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• 1. [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

• 2. [Article] Spatial, Temporal and Frequency Based Climate change Assessment in Columbia River Basin Using Multi Downscaled-Scenarios

  Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional scale. In the present work, we have ...

  Citation × Citation Citation Abstract Copy Title: Spatial, Temporal and Frequency Based Climate change Assessment in Columbia River Basin Using Multi Downscaled-Scenarios Author: Rana, Arun, Moradkhani, Hamid Year: 2016 Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional scale. In the present work, we have analyzed the changes in precipitation and temperature for future scenario period of 2070–2099 with respect to historical period of 1970–2000 from statistically downscaled GCM projections in Columbia River Basin (CRB). Analysis is performed using two different statistically downscaled climate projections (with ten GCMs downscaled products each, for RCP 4.5 and RCP 8.5, from CMIP5 dataset) namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. The two datasets for BCSD and MACA are downscaled from observed data for both scenarios projections i.e. RCP4.5 and RCP8.5. Analysis is performed using spatial change (yearly scale), temporal change (monthly scale), percentile change (seasonal scale), quantile change (yearly scale), and wavelet analysis (yearly scale) in the future period from the historical period, respectively, at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice versa for temperature. Most of the models have indicated considerate positive change in quantiles and percentiles for both precipitation and temperature. Wavelet analysis provided insights into possible explanation to changes in precipitation. Title: Spatial, Temporal and Frequency Based Climate change Assessment in Columbia River Basin Using Multi Downscaled-Scenarios Author: Rana, Arun, Moradkhani, Hamid Year: 2016 Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional scale. In the present work, we have analyzed the changes in precipitation and temperature for future scenario period of 2070–2099 with respect to historical period of 1970–2000 from statistically downscaled GCM projections in Columbia River Basin (CRB). Analysis is performed using two different statistically downscaled climate projections (with ten GCMs downscaled products each, for RCP 4.5 and RCP 8.5, from CMIP5 dataset) namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. The two datasets for BCSD and MACA are downscaled from observed data for both scenarios projections i.e. RCP4.5 and RCP8.5. Analysis is performed using spatial change (yearly scale), temporal change (monthly scale), percentile change (seasonal scale), quantile change (yearly scale), and wavelet analysis (yearly scale) in the future period from the historical period, respectively, at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice versa for temperature. Most of the models have indicated considerate positive change in quantiles and percentiles for both precipitation and temperature. Wavelet analysis provided insights into possible explanation to changes in precipitation. Close

• 3. [Article] Multi-Criteria Evaluation of CMIP5 GCMs for Climate Change Impact Analysis

  Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. ...

  Citation × Citation Citation Abstract Copy Title: Multi-Criteria Evaluation of CMIP5 GCMs for Climate Change Impact Analysis Author: Ahmadalipour, Ali, Rana, Arun, Moradkhani, Hamid, Sharma, Ashish Year: 2015 Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area. Title: Multi-Criteria Evaluation of CMIP5 GCMs for Climate Change Impact Analysis Author: Ahmadalipour, Ali, Rana, Arun, Moradkhani, Hamid, Sharma, Ashish Year: 2015 Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area. Close