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• 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