2015 AWRA Summer Specialty Conference: Climate Change Adaptation
Poster Session
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Monday, June 15 - Tuesday, June 16, 2015
TECHNICAL POSTER SESSIONS

Posters will be on display Monday, from 8:30 AM - 6:30 PM (Poster Presenters will be at their posters from
5:00 PM - 6:30 PM during the Opening Networking Reception) and Tuesday from 8:30 AM - 3:30 PM

Note: The Presenter of each poster is in BOLD type immediately following the poster title.
Co-authors are then listed in parentheses.

Forecasting Extreme Events: Making Sense of Noisy Climate Data in Support of Water Resources Planning - Jenny Bywater, CDM Smith, New Orleans, LA (co-authors: T. Cox, M. Heineman)

Understanding trends in climate data, both past and future, is critical to long term water resources planning. Unfortunately, climate data, precipitation data in particular, are notoriously noisy with large scatter and lacking in readily apparent patterns or trends. Large timescale changes, trends, or non-stationarity in the data are often difficult to decipher from the more random small timescale variability in both historical observations and global climate model (GCM) projections. Uncertainties associated with GCM future precipitation projections are also known to be high. These characteristics can limit the utility of these data in water resources planning studies. In the work to be presented, monthly and daily GCM projections, and historical observations are translated into commonly used summary metrics for extreme event planning and evaluation: peak 24-hour storm events and the Palmer Drought Severity Index (PDSI). Statistical trend analyses on these two metrics were used as a simple means to better understand the data, specifically with respect to suggested climate change, and to support long term forecasting. Two case study cities, Atlanta (GA) and Austin (TX), were used to demonstrate these methods. Results for Atlanta show that while temperatures have been increasing over the past 100+ years at a statistically significant level (p ≤ 0.1), significant trends in historical precipitation data are lacking. For Austin, statistically significant trends were lacking for both historical temperature and precipitation. Additionally, no statistically significant trends were identified for calculated historical (1900 - 2013) PDSI values for either city. As expected, the vast majority of climate model projections, for both cities, exhibit statistically significant positive trends in monthly average temperature through the end of the 21st century. Less expectedly, 36% of the Atlanta GCM projections and 39% of the Austin projections also exhibit significant trends in monthly precipitation though the end of the 21st century. For Atlanta, the majority of these trends are positive (increasing precipitation); while for Austin the trends are primarily negative (decreasing precipitation). Further, approximately 20% of the Atlanta projections display a statistically significant upward trend in annual maximum 24-hour precipitation. None of the analyzed GCM projections for Atlanta indicate a decreasing trend in 24-hour storm events. Austin climate projections offer a lower consensus on future storm trending. Of the 69 GCM projections analyzed, only 8 (12%) displayed a statistically significant increasing trend in 24-hour storm magnitude, while 2 displayed a significant decreasing trend. Future projected PDSI values, show statistically significant decreasing trends for the majority of both Atlanta and Austin GCM projections for the period 2000 - 2100. Thus, droughts are projected to be more frequent and more intense in the future compared to the past. For Atlanta, this is despite the fact that precipitation is generally projected to increase, rather than decrease. The methodology developed is a way to derive important insights from climate data, and in particular climate model projections, that can serve as an early and important step in water resources planning. This methodology is both effective and practical and can be applied at minimal cost.

Adapting to Future Environmental Change: An Assessment of Implications for Recreational Water Quality Standards Using Catchment-Scale Modeling - Rory Coffey, University College Dublin, Dublin, Ireland (co-authors: B. Benham, K. Kline, M. Leigh Wolfe, E. Cummins)

Adapting to the impacts of environmental change, in particular for water quality, is one of the most pressing challenges for water policy globally. Currently, European Union and United States regulatory structures fail to explicitly account for the potential threats caused by environmental change to water quality in recreational freshwaters. The objective of this study was to use watershed modelling to assess future management strategies and adaptation measures that may be required to meet existing microbial water quality standards. The modelling work considers a suite of possible future environmental change scenarios for watersheds in the west of Ireland and Virginia, USA. Increases in microbial load were apparent for all environmental change scenarios across all watersheds. Extensive reductions in microbial source loads (predominantly from point sources) for the Pigg (US) and Black (Ireland) watersheds were required to meet existing US Clean Water Act standards for the future scenarios simulated. However, under the European Union Bathing Water Directive, all watersheds assessed were within the "excellent" classification for the various environmental change scenarios indicating that these standard may be more achievable in future years. Outcomes of the work suggest that managing risks by adapting existing regulatory frameworks and planning for the effects of environmental change must become a critical component of watershed planning in the intermediate term. This will provide safe recreational water for use in society and protect public health

Hydrologic Modeling for Climate Change Adaptation - Claudia Leon, Riverside Technology, Inc., Fort Collins, CO

Climate change adaptation may best be thought of not as a specific action taken in preparation for an impending event but rather as an approach to planning for resource management that a) anticipates and accounts for the uncertainties of climatic variability, and b) prepares for the continuation of current trends and the possible realization of projected future climate trends. Hydrologic models are the best tools for identifying climate change and variability impacts to the hydrologic response of a basin over the long term because the model parameters calibrated with historical data inherently incorporate historical climate variability and therefore should produce reasonable responses to future climate variability. These models first require good baseline data with which to model current conditions.

One case study is presented demonstrating the application of hydrologic models to inform climate change adaptation planning processes. The Nature Conservancy (TNC) under the USAID-TNC Environmental Protection Program requested assistance from Riverside Technology, inc. (Riverside) to evaluate the impact of climate and land use changes in the water and sediment yield in four basins in the Dominican Republic. The Soil and Water Assessment Tool (SWAT) was used for this purpose. Five future land use land cover scenarios along with a subset of five climate change projections representative of a range of possible future outcomes were input into the hydrologic model. Results show that changes in flow volumes and sediment yield are sensitive to both changes in climate forcings and to the characteristics of each particular basin. These results can guide the nature of climate change adaptation strategies in that country.

Water Quality Standards and Protection of Water Resources from Climate Change Impacts - Olga Naidenko, U.S. EPA, Washington, DC

Climate change poses new, previously unanticipated challenges to water resource management, for example from extreme hydrologic events such as droughts and floods, sustained rise in air and water temperatures, altered flow patterns and potentially increased toxicity and reactivity of water pollutants. Water Quality Standards, mandated under the Clean Water Act, are the foundation of water pollution control program for protecting and restoring the Nation's rivers, streams, lakes, wetlands and coastal waters. These standards are adopted by the states and authorized tribes and define the goals for specific water bodies by designating their uses, setting pollutant criteria to protect those uses, and maintaining or improving existing water quality through antidegradation policies. In this presentation we will explore how water quality standards can be used as a holistic tool to help protect water resources and clean freshwater from potential climate change impacts. Different uses of water such as drinking water supplies, recreation and tourism, healthy aquatic ecosystems, fishing and shellfish harvesting as well as agricultural, industrial and energy needs could all be affected by climate change. Designated uses and water quality criteria that consider climate change effects and prepare for climate change adaptation may increase the resiliency of the Nation's waters to extreme weather events and growing urbanization. When developing, adopting and updating water quality standards, states and authorized tribes could start by identifying long-term goals for water resources and working with local, regional and federal partners involved in water resource management to implement priority actions needed to ensure adequate, clean water supplies. In order to assess whether water bodies and watersheds in a state/tribal land might be experiencing climate change impacts, ongoing water quality monitoring is essential. Water quality and quantity data are needed to evaluate water resources' vulnerability to climate change and improve water and climate change information for decision-making. Developing water quality criteria and antidegradation policies to face the challenges of climate change may help to avoid situations where designated uses for water bodies could become difficult to attain or unattainable due, either in part or exclusively, to the effects of climate change and other anthropogenic influences. Ensuring that designated uses are maintained and water bodies and watersheds are resilient to climate change may also help resolve and/or avoid resource-intensive water quality problems that could occur as a consequence of changing climate.

Innovative Meso-scale Framework to Monitor and Forecast Spatiotemporal Estuarine Chlorophyll Water Quality Profile for Assessing Climate Change Mitigation and Adaptation Strategies - Jaewan Yoon, National Research Foundation of Korea, Daejeon, South Korea (co-author: K. Park)

Aggregative and cumulative industrialization, urbanization and anthropogenic activities have led to a number of global environmental issues such as carbon cycle surplus and resultant climate change and sea-level rise in a macro-level. In addition to efforts reducing source-level contributing factors to such global environmental issues, it also becomes increasingly critical to be able to assess and predict the system responses, considering the Nature as a reproducible system, in short- and long-term bases for identifying feasible mitigation and adaptation strategies. Estuaries are vulnerable ecosystems that are sensitive to both natural and anthropogenic disturbances, acting as an acute posteriority of system response to terrestrial, atmospheric and aquatic trigger events. For instance, shift in climate change causes alterations in estuaries in form of frequent eutrophication events, reduced dissolved oxygen (DO), nutrient enrichment and temperature change. Conventionally, closely monitoring of chlorophyll concentration and key water quality parameter derivatives in estuaries in a micro-scale in situ have been standard approach to keep track of eutrophic status and trends. Reflecting current scale limitation and intrinsic latency in monitoring climate change and sea-level rise phenomena, a new paradigm for chlorophyll monitoring approach was developed by incorporating satellite data source to parameterize spatiotemporal estuarine chlorophyll concentration profiles with sustainable reliability and cost effectiveness. A key underpinning of this new paradigm is a meso-scale monitoring and forecasting framework binding spatiotemporal characteristics of Moderate Resolution Imaging Spectroradiameter (MODIS) satellite imagery and time-dependent water quality parameters with ambient water temperature as a conjugating factor to estimate and forecast estuarine eutrophic status and trends. Resultant spatiotemporal estuarine chlorophyll parameterization model (SECPM) framework monitors chlorophyll concentration using MODIS imagery, transfer function models (TFM) describing trigger water quality parameters such as PO4 and DO toward chlorophyll concentration, and seasonal factor model in spring and fall seasons the mesohaline segment in the James River Estuary, Virginia, USA. The TFM model is applicable in the temperature range between 6°C and 23°C in spring and in the temperature range between 19°C and 25°C based on sensitivity analysis results. The SECPMs in spring and fall are comparable in performance to the conventional modelling approach that estimates chlorophyll concentration using in-situ temperature and dissolved oxygen in terms of RMSE and R-square (0.851-0.602 and 0.870-0.903, respectively at α=0.05). Results indicated that the SECPM framework represents the variability of chlorophyll concentration more accurately than other approach over time and over the temperature ranges. SECPM can enhance an existing water quality monitoring and assessment programs in estuaries that are managed by municipal agencies and local water quality decision makers. SECPM framework can be effectively employed as a tool for evaluating various water quality management scenarios in a short-term, especially for complex estuarine systems, and for evaluating a long-term planning, mitigation and adaptation strategies by cross-referencing macro-level, global climate change and sea-level rise trends with estimated meso-scale, local system responses in climate change and sea-level rise. Current framework was developed for the James River Estuary, however paradigm implemented in the framework can be flexibly and rapidly replicated for different estuaries for similar purpose.

WARMF: A Comprehensive Decision Support Tool to Assess Future Water Quality and Quantity Conditions Under Integrated Scenarios of Future Change - Scott Sheeder, Systech Water Resources, Inc., Walnut Creek, CA (co-authors: K. van Werkhoven, Joel Herr)

Water resources across the globe are highly vulnerable to climate change. Increasing average temperature and changing precipitation patterns may cause a range of issues from reduced water supply and elevated water temperatures, to more frequent flooding and higher sediment loads. Water managers are faced with the challenge of minimizing adverse effects in their watershed through climate change adaptation, while at the same time accounting for other projected changes, such as land use, development, and agricultural practices. The Watershed Analysis Risk Management Framework (WARMF) is a comprehensive decision support system that provides water managers with a single, accessible tool to develop, simulate, and analyze the potential effects of climate change in conjunction with other environmental or management-related changes in their watershed. The system incorporates a GIS-based graphical user interface (GUI) that makes development of future scenarios straight forward and intuitive. A climate change module enables the user to define projected changes in future temperature and precipitation, which are integrated with any other defined changes in the watershed for simulations. The underlying physically-based model simulates watershed hydrology along with more than 30 water quality constituents. Examples will be presented that demonstrate the use of WARMF and its associated climate change scenario development tools to assess the impacts of climate change on water quantity and quality in multiple watersheds. Selected examples will include climate change, as well as changes to other factors such as agricultural practices, reservoir management, land use, best management practices, and pollution loads.