ROBUST WATER RESOURCES MANAGEMENT UNDER CLIMATIC AND ENVIRONMENTAL UNCERTAINTY

Climate change is expected to have dramatic impacts on the water resources sector, and there is increasing concern that some degree of adaptation will be required to ensure sustainable water provision in many regions of the world. However, adapting to future climatic conditions is challenged by the considerable uncertainty and disagreement surrounding projections of future hydrologic conditions, particularly at local scales relevant for decision making. Furthermore, many argue that these impacts cannot be confidently represented probabilistically, resulting in uncertainty that confounds traditional approaches for decision support under uncertainty. In the face of these challenges, a number of methods have been developed to better characterize and make decisions in the face of climatic uncertainty. The objective of this dissertation is to critically evaluate methods for impact assessment and decision support in the water resource sector, with a particular emphasis on deep uncertainty surrounding climatic and environmental conditions. This issue is explored through the evaluation of four research questions: 1. How does the choice of modeling approach for empirical streamflow simulation contribute to bias and uncertainty when predicting climate change impacts? 2. How does Robust Decision Making (RDM), a method largely developed in the water resource and climate adaptation field, compare to other methods for risk assessment under deep uncertainty that have been developed in the risk analysis field? 3. How does the method used to aggregate multiple criteria impact the results of the scenario discovery process within the RDM framework? 4. How can methods such as RDM, which generally still rely on complex simulation models and detailed climate model projections, be adapted to data-scarce regions where these models and projections may not be available? By providing a systematic and thorough evaluation of novel methods for climate change impact assessment and adaptation, this dissertation ultimately aims to improve our ability to create robust, sustainable water infrastructure in the face of highly uncertain future climate conditions. Additionally, the use of the Lake Tana basin in Ethiopia as a case study for three of the above questions has led to important applied contributions to infrastructure planning in data-scarce regions of the developing world

Fuel for growth and development

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115971/1/sign854.pd

Anticipating and Adapting to the Future Impacts of Climate Change on the Health, Security and Welfare of Low Elevation Coastal Zone (LECZ) Communities in Southeastern USA

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries

Statistically Evaluating Water Use Historically and Across Multiple Users in Virginia

Virginia boasts plentiful water resources, however, the need for more stringent water management is rising as population increases and climate changes. This study summarizes key aspects of water usage in Virginia via a broad-scale analysis of multiple water users through forty years of time-series records from Virginia Department of Environmental Quality. A full spectrum of users is considered, including energy, industrial, agricultural and municipal. We also investigate explanatory variables associated with higher water use, such as climate and economic conditions, using regression

Folder 02- Climate data (PRISM)

This data is from Oregon State U PRISM group. Thirty years of daily raster values are aggregating using GIS ModelBuilder Function. This is the daily files aggregated to water year in mm (precip) or degrees C (temp). This data is used in regression templates

Folder 04- Mann Kendall Testing

Mann-Kendall (MK) tests were applied to determine if there was a statistically significant upward or downward trend over time. The primary .R package used is 'zyp' which accounts for autocorrelation

GIS Mann Kendall Figures

Shows the tau and pvalues spatially for each sector. Generated in GIS files DEQsummary MK pand tau (sector)

Simulation of Flood-induced Human Migration at the Municipal-Scale: A Stochastic Agent-based Model of Relocation Response to Coastal Flooding

This repository contains Netlogo code for an agent-based model of urban/rural mobility in the face of recurrent flooding

MK Code

contains Statewide, Per facility and by county tau and p value