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Climate extremes threaten human health, economic stability, and the well-being of natural and built environments (e.g., 2003 European heat wave). As the world continues to warm, climate hazards are expected to increase in frequency and intensity. The impacts of extreme events will also be more severe due to the increased exposure (growing population and development) and vulnerability (aging infrastructure) of human settlements. Climate models attribute part of the projected increases in the intensity and frequency of natural disasters to anthropogenic emissions and changes in land use and land cover. Here, we review the impacts, historical and projected changes,and theoretical research gaps of key extreme events (heat waves, droughts, wildfires, precipitation, and flooding). We also highlight the need to improve our understanding of the dependence between individual and interrelated climate extremes because anthropogenic-induced warming increases the risk of not only individual climate extremes but also compound (co-occurring) and cascading hazards. ▪ Climate hazards are expected to increase in frequency and intensity in a warming world. ▪ Anthropogenic-induced warming increases the risk of compound and cascading hazards. ▪ We need to improve our understanding of causes and drivers of compound and cascading hazards.

Most attribution studies tend to focus on the impact of anthropogenic forcing on individual variables. However, studies have already established that many climate variables are interrelated, and therefore, multidimensional changes can occur in response to climate change. Here, we propose a multivariate method which uses copula theory to account for underlying climate conditions while attributing the impact of anthropogenic forcing on a given climate variable. This method can be applied to any relevant pair of climate variables; here we apply the methodology to study high temperature exceedances given specified precipitation conditions (e.g., hot droughts). With this method, we introduce a new conditional probability ratio indicator, which communicates the impact of anthropogenic forcing on the likelihood of conditional exceedances. Since changes in temperatures under droughts have already accelerated faster than average climate conditions in many regions, quantifying anthropogenic impacts on conditional climate behavior is important to better understand climate change.

By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change on surface water availability in Iran over the twenty-first century. Our findings show that increasing water withdrawal in Iran, due to population growth and increased agricultural activities, has been the main source of historical water stress. Increased levels of water stress across Iran are expected to continue or even worsen over the next decades due to projected variability and change in precipitation combined with heightened water withdrawals due to increasing population and socio-economic activities. The greatest rate of decreased water storage is expected in the Urmia Basin, northwest of Iran, (varying from ~ − 8.3 mm/year in 2010–2039 to ~ − 61.6 mm/year in 2070–2099 compared with an observed rate of 4 mm/year in 1976–2005). Human activities, however, strongly dominate the effects of precipitation variability and change. Major shifts toward sustainable land and water management are needed to reduce the impacts of water scarcity in the future, particularly in Iran’s heavily stressed basins like Urmia Basin, which feeds the shrinking Lake Urmia.

California Energy Commission [500-15-005]; National Science Foundation Hazards-SEES Program [DMS 1331611]; National Oceanic and Atmospheric Administration Ecological Effects of Sea Level Rise Program [NA16NOS4780206]

A drought is a multi-dimensional event characterized by changes in the atmospheric and land conditions. Hence, monitoring a single drought indicator may be insufficient for water management. The hybrid drought index (HDI) is presented as a nonparametric composite indicator for monitoring multiple components of the hydrologic cycle. The properties of the HDI can be summarized as follows: (1) HDI describes drought indicated from either climatic anomalies or available water (AW); (2) HDI describes the drought onset as early as a decrease appears in climatic variables, while it shows drought persistence until there is no longer a terrestrial deficit; and (3) HDI shows a more severe drought condition when both the climatic water balance and AW exhibit a deficit. HDI is based on the states of potential meteorological water budget and AW. The proposed integrated drought-monitoring is applied to the Zayandehrud River Basin of Iran to show the status of components and depict drought propagation through each one from climate to groundwater. Finally, HDI announces the general status of the hydrologic cycle. A monitoring system established based on HDI would also allow the managers, local businesses, and farmers to identify the status of water supply capacity and water availability.

Track connections between hurricanes, wildfires, climate change and other risks, urge Amir AghaKouchak and colleagues. Track connections between hurricanes, wildfires, climate change and other risks, urge Amir AghaKouchak and colleagues.

Atmospheric warming is projected to intensify heat wave events, as quantified by multiple descriptors, including intensity, duration, and frequency. While most studies investigate one feature at a time, heat wave characteristics are often interdependent and ignoring the relationships between them can lead to substantial biases in frequency (hazard) analyses. We propose a multivariate approach to construct heat wave intensity, duration, frequency (HIDF) curves, which enables the concurrent analysis of all heat wave properties. Here we show how HIDF curves can be used in various locations to quantitatively describe the likelihood of heat waves with different intensities and durations. We then employ HIDF curves to attribute changes in heat waves to anthropogenic warming by comparing GCM simulations with and without anthropogenic emissions. For example, in Los Angeles, CA, HIDF analysis shows that we can attribute the 21% increase in the likelihood of a four-day heat wave (temperature > 31 °C) to anthropogenic emissions.

Energy, water, and food resources are highly interdependent. Agricultural irrigation accounts for 84% of global consumptive freshwater use, the food supply chain demands up to 30% of global primary energy use, and roughly 80% of global electricity generation depends on water for cooling (an average of nearly 100 L of water withdrawn per kWh). Improving understanding of the complex interactions of this resource nexus is, therefore, a top priority for human well-being, sustainable development, and policymaking. Here, we present an interactive analysis toolbox, Nexus of Food, Energy, and Water (NeFEW), that synthesizes available global data to enable modeling and analysis of these resources and their interdependencies at the country-level and for user-specified categories and quantities. Sample analyses also presented here include country-specific estimates of water resources required to produce different types of food and energy, energy required per quantity of water or agricultural product supplied, and C O 2 -equivalent emissions associated with water and energy provision.

The rapid shrinkage of Lake Urmia, one of the world's largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia's contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin's water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998-2002). The collapse was caused by a marked overshoot of the basin's hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr(-1)) during dry conditions to 5400 mcm yr(-1) during wet periods with the average being 4100 mcm yr(-1). Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia.

Socioeconomic drought broadly refers to conditions whereby the water supply cannot satisfy the demand. Most previous studies describe droughts based on large scale meteorological/hydrologic conditions, ignoring the demand and local resilience to cope with climate variability. Reservoirs provide resilience against climatic extremes and play a key role in water supply and demand management. Here, we outline a unique multivariate approach as a measure of socioeconomic drought, termed Multivariate Standardized Reliability and Resilience Index (MSRRI). The model combines information on the inflow and reservoir storage relative to the demand. MSRRI combines: (I) a “top-down” approach that focuses on processes/phenomena that cannot be simply controlled or altered by decision makers, such as climate change and variability, and (II) a “bottom-up” methodology that represents the local resilience and societal capacity to respond or adapt to droughts. MSRRI is based on a nonparametric multivariate distribution function that links Inflow-Demand Reliability (IDR) indicator to Water Storage Resilience (WSR) indicator. These indicators are used to assess socioeconomic drought during the Australian Millennium Drought (1998–2010) and the 2011–2014 California Drought. The results show that MSRRI is superior to univariate indices because it captures both early onset and persistence of water stress over time. The suggested framework can be applied to both individual reservoirs and a group of reservoirs in a region, and it is consistent with the currently available standardized drought indicators. MSRRI provides complementary information on socioeconomic drought development and recovery based on reservoir storage and demand that cannot be achieved from the commonly used drought indicators.

Droughts have warmed faster than the average climate in southern U.S.; these changes are likely driven by concurrent atmospheric moisture shifts., During droughts, low surface moisture may translate surface heating into warming, since excess energy will be converted into sensible heat instead of evaporating as latent heat. Recent concurrent occurrences of droughts and heatwaves have caused compounding ecosystem and societal stresses, which prompted our investigation of whether there has been a shift in temperatures under meteorological drought conditions in the United States. Using historical observations, we detect that droughts have been warming faster than the average climate in the southern and northeastern United States. Climate model projections also show a pronounced warming shift in southern states between the late 20th and 21st centuries. We argue that concurrent changes in vapor pressure deficit and relative humidity influence the amplified warming, modifying interactions between the land surface and the atmosphere. We anticipate that the magnified shift in temperatures will bring more concurrent extremes in the future, exacerbating individual impacts from high temperatures and droughts.

The aim of this paper is to investigate characteristics of meteorological and agricultural droughts and their trends in Iran, as well as several subregions with different climate conditions from 1980 to 2013. The Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) are used as the primary indicators of meteorological and agricultural droughts, respectively. This study assesses historical droughts using the Multivariate Standardized Drought Index (MSDI), which provides a composite model of meteorological–agricultural drought. Furthermore, this study discusses the behavior of MSDI relative to the other indices (SPI and SSI) over different climatic conditions ranging from humid, semiarid, and hyperarid regions. The Mann–Kendall trend test shows that the northern, northwestern, and central parts of Iran have experienced significant drying trends at a 95 % confidence level. However, no statistically significant drying trend was observed in the eastern part of Iran. The most severe drought across the country occurred between 1998 and 2001, with approximately 80 % of the country experiencing an exceptional drought (

Author(s): Moftakhari, H; AghaKouchak, A; Sanders, BF; Matthew, RA; Mazdiyasni, O | Abstract: Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998–2063) and mid-future (2018–2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950–2015) if adaptation measures are not implemented.

A combination of climate events (e.g., low precipitation and high temperatures) may cause a significant impact on the ecosystem and society, although individual events involved may not be severe extremes themselves. Analyzing historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative effects of climatic change and variability. This study focuses on the changes in concurrences of heatwaves and meteorological droughts from 1960 to 2010. Despite an apparent hiatus in rising temperature and no significant trend in droughts, we show a substantial increase in concurrent droughts and heatwaves across most parts of the United States, and a statistically significant shift in the distribution of concurrent extremes. Although commonly used trend analysis methods do not show any trend in concurrent droughts and heatwaves, a unique statistical approach discussed in this study exhibits a statistically significant change in the distribution of the data.