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• 1. [Article] Accounting For Downscaling and Model Uncertainty in Fine-Resolution Seasonal Climate Projections Over the Columbia River Basin

  Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future ...

  Citation × Citation Citation Abstract Copy Title: Accounting For Downscaling and Model Uncertainty in Fine-Resolution Seasonal Climate Projections Over the Columbia River Basin Author: Ahmadalipour, Ali, Moradkhani, Hamid, Rana, Arun Year: 2017 Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970–2000 as well as future period of 2010–2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation. © 2017 Springer-Verlag Berlin Heidelberg Title: Accounting For Downscaling and Model Uncertainty in Fine-Resolution Seasonal Climate Projections Over the Columbia River Basin Author: Ahmadalipour, Ali, Moradkhani, Hamid, Rana, Arun Year: 2017 Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970–2000 as well as future period of 2010–2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation. © 2017 Springer-Verlag Berlin Heidelberg Close

• 2. [Article] A Comparative Assessment of Projected Meteorological and Hydrological Droughts: Elucidating the Role of Temperature

  The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal ...

  Citation × Citation Citation Abstract Copy Title: A Comparative Assessment of Projected Meteorological and Hydrological Droughts: Elucidating the Role of Temperature Author: Ahmadalipour, Ali, Moradkhani, Hamid, Demirel, Mehmet C. Year: 2017 The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal characteristics of droughts and their future attributes over the Willamette River Basin (WRB) in the Pacific Northwest U.S. The analysis is performed using two subsets of downscaled CMIP5 global climate models (GCMs) each consisting of 10 models from two future scenarios (RCP4.5 and RCP8.5) for 30 years of historical period (1970–1999) and 90 years of future projections (2010–2099). Hydrologic modeling is conducted using the Precipitation Runoff Modeling System (PRMS) as a robust distributed hydrologic model with lower computational cost compared to other models. Meteorological and hydrological droughts are studied using three drought indices (i.e. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Streamflow Index). Results reveal that the intensity and duration of hydrological droughts are expected to increase over the WRB, albeit the annual precipitation is expected to increase. On the other hand, the intensity of meteorological droughts do not indicate an aggravation for most cases. We explore the changes of hydrometeolorogical variables over the basin in order to understand the causes for such differences and to discover the controlling factors of drought. Furthermore, the uncertainty of projections are quantified for model, scenario, and downscaling uncertainty. Title: A Comparative Assessment of Projected Meteorological and Hydrological Droughts: Elucidating the Role of Temperature Author: Ahmadalipour, Ali, Moradkhani, Hamid, Demirel, Mehmet C. Year: 2017 The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal characteristics of droughts and their future attributes over the Willamette River Basin (WRB) in the Pacific Northwest U.S. The analysis is performed using two subsets of downscaled CMIP5 global climate models (GCMs) each consisting of 10 models from two future scenarios (RCP4.5 and RCP8.5) for 30 years of historical period (1970–1999) and 90 years of future projections (2010–2099). Hydrologic modeling is conducted using the Precipitation Runoff Modeling System (PRMS) as a robust distributed hydrologic model with lower computational cost compared to other models. Meteorological and hydrological droughts are studied using three drought indices (i.e. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Streamflow Index). Results reveal that the intensity and duration of hydrological droughts are expected to increase over the WRB, albeit the annual precipitation is expected to increase. On the other hand, the intensity of meteorological droughts do not indicate an aggravation for most cases. We explore the changes of hydrometeolorogical variables over the basin in order to understand the causes for such differences and to discover the controlling factors of drought. Furthermore, the uncertainty of projections are quantified for model, scenario, and downscaling uncertainty. Close

• 3. [Article] Centennial Drought Outlook Over the CONUS Using NASA-NEX Downscaled Climate Ensemble

  Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate ...

  Citation × Citation Citation Abstract Copy Title: Centennial Drought Outlook Over the CONUS Using NASA-NEX Downscaled Climate Ensemble Author: Ahmadalipour, Ali, Moradkhani, Hamid, Svoboda, Mark Year: 2017 Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate in various regions in future. In this study, the impact of climate change on drought characteristics is assessed, and statistical methods are employed to analyse the significance of projections. This is the first study utilizing 21 recently available downscaled global climate models generated by NASA (NEX-GDDP) to evaluate drought projections over various regions across the United States. Drought is investigated through a multi-model dual-index dual-scenario approach to probabilistically analyse drought attributes while characterizing the uncertainty in future drought projections. Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index values at the seasonal scale (3 months) are used to project and analyse meteorological drought conditions from 1950 to 2099 at 0.25° spatial resolution. Two future concentration pathways of RCP4.5 and RCP8.5 are considered for this analysis. Accounting for the combined effects of precipitation and temperature variations reveals a considerable aggravation in severity and extent of future drought in the western United States and a tendency toward more frequent and intense summer droughts across the Contiguous United States. Title: Centennial Drought Outlook Over the CONUS Using NASA-NEX Downscaled Climate Ensemble Author: Ahmadalipour, Ali, Moradkhani, Hamid, Svoboda, Mark Year: 2017 Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate in various regions in future. In this study, the impact of climate change on drought characteristics is assessed, and statistical methods are employed to analyse the significance of projections. This is the first study utilizing 21 recently available downscaled global climate models generated by NASA (NEX-GDDP) to evaluate drought projections over various regions across the United States. Drought is investigated through a multi-model dual-index dual-scenario approach to probabilistically analyse drought attributes while characterizing the uncertainty in future drought projections. Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index values at the seasonal scale (3 months) are used to project and analyse meteorological drought conditions from 1950 to 2099 at 0.25° spatial resolution. Two future concentration pathways of RCP4.5 and RCP8.5 are considered for this analysis. Accounting for the combined effects of precipitation and temperature variations reveals a considerable aggravation in severity and extent of future drought in the western United States and a tendency toward more frequent and intense summer droughts across the Contiguous United States. Close

• 4. [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