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We introduce a hierarchical Bayesian model for the spatial distribution of rainfall corresponding to an extreme event of a specified duration that could be used with regional hydrologic models to perform a regional hydrologic risk analysis. An extreme event is defined if any gaging site in the watershed experiences an annual maximum rainfall event and the spatial field of rainfall at all sites corresponding to that occurrence is modeled. Applications to data from New York City demonstrate the effectiveness of the model for providing spatial scenarios that could be used for simulating loadings into the urban drainage system. Insights as to the homogeneity in spatial rainfall and its implications for modeling are provided by considering partial pooling in the hierarchical Bayesian framework. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 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, which in turn makes livestock more vulnerable to harsh winter conditions. However, these studies typically look at a short time frame (i.e., after 1940); few have investigated either 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 levels 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 extends to the early 19th century. The Generalized Extreme Value (i.e., the statistical method to infer the probability of very rare or extreme events) shows that the return levels of drought conditions are changing over time, with variability increasing for all the regions. Winter severity, however, is constant. The median 100-year return levels of the winter minimum temperature in Mongolia have been, over the past 300 years, -26.08 °C for the Southwest, -27.99 °C for the Northwest, and -25.31 °C for the East. This study thus suggests that continued summer drought would lead to increased vulnerability and malnutrition. Here, we link meteorological characteristics to socioeconomic impacts related to livestock populations and draws attention to the need for livestock index insurance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001-2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions. [ABSTRACT FROM AUTHOR]

As the largest water conservancy project, Three Gorges Dam starts its impoundment in 2003 and henceforth the efficient operation of a multi-purpose dam has aroused a great concern on the effectiveness on flood control and water management over Yangtze River Basin. In this paper, we consider the relationship between rainfall from 136 weather stations and streamflow from 5 hydrological stations including Cuntan, Yichang, Luoshan, Hankou and Datong. Meanwhile, the spatial average rainfall over 21 subbasins was computed. The analysis of the correlation demonstrated that the correlation of spatial average rainfall and streamflow for each station is consistent with that between rainfall by stations and streamflow. Then, two options were selected to develop the linear models, including option a) using rainfall by stations to forecast streamflow and option b) using spatial average rainfall to forecast streamflow. The canonical correlation analysis enabled a large degree of spatially coherent information of rainfall by linear transforms to maximize the correlation of rainfall and streamflow for developing linear models. The model resulting from option b) best fits the observations. Coefficient of determination for each model and statistics such as reduction of error, coefficient of efficiency under cross validation serve as a test of good performance of the model. An analysis of streamflow characteristics across the stations illustrated that the streamflow of Cuntan and Yichang have different modes of variability from that of the rest stations below the dam. The comparison of risk type changes between observations and predictions during the post-dam period showed the dam effectively relieved the risk of upstream while not always played a positive role in risk mitigation of downstream. The spatial and temporal patterns of rainfall anomalies over the whole basin helps to explain how the dam impact the streamflow. Some applications as to possible operational strategies are discussed. [ABSTRACT FROM AUTHOR]

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 multidimensional. Water related 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 time scales, 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 climate induced 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 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 data sets 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 tacitly assumed usually. 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. [ABSTRACT FROM AUTHOR]

A statistically and physically based framework is put forward that investigates the relationship between Tropical Moisture Exports (TME), and extreme Precipitation and floods in the Northeast United States (N.E. USA). TME correspond to the meridional transport of moist air masses, primarily born in tropical oceanic areas, to higher latitudes; contribute to the global climatology precipitation and its extremes; and are closely related to flood events, especially in the mid-latitudes. The birth process and the steering of TME have seasonal and interannual variability. In this study, we explore how the TME are related to extreme precipitation and floods in the N.E. USA with a focus on seasonal variability and the potential impact of the El Niño Southern Oscillation. Links of TME to large floods events in N.E. USA in different seasons are first identified. The major moisture sources of the TME that contribute to precipitation extremes and floods in N.E. USA are then identified, together with the seasonally and interannually varying characteristics in terms of both TME birth and entrance to the N.E. USA, and their subsequent contribution to extreme precipitation. We show that the extreme daily precipitation events are dominated by extreme TME entering the N.E. USA events in every season. [ABSTRACT FROM AUTHOR]