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• 1. [Article] Investigating the Impact of Remotely Sensed Precipitation and Hydrologic Model Uncertainties on the Ensemble Streamflow Forecasting

  In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies ...

  Citation × Citation Citation Abstract Copy Title: Investigating the Impact of Remotely Sensed Precipitation and Hydrologic Model Uncertainties on the Ensemble Streamflow Forecasting Author: Moradkhani, Hamid, Hsu, K., Hong, Y., Sorooshian, S. Year: 2006 In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measures are used to quantify the quality of ensemble prediction. Title: Investigating the Impact of Remotely Sensed Precipitation and Hydrologic Model Uncertainties on the Ensemble Streamflow Forecasting Author: Moradkhani, Hamid, Hsu, K., Hong, Y., Sorooshian, S. Year: 2006 In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measures are used to quantify the quality of ensemble prediction. Close

• 2. [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

• 3. [Article] Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response

  The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence ...

  Citation × Citation Citation Abstract Copy Title: Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response Author: Moradkhani, Hamid, Hong, Yang, Sorooshian, Soroosh Year: 2006 The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5ø ? 5ø latitude-longitude grids shows about 12?16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval. Title: Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response Author: Moradkhani, Hamid, Hong, Yang, Sorooshian, Soroosh Year: 2006 The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5ø ? 5ø latitude-longitude grids shows about 12?16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval. Close

• 4. [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

• 5. [Article] Towards an Improved Ensemble Precipitation Forecast: A Probabilistic Post-Processing Approach

  Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble ...

  Citation × Citation Citation Abstract Copy Title: Towards an Improved Ensemble Precipitation Forecast: A Probabilistic Post-Processing Approach Author: Khajehei, Sepideh, Moradkhani, Hamid Year: 2017 Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons. Title: Towards an Improved Ensemble Precipitation Forecast: A Probabilistic Post-Processing Approach Author: Khajehei, Sepideh, Moradkhani, Hamid Year: 2017 Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons. Close

• 6. [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

• 7. [Article] Understanding the Joint Behavior of Temperature and Precipitation for Climate Change Impact Studies

  The multiple downscaled-scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic ...

  Citation × Citation Citation Abstract Copy Title: Understanding the Joint Behavior of Temperature and Precipitation for Climate Change Impact Studies Author: Rana, Arun, Moradkhani, Hamid, Qin, Yueyue Year: 2016 The multiple downscaled-scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two, and thus in-turn help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 Global Climate Models (GCMs) downscaled product from CMIP5 daily dataset, using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends varies, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins. Title: Understanding the Joint Behavior of Temperature and Precipitation for Climate Change Impact Studies Author: Rana, Arun, Moradkhani, Hamid, Qin, Yueyue Year: 2016 The multiple downscaled-scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two, and thus in-turn help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 Global Climate Models (GCMs) downscaled product from CMIP5 daily dataset, using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends varies, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins. Close

• 8. [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

• 9. [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

• 10. [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

• 11. [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

• 12. [Article] Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States

  In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated ...

  Citation × Citation Citation Abstract Copy Title: Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States Author: Risley, John, Moradkhani, Hamid, Hay, Lauren, Markstrom, Steve Year: 2011 In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov?Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. Title: Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States Author: Risley, John, Moradkhani, Hamid, Hay, Lauren, Markstrom, Steve Year: 2011 In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov?Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. Close

• 13. [Article] Assessment of Climate Change Impacts over the Willamette River Basin Using NARCCAP Dynamically Downscaled Datasets

  Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR.

  Citation × Citation Citation Abstract Copy Title: Assessment of Climate Change Impacts over the Willamette River Basin Using NARCCAP Dynamically Downscaled Datasets Author: Halmstad, Andrew, Moradkhani, Hamid, Najafi, Mohammad Reza Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR. Title: Assessment of Climate Change Impacts over the Willamette River Basin Using NARCCAP Dynamically Downscaled Datasets Author: Halmstad, Andrew, Moradkhani, Hamid, Najafi, Mohammad Reza Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR. Close

• 14. [Article] Atmospheric Circulation Patterns Explaining Climatological Drought Dynamics in the Boreal Environment of Finland, 1962-2011

  Understanding the atmospheric circulation patterns (ACPs) influencing drought development is important in management of this spatially extensive and recurrently prolonged natural hazard. Using the Standardized ...

  Citation × Citation Citation Abstract Copy Title: Atmospheric Circulation Patterns Explaining Climatological Drought Dynamics in the Boreal Environment of Finland, 1962-2011 Author: Irannezhad, Masoud, Ahmadi, Behzad, Kløve, Bjørn, Moradkhani, Hamid Year: 2017 Understanding the atmospheric circulation patterns (ACPs) influencing drought development is important in management of this spatially extensive and recurrently prolonged natural hazard. Using the Standardized Precipitation Evapotranspiration Index (SPEI), this study evaluates the spatio-temporal variability in climatological drought dynamics (in terms of intensity, frequency, duration, and extension) in Finland in 1962–2011. The SPEI values are calculated in 12-, 3-, and 1-month time steps (SPEI-12, -3, and -1, respectively) and the analyses are based on annual (SPEI-12Ann), seasonal (e.g. SPEI-3Win for winter), and monthly (e.g. SPEI-1Dec for December) time scales. The relationships between SPEI values and well-known ACPs of climate variability over Finland are also investigated. Our analysis shows that on national scale, the longest observed drought is 12 months (October 2002–September 2003) observed in SPEI-12 values. Droughts intensify in April, August, and September, and largely occur over the south, centre, east, and west of Finland. December has the highest frequency of droughts, but with significant wetting trends. Analysis of annual SPEI-12 indicates an increase in the probability of drought extent over time. In warm months (April–September), influential ACPs for temperature variability and drought conditions are mainly the Arctic Oscillation (AO) and the Scandinavia pattern. For the cold months (October–March), the AO, the North Atlantic Oscillation, the East Atlantic, and the Polar patterns influence precipitation deficit and SPEI. Although Finland has experienced wetting trends during the period 1962–2011, this study provides a detailed spatial assessment of climatological drought variability and the plausible atmospheric circulation drivers for the boreal environment of Finland in recent decades. Title: Atmospheric Circulation Patterns Explaining Climatological Drought Dynamics in the Boreal Environment of Finland, 1962-2011 Author: Irannezhad, Masoud, Ahmadi, Behzad, Kløve, Bjørn, Moradkhani, Hamid Year: 2017 Understanding the atmospheric circulation patterns (ACPs) influencing drought development is important in management of this spatially extensive and recurrently prolonged natural hazard. Using the Standardized Precipitation Evapotranspiration Index (SPEI), this study evaluates the spatio-temporal variability in climatological drought dynamics (in terms of intensity, frequency, duration, and extension) in Finland in 1962–2011. The SPEI values are calculated in 12-, 3-, and 1-month time steps (SPEI-12, -3, and -1, respectively) and the analyses are based on annual (SPEI-12Ann), seasonal (e.g. SPEI-3Win for winter), and monthly (e.g. SPEI-1Dec for December) time scales. The relationships between SPEI values and well-known ACPs of climate variability over Finland are also investigated. Our analysis shows that on national scale, the longest observed drought is 12 months (October 2002–September 2003) observed in SPEI-12 values. Droughts intensify in April, August, and September, and largely occur over the south, centre, east, and west of Finland. December has the highest frequency of droughts, but with significant wetting trends. Analysis of annual SPEI-12 indicates an increase in the probability of drought extent over time. In warm months (April–September), influential ACPs for temperature variability and drought conditions are mainly the Arctic Oscillation (AO) and the Scandinavia pattern. For the cold months (October–March), the AO, the North Atlantic Oscillation, the East Atlantic, and the Polar patterns influence precipitation deficit and SPEI. Although Finland has experienced wetting trends during the period 1962–2011, this study provides a detailed spatial assessment of climatological drought variability and the plausible atmospheric circulation drivers for the boreal environment of Finland in recent decades. Close

• 15. [Article] Improving the Characterization of Initial Condition for Ensemble Streamflow Prediction Using Data Assimilation

  Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and ...

  Citation × Citation Citation Abstract Copy Title: Improving the Characterization of Initial Condition for Ensemble Streamflow Prediction Using Data Assimilation Author: DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2011 Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations. Title: Improving the Characterization of Initial Condition for Ensemble Streamflow Prediction Using Data Assimilation Author: DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2011 Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations. Close

• 16. [Article] Analysis of Dam-Induced Cyclic Patterns on River Flow Dynamics

  This paper investigates the impact of the Hungry Horse Dam on streamflow dynamics in the South Fork of the Flathead River, Montana, USA. To this end, pre- and post-dam periods of raw and naturalized streamflow ...

  Citation × Citation Citation Abstract Copy Title: Analysis of Dam-Induced Cyclic Patterns on River Flow Dynamics Author: Tongal, Hakan, Demirel, Mehmet C., Moradkhani, Hamid Year: 2016 This paper investigates the impact of the Hungry Horse Dam on streamflow dynamics in the South Fork of the Flathead River, Montana, USA. To this end, pre- and post-dam periods of raw and naturalized streamflow data were analysed. Pettitt’s change point analysis indicated a significant change point in streamflow dynamics due to dam construction. Complexities in the pre- and post-dam periods were evaluated by sample and multi-scale entropy analyses, and the entropies of the post-dam period were found to be higher than those of the pre-dam period. Possible reasons for this, unrelated to the natural hydrological cycle caused by the dam, were analysed using wavelet analyses. The wavelet analyses showed a clear change in the phase relationship between precipitation and streamflow. Finally, weak positive trends found in the hydrological variables indicated the effects of human activities (e.g. dam construction). The results also revealed distorted lead times, which can improve the streamflow forecasts for different lead times. Title: Analysis of Dam-Induced Cyclic Patterns on River Flow Dynamics Author: Tongal, Hakan, Demirel, Mehmet C., Moradkhani, Hamid Year: 2016 This paper investigates the impact of the Hungry Horse Dam on streamflow dynamics in the South Fork of the Flathead River, Montana, USA. To this end, pre- and post-dam periods of raw and naturalized streamflow data were analysed. Pettitt’s change point analysis indicated a significant change point in streamflow dynamics due to dam construction. Complexities in the pre- and post-dam periods were evaluated by sample and multi-scale entropy analyses, and the entropies of the post-dam period were found to be higher than those of the pre-dam period. Possible reasons for this, unrelated to the natural hydrological cycle caused by the dam, were analysed using wavelet analyses. The wavelet analyses showed a clear change in the phase relationship between precipitation and streamflow. Finally, weak positive trends found in the hydrological variables indicated the effects of human activities (e.g. dam construction). The results also revealed distorted lead times, which can improve the streamflow forecasts for different lead times. Close

• 17. [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

• 18. [Article] Multi-Model Ensemble Analysis of Runoff Extremes for Climate Change Impact Assessments

  In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). ...

  Citation × Citation Citation Abstract Copy Title: Multi-Model Ensemble Analysis of Runoff Extremes for Climate Change Impact Assessments Author: Najafi, Mohammad Reza, Moradkhani, Hamid Year: 2015 In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades. Title: Multi-Model Ensemble Analysis of Runoff Extremes for Climate Change Impact Assessments Author: Najafi, Mohammad Reza, Moradkhani, Hamid Year: 2015 In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades. Close

• 19. [Article] A Regional Bayesian Hierarchical Model for Flood Frequency Analysis

  In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying ...

  Citation × Citation Citation Abstract Copy Title: A Regional Bayesian Hierarchical Model for Flood Frequency Analysis Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2015 In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the Bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical Bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the Bayesian hierarchical model, we add a new layer referred to as the “L-moments layer”. The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and Bayesian methods. A standard process of covariates selection is also proposed in the Bayesian hierarchical model. The performance of the Bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management. Title: A Regional Bayesian Hierarchical Model for Flood Frequency Analysis Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2015 In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the Bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical Bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the Bayesian hierarchical model, we add a new layer referred to as the “L-moments layer”. The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and Bayesian methods. A standard process of covariates selection is also proposed in the Bayesian hierarchical model. The performance of the Bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management. Close

• 20. [Article] Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree

  Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. ...

  Citation × Citation Citation Abstract Copy Title: Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree Author: Sahraei, Shahram, Andalani, Saeed Zare, Zakermoshfegh, Mohammad, Sisakht, Babak Nikeghbal, Talebbeydokhti, Nasser, Moradkhani, Hamid Year: 2015 Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. In the current study, two newly developed models called Least Square Support Vector Regression (LSSVR) and Regression Tree (RT) are used. The LSSVR model is based on the constrained optimization method and applies structural risk minimization in order to yield a general optimized result. Also in the RT, data movement is based on laws discovered in the tree. Both models have been applied to the data in the Kashkan watershed. Variables include (a) recorded precipitation values in the Kashkan watershed stations, and (b) outlet discharge values of one and two previous days. Present discharge is considered as output of the two models. Following that, a sensitivity analysis has been carried out on the input features and less important features has been diminished so that both models have provided better prediction on the data. The final results of both models have been compared. It was found that the LSSVR model has better performance. Finally, the results present these models as a suitable models in river flow forecasting. Title: Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree Author: Sahraei, Shahram, Andalani, Saeed Zare, Zakermoshfegh, Mohammad, Sisakht, Babak Nikeghbal, Talebbeydokhti, Nasser, Moradkhani, Hamid Year: 2015 Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. In the current study, two newly developed models called Least Square Support Vector Regression (LSSVR) and Regression Tree (RT) are used. The LSSVR model is based on the constrained optimization method and applies structural risk minimization in order to yield a general optimized result. Also in the RT, data movement is based on laws discovered in the tree. Both models have been applied to the data in the Kashkan watershed. Variables include (a) recorded precipitation values in the Kashkan watershed stations, and (b) outlet discharge values of one and two previous days. Present discharge is considered as output of the two models. Following that, a sensitivity analysis has been carried out on the input features and less important features has been diminished so that both models have provided better prediction on the data. The final results of both models have been compared. It was found that the LSSVR model has better performance. Finally, the results present these models as a suitable models in river flow forecasting. Close

• 21. [Article] Water Supply, Demand, and Quality Indicators for Assessing the Spatial Distribution of Water Resource Vulnerability in the Columbia River Basin

  We investigated water resource vulnerability in the US portion of the Columbia River basin (CRB) using multiple indicators representing water supply, water demand, and water quality. Based on the US county ...

  Citation × Citation Citation Abstract Copy Title: Water Supply, Demand, and Quality Indicators for Assessing the Spatial Distribution of Water Resource Vulnerability in the Columbia River Basin Author: Chang, Heejun, Jung, Il-Won, Strecker, Angela L., Wise, Daniel, Lafrenz, Martin, Shandas, Vivek, Moradkhani, Hamid, Yeakley, J. Alan, Pan, Yangdong, Bean, Robert Allen, Johnson, Gunnar, Psaris, Mike Year: 2013 We investigated water resource vulnerability in the US portion of the Columbia River basin (CRB) using multiple indicators representing water supply, water demand, and water quality. Based on the US county scale, spatial analysis was conducted using various biophysical and socio-economic indicators that control water vulnerability. Water supply vulnerability and water demand vulnerability exhibited a similar spatial clustering of hotspots in areas where agricultural lands and variability of precipitation were high but dam storage capacity was low. The hotspots of water quality vulnerability were clustered around the main stem of the Columbia River where major population and agricultural centres are located. This multiple equal weight indicator approach confirmed that different drivers were associated with different vulnerability maps in the sub-basins of the CRB. Water quality variables are more important than water supply and water demand variables in the Willamette River basin, whereas water supply and demand variables are more important than water quality variables in the Upper Snake and Upper Columbia River basins. This result suggests that current water resources management and practices drive much of the vulnerability within the study area. The analysis suggests the need for increased coordination of water management across multiple levels of water governance to reduce water resource vulnerability in the CRB and a potentially different weighting scheme that explicitly takes into account the input of various water stakeholders. Title: Water Supply, Demand, and Quality Indicators for Assessing the Spatial Distribution of Water Resource Vulnerability in the Columbia River Basin Author: Chang, Heejun, Jung, Il-Won, Strecker, Angela L., Wise, Daniel, Lafrenz, Martin, Shandas, Vivek, Moradkhani, Hamid, Yeakley, J. Alan, Pan, Yangdong, Bean, Robert Allen, Johnson, Gunnar, Psaris, Mike Year: 2013 We investigated water resource vulnerability in the US portion of the Columbia River basin (CRB) using multiple indicators representing water supply, water demand, and water quality. Based on the US county scale, spatial analysis was conducted using various biophysical and socio-economic indicators that control water vulnerability. Water supply vulnerability and water demand vulnerability exhibited a similar spatial clustering of hotspots in areas where agricultural lands and variability of precipitation were high but dam storage capacity was low. The hotspots of water quality vulnerability were clustered around the main stem of the Columbia River where major population and agricultural centres are located. This multiple equal weight indicator approach confirmed that different drivers were associated with different vulnerability maps in the sub-basins of the CRB. Water quality variables are more important than water supply and water demand variables in the Willamette River basin, whereas water supply and demand variables are more important than water quality variables in the Upper Snake and Upper Columbia River basins. This result suggests that current water resources management and practices drive much of the vulnerability within the study area. The analysis suggests the need for increased coordination of water management across multiple levels of water governance to reduce water resource vulnerability in the CRB and a potentially different weighting scheme that explicitly takes into account the input of various water stakeholders. Close