Scott Steinschneider

A climate change range‐based method for estimating robustness for water resources supply

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.

Dynamic reservoir management with real-option risk hedging as a robust adaptation to nonstationary climate

[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.

A hierarchical Bayesian regional model for nonstationary precipitation extremes in Northern California conditioned on tropical moisture exports

Warm, moist, and longitudinally confined tropical air masses are being linked to some of the most extreme precipitation and flooding events in the midlatitudes. The interannual frequency and intensity of such atmospheric rivers (ARs), or tropical moisture exports (TMEs), are connected to the risk of extreme precipitation events in areas where moisture convergence occurs. This study presents a nonstationary, regional frequency analysis of precipitation extremes in Northern California that is conditioned on the interannual variability of TMEs entering the region. Parameters of a multisite peaks-over-threshold model are allowed to vary conditional on the integrated moisture delivery from TMEs over the area. Parameters are also related to time-invariant, local characteristics to facilitate regionalization to ungaged sites. The model is developed and calibrated in a hierarchical Bayesian framework to support partial pooling and enhance regionalization skill. The model is cross validated along with two alternative, increasingly parsimonious formulations to assess the additional skill provided by the covariates. Climate diagnostics are also used to better understand the instances where TMEs fail to explain variations in rainfall extremes to provide a path forward for further model improvement. The modeling structure is designed to link seasonal forecasting and long-term projections of TMEs directly to regional models of extremes used for risk estimation. Results suggest that the inclusion of TME-based information greatly improves the characterization of extremes, particularly for their frequency of occurrence. Diagnostics indicate that the model could be further improved by considering an index for frontal systems as an additional covariate.

Expanded decision-scaling framework to select robust long-term water-system plans under hydroclimatic uncertainties

AbstractThis paper presents a decision-scaling based framework to determine whether one or more preselected planning alternatives for a multiobjective water-resources system are robust to a variety of nonstationary hydroclimatic conditions and modeling uncertainties. The decision-scaling methodology is advanced beyond previous applications with an efficient procedure to select realizations of climate variability and Bayesian methods to assess the effects of hydrologic uncertainty. Monte Carlo simulations are used to identify long-term planning alternatives that are robust despite the hydroclimatic uncertainties. A new metric is proposed to define robustness in this context. The framework is coupled with a host of long-term projections to understand the likelihood of potential future changes and provide useful guidance for planning. The effects of climate model downscaling and credibility on the decision process are discussed. The approach is demonstrated in a case study for a dual-purpose surface water re...

Reservoir management optimization for basin-wide ecological restoration in the Connecticut River.

AbstractEvidence 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 mo...

Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima

The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.

The effects of climate model similarity on probabilistic climate projections and the implications for local, risk‐based adaptation planning

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.

Daily Precipitation and Tropical Moisture Exports across the Eastern United States: An Application of Archetypal Analysis to Identify Spatiotemporal Structure

AbstractThis 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 nonparametric generalized linear models (GLMs). Results show that t...

Can PDSI inform extreme precipitation?: An exploration with a 500 year long paleoclimate reconstruction over the U.S.

This study explores whether it is possible to reconstruct the frequency of extreme precipitation occurrence across the contiguous United States (CONUS) using the Living Blended Drought Atlas (LBDA), a 500 year paleoclimate reconstruction of the summer (June–August) Palmer Drought Severity Index (PDSI). We first identify regions of the country where the LBDA may reflect the occurrence of extremes based on their seasonality and contribution to total annual moisture delivery. Correlation measures are used to assess the relationship between the frequencies of extreme precipitation occurrence and both the instrumental monthly PDSI and the annual LBDA-estimated PDSI. Extreme precipitation is found to account for a large portion of total precipitation west of the Mississippi River and clusters in particular seasons (winter and summer), supporting a strong relationship with the LBDA without much information loss from the instrumental PDSI data. Dimension reduction techniques are used to explore the joint spatiotemporal structure of extreme precipitation occurrence and LBDA across the country. The primary modes of variability of the LBDA and extreme precipitation occurrence relate remarkably well for a region centered over the southwest that exhibits an ENSO-like time-frequency structure. Generalized linear models (GLMs) are used to demonstrate the feasibility of reconstructing the annual extreme precipitation frequency over the 500 year prehistoric record at two sites in the southwest and Southern Plains. GLM-based reconstructions show a high degree of structured variability in the likelihood of extreme precipitation occurrences over the prehistoric record.

Selecting Stochastic Climate Realizations to Efficiently Explore a Wide Range of Climate Risk to Water Resource Systems

AbstractThere are significant computational requirements for assessing climate change impacts on water resource system reliability and vulnerability, particularly when analyzing a wide range of plausible scenarios. These requirements often deter analysts from exhaustively identifying climate hazards. This technical note investigates two approaches for generating a subset of stochastic climate realizations that efficiently explore a range of risk to water supply systems. In both methods, a large ensemble of stochastic weather time series is generated to simulate the natural variability of the local climate system, and a selected subset of these sequences is used in the impacts assessment. Method 1 selects the subset by first passing the entire ensemble through a rainfall-runoff model and then screening the hydrologic sequences using the sequent peak algorithm. Method 2 selects a subset of climate sequences based on climate statistics alone, prior to hydrological modeling. Both methods provide insight for i...