Kathleen D. White

Projected Changes in Discharge in an Agricultural Watershed in Iowa

Our improved capability to adapt to the future changes in discharge is linked to our capability to predict the magnitude or at least the direction of these changes. For the agricultural United States Midwest, too much or too little water has severe socioeconomic impacts. Here, we focus on the Raccoon River at Van Meter, Iowa, and use a statistical approach to examine projected changes in discharge. We build on statistical models using rainfall and harvested corn and soybean acreage to explain the observed discharge variability. We then use projections of these two predictors to examine the projected discharge response. Results are based on seven global climate models part of the Coupled Model Intercomparison Project Phase 5 and two representative concentration pathways (RCPs 4.5 and 8.5). There is not a strong signal of change in the discharge projections under the RCP 4.5. However, the results for the RCP 8.5 point to a stronger changing signal related to larger projected increases in rainfall, resulting in increased trends, in particular, in the upper part of the discharge distribution (i.e., 60th percentile and above). Examination of two hypothetical agricultural scenarios indicates that these increasing trends could be alleviated by decreasing the extent of the agricultural production. We also discuss how the methodology presented in this study represents a viable approach to move forward with the concept of return period for engineering design and management in a nonstationary world.

A multi-criteria geographic information systems approach for the measurement of vulnerability to climate change

A flexible procedure for the development of a multi-criteria composite index to measure relative vulnerability under future climate change scenarios is presented. The composite index is developed using the Weighted Ordered Weighted Average (WOWA) aggregation technique which enables the selection of different levels of trade-off, which controls the degree to which indicators are able to average out others. We explore this approach in an illustrative case study of the United States (US), using future projections of widely available indicators quantifying flood vulnerability under two scenarios of climate change. The results are mapped for two future time intervals for each climate scenario, highlighting areas that may exhibit higher future vulnerability to flooding events. Based on a Monte Carlo robustness analysis, we find that the WOWA aggregation technique can provide a more flexible and potentially robust option for the construction of vulnerability indices than traditionally used approaches such as Weighted Linear Combinations (WLC). This information was used to develop a proof-of-concept vulnerability assessment to climate change impacts for the US Army Corps of Engineers. Lessons learned in this study informed the climate change screening analysis currently under way.

International Opportunities for Broad Scale Coastal Simulation

The preceding chapters of this book have looked at the details of Integrated Assessment on the UK coast, especially in Norfolk. In addition to explaining this analysis in detail, the book aims to look for wider and more generic lessons about Integrated Assessment for coasts. In this regard, this chapter turns the focus to other parts of the world and the ‘global’ coast in general. Through diverse coastal examples from Australia, Bangladesh, Barbados, Italy, Japan and the USA, the opportunities and challenges associated with transferring the Tyndall Coastal Simulator approach to other locations are critically evaluated.