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• 1. [Article] Sensitivity of Columbia Basin Runoff to Long-Term Changes in Multi-Model CMIP5 Precipitation Simulations

  In this study, we used precipitation elasticity index of streamflow, to reflect on the sensitivity of streamflow to changes in future precipitation. We estimated precipitation elasticity of streamflow ...

  Citation × Citation Citation Abstract Copy Title: Sensitivity of Columbia Basin Runoff to Long-Term Changes in Multi-Model CMIP5 Precipitation Simulations Author: Demirel, Mehmet, Moradkhani, Hamid Year: 2014 In this study, we used precipitation elasticity index of streamflow, to reflect on the sensitivity of streamflow to changes in future precipitation. We estimated precipitation elasticity of streamflow from: (1) simulated streamflow by the VIC model using observed precipitation for the current climate (1963–2003); (2) simulated streamflow by the VIC model using simulated precipitation from 10 GCM - CMIP5 dataset for the future climate (2010–2099) including two different pathways (RCP4.5 and RCP8.5) and two different downscaled products (BCSD and MACA). The hydrological model was calibrated at 1/16 latitude-longitude resolution and the simulated streamflow was routed to the subbasin outlets of interest i.e. Hungry Horse subbasin. We used hydrological model simulations from 19063-2003 and calculated streamflow sensitivities and precipitation elasticity for the same period using observed climate (case 1) and simulated climate (case 2). The runoff sensitivity to long-term (e.g., 30-year) average annual changes in precipitation is calculated based on the elasticity of streamflow for three different 30 year blocks (2010-40, 2040-70 and 2070-99), which are of importance to reservoir management in the Columbia River basin. These two cases and different periods are compared to assess the effects of forcing by different climate models and different pathways on the precipitation elasticity of streamflow. Title: Sensitivity of Columbia Basin Runoff to Long-Term Changes in Multi-Model CMIP5 Precipitation Simulations Author: Demirel, Mehmet, Moradkhani, Hamid Year: 2014 In this study, we used precipitation elasticity index of streamflow, to reflect on the sensitivity of streamflow to changes in future precipitation. We estimated precipitation elasticity of streamflow from: (1) simulated streamflow by the VIC model using observed precipitation for the current climate (1963–2003); (2) simulated streamflow by the VIC model using simulated precipitation from 10 GCM - CMIP5 dataset for the future climate (2010–2099) including two different pathways (RCP4.5 and RCP8.5) and two different downscaled products (BCSD and MACA). The hydrological model was calibrated at 1/16 latitude-longitude resolution and the simulated streamflow was routed to the subbasin outlets of interest i.e. Hungry Horse subbasin. We used hydrological model simulations from 19063-2003 and calculated streamflow sensitivities and precipitation elasticity for the same period using observed climate (case 1) and simulated climate (case 2). The runoff sensitivity to long-term (e.g., 30-year) average annual changes in precipitation is calculated based on the elasticity of streamflow for three different 30 year blocks (2010-40, 2040-70 and 2070-99), which are of importance to reservoir management in the Columbia River basin. These two cases and different periods are compared to assess the effects of forcing by different climate models and different pathways on the precipitation elasticity of streamflow. Close

• 2. [Article] Assessing the Impact of CMIP5 Climate Multi-Modeling on Estimating the Precipitation Seasonality and Timing

  This paper investigates the effect of a Bayesian Model Averaging (BMA) method on simulated precipitation over the Columbia River Basin using two statistically downscaled climate datasets, i.e., Bias-Correction ...

  Citation × Citation Citation Abstract Copy Title: Assessing the Impact of CMIP5 Climate Multi-Modeling on Estimating the Precipitation Seasonality and Timing Author: Demirel, Mehmet, Moradkhani, Hamid Year: 2015 This paper investigates the effect of a Bayesian Model Averaging (BMA) method on simulated precipitation over the Columbia River Basin using two statistically downscaled climate datasets, i.e., Bias-Correction Spatial Disaggregation (BCSD) and Multivariate Adaptive Constructed Analogs (MACA). To this end daily observed and simulated precipitation are used to calculate different indices focusing solely on seasonality, event-timing, and variability in timing (persistence) of the precipitation. The climate model weights are estimated for each cell (6 × 6 km) of the Columbia River Basin using daily time series for the historical period 1970–1999 from the ten Global Climate Models (GCMs) participating to the Coupled Model Intercomparison Project platform, Phase 5. The results show that BMA results in more than 15 % improvement/reduction in mean absolute error as compared to the individual GCMs. The improvement is, in general, higher for the MACA models than for the BCSD models. The results of variability in precipitation timing show that extreme precipitation events are mostly not persistent (i.e., occurring in different periods throughout the year), i.e., more than 75 % of the grid cells with an elevation above 900 m indicate persistence values less than 0.2 whereas nearly 70 % of the high elevation cells indicate such low persistence. Further we find that the variability in persistence is higher in high elevation cells than those with low elevation. The picture is different for MACA ensembles as the simulated persistence of extreme precipitation events is higher than that for the observed and BCSD datasets. Title: Assessing the Impact of CMIP5 Climate Multi-Modeling on Estimating the Precipitation Seasonality and Timing Author: Demirel, Mehmet, Moradkhani, Hamid Year: 2015 This paper investigates the effect of a Bayesian Model Averaging (BMA) method on simulated precipitation over the Columbia River Basin using two statistically downscaled climate datasets, i.e., Bias-Correction Spatial Disaggregation (BCSD) and Multivariate Adaptive Constructed Analogs (MACA). To this end daily observed and simulated precipitation are used to calculate different indices focusing solely on seasonality, event-timing, and variability in timing (persistence) of the precipitation. The climate model weights are estimated for each cell (6 × 6 km) of the Columbia River Basin using daily time series for the historical period 1970–1999 from the ten Global Climate Models (GCMs) participating to the Coupled Model Intercomparison Project platform, Phase 5. The results show that BMA results in more than 15 % improvement/reduction in mean absolute error as compared to the individual GCMs. The improvement is, in general, higher for the MACA models than for the BCSD models. The results of variability in precipitation timing show that extreme precipitation events are mostly not persistent (i.e., occurring in different periods throughout the year), i.e., more than 75 % of the grid cells with an elevation above 900 m indicate persistence values less than 0.2 whereas nearly 70 % of the high elevation cells indicate such low persistence. Further we find that the variability in persistence is higher in high elevation cells than those with low elevation. The picture is different for MACA ensembles as the simulated persistence of extreme precipitation events is higher than that for the observed and BCSD datasets. Close

• 3. [Article] The Effect of Multi-Model Averaging of Climate Model Outputs on the Seasonality of Rainfall Over the Columbia River Basin

  The rainfall seasonality index is the measure of precipitation distribution throughout the seasonal cycle. The aim of this study is to compare the effect of different multi-model averaging methods on the ...

  Citation × Citation Citation Abstract Copy Title: The Effect of Multi-Model Averaging of Climate Model Outputs on the Seasonality of Rainfall Over the Columbia River Basin Author: Demirel, Mehmet, Rana, Arun, Moradkhani, Hamid Year: 2014 The rainfall seasonality index is the measure of precipitation distribution throughout the seasonal cycle. The aim of this study is to compare the effect of different multi-model averaging methods on the rainfall seasonality index at each 1/16 latitude-longitude cells covering the Columbia River Basin. In accordance with the same, ten different climate model outputs are selected from 45 available climate models from CMIP5 dataset. The reanalysis precipitation data is used to estimate the errors in rainfall seasonality for the climate model outputs. The inverse variance method and statistical multi criteria analysis (SMCA) method were used to estimate the weights for each climate model output. The precipitation amounts from the climate model outputs were then averaged using these model weights. The rainfall seasonality index was estimated from: (1) observed reanalysis data; (2) averaged precipitation amount from ten combinations of CMIP5 outputs for the current climate (1979–2005) using inverse variance method; (3) averaged precipitation amount from the ten combinations of CMIP5 outputs for the current climate using SMCA. The results showed the large differences in rainfall seasonality index for each climate model averaging. Moreover, the multi-modelling of climate models resulted in relative improvements in the performance of the rainfall seasonality over the Columbia River Basin. The estimated model weights for the current climate can be useful to combine the model outputs for the future climate. Title: The Effect of Multi-Model Averaging of Climate Model Outputs on the Seasonality of Rainfall Over the Columbia River Basin Author: Demirel, Mehmet, Rana, Arun, Moradkhani, Hamid Year: 2014 The rainfall seasonality index is the measure of precipitation distribution throughout the seasonal cycle. The aim of this study is to compare the effect of different multi-model averaging methods on the rainfall seasonality index at each 1/16 latitude-longitude cells covering the Columbia River Basin. In accordance with the same, ten different climate model outputs are selected from 45 available climate models from CMIP5 dataset. The reanalysis precipitation data is used to estimate the errors in rainfall seasonality for the climate model outputs. The inverse variance method and statistical multi criteria analysis (SMCA) method were used to estimate the weights for each climate model output. The precipitation amounts from the climate model outputs were then averaged using these model weights. The rainfall seasonality index was estimated from: (1) observed reanalysis data; (2) averaged precipitation amount from ten combinations of CMIP5 outputs for the current climate (1979–2005) using inverse variance method; (3) averaged precipitation amount from the ten combinations of CMIP5 outputs for the current climate using SMCA. The results showed the large differences in rainfall seasonality index for each climate model averaging. Moreover, the multi-modelling of climate models resulted in relative improvements in the performance of the rainfall seasonality over the Columbia River Basin. The estimated model weights for the current climate can be useful to combine the model outputs for the future climate. Close