Scott Steinschneider - Academia.edu (original) (raw)

Papers by Scott Steinschneider

Water Resources Research, 2012

ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydro... more ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. This study presents a statistical framework that quantifies and propagates the uncertainties of hydrologic model response through projections of future streamflow under climate change. Different sources of hydrologic model uncertainty are accounted for using Bayesian modeling. The distribution of model residuals is formally characterized to quantify predictive skill, and Markov chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and residual error uncertainties are integrated to develop reliable prediction intervals for streamflow estimates. The Bayesian hydrologic modeling framework is then extended to a climate change impact assessment. Ensembles of baseline and future climate are downscaled from global circulation models and are used to drive simulations of streamflow over parameters drawn from the posterior space. Time series of streamflow statistics are calculated from baseline and future ensembles of simulated flows. Uncertainties in hydrologic model response, sampling error, and the range of future climate projections are integrated to help determine the level of confidence associated with hydrologic alteration between baseline and future climate regimes. A case study is conducted on the White River in Vermont, USA. Results indicate that the framework can be used to present a reliable depiction of the range of hydrologic alterations that may occur in the future.

World Environmental and Water Resources Congress 2010, 2010

Streamflow timing has shifted for many streams across New England (Hodgkins 2003, USGS 2005). Cur... more Streamflow timing has shifted for many streams across New England (Hodgkins 2003, USGS 2005). Current climate change forecasts project this trend to continue as a result of earlier snow melt, increased evapotranspiration, and more precipitation falling as rain during the ...

World Environmental and Water Resources Congress 2011, 2011

World Environmental and Water Resources Congress 2013, 2013

The cascade of uncertainty that underscores climate impact assessments of regional hydrology unde... more The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. Innovative approaches are required to unravel these uncertainties and formally test the significance of hydrologic alteration under future climate scenarios. This study presents a statistical framework that tests the likelihood of significant hydrologic alteration under assumed future climates. Hydrologic model uncertainty is formally characterized to enable accurate prediction intervals that can determine the statistical significance of differences between altered and baseline hydrologic simulations. Different sources of hydrologic model error are accounted for using a Bayesian approach. The sampling distribution of model errors is formally characterized to quantify predictive skill, and Markov-Chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Pa...

The El-Niño Southern Oscillation (ENSO) is a variation in the sea surface temperature (SST) in th... more The El-Niño Southern Oscillation (ENSO) is a variation in the sea surface temperature (SST) in the tropical eastern Pacific Ocean, and corresponding air surface pressure in the tropical western Pacific.

World Environmental and Water Resources Congress 2010, 2010

The purpose of this paper is to demonstrate the efficacy of using an optimization model to sugges... more The purpose of this paper is to demonstrate the efficacy of using an optimization model to suggest reservoir rules that address emerging environmental concerns while maintaining historical operating objectives for management in the Connecticut River. This is demonstrated by exploring ...

ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydro... more ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. Innovative approaches are required to unravel these uncertainties and formally test the significance of hydrologic alteration under future climate scenarios. This study presents a statistical framework that tests the likelihood of significant hydrologic alteration under assumed future climates. Hydrologic model uncertainty is formally characterized to enable accurate prediction intervals that can determine the statistical significance of differences between altered and baseline hydrologic simulations. Different sources of hydrologic model error are accounted for using a Bayesian approach. The sampling distribution of model errors is formally characterized to quantify predictive skill, and Markov-Chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and model uncertainties are integrated to develop accurate prediction intervals for streamflow estimates. Baseline and future hydrologic regimes are then simulated from historic and future climate data downscaled from global circulation model simulations. Predictive inference is utilized to determine the significance of hydrologic alteration between the baseline and future regimes. A case study is conducted on the White River in Vermont. Results indicate that the statistical method can help distinguish between significant alterations and errors inherent to the hydrologic model. The proposed method could prove valuable for informing climate change adaptation investments for water resources systems.

Keywords: Connecticut River Hydroclimate North Atlantic Oscillation East coast pressure trough No... more Keywords: Connecticut River Hydroclimate North Atlantic Oscillation East coast pressure trough North Atlantic Tripole Forecasting s u m m a r y Connections between summertime, ecologically relevant low-flow indicators and both winter and spring climate phenomena are explored for the Connecticut River Basin, with an emphasis on assessing forecast potential. Low-flow streamflow statistics deemed important for ecological health, including minimum 1-day mean flows, minimum 7-day mean flows, and monthly streamflow averages from June to Septem-ber, are derived from 61 years of continuous, daily streamflow data at 15 United States Geological Survey streamflow gauging stations across the basin. Relationships between the ecological flow indicators with leading sea-surface temperature and sea-level pressure are investigated using correlation and composite analysis. Results suggest lagged relationships of up to 5 months between summer streamflow and the wintertime North Atlantic Oscillation, springtime east coast pressure trough, and springtime North Atlantic Tripole. These climate states have been linked to shifts between zonal and meridonal airflow as well as sea-surface temperature anomalies off the coast of the eastern US, both of which have implications for the movement of moisture systems over the study region. This study suggests that residual influences on airflow and sea-surface temperature persist into the summer following these earlier climate states, influencing low-flow hydrology in the region. As eco-hydrologic flow targets often conflict with other stakeholder objectives within a watershed, reservoir operators may utilize such lagged tele-connection patterns to predict annual low-flow characteristics in the region and help negotiate tradeoffs between traditional water management objectives and those emphasizing ecological conservation.

[1] The implications of climate change and the potential nonstationarity of the hydrologic record... more [1] The implications of climate change and the potential nonstationarity of the hydrologic record necessitate innovative approaches to water management. This study presents a novel adaptation strategy for water reservoir management under nonstationary hydrologic conditions. Seasonal hydrologic forecasts and a real-option instrument allow reservoir operations that dynamically adapt to an evolving hydrologic record. System operating policies are conditioned on seasonal hydrologic forecasts to account for year-to-year variability and climate change and a real option is established to hedge against the risk associated with operational forecasts and unexpected climate outcomes. This scheme is implemented over an ensemble of climate futures based on general circulation model (GCM) simulations. Two alternative management strategies are considered, one in which system operations are optimized for the GCM-based ensemble mean projection of the future and a baseline strategy in which assumptions of stationarity are maintained and operations are left unchanged from historic norms. The approach is evaluated for a water supply– hydropower facility on the Westfield River in the northeast United States. Results suggest that seasonal hydrologic forecasts are a promising adaptation to nonstationary hydrology, even without the support of a risk hedging option. Surprisingly, the option approach enabled even a stationary assumption to perform well in the future, suggesting that option instruments alone can act as a robust adaptation mechanism. Citation: Steinschneider, S., and C. Brown (2012), Dynamic reservoir management with real-option risk hedging as a robust adaptation to nonstationary climate, Water Resour. Res., 48, W05524,

[1] The cascade of uncertainty that underscores climate impact assessments of regional hydrology ... more [1] The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. This study presents a statistical framework that quantifies and propagates the uncertainties of hydrologic model response through projections of future streamflow under climate change. Different sources of hydrologic model uncertainty are accounted for using Bayesian modeling. The distribution of model residuals is formally characterized to quantify predictive skill, and Markov chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and residual error uncertainties are integrated to develop reliable prediction intervals for streamflow estimates. The Bayesian hydrologic modeling framework is then extended to a climate change impact assessment. Ensembles of baseline and future climate are downscaled from global circulation models and are used to drive simulations of streamflow over parameters drawn from the posterior space. Time series of streamflow statistics are calculated from baseline and future ensembles of simulated flows. Uncertainties in hydrologic model response, sampling error, and the range of future climate projections are integrated to help determine the level of confidence associated with hydrologic alteration between baseline and future climate regimes. A case study is conducted on the White River in Vermont, USA. Results indicate that the framework can be used to present a reliable depiction of the range of hydrologic alterations that may occur in the future. Citation: Steinschneider, S., A. Polebitski, C. Brown, and B. H. Letcher (2012), Toward a statistical framework to quantify the uncertainties of hydrologic response under climate change, Water Resour.

1] Structured variation in the frequency spectrum of critical hydrologic variables can have impor... more 1] Structured variation in the frequency spectrum of critical hydrologic variables can have important implications for the design and management of water resources infrastructure, yet traditional hydrologic frequency analysis often ignores the influence of exogenous factors that can both precede and exert control over hydrologic responses. Moreover, emerging literature that has addressed predictable low-frequency oscillations in the probabilistic nature of hydrologic variables has focused almost exclusively on flood flows. This study explores a new approach for conditioning the frequency spectrum of hydrologic extremes on seasonal predictors and applies the method to annual minimum 7 day low flows, a critical low-flow statistic often utilized in water quality management and planning. A semiparametric local likelihood method is used to condition quantile estimates of the 7 day low flow on year-to-year hydroclimatic forecasts for two major rivers in the northeast United States. The local likelihood approach is employed in a Bayesian framework in which regional information is used to inform prior distributions of model parameters. The method is compared against a baseline approach that applies a static Bayesian inference with noninformative priors to derive unconditional parameter and quantile estimates. The implications of the approach for the efficacy of water quality regulations and as an adaptation to climate change are discussed.

Many water planning and operation decisions are affected by climate uncertainty. Given concerns a... more Many water planning and operation decisions are affected by climate uncertainty. Given concerns about the effects of uncertainty on the outcomes of long-term decisions, many water planners seek adaptation alternatives that are robust given a wide range of possible climate futures. However, there is no standardized paradigm for quantifying robustness in the water sector. This study uses a new framework for assessing the impact of future climate change and uncertainty on water supply systems and defines and demonstrates a new metric for quantifying climate robustness. The metric is based on the range of climate change space over which an alternative provides acceptable performance. The metric is independent of assumptions regarding future climate; however, GCM-based (or other) climate projections can be used to create a ''climate-informed'' version of the metric. The method is demonstrated for a water supply system in the northeast United States to evaluate the additional robustness that can be attained through optimal operational changes, by comparing optimal reservoir operations with current reservoir operations. Results show the additional robustness gained through adaptation. They also reveal the additional insight regarding robust adaptation gained from the decision-scaling approach that would not be discerned using a GCM projection-based analysis.

This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-In... more This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using non-parametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA.

This paper introduces the use of hierarchical Bayesian modeling as a method to estimate how kinet... more This paper introduces the use of hierarchical Bayesian modeling as a method to estimate how kinetic properties of environmental contaminants vary across experimental factors. The hierarchical modeling framework utilizes a multilevel statistical structure, in which kinetic rate constants represent contaminant degradation behavior for individual, lower-level experiments, but also exhibit a higher-level structure across different experimental conditions. The main benefit of this modeling approach is the ability to pool information between experiments in order to reduce the influence of outliers and produce more robust predictions of degradation rate constants in an out-of-sample context. The Bayesian estimation method is also very flexible, with the ability to relax distributional assumptions on parameters, account for heteroscedasticity in model residuals, include prior information or expert knowledge when available, and propagate all uncertainties into model predictions, all with relative ease. The benefits of the hierarchical Bayesian approach are demonstrated in a case study examining the pH dependence of hydrolysis rates of haloacetamides (HAMs). A comparison is provided against a simpler least-squares method to highlight the differences and benefits of the proposed method.dividual papers. This technical note is part of the Journal of Environmental Engineering, © ASCE, ISSN 0733-9372/06015008 /$25.00. © ASCE 06015008-1 J. Environ. Eng. J. Environ. Eng., 2015, 141(12): 06015008 Downloaded from ascelibrary.org by University of Massachusetts, Amherst on 12/12/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE 06015008-5 J. Environ. Eng. J. Environ. Eng., 2015, 141(12): 06015008 Downloaded from ascelibrary.org by University of Massachusetts, Amherst on 12/12/15.

Evidence from ecological studies suggests that the alteration of river flows downstream of reserv... more Evidence from ecological studies suggests that the alteration of river flows downstream of reservoirs can threaten native aquatic ecosystems and the services they offer. Innovative revisions to water management practices are required to improve the health of aquatic species while maintaining the benefits from current infrastructure projects. The impacts of individual reservoir operations on ecosystem vitality are often masked by the uncoordinated and compounding influences of several impoundments upstream, undermining the examination of environmental impacts from particular reservoirs in a large watershed system. This paper presents a large-scale optimization model that investigates the value of coordinated reservoir management practices for ecological benefits in a large watershed with several major reservoir systems operating for a range of management objectives. An application of the model is presented for the Connecticut River watershed, the largest river basin in New England and one of the most impounded rivers in the United States. The model can examine trade-offs between the maintenance of ecologically acceptable, daily streamflows at key locations throughout the watershed and traditional reservoir objectives, including flood risk reduction, municipal and riparian water supply, hydropower production, and recreation. The ecological streamflow targets are designed specifically for the basin's ecology in a collaborative process engaging regional experts and constitute a unique and innovative component of the modeling approach. This study focuses on the re-operation of a network of federal flood control dams for the restoration of environmental flows. Results suggest that coordinated changes to current flood control reservoir operations can restore aspects of the natural hydrologic flow regime necessary for ecosystem persistence without significantly reducing current flood risk reduction capabilities.

Approaches for probability density function (pdf) development of future climate often assume that... more Approaches for probability density function (pdf) development of future climate often assume that different climate models provide independent information, despite model similarities that stem from a common genealogy (models with shared code or developed at the same institution). Here we use an ensemble of projections from the Coupled Model Intercomparison Project Phase 5 to develop probabilistic climate information, with and without an accounting of intermodel correlations, for seven regions across the United States. We then use the pdfs to estimate midcentury climate-related risks to a water utility in one of the regions. We show that the variance of climate changes is underestimated across all regions if model correlations are ignored, and in some cases, the mean change shifts as well. When coupled with impact models of the hydrology and infrastructure of a water utility, the underestimated likelihood of large climate changes significantly alters the quantification of risk for water shortages by midcentury. STEINSCHNEIDER ET AL. INTERMODEL CORRELATION AND RISK 5014 Citation: Steinschneider, S., R. McCrary, L. O. Mearns, and C. Brown , The effects of climate model similarity on probabilistic climate projections and the implications for local, risk-based adaptation planning, Geophys.

Water Resources Research, 2012

ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydro... more ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. This study presents a statistical framework that quantifies and propagates the uncertainties of hydrologic model response through projections of future streamflow under climate change. Different sources of hydrologic model uncertainty are accounted for using Bayesian modeling. The distribution of model residuals is formally characterized to quantify predictive skill, and Markov chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and residual error uncertainties are integrated to develop reliable prediction intervals for streamflow estimates. The Bayesian hydrologic modeling framework is then extended to a climate change impact assessment. Ensembles of baseline and future climate are downscaled from global circulation models and are used to drive simulations of streamflow over parameters drawn from the posterior space. Time series of streamflow statistics are calculated from baseline and future ensembles of simulated flows. Uncertainties in hydrologic model response, sampling error, and the range of future climate projections are integrated to help determine the level of confidence associated with hydrologic alteration between baseline and future climate regimes. A case study is conducted on the White River in Vermont, USA. Results indicate that the framework can be used to present a reliable depiction of the range of hydrologic alterations that may occur in the future.

World Environmental and Water Resources Congress 2010, 2010

Streamflow timing has shifted for many streams across New England (Hodgkins 2003, USGS 2005). Cur... more Streamflow timing has shifted for many streams across New England (Hodgkins 2003, USGS 2005). Current climate change forecasts project this trend to continue as a result of earlier snow melt, increased evapotranspiration, and more precipitation falling as rain during the ...

World Environmental and Water Resources Congress 2011, 2011

World Environmental and Water Resources Congress 2013, 2013

The cascade of uncertainty that underscores climate impact assessments of regional hydrology unde... more The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. Innovative approaches are required to unravel these uncertainties and formally test the significance of hydrologic alteration under future climate scenarios. This study presents a statistical framework that tests the likelihood of significant hydrologic alteration under assumed future climates. Hydrologic model uncertainty is formally characterized to enable accurate prediction intervals that can determine the statistical significance of differences between altered and baseline hydrologic simulations. Different sources of hydrologic model error are accounted for using a Bayesian approach. The sampling distribution of model errors is formally characterized to quantify predictive skill, and Markov-Chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Pa...

The El-Niño Southern Oscillation (ENSO) is a variation in the sea surface temperature (SST) in th... more The El-Niño Southern Oscillation (ENSO) is a variation in the sea surface temperature (SST) in the tropical eastern Pacific Ocean, and corresponding air surface pressure in the tropical western Pacific.

World Environmental and Water Resources Congress 2010, 2010

The purpose of this paper is to demonstrate the efficacy of using an optimization model to sugges... more The purpose of this paper is to demonstrate the efficacy of using an optimization model to suggest reservoir rules that address emerging environmental concerns while maintaining historical operating objectives for management in the Connecticut River. This is demonstrated by exploring ...

ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydro... more ABSTRACT The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. Innovative approaches are required to unravel these uncertainties and formally test the significance of hydrologic alteration under future climate scenarios. This study presents a statistical framework that tests the likelihood of significant hydrologic alteration under assumed future climates. Hydrologic model uncertainty is formally characterized to enable accurate prediction intervals that can determine the statistical significance of differences between altered and baseline hydrologic simulations. Different sources of hydrologic model error are accounted for using a Bayesian approach. The sampling distribution of model errors is formally characterized to quantify predictive skill, and Markov-Chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and model uncertainties are integrated to develop accurate prediction intervals for streamflow estimates. Baseline and future hydrologic regimes are then simulated from historic and future climate data downscaled from global circulation model simulations. Predictive inference is utilized to determine the significance of hydrologic alteration between the baseline and future regimes. A case study is conducted on the White River in Vermont. Results indicate that the statistical method can help distinguish between significant alterations and errors inherent to the hydrologic model. The proposed method could prove valuable for informing climate change adaptation investments for water resources systems.

Keywords: Connecticut River Hydroclimate North Atlantic Oscillation East coast pressure trough No... more Keywords: Connecticut River Hydroclimate North Atlantic Oscillation East coast pressure trough North Atlantic Tripole Forecasting s u m m a r y Connections between summertime, ecologically relevant low-flow indicators and both winter and spring climate phenomena are explored for the Connecticut River Basin, with an emphasis on assessing forecast potential. Low-flow streamflow statistics deemed important for ecological health, including minimum 1-day mean flows, minimum 7-day mean flows, and monthly streamflow averages from June to Septem-ber, are derived from 61 years of continuous, daily streamflow data at 15 United States Geological Survey streamflow gauging stations across the basin. Relationships between the ecological flow indicators with leading sea-surface temperature and sea-level pressure are investigated using correlation and composite analysis. Results suggest lagged relationships of up to 5 months between summer streamflow and the wintertime North Atlantic Oscillation, springtime east coast pressure trough, and springtime North Atlantic Tripole. These climate states have been linked to shifts between zonal and meridonal airflow as well as sea-surface temperature anomalies off the coast of the eastern US, both of which have implications for the movement of moisture systems over the study region. This study suggests that residual influences on airflow and sea-surface temperature persist into the summer following these earlier climate states, influencing low-flow hydrology in the region. As eco-hydrologic flow targets often conflict with other stakeholder objectives within a watershed, reservoir operators may utilize such lagged tele-connection patterns to predict annual low-flow characteristics in the region and help negotiate tradeoffs between traditional water management objectives and those emphasizing ecological conservation.

[1] The implications of climate change and the potential nonstationarity of the hydrologic record... more [1] The implications of climate change and the potential nonstationarity of the hydrologic record necessitate innovative approaches to water management. This study presents a novel adaptation strategy for water reservoir management under nonstationary hydrologic conditions. Seasonal hydrologic forecasts and a real-option instrument allow reservoir operations that dynamically adapt to an evolving hydrologic record. System operating policies are conditioned on seasonal hydrologic forecasts to account for year-to-year variability and climate change and a real option is established to hedge against the risk associated with operational forecasts and unexpected climate outcomes. This scheme is implemented over an ensemble of climate futures based on general circulation model (GCM) simulations. Two alternative management strategies are considered, one in which system operations are optimized for the GCM-based ensemble mean projection of the future and a baseline strategy in which assumptions of stationarity are maintained and operations are left unchanged from historic norms. The approach is evaluated for a water supply– hydropower facility on the Westfield River in the northeast United States. Results suggest that seasonal hydrologic forecasts are a promising adaptation to nonstationary hydrology, even without the support of a risk hedging option. Surprisingly, the option approach enabled even a stationary assumption to perform well in the future, suggesting that option instruments alone can act as a robust adaptation mechanism. Citation: Steinschneider, S., and C. Brown (2012), Dynamic reservoir management with real-option risk hedging as a robust adaptation to nonstationary climate, Water Resour. Res., 48, W05524,

[1] The cascade of uncertainty that underscores climate impact assessments of regional hydrology ... more [1] The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. This study presents a statistical framework that quantifies and propagates the uncertainties of hydrologic model response through projections of future streamflow under climate change. Different sources of hydrologic model uncertainty are accounted for using Bayesian modeling. The distribution of model residuals is formally characterized to quantify predictive skill, and Markov chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and residual error uncertainties are integrated to develop reliable prediction intervals for streamflow estimates. The Bayesian hydrologic modeling framework is then extended to a climate change impact assessment. Ensembles of baseline and future climate are downscaled from global circulation models and are used to drive simulations of streamflow over parameters drawn from the posterior space. Time series of streamflow statistics are calculated from baseline and future ensembles of simulated flows. Uncertainties in hydrologic model response, sampling error, and the range of future climate projections are integrated to help determine the level of confidence associated with hydrologic alteration between baseline and future climate regimes. A case study is conducted on the White River in Vermont, USA. Results indicate that the framework can be used to present a reliable depiction of the range of hydrologic alterations that may occur in the future. Citation: Steinschneider, S., A. Polebitski, C. Brown, and B. H. Letcher (2012), Toward a statistical framework to quantify the uncertainties of hydrologic response under climate change, Water Resour.

1] Structured variation in the frequency spectrum of critical hydrologic variables can have impor... more 1] Structured variation in the frequency spectrum of critical hydrologic variables can have important implications for the design and management of water resources infrastructure, yet traditional hydrologic frequency analysis often ignores the influence of exogenous factors that can both precede and exert control over hydrologic responses. Moreover, emerging literature that has addressed predictable low-frequency oscillations in the probabilistic nature of hydrologic variables has focused almost exclusively on flood flows. This study explores a new approach for conditioning the frequency spectrum of hydrologic extremes on seasonal predictors and applies the method to annual minimum 7 day low flows, a critical low-flow statistic often utilized in water quality management and planning. A semiparametric local likelihood method is used to condition quantile estimates of the 7 day low flow on year-to-year hydroclimatic forecasts for two major rivers in the northeast United States. The local likelihood approach is employed in a Bayesian framework in which regional information is used to inform prior distributions of model parameters. The method is compared against a baseline approach that applies a static Bayesian inference with noninformative priors to derive unconditional parameter and quantile estimates. The implications of the approach for the efficacy of water quality regulations and as an adaptation to climate change are discussed.

Many water planning and operation decisions are affected by climate uncertainty. Given concerns a... more Many water planning and operation decisions are affected by climate uncertainty. Given concerns about the effects of uncertainty on the outcomes of long-term decisions, many water planners seek adaptation alternatives that are robust given a wide range of possible climate futures. However, there is no standardized paradigm for quantifying robustness in the water sector. This study uses a new framework for assessing the impact of future climate change and uncertainty on water supply systems and defines and demonstrates a new metric for quantifying climate robustness. The metric is based on the range of climate change space over which an alternative provides acceptable performance. The metric is independent of assumptions regarding future climate; however, GCM-based (or other) climate projections can be used to create a ''climate-informed'' version of the metric. The method is demonstrated for a water supply system in the northeast United States to evaluate the additional robustness that can be attained through optimal operational changes, by comparing optimal reservoir operations with current reservoir operations. Results show the additional robustness gained through adaptation. They also reveal the additional insight regarding robust adaptation gained from the decision-scaling approach that would not be discerned using a GCM projection-based analysis.

This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-In... more This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using non-parametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA.

This paper introduces the use of hierarchical Bayesian modeling as a method to estimate how kinet... more This paper introduces the use of hierarchical Bayesian modeling as a method to estimate how kinetic properties of environmental contaminants vary across experimental factors. The hierarchical modeling framework utilizes a multilevel statistical structure, in which kinetic rate constants represent contaminant degradation behavior for individual, lower-level experiments, but also exhibit a higher-level structure across different experimental conditions. The main benefit of this modeling approach is the ability to pool information between experiments in order to reduce the influence of outliers and produce more robust predictions of degradation rate constants in an out-of-sample context. The Bayesian estimation method is also very flexible, with the ability to relax distributional assumptions on parameters, account for heteroscedasticity in model residuals, include prior information or expert knowledge when available, and propagate all uncertainties into model predictions, all with relative ease. The benefits of the hierarchical Bayesian approach are demonstrated in a case study examining the pH dependence of hydrolysis rates of haloacetamides (HAMs). A comparison is provided against a simpler least-squares method to highlight the differences and benefits of the proposed method.dividual papers. This technical note is part of the Journal of Environmental Engineering, © ASCE, ISSN 0733-9372/06015008 /$25.00. © ASCE 06015008-1 J. Environ. Eng. J. Environ. Eng., 2015, 141(12): 06015008 Downloaded from ascelibrary.org by University of Massachusetts, Amherst on 12/12/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE 06015008-5 J. Environ. Eng. J. Environ. Eng., 2015, 141(12): 06015008 Downloaded from ascelibrary.org by University of Massachusetts, Amherst on 12/12/15.

Evidence from ecological studies suggests that the alteration of river flows downstream of reserv... more Evidence from ecological studies suggests that the alteration of river flows downstream of reservoirs can threaten native aquatic ecosystems and the services they offer. Innovative revisions to water management practices are required to improve the health of aquatic species while maintaining the benefits from current infrastructure projects. The impacts of individual reservoir operations on ecosystem vitality are often masked by the uncoordinated and compounding influences of several impoundments upstream, undermining the examination of environmental impacts from particular reservoirs in a large watershed system. This paper presents a large-scale optimization model that investigates the value of coordinated reservoir management practices for ecological benefits in a large watershed with several major reservoir systems operating for a range of management objectives. An application of the model is presented for the Connecticut River watershed, the largest river basin in New England and one of the most impounded rivers in the United States. The model can examine trade-offs between the maintenance of ecologically acceptable, daily streamflows at key locations throughout the watershed and traditional reservoir objectives, including flood risk reduction, municipal and riparian water supply, hydropower production, and recreation. The ecological streamflow targets are designed specifically for the basin's ecology in a collaborative process engaging regional experts and constitute a unique and innovative component of the modeling approach. This study focuses on the re-operation of a network of federal flood control dams for the restoration of environmental flows. Results suggest that coordinated changes to current flood control reservoir operations can restore aspects of the natural hydrologic flow regime necessary for ecosystem persistence without significantly reducing current flood risk reduction capabilities.

Approaches for probability density function (pdf) development of future climate often assume that... more Approaches for probability density function (pdf) development of future climate often assume that different climate models provide independent information, despite model similarities that stem from a common genealogy (models with shared code or developed at the same institution). Here we use an ensemble of projections from the Coupled Model Intercomparison Project Phase 5 to develop probabilistic climate information, with and without an accounting of intermodel correlations, for seven regions across the United States. We then use the pdfs to estimate midcentury climate-related risks to a water utility in one of the regions. We show that the variance of climate changes is underestimated across all regions if model correlations are ignored, and in some cases, the mean change shifts as well. When coupled with impact models of the hydrology and infrastructure of a water utility, the underestimated likelihood of large climate changes significantly alters the quantification of risk for water shortages by midcentury. STEINSCHNEIDER ET AL. INTERMODEL CORRELATION AND RISK 5014 Citation: Steinschneider, S., R. McCrary, L. O. Mearns, and C. Brown , The effects of climate model similarity on probabilistic climate projections and the implications for local, risk-based adaptation planning, Geophys.