Search

Search Results

• 1. [Article] Century-Long Variability and Trends in Daily Precipitation Characteristics at Three Finnish Stations

  Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records ...

  Citation × Citation Citation Abstract Copy Title: Century-Long Variability and Trends in Daily Precipitation Characteristics at Three Finnish Stations Author: Irannezhad, Masoud, Marttila, Hannu, De-liang, Chen, Kløve, Bjørn Year: 2016 Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records during 1908－2008 from 3 meteorological stations in the south (Kaisaniemi), center (Kajaani) and north (Sodankylä). Although precipitation days in northern part were more frequent than in central and southern parts, daily precipitation intensity in the south was generally higher than those in the centre and north of the country. Annual sum of very light precipitation (0 mm < daily precipitation ≤ long-term 50th percentile of daily precipitation more than 0 mm) significantly (p Title: Century-Long Variability and Trends in Daily Precipitation Characteristics at Three Finnish Stations Author: Irannezhad, Masoud, Marttila, Hannu, De-liang, Chen, Kløve, Bjørn Year: 2016 Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records during 1908－2008 from 3 meteorological stations in the south (Kaisaniemi), center (Kajaani) and north (Sodankylä). Although precipitation days in northern part were more frequent than in central and southern parts, daily precipitation intensity in the south was generally higher than those in the centre and north of the country. Annual sum of very light precipitation (0 mm < daily precipitation ≤ long-term 50th percentile of daily precipitation more than 0 mm) significantly (p Close

• 2. [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

• 3. [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

• 4. [Article] Detecting Change in Rainstorm Properties from 1977-2016 and Associated Future Flood Risks in Portland, Oregon

  In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these ...

  Citation × Citation Citation Abstract Copy Title: Detecting Change in Rainstorm Properties from 1977-2016 and Associated Future Flood Risks in Portland, Oregon Author: Cooley, Alexis Kirsten Year: 2017 In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these characteristics is facilitated by common indices to measure precipitation and temperature developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can be used to describe the likely consequences of climate change such as increased daily precipitation intensity (SDII) and heavier rainfall events (R95p). This study calculates a subset of these indices from observed and modelled precipitation data in Portland, Oregon. Five rainfall gages from a high resolution rain gage network and projections from three downscaled global climate models including CanESM2, CESM1, CNRM-CM5 are used to calculate precipitation indices. Mann-Kendall's tau is used to detect monotonic trends in indices. The observational record is compared with models for the historic period (1977-2005) and these past trends are compared with projected future trends (2006-2100). The influence of study unit on trend detection is analyzed by computing trends at the annual and monthly scale. Study unit is show to be important for trend detection. When the annual study unit is used, projected future trends towards increased precipitation intensity and event volumes are not observed in the historic data. However, when analyzed with a monthly study unit, trends towards increased precipitation intensity and event volumes are observed in the historic data. These trends are shown to be important for Portland area flooding, as precipitation indices are shown to significantly correlate with 40 maximum peak flow events that occurred during the period of study. Title: Detecting Change in Rainstorm Properties from 1977-2016 and Associated Future Flood Risks in Portland, Oregon Author: Cooley, Alexis Kirsten Year: 2017 In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these characteristics is facilitated by common indices to measure precipitation and temperature developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can be used to describe the likely consequences of climate change such as increased daily precipitation intensity (SDII) and heavier rainfall events (R95p). This study calculates a subset of these indices from observed and modelled precipitation data in Portland, Oregon. Five rainfall gages from a high resolution rain gage network and projections from three downscaled global climate models including CanESM2, CESM1, CNRM-CM5 are used to calculate precipitation indices. Mann-Kendall's tau is used to detect monotonic trends in indices. The observational record is compared with models for the historic period (1977-2005) and these past trends are compared with projected future trends (2006-2100). The influence of study unit on trend detection is analyzed by computing trends at the annual and monthly scale. Study unit is show to be important for trend detection. When the annual study unit is used, projected future trends towards increased precipitation intensity and event volumes are not observed in the historic data. However, when analyzed with a monthly study unit, trends towards increased precipitation intensity and event volumes are observed in the historic data. These trends are shown to be important for Portland area flooding, as precipitation indices are shown to significantly correlate with 40 maximum peak flow events that occurred during the period of study. Close

• 5. [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

• 6. [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

• 7. [Article] The Relationship Between Precipitation and Temperature Over the Contiguous United States

  The correlation between monthly total precipitation and monthly mean temperature over the 80-year period from 1905 to 1984 at nearly 1000 stations in the contiguous United States was calculated and analyzed. ...

  Citation × Citation Citation Abstract Copy Title: The Relationship Between Precipitation and Temperature Over the Contiguous United States Author: Zhao, Weining, Khalil, M. A. K. Year: 1993 The correlation between monthly total precipitation and monthly mean temperature over the 80-year period from 1905 to 1984 at nearly 1000 stations in the contiguous United States was calculated and analyzed. Both local and field significances were tested by using statistical methods. Areas of both negative and positive precipitation-temperature correlations were found. Over most of the United States, summer precipitation and temperature were negatively correlated, which indicates that warm summers tended to be dryer. This is particularly true in the central and southern Great Plains. The area south of the Great Lakes covering the eastern portion of the Corn Belt was the only major area where a significant positive correlation was found in winter. Title: The Relationship Between Precipitation and Temperature Over the Contiguous United States Author: Zhao, Weining, Khalil, M. A. K. Year: 1993 The correlation between monthly total precipitation and monthly mean temperature over the 80-year period from 1905 to 1984 at nearly 1000 stations in the contiguous United States was calculated and analyzed. Both local and field significances were tested by using statistical methods. Areas of both negative and positive precipitation-temperature correlations were found. Over most of the United States, summer precipitation and temperature were negatively correlated, which indicates that warm summers tended to be dryer. This is particularly true in the central and southern Great Plains. The area south of the Great Lakes covering the eastern portion of the Corn Belt was the only major area where a significant positive correlation was found in winter. Close

• 8. [Article] Climate Change Impacts on Precipitation Extremes over the Columbia River Basin Based on Downscaled CMIP5 Climate Scenarios

  Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, ...

  Citation × Citation Citation Abstract Copy Title: Climate Change Impacts on Precipitation Extremes over the Columbia River Basin Based on Downscaled CMIP5 Climate Scenarios Author: Dars, Ghulam Hussain Year: 2013 Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, forecasting floods and an effective decision making in the field of water resources design and management. The study evaluates extreme precipitation events over the Columbia River Basin (CRB), the fourth largest basin in the U.S., by simulating four CMIP5 global climate models (GCMs) for the historical period (1970-1999) and future period (2041-2070) under RCP85 GHG scenario. We estimated the intensity of extreme and average precipitation for both winter (DJF) and summer (JJA) seasons by using the GEV distribution and multi-model ensemble average over the domain of the Columbia River Basin. The four CMIP5 models performed very well at simulating precipitation extremes in the winter season. The CMIP5 climate models showed heterogeneous spatial pattern of summer extreme precipitation over the CRB for the future period. It was noticed that multi-model ensemble mean outperformed compared to the individual performance of climate models for both seasons. We have found that the multi-model ensemble shows a consistent and significant increase in the extreme precipitation events in the west of the Cascades Range, Coastal Ranges of Oregon and Washington State, the Canadian portion of the basin and over the Rocky Mountains. However, the mean precipitation is projected to decrease in both winter and summer seasons in the future period. The Columbia River is dominated by the glacial snowmelt, so the increase in the intensity of extreme precipitation and decrease in mean precipitation in the future period, as simulated by four CMIP5 models, is expected to aggravate the earlier snowmelt and contribute to the flooding in the low lying areas especially in the west of the Cascades Range. In addition, the climate change shift could have serious implications on transboundary water issues in between the United States and Canada. Therefore, adaptation strategies should be devised to cope the possible adverse effects of the changing the future climate so that it could have minimal influence on hydrology, agriculture, aquatic species, hydro-power generation, human health and other water related infrastructure. Title: Climate Change Impacts on Precipitation Extremes over the Columbia River Basin Based on Downscaled CMIP5 Climate Scenarios Author: Dars, Ghulam Hussain Year: 2013 Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, forecasting floods and an effective decision making in the field of water resources design and management. The study evaluates extreme precipitation events over the Columbia River Basin (CRB), the fourth largest basin in the U.S., by simulating four CMIP5 global climate models (GCMs) for the historical period (1970-1999) and future period (2041-2070) under RCP85 GHG scenario. We estimated the intensity of extreme and average precipitation for both winter (DJF) and summer (JJA) seasons by using the GEV distribution and multi-model ensemble average over the domain of the Columbia River Basin. The four CMIP5 models performed very well at simulating precipitation extremes in the winter season. The CMIP5 climate models showed heterogeneous spatial pattern of summer extreme precipitation over the CRB for the future period. It was noticed that multi-model ensemble mean outperformed compared to the individual performance of climate models for both seasons. We have found that the multi-model ensemble shows a consistent and significant increase in the extreme precipitation events in the west of the Cascades Range, Coastal Ranges of Oregon and Washington State, the Canadian portion of the basin and over the Rocky Mountains. However, the mean precipitation is projected to decrease in both winter and summer seasons in the future period. The Columbia River is dominated by the glacial snowmelt, so the increase in the intensity of extreme precipitation and decrease in mean precipitation in the future period, as simulated by four CMIP5 models, is expected to aggravate the earlier snowmelt and contribute to the flooding in the low lying areas especially in the west of the Cascades Range. In addition, the climate change shift could have serious implications on transboundary water issues in between the United States and Canada. Therefore, adaptation strategies should be devised to cope the possible adverse effects of the changing the future climate so that it could have minimal influence on hydrology, agriculture, aquatic species, hydro-power generation, human health and other water related infrastructure. Close

• 9. [Article] Assessing Hydrologic and Water Quality Sensitivities to Precipitation Changes, Urban Growth and Land Management Using SWAT

  Precipitation changes and urban growth are two factors altering the state of water quality. Changes in precipitation will alter the amount and timing of flows, and the corresponding sediment and nutrient ...

  Citation × Citation Citation Abstract Copy Title: Assessing Hydrologic and Water Quality Sensitivities to Precipitation Changes, Urban Growth and Land Management Using SWAT Author: Psaris, Alexander Michael Year: 2014 Precipitation changes and urban growth are two factors altering the state of water quality. Changes in precipitation will alter the amount and timing of flows, and the corresponding sediment and nutrient dynamics. Meanwhile, densification associated with urban growth will create more impervious surfaces which will alter sediment and nutrient loadings. Land and water managers often rely on models to develop possible future scenarios and devise management responses to these projected changes. We use the Soil and Water Assessment Tool (SWAT) to assess the sensitivities of stream flow, sediment, and nutrient loads in two urbanizing watersheds in Northwest Oregon, USA to various climate and urbanization scenarios. We evaluate the spatial patterns climate change and urban growth will have on water, sediment and nutrient yields. We also identify critical source areas (CSAs) and investigate how implementation of vegetative filter strips (VFS) could ameliorate the effects of these changes. Our findings suggest that: 1) Water yield is tightly coupled to precipitation. 2) Large increases in winter and spring precipitation provide enough sub-surface storage to increase summertime water yields despite a moderate decrease in summer precipitation. 3) Expansion of urban areas increases surface runoff and has mixed effects on sediment and nutrients. 4) Implementation of VFS reduces pollutant loads helping overall watershed health. This research demonstrates the usefulness of SWAT in facilitating informed land and water management decisions. Title: Assessing Hydrologic and Water Quality Sensitivities to Precipitation Changes, Urban Growth and Land Management Using SWAT Author: Psaris, Alexander Michael Year: 2014 Precipitation changes and urban growth are two factors altering the state of water quality. Changes in precipitation will alter the amount and timing of flows, and the corresponding sediment and nutrient dynamics. Meanwhile, densification associated with urban growth will create more impervious surfaces which will alter sediment and nutrient loadings. Land and water managers often rely on models to develop possible future scenarios and devise management responses to these projected changes. We use the Soil and Water Assessment Tool (SWAT) to assess the sensitivities of stream flow, sediment, and nutrient loads in two urbanizing watersheds in Northwest Oregon, USA to various climate and urbanization scenarios. We evaluate the spatial patterns climate change and urban growth will have on water, sediment and nutrient yields. We also identify critical source areas (CSAs) and investigate how implementation of vegetative filter strips (VFS) could ameliorate the effects of these changes. Our findings suggest that: 1) Water yield is tightly coupled to precipitation. 2) Large increases in winter and spring precipitation provide enough sub-surface storage to increase summertime water yields despite a moderate decrease in summer precipitation. 3) Expansion of urban areas increases surface runoff and has mixed effects on sediment and nutrients. 4) Implementation of VFS reduces pollutant loads helping overall watershed health. This research demonstrates the usefulness of SWAT in facilitating informed land and water management decisions. Close

• 10. [Article] Application of Tree-Structured Regression for Regional Precipitation Prediction Using General Circulation Model Output

  This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns ...

  Citation × Citation Citation Abstract Copy Title: Application of Tree-Structured Regression for Regional Precipitation Prediction Using General Circulation Model Output Author: Li, Xiangshang, Sailor, David J. Year: 2000 This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of 2 US sites: San Francisco, California, and San Antonio, Texas. The average correlation between observed and simulated daily precipitation data series is 0.75 for the training set and 0.68 for the validation set. Relative humidity was found to be the dominant variable in these TSR models. Output from an NCAR CSM (climate system model) transient simulation of climate change were then used to drive the TSR models in the prediction of precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the San Antonio site. Specifically, the CSM missed the annual trends in humidity for the grid cell containing this site. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. Title: Application of Tree-Structured Regression for Regional Precipitation Prediction Using General Circulation Model Output Author: Li, Xiangshang, Sailor, David J. Year: 2000 This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of 2 US sites: San Francisco, California, and San Antonio, Texas. The average correlation between observed and simulated daily precipitation data series is 0.75 for the training set and 0.68 for the validation set. Relative humidity was found to be the dominant variable in these TSR models. Output from an NCAR CSM (climate system model) transient simulation of climate change were then used to drive the TSR models in the prediction of precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the San Antonio site. Specifically, the CSM missed the annual trends in humidity for the grid cell containing this site. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. Close

• 11. [Article] Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation

  The multi downscaled-scenario products allow us to better assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions ...

  Citation × Citation Citation Abstract Copy Title: Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation Author: Qin, Yueyue Year: 2015 The multi downscaled-scenario products allow us to better assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. In the present study, we have used multi-modelled statistically downscaled ensemble of precipitation and temperature variables. 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 PDFs of both precipitation and temperature is evaluated for summer (dry) and winter (wet) periods for 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. Results have indicated that the probabilistic distribution helps remove the limitations on marginal distributions of variables in question and helps in better prediction. 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 are varied, but certainly positive, for summer and winter time scales based on sub-basins. Dry season, generally, is indicating towards higher positive changes in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated by the joint precipitation and temperature, also indicates varied degree and forms during dry season whereas the wet season is rather constant across all the sub-basins. Title: Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation Author: Qin, Yueyue Year: 2015 The multi downscaled-scenario products allow us to better assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. In the present study, we have used multi-modelled statistically downscaled ensemble of precipitation and temperature variables. 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 PDFs of both precipitation and temperature is evaluated for summer (dry) and winter (wet) periods for 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. Results have indicated that the probabilistic distribution helps remove the limitations on marginal distributions of variables in question and helps in better prediction. 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 are varied, but certainly positive, for summer and winter time scales based on sub-basins. Dry season, generally, is indicating towards higher positive changes in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated by the joint precipitation and temperature, also indicates varied degree and forms during dry season whereas the wet season is rather constant across all the sub-basins. Close

• 12. [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

• 13. [Article] A Study of Oxygen Precipitation in Heavily Doped Silicon

  Gettering of impurities with oxygen precipitates is widely used during the fabrication of semiconductors to improve the performance and yield of the devices. Since the effectiveness of the gettering process ...

  Citation × Citation Citation Abstract Copy Title: A Study of Oxygen Precipitation in Heavily Doped Silicon Author: Graupner, Robert Kurt Year: 1989 Gettering of impurities with oxygen precipitates is widely used during the fabrication of semiconductors to improve the performance and yield of the devices. Since the effectiveness of the gettering process is largely dependent on the initial interstitial oxygen concentration, accurate measurements of this parameter are of considerable importance. Measurements of interstitial oxygen following thermal cycles are required for development of semiconductor fabrication processes and for research into the mechanisms of oxygen precipitate nucleation and growth. Efforts by industrial associations have led to the development of standard procedures for the measurement of interstitial oxygen in wafers. However practical oxygen measurements often do not satisfy the requirements of such standard procedures. An additional difficulty arises when the silicon wafer has a low resitivity (high dopant concentration). In such cases the infrared light used for the measurement is severely attenuated by the electrons of holes introduced by the dopant. Since such wafers are the substrates used for the production of widely used epitaxial wafers, this measurement problem is economically important. Alternative methods such as Secondary Ion Mass Spectroscopy or Gas Fusion Analysis have been developed to measure oxygen in these cases. However, neither of these methods is capable of distinguishing interstitial oxygen from precipitated oxygen as required for precipitation studies. In addition to the commercial interest in heavily doped silicon substrates, they are also of interest for research into the role of point defects in nucleation and precipitation processes. Despite considerable research effort, there is still disagreement concerning the type of point defect and its role in semiconductor processes. Studies of changes in the interstitial oxygen concentration of heavily doped and lightly doped silicon wafers could help clarify the role of point defects in oxygen nucleation and precipitation processes. This could lead to more effective control and use of oxygen precipitation for gettering. One of the principal purposes of this thesis is the extension of the infrared interstitial oxygen measurement technique to situations outside the measurement capacities of the standard technique. These situations include silicon slices exhibiting interfering precipitate absorption bands and heavily doped n-type silicon wafers. A new method is presented for correcting for the effect of multiple reflections in silicon wafers with optically rough surfaces. The technique for the measurement of interstitial oxygen in heavily doped n-type wafers is then used to perform a comparative study of oxygen precipitation in heavily antimony doped (.035 ohm-cm) silicon and lightly doped p-type silicon. A model is presented to quantitatively explain the observed suppression of defect formation in heavily doped n-type wafers. Title: A Study of Oxygen Precipitation in Heavily Doped Silicon Author: Graupner, Robert Kurt Year: 1989 Gettering of impurities with oxygen precipitates is widely used during the fabrication of semiconductors to improve the performance and yield of the devices. Since the effectiveness of the gettering process is largely dependent on the initial interstitial oxygen concentration, accurate measurements of this parameter are of considerable importance. Measurements of interstitial oxygen following thermal cycles are required for development of semiconductor fabrication processes and for research into the mechanisms of oxygen precipitate nucleation and growth. Efforts by industrial associations have led to the development of standard procedures for the measurement of interstitial oxygen in wafers. However practical oxygen measurements often do not satisfy the requirements of such standard procedures. An additional difficulty arises when the silicon wafer has a low resitivity (high dopant concentration). In such cases the infrared light used for the measurement is severely attenuated by the electrons of holes introduced by the dopant. Since such wafers are the substrates used for the production of widely used epitaxial wafers, this measurement problem is economically important. Alternative methods such as Secondary Ion Mass Spectroscopy or Gas Fusion Analysis have been developed to measure oxygen in these cases. However, neither of these methods is capable of distinguishing interstitial oxygen from precipitated oxygen as required for precipitation studies. In addition to the commercial interest in heavily doped silicon substrates, they are also of interest for research into the role of point defects in nucleation and precipitation processes. Despite considerable research effort, there is still disagreement concerning the type of point defect and its role in semiconductor processes. Studies of changes in the interstitial oxygen concentration of heavily doped and lightly doped silicon wafers could help clarify the role of point defects in oxygen nucleation and precipitation processes. This could lead to more effective control and use of oxygen precipitation for gettering. One of the principal purposes of this thesis is the extension of the infrared interstitial oxygen measurement technique to situations outside the measurement capacities of the standard technique. These situations include silicon slices exhibiting interfering precipitate absorption bands and heavily doped n-type silicon wafers. A new method is presented for correcting for the effect of multiple reflections in silicon wafers with optically rough surfaces. The technique for the measurement of interstitial oxygen in heavily doped n-type wafers is then used to perform a comparative study of oxygen precipitation in heavily antimony doped (.035 ohm-cm) silicon and lightly doped p-type silicon. A model is presented to quantitatively explain the observed suppression of defect formation in heavily doped n-type wafers. Close

• 14. [Article] Evaluating Hourly Rainfall Characteristics over the U.S. Great Plains in Dynamically Downscaled Climate Model Simulations using NASA-Unified WRF

  Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall ...

  Citation × Citation Citation Abstract Copy Title: Evaluating Hourly Rainfall Characteristics over the U.S. Great Plains in Dynamically Downscaled Climate Model Simulations using NASA-Unified WRF Author: Lee, Huikyo, Waliser, Duane E., Ferraro, Robert, Iguchi, Takamichi, Peters-Lidard, Christa D., Tian, Baijun, Loikith, Paul C., Wright, Daniel B. Year: 2017 Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12, and 24 km over the Great Plains of the United States. We also examined the sensitivity of the simulated precipitation to different spectral nudging approaches and the cumulus parameterizations. The rainfall characteristics in the observations and simulations were defined as an hourly diurnal cycle of precipitation and a joint probability distribution function (JPDF) between duration and peak intensity of precipitation events over the Great Plains in summer. We calculated a JPDF for each data set and the overlapping area between observed and simulated JPDFs to measure the similarity between the two JPDFs. Comparison of the diurnal precipitation cycles between observations and simulations does not reveal the added value of high-resolution simulations. However, the performance of NU-WRF simulations measured by the JPDF metric strongly depends on horizontal resolution. The simulation with the highest resolution of 4 km shows the best agreement with the observations in simulating duration and intensity of wet spells. Spectral nudging does not affect the JPDF significantly. The effect of cumulus parameterizations on the JPDFs is considerable but smaller than that of horizontal resolution. The simulations with lower resolutions of 12 and 24 km show reasonable agreement but only with the high-resolution observational data that are aggregated into coarse resolution and spatially averaged. Title: Evaluating Hourly Rainfall Characteristics over the U.S. Great Plains in Dynamically Downscaled Climate Model Simulations using NASA-Unified WRF Author: Lee, Huikyo, Waliser, Duane E., Ferraro, Robert, Iguchi, Takamichi, Peters-Lidard, Christa D., Tian, Baijun, Loikith, Paul C., Wright, Daniel B. Year: 2017 Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12, and 24 km over the Great Plains of the United States. We also examined the sensitivity of the simulated precipitation to different spectral nudging approaches and the cumulus parameterizations. The rainfall characteristics in the observations and simulations were defined as an hourly diurnal cycle of precipitation and a joint probability distribution function (JPDF) between duration and peak intensity of precipitation events over the Great Plains in summer. We calculated a JPDF for each data set and the overlapping area between observed and simulated JPDFs to measure the similarity between the two JPDFs. Comparison of the diurnal precipitation cycles between observations and simulations does not reveal the added value of high-resolution simulations. However, the performance of NU-WRF simulations measured by the JPDF metric strongly depends on horizontal resolution. The simulation with the highest resolution of 4 km shows the best agreement with the observations in simulating duration and intensity of wet spells. Spectral nudging does not affect the JPDF significantly. The effect of cumulus parameterizations on the JPDFs is considerable but smaller than that of horizontal resolution. The simulations with lower resolutions of 12 and 24 km show reasonable agreement but only with the high-resolution observational data that are aggregated into coarse resolution and spatially averaged. Close

• 15. [Article] Winter Precipitation Intensity and ENSO/PDO Variability in the Willamette Valley of Oregon

  There is growing concern about the effects of inter-annual climatic variability, such as the El-Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on regional hydrology and water ...

  Citation × Citation Citation Abstract Copy Title: Winter Precipitation Intensity and ENSO/PDO Variability in the Willamette Valley of Oregon Author: Praskievicz, Sarah, Chang, Heejun Year: 2009 There is growing concern about the effects of inter-annual climatic variability, such as the El-Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on regional hydrology and water resources. We analyzed patterns of wintertime precipitation intensity, using both simple intensity and number of heavy precipitation days per year, for eight stations in northwestern Oregon’s Willamette Valley for the period 1972–2006, and examined the separate and combined influence of ENSO and PDO on precipitation intensity. The analysis was accomplished using Kendall’s tau and analysis of variance (ANOVA) to determine differences in precipitation intensity among combinations of positive and negative ENSO and PDO phases. The results suggest that the relation between ENSO phase and precipitation intensity is negative in November and positive in April, while the relation between PDO and intensity is negative and strongest in January and March. These varying seasonal associations with ENSO/PDO phase may be related to the Willamette Valley’s location in the transitional zone between positive and negative El Niño response and to the moderating effects of out-of-phase ENSO/PDO. This seasonal variability of heavy rainfall has implications for the quantity and quality of regional water resources. Title: Winter Precipitation Intensity and ENSO/PDO Variability in the Willamette Valley of Oregon Author: Praskievicz, Sarah, Chang, Heejun Year: 2009 There is growing concern about the effects of inter-annual climatic variability, such as the El-Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on regional hydrology and water resources. We analyzed patterns of wintertime precipitation intensity, using both simple intensity and number of heavy precipitation days per year, for eight stations in northwestern Oregon’s Willamette Valley for the period 1972–2006, and examined the separate and combined influence of ENSO and PDO on precipitation intensity. The analysis was accomplished using Kendall’s tau and analysis of variance (ANOVA) to determine differences in precipitation intensity among combinations of positive and negative ENSO and PDO phases. The results suggest that the relation between ENSO phase and precipitation intensity is negative in November and positive in April, while the relation between PDO and intensity is negative and strongest in January and March. These varying seasonal associations with ENSO/PDO phase may be related to the Willamette Valley’s location in the transitional zone between positive and negative El Niño response and to the moderating effects of out-of-phase ENSO/PDO. This seasonal variability of heavy rainfall has implications for the quantity and quality of regional water resources. Close

• 16. [Article] Preliminary Study Comparing Precipitation Quality Between Nominal Land Uses in Portland, Oregon

  The purpose of this study is to analyze the precipitation quality in various land use categories throughout Portland. Because of its significance in the removal of atmospheric pollutants, wet deposition ...

  Citation × Citation Citation Abstract Copy Title: Preliminary Study Comparing Precipitation Quality Between Nominal Land Uses in Portland, Oregon Author: Sullivan, Lacey Year: 2005 The purpose of this study is to analyze the precipitation quality in various land use categories throughout Portland. Because of its significance in the removal of atmospheric pollutants, wet deposition is an important component to study (Rocha et al. 2003). In addition to its significant role in cleansing the atmosphere, wet deposition can have a direct impact on the ecosystem due to its potential pollutant load (Khare et al. 2004). Various land uses may provide different emission sources, each uniquely affecting the composition of the atmosphere. A literature review found few studies comparing precipitation quality between local land uses within an urban area. Wet deposition studies seem to be fairly limited in the western United States, specifically in the Pacific Northwest. By collecting wet deposition in four different land uses: industrial, commercial, residential, and open space, precipitation quality is compared between each land use using a chosen group of analytes (ammonia, total phosphorus, copper, lead, mercury, zinc, pH, and conductivity). Significant differences were found between a number of land use category pairs for each of the eight analytes, except pH and conductivity. In order to give more insight into the precipitation quality, the results were also analyzed for differences between sites within each land use and differences between storm events. Differences were found between a number of sites, suggesting local effects in precipitation quality throughout Portland. Differences were also found between events, suggesting the importance of meteorological characteristics. Title: Preliminary Study Comparing Precipitation Quality Between Nominal Land Uses in Portland, Oregon Author: Sullivan, Lacey Year: 2005 The purpose of this study is to analyze the precipitation quality in various land use categories throughout Portland. Because of its significance in the removal of atmospheric pollutants, wet deposition is an important component to study (Rocha et al. 2003). In addition to its significant role in cleansing the atmosphere, wet deposition can have a direct impact on the ecosystem due to its potential pollutant load (Khare et al. 2004). Various land uses may provide different emission sources, each uniquely affecting the composition of the atmosphere. A literature review found few studies comparing precipitation quality between local land uses within an urban area. Wet deposition studies seem to be fairly limited in the western United States, specifically in the Pacific Northwest. By collecting wet deposition in four different land uses: industrial, commercial, residential, and open space, precipitation quality is compared between each land use using a chosen group of analytes (ammonia, total phosphorus, copper, lead, mercury, zinc, pH, and conductivity). Significant differences were found between a number of land use category pairs for each of the eight analytes, except pH and conductivity. In order to give more insight into the precipitation quality, the results were also analyzed for differences between sites within each land use and differences between storm events. Differences were found between a number of sites, suggesting local effects in precipitation quality throughout Portland. Differences were also found between events, suggesting the importance of meteorological characteristics. Close

• 17. [Article] Precipitation Intensity Required for Landslide Initiation in Rwanda

  Rwanda has a high density of landslides, heavy precipitation events and a shortage of resources to study them, making it an excellent candidate for study using satellite-based remote sensing data. To assess ...

  Citation × Citation Citation Abstract Copy Title: Precipitation Intensity Required for Landslide Initiation in Rwanda Author: Piller, Angela N. Year: 2016 Rwanda has a high density of landslides, heavy precipitation events and a shortage of resources to study them, making it an excellent candidate for study using satellite-based remote sensing data. To assess landslide hazards countrywide, I first built a landslide inventory of 254 landslides and used a statistical methodology. Using logistic regression on 24 test variables, I determined that slope and population density are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda was produced, with an overall predictive accuracy of 79.6%. Second, I worked to define a relationship between precipitation intensity and landslide density for a landslide-prone study area in western Rwanda. In the 1180 km2 study area, I mapped 577 landslides, using CNES/Astrium and WorldView satellite imagery in Google Earth over a study period of 2000 to 2015. One 400 km2 part of the study area has a high landslide density of 1.4 landslides/km2, while another 780 km2 part with identical geology, soils, land-use, and vegetation has a much lower landslide density. To identify possible triggering events for these landslides, I analyzed a 16 year record of TRMM (Tropical Rainfall Measuring Mission) satellite precipitation data. The high landslide density region and the low landslide density region were not notably different in rainfall, as quantified by recurrence interval analysis. A relationship between precipitation and landslide density could therefore not be developed, and the null hypothesis cannot be ruled out. This apparent lack of connection could result from a variety of factors including TRMM grid size, satellite imagery temporal resolution, antecedent soil moisture, or vegetation regrowth rates. Title: Precipitation Intensity Required for Landslide Initiation in Rwanda Author: Piller, Angela N. Year: 2016 Rwanda has a high density of landslides, heavy precipitation events and a shortage of resources to study them, making it an excellent candidate for study using satellite-based remote sensing data. To assess landslide hazards countrywide, I first built a landslide inventory of 254 landslides and used a statistical methodology. Using logistic regression on 24 test variables, I determined that slope and population density are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda was produced, with an overall predictive accuracy of 79.6%. Second, I worked to define a relationship between precipitation intensity and landslide density for a landslide-prone study area in western Rwanda. In the 1180 km2 study area, I mapped 577 landslides, using CNES/Astrium and WorldView satellite imagery in Google Earth over a study period of 2000 to 2015. One 400 km2 part of the study area has a high landslide density of 1.4 landslides/km2, while another 780 km2 part with identical geology, soils, land-use, and vegetation has a much lower landslide density. To identify possible triggering events for these landslides, I analyzed a 16 year record of TRMM (Tropical Rainfall Measuring Mission) satellite precipitation data. The high landslide density region and the low landslide density region were not notably different in rainfall, as quantified by recurrence interval analysis. A relationship between precipitation and landslide density could therefore not be developed, and the null hypothesis cannot be ruled out. This apparent lack of connection could result from a variety of factors including TRMM grid size, satellite imagery temporal resolution, antecedent soil moisture, or vegetation regrowth rates. Close

• 18. [Article] Climate Change and Freshwater Resources in Oregon

  Climate change will affect various sectors of water resources in Oregon in the 21st century. The observed trends in streamflow show significant declines in September flow and, although not significant, ...

  Citation × Citation Citation Abstract Copy Title: Climate Change and Freshwater Resources in Oregon Author: Chang, Heejun, Jones, Julie Year: 2010 Climate change will affect various sectors of water resources in Oregon in the 21st century. The observed trends in streamflow show significant declines in September flow and, although not significant, increases in March flow in many transient rain-snow basins. These streamflow trends are associated with rising temperature and coincident declines in snowpack in spring in the latter half of the 20th century. While there are no distinct trends in high precipitation events, such events are associated with climate variability such as ENSO and PDO. Effects of ENSO and PDO are more pronounced at the beginning and end of the wet season in the Willamette River basin. The amount and seasonality of water supply is projected to shift as the distribution of precipitation changes and temperatures rise. Higher summer air temperatures accompanied by reduced precipitation are projected to increase evapotranspiration and decrease stream flow in summer. Although there are no distinct spatial patterns of changes in precipitation and temperature across the State in the 21st century (uniform increase in temperature across the region), significant regional variations do exist. The magnitude of change depends on the importance of snow in the current water budget, so projected changes are greater for mountainous regions than for low-elevation areas. Transient rain-snow basins, such as those in the Western Cascade basins, are projected to be more sensitive to these changes in precipitation and temperature. The high Cascade basins that are primarily fed by deep groundwater systems could sustain low flow during summer months. Basins in the east of the Cascades are projected to have low summer flow in a distant future as groundwater recharge declines over time. April 1 snow water equivalent (SWE) will decline and the center timing of runoff will become earlier in transient rain-snow basins as snowpack is projected to decline consistently in the 21st century. These model projections should be viewed with caution for several reasons when considering climate change impacts on water supply in Oregon. First, this chapter shows that few consistent trends in runoff are apparent in streamflow records from Oregon; instead, the direction and magnitude of change in streamflow varies by season, by basin size, and among ecoregions in Oregon. Second, observed streamflow trends (e.g., declining flows in summer, or in September) may be explained by factors that may not be directly related to global climate change. For example, recent low-flow years are attributable to low precipitation years (especially 2001 and 2005) and perhaps to interannual variations in snowpack associated with cyclical variation in ocean temperatures, while long-term decreases in summer flows are attributable to the combination of summer precipitation decline and increasing water withdrawals for consumptive use. Third, model projections do not account for possible resilience and adaptations in natural ecosystems that may alter water use and lead to smaller than expected changes in streamflow. More work is needed on vegetation responses to climate variability, the interactions between soil water and vegetation, and the relationship between streamflow and precipitation (runoff ratios) in large basins. Title: Climate Change and Freshwater Resources in Oregon Author: Chang, Heejun, Jones, Julie Year: 2010 Climate change will affect various sectors of water resources in Oregon in the 21st century. The observed trends in streamflow show significant declines in September flow and, although not significant, increases in March flow in many transient rain-snow basins. These streamflow trends are associated with rising temperature and coincident declines in snowpack in spring in the latter half of the 20th century. While there are no distinct trends in high precipitation events, such events are associated with climate variability such as ENSO and PDO. Effects of ENSO and PDO are more pronounced at the beginning and end of the wet season in the Willamette River basin. The amount and seasonality of water supply is projected to shift as the distribution of precipitation changes and temperatures rise. Higher summer air temperatures accompanied by reduced precipitation are projected to increase evapotranspiration and decrease stream flow in summer. Although there are no distinct spatial patterns of changes in precipitation and temperature across the State in the 21st century (uniform increase in temperature across the region), significant regional variations do exist. The magnitude of change depends on the importance of snow in the current water budget, so projected changes are greater for mountainous regions than for low-elevation areas. Transient rain-snow basins, such as those in the Western Cascade basins, are projected to be more sensitive to these changes in precipitation and temperature. The high Cascade basins that are primarily fed by deep groundwater systems could sustain low flow during summer months. Basins in the east of the Cascades are projected to have low summer flow in a distant future as groundwater recharge declines over time. April 1 snow water equivalent (SWE) will decline and the center timing of runoff will become earlier in transient rain-snow basins as snowpack is projected to decline consistently in the 21st century. These model projections should be viewed with caution for several reasons when considering climate change impacts on water supply in Oregon. First, this chapter shows that few consistent trends in runoff are apparent in streamflow records from Oregon; instead, the direction and magnitude of change in streamflow varies by season, by basin size, and among ecoregions in Oregon. Second, observed streamflow trends (e.g., declining flows in summer, or in September) may be explained by factors that may not be directly related to global climate change. For example, recent low-flow years are attributable to low precipitation years (especially 2001 and 2005) and perhaps to interannual variations in snowpack associated with cyclical variation in ocean temperatures, while long-term decreases in summer flows are attributable to the combination of summer precipitation decline and increasing water withdrawals for consumptive use. Third, model projections do not account for possible resilience and adaptations in natural ecosystems that may alter water use and lead to smaller than expected changes in streamflow. More work is needed on vegetation responses to climate variability, the interactions between soil water and vegetation, and the relationship between streamflow and precipitation (runoff ratios) in large basins. Close

• 19. [Article] Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades?

  Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. ...

  Citation × Citation Citation Abstract Copy Title: Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades? Author: de Barros Soares, Daniel, Lee, Huikyo, Loikith, Paul C., Barkhordarian, Armineh, Mechoso, Carlos R. Year: 2016 Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. The results for observed near-surface air temperature show an overall warming trend over much of the continent, with the largest magnitudes over central Brazil. These observed trends are found to be statistically significant using pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) as the baseline to estimate natural climate variability. The observed trends are compared with those obtained in natural-only CMIP5 simulations, in which only natural forcings (i.e. volcanoes and solar variability) are included, and in historical CMIP5 simulations, in which anthropogenic forcings (i.e. changes in the atmospheric composition) are further incorporated. The historical CMIP5 simulations are more successful in capturing the observed temperature trends than the simulations with natural forcings only. It is suggested that anthropogenic warming is already evident over much of South America. Unlike the warming trends, observed precipitation trends over South America are less spatially coherent with both negative and positive values across the continent. Significant positive trends are found over South America in only one of the data sets used, and over a region that roughly encompasses the southern part of La Plata Basin (southern Brazil, Uruguay, and northeastern Argentina) in all data sets used. The historical CMIP5 simulations do not capture this feature. No firm conclusions are reached, therefore, for anthropogenic influences on precipitation changes in the period selected for study. Title: Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades? Author: de Barros Soares, Daniel, Lee, Huikyo, Loikith, Paul C., Barkhordarian, Armineh, Mechoso, Carlos R. Year: 2016 Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. The results for observed near-surface air temperature show an overall warming trend over much of the continent, with the largest magnitudes over central Brazil. These observed trends are found to be statistically significant using pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) as the baseline to estimate natural climate variability. The observed trends are compared with those obtained in natural-only CMIP5 simulations, in which only natural forcings (i.e. volcanoes and solar variability) are included, and in historical CMIP5 simulations, in which anthropogenic forcings (i.e. changes in the atmospheric composition) are further incorporated. The historical CMIP5 simulations are more successful in capturing the observed temperature trends than the simulations with natural forcings only. It is suggested that anthropogenic warming is already evident over much of South America. Unlike the warming trends, observed precipitation trends over South America are less spatially coherent with both negative and positive values across the continent. Significant positive trends are found over South America in only one of the data sets used, and over a region that roughly encompasses the southern part of La Plata Basin (southern Brazil, Uruguay, and northeastern Argentina) in all data sets used. The historical CMIP5 simulations do not capture this feature. No firm conclusions are reached, therefore, for anthropogenic influences on precipitation changes in the period selected for study. Close

• 20. [Article] Investigating Future Variation of Extreme Precipitation Events over the Willamette River Basin Using Dynamically Downscaled Climate Scenarios

  One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods ...

  Citation × Citation Citation Abstract Copy Title: Investigating Future Variation of Extreme Precipitation Events over the Willamette River Basin Using Dynamically Downscaled Climate Scenarios Author: Halmstad, Andrew Jason Year: 2011 One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena. Title: Investigating Future Variation of Extreme Precipitation Events over the Willamette River Basin Using Dynamically Downscaled Climate Scenarios Author: Halmstad, Andrew Jason Year: 2011 One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena. Close