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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.

Significant groundwater depletion in regions where grains are procured for public distribution is a primary sustainability challenge in India. We identify specific changes in the Indian Government's Procurement & Distribution System as a primary solution lever. Irrigation, using groundwater, facilitated by subsidized electricity, is seen as vital for meeting India's food security goals. Using over a century of daily climate data and recent spatially detailed economic, crop yield, and related parameters, we use an optimization model to show that by shifting the geographies where crops are procured from and grown, the government's procurement targets could be met on average even without irrigation, while increasing net farm income and arresting groundwater depletion. Allowing irrigation increases the average net farm income by 30%. The associated reduction in electricity subsidies in areas with significant groundwater depletion can help offset the needed spatial re-distribution of farm income, a key political obstacle to changes in the procurement system.

Multivariate simulations of a set of random variables are often needed for risk analysis. Given a historical data set, the goal is to develop simulations that reproduce the dependence structure in that data set so that the risk of potentially correlated factors can be evaluated. A nonparametric, copula‐based simulation approach is developed and exemplified. It can be applied to multiple variables or to spatial fields with arbitrary dependence structures and marginal densities. The nonparametric simulator uses logspline density estimation in the univariate setting, together with a sampling strategy to reproduce dependence across variables or spatial instances, through a nonparametric numerical approximation of the underlying copula function. The multivariate data vectors are assumed to be independent and identically distributed. A synthetic example is provided to illustrate the method, followed by an application to the risk of livestock losses in Mongolia.

Abstract. Mass livestock mortality events during severe winters, a phenomenon that Mongolians call dzud, cause the country significant socioeconomic problems. Dzud is an example of a compound event, meaning that multiple climatic and social drivers contribute to the risk of occurrence. Existing studies argue that the frequency and intensity of dzud events are rising due to the combined effects of climate change and variability, most notably summer drought and severe winter conditions, on top of socioeconomic dynamics such as overgrazing. Summer droughts are a precondition for dzud because scarce grasses cause malnutrition, making livestock more vulnerable to harsh winter conditions. However, studies investigating the association between climate and dzud typically look at a short time frame (i.e., after 1940), and few have investigated the risk or the recurrence of dzud over a century-scale climate record. This study aims to fill the gaps in technical knowledge about the recurrence probability of dzud by estimating the return
periods of relevant climatic variables: summer drought conditions and winter minimum temperature. We divide the country into three regions (northwest, southwest, and east Mongolia) based on the mortality index at the soum (county) level. For droughts, our study uses as a proxy the tree-ring-reconstructed Palmer drought severity index (PDSI) for three regions between 1700–2013. For winter severity, our study uses observational data of winter minimum temperature after 1901 while inferring winter minimum temperature in Mongolia from instrumental data in Siberia that extend to the early 19th century. Using a generalized extreme value distribution with time-varying parameters, we find that the return periods of drought conditions vary over time, with variability increasing for all the regions. Winter temperature severity, however, does not change with time. The median temperature of the 100-year return period for winter minimum temperature in Mongolia over the past 300 years is estimated as −26.08 ∘C for the southwest, −27.99 ∘C for the northwest, and −25.31 ∘C for the east. The co-occurrence of summer drought and winter severity increases in all the regions in the early 21st century. The analysis suggests that a continued trend in summer drought would lead to increased vulnerability and malnutrition. Prospects for climate index insurance for livestock are also discussed.

Abstract
Because of declining public investments in irrigation projects in India, the growth of irrigated agricultural production has increasingly become reliant on unsustainable allocation of groundwater. As a result, groundwater resources are increasingly depleted and their role in buffering climate variability is lost. Given future climate and food supply uncertainty under mounting population pressure, it is vital that the connections between climate variability, unsustainable irrigation practices, and their impacts on regional-scale agricultural production are quantified.
Here, the focus is on rice and maize production in the semiarid Telangana region in Andhra Pradesh, where the advent of inexpensive pump technology in the late twentieth century, coupled with governmentally subsidized electricity, has allowed year-round planting of water-intensive crops. Using a 35-yr climate and agricultural dataset from Telangana, nonlinear Gaussian process district-level regression models are developed to model dry-season irrigated area, which is a proxy for total groundwater use, in the function of climate-related predictors. The resulting models are able to accurately reproduce dry-season cropped area in most districts. Interannual climate variations play a significant role in determining groundwater use for irrigation. Nonlinear interactions between selected climate features are likely to influence irrigation water use significantly. These results suggest that the authors' modeling approach, combined with monsoon predictions, allow the forecasting of cropped area and agricultural water requirements at seasonal time scales within the bounds of uncertainty. The usefulness of such data to decision makers and stakeholders is discussed, as they attempt to use scarce surface and subsurface water resources more efficiently and sustainably.

Groundwater depletion is becoming a global threat to food security, yet the ultimate impacts of depletion on agricultural production and the efficacy of available adaptation strategies remain poorly quantified. We use high-resolution satellite and census data from India, the world's largest consumer of groundwater, to quantify the impacts of groundwater depletion on cropping intensity, a crucial driver of agricultural production. Our results suggest that, given current depletion trends, cropping intensity may decrease by 20% nationwide and by 68% in groundwater-depleted regions. Even if surface irrigation delivery is increased as a supply-side adaptation strategy, which is being widely promoted by the Indian government, cropping intensity will decrease, become more vulnerable to interannual rainfall variability, and become more spatially uneven. We find that groundwater and canal irrigation are not substitutable and that additional adaptation strategies will be necessary to maintain current levels of production in the face of groundwater depletion.

Groundwater depletion is becoming a global threat to food security, yet the ultimate impacts of depletion on agricultural production and the efficacy of available adaptation strategies remain poorly quantified. We use high-resolution satellite and census data from India, the world's largest consumer of groundwater, to quantify the impacts of groundwater depletion on cropping intensity, a crucial driver of agricultural production. Our results suggest that, given current depletion trends, cropping intensity may decrease by 20% nationwide and by 68% in groundwater-depleted regions. Even if surface irrigation delivery is increased as a supply-side adaptation strategy, which is being widely promoted by the Indian government, cropping intensity will decrease, become more vulnerable to interannual rainfall variability, and become more spatially uneven. We find that groundwater and canal irrigation are not substitutable and that additional adaptation strategies will be necessary to maintain current levels of production in the face of groundwater depletion.