A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry

Abstract

Abstract. Corporations, industries and non-governmental organizations have become increasingly concerned with growing water risks in many parts of the world. Most of the focus has been on water scarcity and competition for the resource between agriculture, urban users, ecology and industry. However, water risks are multi-dimensional. Waterrelated hazards include flooding due to extreme rainfall, persistent drought and pollution, either due to industrial operations themselves, or to the failure of infrastructure. Most companies have risk management plans at each operational location to address these risks to a certain design level. The residual risk may or may not be managed, and is typically not quantified at a portfolio scale, i.e. across many sites. Given that climate is the driver of many of these extreme events, and there is evidence of quasi-periodic climate regimes at inter-annual and decadal timescales, it is possible that a portfolio is subject to persistent, multi-year exceedances of the design level. In other words, for a multi-national corporation, it is possible that there is correlation in the climateinduced portfolio water risk across its operational sites as multiple sites may experience a hazard beyond the design level in a given year. Therefore, from an investor's perspective, a need exists for a water risk index that allows for an exploration of the possible space and/or time clustering in exposure across many sites contained in a portfolio. This paper represents a first attempt to develop an index for financial exposure of a geographically diversified, global portfolio to the time-varying risk of climatic extremes using long daily global rainfall datasets derived from climate re-analysis models. Focusing on extreme daily rainfall amounts and using examples from major mining companies, we illustrate how the index can be developed. We discuss how companies can use it to explore their corporate exposure, and what they may need to disclose to investors and regulators to promote transparency as to risk exposure and mitigation efforts. For the examples of mining companies provided, we note that the actual exposure is substantially higher than would be expected in the absence of space and time correlation of risk as is usually tacitly assumed. We also find evidence for the increasing exposure to climate-induced risk, and for decadal variability in exposure. The relative vulnerability of different portfolios to multiple extreme events in a given year is also demonstrated.

The Global Food‐Energy‐Water Nexus

“…More specifically, (1) there is an urgent need to link sustainable FEW solutions with real‐world outcomes and to engage in research that interacts with local experts and stakeholders; (2) more emphasis needs to be placed on nutrition instead of just food to examine the nutritional implications of different climate and management scenarios; (3) while substantial additional water will be required to support future food and energy production, it is not clear whether and where local freshwater availability is sufficient to sustainably meet future water needs. For instance, the extent to which irrigation can be expanded within presently rainfed cultivated land to close the yield gap without depleting environmental flows remains poorly understood; (4) new energy systems (e.g., unconventional fossil fuels such as shale oil, shale, gas, or oil sands) require much greater water amounts than their conventional counterparts; their impacts on the FEW nexus have just started to be explored (Rosa et al, , ); (5) investments in energy production and mining should also account for the possibility that some of these economic activities may remain stranded (i.e., not developed) because of water scarcity (Bonnafous et al, ; Northey et al, , ); (6) in addition to effects on water resources there is a myriad of environmental impacts (e.g., GHG emissions, pollution, depletion of high‐grade phosphate rock reserves, and soil losses) that need to be accounted for while evaluating the environmental trade‐offs of energy and food production; and finally, (7) research on FEW systems and sustainability often suffers from limited and incomplete data (e.g., sub‐Saharan Africa). Therefore, there is the need for creative strategies aiming at identifying new data sources or proxies that can improve our understanding of the FEW nexus.…”

Space-time clustering of climate extremes amplify global climate impacts, leading to fat-tailed risk

“…On the one hand, the quality of historical climate datasets degrades especially as one goes back before 1950. On the other hand, climate reanalysis products, as well as the Intergovernmental Panel on Climate Change (IPCC) climate model integrations for the 20th century, are known to show significant biases for hydroclimatic variables (Bozkurt et al, 2017;Ficklin et al, 2016;Liu et al, 2014). However, we expect the conclusion as to the space and time clustering that translates into a fat-tailed risk for global enterprises is robust.…”

The Future of Water: A Collection of Essays on “Disruptive” Technologies that may Transform the Water Sector in the Next 10 Years

“…With growing populations, changing social preferences, increasing economic activity, and changing land use and climate, the inefficiency of this traditional approach has become increasingly apparent, as impacts increase and are not effectively managed. Further, as (Bonnafous, Lall, & Siegel, 2017a, 2017b show, a consequence of globalization is that supply chains or even a single company may experience significant flood and drought risk across their portfolio of global assets in the same year, due to the space-time clustering of climate extremes. This clustering emerges from the nature of the underlying climate variability -a combination of nearly cyclical climate patterns at global scales with preferred time scales of recurrence every 3-7 years (El Nino), 8-12 years (North Atlantic Oscillation), 16-20 years (Pacific Decadal Oscillations), 40-80 years (Atlantic Meridional Oscillation) in addition to the trends imposed by anthropogenic climate change.…”