A bottom‐up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

Abstract

Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large‐scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom‐up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy—a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes.

Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones

“…Furthermore, potential changes to precipitation, which were not analyzed here but that can have a significant impact on future runoff, would need to be considered. Within this context, the incorporation of alternative lines of evidence can therefore not only be used to define the bounds of the perturbations, but also can be superimposed onto the exposure space (e.g., as in Prudhomme et al, 2013a;Culley et al, 2016) to provide insight into the likelihood of possible changes. The outcomes of our study can feed into such a scenario-neutral analysis by providing guidance on the variables that are likely to be most important for a particular location, as well as providing insights on the potential implications of using alternative PET models on the overall sensitivity results.…”

“…Therefore, to quantify the specific impact of changes in ET on the water balance, a good understanding of the sensitivity of PET to potential changes in its key influencing climatic variables is required (Goyal, 2004;Tabari and Hosseinzadeh Talaee, 2014). This is particularly relevant given the recent focus on "scenario-neutral" (or "bottomup") approaches to climate impact assessment (Brown et al, 2012;Prudhomme et al, 2010;Culley et al, 2016), which require the sensitivity of a given system to potential changes in climate forcings to be estimated (Prudhomme et al, 2013a, b;Steinschneider and Brown, 2013;Kay et al, 2014;Guo et al, 2016a).…”

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

“…These scenarios, displayed in Figure 2, present daily inflow values for 730 days and are obtained via perturbation of the historical inflow w h by reducing or increasing the historical average by 3% and 5% (Culley et al, 2016).…”

Using crowdsourced web content for informing water systems operations in snow-dominated catchments

“…Yet, storing such water increases the lake level and, consequently, the flood risk, which would be instead minimized by keeping the lake level as low as possible. On the basis of previous works (e.g., Castelletti et al, 2010;Giuliani and Castelletti, 2016;Culley et al, 2016;Giuliani et al, 2016b), these two objectives are formulated as follows: -Flood control: the average annual number of flooding days in the evaluation horizon H , defined as days when the lake level h t is higher than the flooding threshold (h = 1.24 m):…”