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• 1. [Article] Climate-Driven Variability and Trends in Mountain Snowpack in Western North America

  Records of 1 April snow water equivalent (SWE) are examined here using multiple linear regression against reference time series of temperature and precipitation. This method permits 1) an examination of ...

  Citation × Citation Citation Abstract Copy Title: Climate-Driven Variability and Trends in Mountain Snowpack in Western North America Author: Mote, Philip W. Records of 1 April snow water equivalent (SWE) are examined here using multiple linear regression against reference time series of temperature and precipitation. This method permits 1) an examination of the separate roles of temperature and precipitation in determining the trends in SWE; 2) an estimation of the sensitivity of SWE to warming trends, and its distribution across western North America and as a function of elevation; and 3) inferences about responses of SWE to future warming. These results emphasize the sensitivity to warming of the mountains of northern California and the Cascades of Oregon and Washington. In addition, the contribution of modes of Pacific climate variability is examined and found to be responsible for about 10%–60% of the trends in SWE, depending on the period of record and climate index. Title: Climate-Driven Variability and Trends in Mountain Snowpack in Western North America Author: Mote, Philip W. Records of 1 April snow water equivalent (SWE) are examined here using multiple linear regression against reference time series of temperature and precipitation. This method permits 1) an examination of the separate roles of temperature and precipitation in determining the trends in SWE; 2) an estimation of the sensitivity of SWE to warming trends, and its distribution across western North America and as a function of elevation; and 3) inferences about responses of SWE to future warming. These results emphasize the sensitivity to warming of the mountains of northern California and the Cascades of Oregon and Washington. In addition, the contribution of modes of Pacific climate variability is examined and found to be responsible for about 10%–60% of the trends in SWE, depending on the period of record and climate index. Close

• 2. [Image] Precipitation (Meteorology) Water Quality in Jordan

  	Citation × Citation Citation Abstract Copy Title: Precipitation (Meteorology) Water Quality in Jordan Author: Salāmah, Ilyās Year: 1991 Title: Precipitation (Meteorology) Water Quality in Jordan Author: Salāmah, Ilyās Year: 1991 Close

• 3. [Image] Report to the Government of Lebanon on the prospect for increasing the useful water supply in Lebanon by Precipitation (Meteorology) management

  	Citation × Citation Citation Abstract Copy Title: Report to the Government of Lebanon on the prospect for increasing the useful water supply in Lebanon by Precipitation (Meteorology) management Year: 1969 Title: Report to the Government of Lebanon on the prospect for increasing the useful water supply in Lebanon by Precipitation (Meteorology) management Year: 1969 Close

• 4. [Article] A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications

  Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. ...

  Citation × Citation Citation Abstract Copy Title: A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications Author: Khajehei, Sepideh Year: 2015 Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach. Title: A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications Author: Khajehei, Sepideh Year: 2015 Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach. Close

• 5. [Article] Streamflow Modeling of Johnson Creek Subwatersheds Using the Precipitation Runoff Modeling System

  Johnson Creek, in the Portland, Oregon metropolitan region, has several pollutants on the U.S. Environmental Protection Agency’s (EPA) 303(d) list including excess heat, low dissolved oxygen, and harmful ...

  Citation × Citation Citation Abstract Copy Title: Streamflow Modeling of Johnson Creek Subwatersheds Using the Precipitation Runoff Modeling System Author: Malone, Theophilus M Year: 2014 Johnson Creek, in the Portland, Oregon metropolitan region, has several pollutants on the U.S. Environmental Protection Agency’s (EPA) 303(d) list including excess heat, low dissolved oxygen, and harmful bacteria. Understanding streamflow response to precipitation events is an important component to evaluating water quality trends and calculating the Total Maximum Daily Load (TMDL) for pollutants of concern. Investigating the streamflow-precipitation relationship on the subwatershed scale can give insight to the hydrologic response of a given watershed. However, developing rating curves for several subwatersheds can be cost and time prohibitive. The objective of this project was to develop a hydrologic model using the Precipitation Runoff Modeling System (PRMS), developed by the United States Geological Survey (USGS) to validate streamflow estimates for subwatersheds lacking a significant period of record. ESRI’s ArcMap, a geographic information system (GIS), was used to characterize the target drainage basins and extract basin-specific parameters for upper watersheds of Johnson Creek. The Johnson Creek Upper Watershed (JCUW) model was calibrated to an existing streamflow gage at Regner Road, in Gresham, Oregon. Calibrated parameters were substituted into a second PRMS model characterizing the Sunshine Creek Subwatershed, which lies within the JCUW. The Sunshine Creek model was used to validate a flow time-series derived from a pressure transducer and a rating curve. Title: Streamflow Modeling of Johnson Creek Subwatersheds Using the Precipitation Runoff Modeling System Author: Malone, Theophilus M Year: 2014 Johnson Creek, in the Portland, Oregon metropolitan region, has several pollutants on the U.S. Environmental Protection Agency’s (EPA) 303(d) list including excess heat, low dissolved oxygen, and harmful bacteria. Understanding streamflow response to precipitation events is an important component to evaluating water quality trends and calculating the Total Maximum Daily Load (TMDL) for pollutants of concern. Investigating the streamflow-precipitation relationship on the subwatershed scale can give insight to the hydrologic response of a given watershed. However, developing rating curves for several subwatersheds can be cost and time prohibitive. The objective of this project was to develop a hydrologic model using the Precipitation Runoff Modeling System (PRMS), developed by the United States Geological Survey (USGS) to validate streamflow estimates for subwatersheds lacking a significant period of record. ESRI’s ArcMap, a geographic information system (GIS), was used to characterize the target drainage basins and extract basin-specific parameters for upper watersheds of Johnson Creek. The Johnson Creek Upper Watershed (JCUW) model was calibrated to an existing streamflow gage at Regner Road, in Gresham, Oregon. Calibrated parameters were substituted into a second PRMS model characterizing the Sunshine Creek Subwatershed, which lies within the JCUW. The Sunshine Creek model was used to validate a flow time-series derived from a pressure transducer and a rating curve. Close

• 6. [Article] A macroscale measurement and modeling approach to improve understanding of the hydrology of steep, forested hillslopes

  The dominant controls on flow generation in steep, forested hillslopes are poorly understood. This dissertation examined the dominant flow processes operating at the hillslope scale, using a combined ...

  Citation × Citation Citation Abstract Copy Title: A macroscale measurement and modeling approach to improve understanding of the hydrology of steep, forested hillslopes Author: Graham, Christopher Brian The dominant controls on flow generation in steep, forested hillslopes are poorly understood. This dissertation examined the dominant flow processes operating at the hillslope scale, using a combined macroscale measurement and model development and analysis framework. Irrigation experiments at two steep forested hillslopes were conducted to isolate individual hillslope flow components and reveal the dominant controls on flow routing to the stream. A new perceptual model of flow processes at the hillslope scale was developed from these field experiments that included three key components: 1) A connected preferential flow network located at the soil/bedrock interface controls lateral water and solute transport, 2) The bedrock surface controls the subsurface flow routing, and 3) Bedrock is permeable, and acts as a sink for precipitation at the hillslope scale. These components formed the basis of a new, low dimensional numerical model that was used to represent, quantitatively, qualitative experimental findings. A multiple criteria calibration using hydrometric and tracer experimental data was conducted to evaluate the model and determine parameter identifiability. The model was able to adequately reproduce both hydrometric response to precipitation, and a tracer application breakthrough. The model was then used within a virtual experiment framework to test the dominant controls on the whole storm precipitation / discharge threshold relationship at the catchment scale. The modeling experiments showed that the macroscale precipitation / discharge threshold was controlled by a balance between antecedent evaporation (evaporation rate times antecedent drainage time), and geologic factors (bedrock permeability and subsurface storage volume). Overall, these findings suggest that making measurements at the scale of the processes one wishes to understand, and constructing numerical models based on the dominant processes at the same scale may be a first step towards the development of macroscale laws of hillslope behavior. Title: A macroscale measurement and modeling approach to improve understanding of the hydrology of steep, forested hillslopes Author: Graham, Christopher Brian The dominant controls on flow generation in steep, forested hillslopes are poorly understood. This dissertation examined the dominant flow processes operating at the hillslope scale, using a combined macroscale measurement and model development and analysis framework. Irrigation experiments at two steep forested hillslopes were conducted to isolate individual hillslope flow components and reveal the dominant controls on flow routing to the stream. A new perceptual model of flow processes at the hillslope scale was developed from these field experiments that included three key components: 1) A connected preferential flow network located at the soil/bedrock interface controls lateral water and solute transport, 2) The bedrock surface controls the subsurface flow routing, and 3) Bedrock is permeable, and acts as a sink for precipitation at the hillslope scale. These components formed the basis of a new, low dimensional numerical model that was used to represent, quantitatively, qualitative experimental findings. A multiple criteria calibration using hydrometric and tracer experimental data was conducted to evaluate the model and determine parameter identifiability. The model was able to adequately reproduce both hydrometric response to precipitation, and a tracer application breakthrough. The model was then used within a virtual experiment framework to test the dominant controls on the whole storm precipitation / discharge threshold relationship at the catchment scale. The modeling experiments showed that the macroscale precipitation / discharge threshold was controlled by a balance between antecedent evaporation (evaporation rate times antecedent drainage time), and geologic factors (bedrock permeability and subsurface storage volume). Overall, these findings suggest that making measurements at the scale of the processes one wishes to understand, and constructing numerical models based on the dominant processes at the same scale may be a first step towards the development of macroscale laws of hillslope behavior. Close

• 7. [Image] Bibliography

  	Citation × Citation Citation Abstract Copy Title: Bibliography Year: 2008 Title: Bibliography Year: 2008 Close

• 8. [Image] References

  	Citation × Citation Citation Abstract Copy Title: References Year: 1984 Title: References Year: 1984 Close

• 9. [Image] Water Resources Development Plans in Syria: Role of the Euphrates basin

  	Citation × Citation Citation Abstract Copy Title: Water Resources Development Plans in Syria: Role of the Euphrates basin Year: 2013 Title: Water Resources Development Plans in Syria: Role of the Euphrates basin Year: 2013 Close

• 10. [Image] Volume 1, Proceedings

  	Citation × Citation Citation Abstract Copy Title: Volume 1, Proceedings Year: 1973 Title: Volume 1, Proceedings Year: 1973 Close