Meanwhile, VIC was calibrated using the University of Arizona Shuffled Complex Evolution method (SCE-UA). The performance of the VIC model in replicating the characteristics of the observed streamflow was validated for 1994–2003. Changes in runoff and streamflow under RCPs were then simulated by the validated VIC model. The validation results demonstrate that the ensemble-RCM-driven VIC model can effectively reproduce historical climatological and hydrological patterns in the Athabasca River basin. The ensemble-RCM-driven VIC model shows that monthly streamflow is projected to increase in the 2050s and 2080s under RCPs, with notably higher flows expected in the spring for the 2080s. This will have substantial impacts on water balance on the Athabasca River basin, thus affecting the surrounding industry and ecosystems. The developed framework can be applied to other regions for exploration of hydrologic impacts under climate change.
