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• 1. [Article] Continuous Deflection Separation of Stormwater Particulates

  Capture of stormwater particulate matter is of concern to watershed managers trying to meet water quality guidelines by removing toxics associated with particulates from natural water bodies. A laboratory ...

  Citation × Citation Citation Abstract Copy Title: Continuous Deflection Separation of Stormwater Particulates Author: Schwarz, Tracy S., Wells, Scott A. Year: 1999 Capture of stormwater particulate matter is of concern to watershed managers trying to meet water quality guidelines by removing toxics associated with particulates from natural water bodies. A laboratory investigation into particle removal efficiency of a prototype Continuous Deflective Separation (CDS) device was made in the laboratory. Particle size distributions were obtained for roadway drainage surfaces. These distributions were used to assess the predicted performance of a CDS unit in the field. Flow rates in the laboratory tests ranged from 125 GPM (7.9 l/s) to 270 GPM (17.0 l/s). Screen sizes used for testing were 1200 m and 2400 m. Using four different sand types and the two screen sizes, particle capture rates were determined for the CDS unit. Internal velocities were measured at seventy-two measuring positions within the main chamber of the CDS unit. At 270 GPM capture rates based on particle size ranged from 12% to 100% with the 2400 m screen, and 22% to 100% with the 1200 m screen. Cumulative mass capture rates were 73% and 70% for the 1200 m and 2400 m screens, respectively, at 270 gpm and 84% at 125 gpm for the given particle distribution used. Title: Continuous Deflection Separation of Stormwater Particulates Author: Schwarz, Tracy S., Wells, Scott A. Year: 1999 Capture of stormwater particulate matter is of concern to watershed managers trying to meet water quality guidelines by removing toxics associated with particulates from natural water bodies. A laboratory investigation into particle removal efficiency of a prototype Continuous Deflective Separation (CDS) device was made in the laboratory. Particle size distributions were obtained for roadway drainage surfaces. These distributions were used to assess the predicted performance of a CDS unit in the field. Flow rates in the laboratory tests ranged from 125 GPM (7.9 l/s) to 270 GPM (17.0 l/s). Screen sizes used for testing were 1200 m and 2400 m. Using four different sand types and the two screen sizes, particle capture rates were determined for the CDS unit. Internal velocities were measured at seventy-two measuring positions within the main chamber of the CDS unit. At 270 GPM capture rates based on particle size ranged from 12% to 100% with the 2400 m screen, and 22% to 100% with the 1200 m screen. Cumulative mass capture rates were 73% and 70% for the 1200 m and 2400 m screens, respectively, at 270 gpm and 84% at 125 gpm for the given particle distribution used. Close

• 2. [Article] Towards Improving Drought Forecasts Across Different Spatial and Temporal Scales

  Recent water scarcities across the southwestern U.S. with severe effects on the living environment inspire the development of new methodologies to achieve reliable drought forecasting in seasonal scale. ...

  Citation × Citation Citation Abstract Copy Title: Towards Improving Drought Forecasts Across Different Spatial and Temporal Scales Author: Madadgar, Shahrbanou Year: 2014 Recent water scarcities across the southwestern U.S. with severe effects on the living environment inspire the development of new methodologies to achieve reliable drought forecasting in seasonal scale. Reliable forecast of hydrologic variables, in general, is a preliminary requirement for appropriate planning of water resources and developing effective allocation policies. This study aims at developing new techniques with specific probabilistic features to improve the reliability of hydrologic forecasts, particularly the drought forecasts. The drought status in the future is determined by certain hydrologic variables that are basically estimated by the hydrologic models with rather simple to complex structures. Since the predictions of hydrologic models are prone to different sources of uncertainties, there have been several techniques examined during past several years which generally attempt to combine the predictions of single (multiple) hydrologic models to generate an ensemble of hydrologic forecasts addressing the inherent uncertainties. However, the imperfect structure of hydrologic models usually lead to systematic bias of hydrologic predictions that further appears in the forecast ensembles. This study proposes a post-processing method that is applied to the raw forecast of hydrologic variables and can develop the entire distribution of forecast around the initial single-value prediction. To establish the probability density function (PDF) of the forecast, a group of multivariate distribution functions, the so-called copula functions, are incorporated in the post-processing procedure. The performance of the new post-processing technique is tested on 2500 hypothetical case studies and the streamflow forecast of Sprague River Basin in southern Oregon. Verified by some deterministic and probabilistic verification measures, the method of Quantile Mapping as a traditional post-processing technique cannot generate the qualified forecasts as comparing with the copula-based method. The post-processing technique is then expanded to exclusively study the drought forecasts across the different spatial and temporal scales. In the proposed drought forecasting model, the drought status in the future is evaluated based on the drought status of the past seasons while the correlations between the drought variables of consecutive seasons are preserved by copula functions. The main benefit of the new forecast model is its probabilistic features in analyzing future droughts. It develops conditional probability of drought status in the forecast season and generates the PDF and cumulative distribution function (CDF) of future droughts given the past status. The conditional PDF can return the highest probable drought in the future along with an assessment of the uncertainty around that value. Using the conditional CDF for forecast season, the model can generate the maps of drought status across the basin with particular chance of occurrence in the future. In a different analysis of the conditional CDF developed for the forecast season, the chance of a particular drought in the forecast period can be approximated given the drought status of earlier seasons. The forecast methodology developed in this study shows promising results in hydrologic forecasts and its particular probabilistic features are inspiring for future studies. Title: Towards Improving Drought Forecasts Across Different Spatial and Temporal Scales Author: Madadgar, Shahrbanou Year: 2014 Recent water scarcities across the southwestern U.S. with severe effects on the living environment inspire the development of new methodologies to achieve reliable drought forecasting in seasonal scale. Reliable forecast of hydrologic variables, in general, is a preliminary requirement for appropriate planning of water resources and developing effective allocation policies. This study aims at developing new techniques with specific probabilistic features to improve the reliability of hydrologic forecasts, particularly the drought forecasts. The drought status in the future is determined by certain hydrologic variables that are basically estimated by the hydrologic models with rather simple to complex structures. Since the predictions of hydrologic models are prone to different sources of uncertainties, there have been several techniques examined during past several years which generally attempt to combine the predictions of single (multiple) hydrologic models to generate an ensemble of hydrologic forecasts addressing the inherent uncertainties. However, the imperfect structure of hydrologic models usually lead to systematic bias of hydrologic predictions that further appears in the forecast ensembles. This study proposes a post-processing method that is applied to the raw forecast of hydrologic variables and can develop the entire distribution of forecast around the initial single-value prediction. To establish the probability density function (PDF) of the forecast, a group of multivariate distribution functions, the so-called copula functions, are incorporated in the post-processing procedure. The performance of the new post-processing technique is tested on 2500 hypothetical case studies and the streamflow forecast of Sprague River Basin in southern Oregon. Verified by some deterministic and probabilistic verification measures, the method of Quantile Mapping as a traditional post-processing technique cannot generate the qualified forecasts as comparing with the copula-based method. The post-processing technique is then expanded to exclusively study the drought forecasts across the different spatial and temporal scales. In the proposed drought forecasting model, the drought status in the future is evaluated based on the drought status of the past seasons while the correlations between the drought variables of consecutive seasons are preserved by copula functions. The main benefit of the new forecast model is its probabilistic features in analyzing future droughts. It develops conditional probability of drought status in the forecast season and generates the PDF and cumulative distribution function (CDF) of future droughts given the past status. The conditional PDF can return the highest probable drought in the future along with an assessment of the uncertainty around that value. Using the conditional CDF for forecast season, the model can generate the maps of drought status across the basin with particular chance of occurrence in the future. In a different analysis of the conditional CDF developed for the forecast season, the chance of a particular drought in the forecast period can be approximated given the drought status of earlier seasons. The forecast methodology developed in this study shows promising results in hydrologic forecasts and its particular probabilistic features are inspiring for future studies. Close