Climate Adaptation under Uncertainty

Abstract

Managing water resources for the future is challenging, given the wide range of climatic and hydrological uncertainty. To support decision makers in formulating robust adaptation plans and finding their way through the broad range of available climate data and models, decision scaling was introduced: an approach for bottom-up climate vulnerability assessments, informed by Global Climate Models (GCMs). This study aims to improve decision scaling as developed by Brown et al. (2012) by introducing three recent advances in climate adaptation and uncertainty science.

First, the concept of environmental flows (eflows) was adopted to represent the local ecology and variable hydrology with a broad range of indicators for evaluating the impact of climate change. Second, the GCM weighting strategy of Knutti et al. (2017) was applied to account for model performance and interdependency when estimating the plausibility of future climate conditions. Lastly, climate stress testing was not only done for annual average climate changes, but also for a prolonged dry season to represent the interannually variable character of climate change. The potential application of the novel decision scaling approach was illustrated through a case study of the Mokolo River. This river is situated in the South African Waterberg Biosphere Reserve, which faces competing water demands from tourism, industry, agriculture, and ecology under a changing climate.

It was found that the additions contribute to decision scaling, as eflows indicators introduced the climate impact on multiple flow components, which provides extra information on the climate vulnerability of the river during different flow conditions. In Waterberg, low and average flow conditions were found similarly sensitive to climate change. Moreover, GCM weighting increased the range of temperature uncertainty and showed high weights for both wet and dry GCM projections, which emphasizes the need for robust climate adaptation in Waterberg. Next, the additional stress test showed that prolonging the dry season by one month influences flows throughout the following year, especially in the posterior months. In this way, understanding the impact of plausible characteristics of future climate was improved.

Finally, this study revealed that local activities, such as groundwater extractions and land use changes, and available knowledge challenges the application of decision scaling to a real case study as it requires models and quantification of indicators. Therefore, carefully matching models, performance indicators, local concerns and knowledge are required for formulating climate adaptation strategies with decision scaling.