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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.

Water scarcity has emerged as a potential risk for mining operations. High capital spending for desalination and water conflicts leading to asset stranding have recently occurred. Investors in mining companies are interested in the exposure to such risks across portfolios of mining assets (whether the practical at‐site consequences are foregone production, higher OPEX and CAPEX and ensuing lost revenues, or asset‐stranding). In this paper, an index of the potential financial exposure of a portfolio is developed and its application is illustrated. Since the likely loss at each mine is hard to estimate a priori, one needs a proxy for potential loss. The index considers drought duration, severity and frequency (defined by a return‐level in years) at each mining asset, and provides a measure of financial exposure through weighing of production or Net Asset Value. Changes in human needs are not considered, but are relevant, and could be incorporated if global data on mine and other water use were available at the appropriate resolution. Potential for contemporaneous drought incidence across sites in a portfolio is considered specifically. Through an appropriate choice of drought thresholds, an analyst can customize a scenario to assess potential losses in production value or profits, or whether conflicts could emerge that would lead to stranded assets or capital expenditure to secure alternate water supplies. Global climate data sets that allow a customized development of such an index are identified, and selected mining company portfolios are scored as to the risk associated with one publicly available drought index.

Abstract. We present evidence that the global juxtaposition of major assets relevant to the economy with the space and time expression of extreme floods or droughts leads to a much higher aggregate risk than would be expected by chance. Using a century long, globally gridded time series that indexes net water availability, we compute local occurrences of an extreme dry or wet condition for a specified duration and return period, every year. A global exposure index is then derived for major mining commodities, by weighting extreme event occurrence by local production exposed. We note significant spatial and temporal clustering of exposure leading to the potential for fat tail risk associated with investment portfolios and supply chains. The traditional approach of climate risk analysis only considers local or point extreme value analysis and hence does not account for this spatially and temporally clustered exposure. Consequently, the global economic implications of the past or future financial and social exposure are understated in current climate risk analyses.

Abstract. We present evidence that the global juxtaposition of major assets relevant to the economy with the space and time expression of extreme floods or droughts leads to a much higher aggregate risk than would be expected by chance. Using a century-long, globally gridded time series that indexes net water availability, every year we compute local occurrences of an extreme “dry” or “wet” condition for a specified duration and return period. A global exposure index is then derived for major mining commodities by weighting extreme event occurrence by local production exposed. We note significant spatial and temporal clustering of exposure at the global level leading to the potential for fat-tailed risk associated with investment portfolios and supply chains. This may not be a surprise to climate scientists familiar with the space-time patterns of interannual to decadal climate oscillations that can affect remote regions through teleconnections. However, the traditional approach of climate risk analysis only considers local or point extreme value analysis and hence does not account for temporal and spatial clustering of exposure for global portfolios. As multinational enterprises and supply chains assess and disclose physical climate risks, they need to consider the much higher chance that they may have multiple assets that may be exposed to extreme wet and/or dry climate extremes in the same year.