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• 21. [Article] TMDLS: Statistical Correlations or Mechanistic Modeling?

  Western Idaho and Eastern Oregon, determinations of the assimilative capacity of the system and the impact of pollutant reduction strategies has been performed using both a statistical-correlation approach ...

  Citation × Citation Citation Abstract Copy Title: TMDLS: Statistical Correlations or Mechanistic Modeling? Author: Wells, Scott A., Cole, Thomas M. Year: 2002 Western Idaho and Eastern Oregon, determinations of the assimilative capacity of the system and the impact of pollutant reduction strategies has been performed using both a statistical-correlation approach and a mechanistic modeling approach. The system included the Lower Snake River, Brownlee Reservoir, Oxbow Reservoir, and Hells Canyon Reservoir with the focus was on Brownlee Reservoir. The statistical approach used on Brownlee Reservoir divided the system into riverine and lacustrine zones. Field data were then averaged over season and location to provide statistical correlations, such as between total phosphorus (TP) and chlorophyll a (chl a), between an anoxic factor (AF) and TP, and between a hypoxic factor (HP) and TP. The anoxic factor is defined as the number of days when dissolved oxygen was less than 2 mg/l and the hypoxic factor was defined as the number of days when dissolved oxygen was less than 6.5 mg/l. The mechanistic approach used a two-dimensional, unsteady, hydrodynamic and water quality model called CE-QUAL-W2. The model was calibrated to field data over a three year period for 1992 (a low flow year), 1995 (an average flow year), and 1997 (a high flow year). Some of the important questions that the TMDL process was to answer included the following. What level of TP is necessary in the river coming into Brownlee Reservoir to reduce the number of days of hypoxia in the reservoir? What are the causes of low2 dissolved oxygen in Brownlee Reservoir and what management strategies can be implemented to improve water quality? This paper examines the strengths and weaknesses of answering management questions using these two approaches. Recommendations are suggested for answering assimilative capacity questions that arise in TMDL studies. Title: TMDLS: Statistical Correlations or Mechanistic Modeling? Author: Wells, Scott A., Cole, Thomas M. Year: 2002 Western Idaho and Eastern Oregon, determinations of the assimilative capacity of the system and the impact of pollutant reduction strategies has been performed using both a statistical-correlation approach and a mechanistic modeling approach. The system included the Lower Snake River, Brownlee Reservoir, Oxbow Reservoir, and Hells Canyon Reservoir with the focus was on Brownlee Reservoir. The statistical approach used on Brownlee Reservoir divided the system into riverine and lacustrine zones. Field data were then averaged over season and location to provide statistical correlations, such as between total phosphorus (TP) and chlorophyll a (chl a), between an anoxic factor (AF) and TP, and between a hypoxic factor (HP) and TP. The anoxic factor is defined as the number of days when dissolved oxygen was less than 2 mg/l and the hypoxic factor was defined as the number of days when dissolved oxygen was less than 6.5 mg/l. The mechanistic approach used a two-dimensional, unsteady, hydrodynamic and water quality model called CE-QUAL-W2. The model was calibrated to field data over a three year period for 1992 (a low flow year), 1995 (an average flow year), and 1997 (a high flow year). Some of the important questions that the TMDL process was to answer included the following. What level of TP is necessary in the river coming into Brownlee Reservoir to reduce the number of days of hypoxia in the reservoir? What are the causes of low2 dissolved oxygen in Brownlee Reservoir and what management strategies can be implemented to improve water quality? This paper examines the strengths and weaknesses of answering management questions using these two approaches. Recommendations are suggested for answering assimilative capacity questions that arise in TMDL studies. Close

• 22. [Article] Investigating the Link Between Surface Water and Groundwater in the Tule Lake Subbasin, Oregon and California

  Water allocation in the upper Klamath Basin of Oregon and California has been challenging. Irrigators have increasingly turned to groundwater to make up for surface water shortages because of shifts in ...

  Citation × Citation Citation Abstract Copy Title: Investigating the Link Between Surface Water and Groundwater in the Tule Lake Subbasin, Oregon and California Author: Pischel, Esther Maria Year: 2014 Water allocation in the upper Klamath Basin of Oregon and California has been challenging. Irrigators have increasingly turned to groundwater to make up for surface water shortages because of shifts in allocation toward in-stream flows for Endangered Species Act listed fishes. The largest increase in groundwater pumping has been in and around the Bureau of Reclamation's Klamath Irrigation Project, which includes the Tule Lake subbasin in the southern part of the upper Klamath Basin. Previous groundwater flow model simulations indicate that water level declines from pumping may result in decreased flow to agricultural drains in the Tule Lake subbasin. Agricultural drains on the Klamath Project are an important source of water for downstream irrigators and for the Tule Lake and Lower Klamath Lake National Wildlife Refuges. To better assess the impact of increased pumping on drain flow and on the water balance of the groundwater system, flow data from agricultural drains were evaluated to investigate the changes that have taken place in groundwater discharge to drains since pumping volumes increased. Additionally, a fine-grid groundwater model of the Tule Lake subbasin was developed based on the existing regional flow model. The fine-grid model has sufficient vertical and horizontal resolution to simulate vertical head gradients, takes advantage of time-series data from 38 observation wells for model calibration, and allows agricultural drains to be more explicitly represented. Results of the drain flow analysis show that the groundwater discharge to agricultural drains has decreased by approximately 4000 hectare-meters from the 1997-2000 average discharge. Most of this decrease takes place in the northern and southeastern portions of the subbasin. Results of the groundwater model show that the initial source of water to wells is groundwater storage. By 2006, approximately 56% of the water from wells is sourced from agricultural drains. Title: Investigating the Link Between Surface Water and Groundwater in the Tule Lake Subbasin, Oregon and California Author: Pischel, Esther Maria Year: 2014 Water allocation in the upper Klamath Basin of Oregon and California has been challenging. Irrigators have increasingly turned to groundwater to make up for surface water shortages because of shifts in allocation toward in-stream flows for Endangered Species Act listed fishes. The largest increase in groundwater pumping has been in and around the Bureau of Reclamation's Klamath Irrigation Project, which includes the Tule Lake subbasin in the southern part of the upper Klamath Basin. Previous groundwater flow model simulations indicate that water level declines from pumping may result in decreased flow to agricultural drains in the Tule Lake subbasin. Agricultural drains on the Klamath Project are an important source of water for downstream irrigators and for the Tule Lake and Lower Klamath Lake National Wildlife Refuges. To better assess the impact of increased pumping on drain flow and on the water balance of the groundwater system, flow data from agricultural drains were evaluated to investigate the changes that have taken place in groundwater discharge to drains since pumping volumes increased. Additionally, a fine-grid groundwater model of the Tule Lake subbasin was developed based on the existing regional flow model. The fine-grid model has sufficient vertical and horizontal resolution to simulate vertical head gradients, takes advantage of time-series data from 38 observation wells for model calibration, and allows agricultural drains to be more explicitly represented. Results of the drain flow analysis show that the groundwater discharge to agricultural drains has decreased by approximately 4000 hectare-meters from the 1997-2000 average discharge. Most of this decrease takes place in the northern and southeastern portions of the subbasin. Results of the groundwater model show that the initial source of water to wells is groundwater storage. By 2006, approximately 56% of the water from wells is sourced from agricultural drains. Close

• 23. [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