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Water pollution incidents pose a significant threat to the safety of drinking water supplies and directly impact the quality of life of the residents when multiple pollutants contaminate drinking water sources. The majority of drinking water sources in China are derived from rivers and lakes that are often significantly impacted by water pollution incidents. To tackle the internal mechanisms between water quality and quantity, in this study, a Copula-based spatiotemporal probabilistic model for drinking water sources at the watershed scale is proposed. A spatiotemporal distribution simulation model was constructed to predict the spatiotemporal variations for water discharge and pollution to each drinking water source. This method was then applied to the joint probabilistic assessment for the entire Yangtze River downstream watershed in Nanjing City. The results demonstrated a significant negative correlation between water discharge and pollutant concentration following a water emergency. The water quantity-quality joint probability distribution reached the highest value (0.8523) after 14 hours of exposure during the flood season, much higher than it was (0.4460) during the dry season. As for the Yangtze River downstream watershed, five key risk sources (N1–N5) and two high-exposure drinking water sources (W3–W4; AEI=1) should be paid more attention. Overall, this research highlights a comprehensive mode between water quantity and quality for drinking water sources to cope with accidental water pollution.

To calculate warning thresholds for compound-flooding events triggered by heavy rainfall coupled with storm tides in Taiwan’s coastal urban areas, we applied copula-based analysis to observation data collected from 2001 till 2022 for Taipei City and New Taipei City and developed an empirical formula that accounts for both the capacity of the drainage infrastructure, which partially depends on the coastal sea level and varies over time, and the amount of precipitation. Compared against observation data from flood detectors, our predictions exhibited an accuracy of 85.2 % and 78.8 % for Taipei City and New Taipei City, respectively, thus improving upon the 62.8 % and 68.5 % success rates for thresholds estimated using only the hourly accumulated rainfall. These promising preliminary results suggest that reliable flood warnings for tidal-basin regions can be expedited by employing our formula and inputting rainfall and sea-level values from ensemble typhoon and storm-surge forecasts.

Proper management of water resources relies on precise hydrological data interpretation, with runoff data being essential for handling drought risks. In this research, we evaluated the Mun watershed's hydrological drought patterns using the Standardized Runoff Index (SRI) for quarterly periods. We analyzed runoff data spanning 24 years (1999–2022) sourced from 33 Royal Irrigation Department stations across 10 watershed provinces. We applied both the Archimedean Copula to determine return intervals for drought intensity and length. Results indicate that these methods effectively gauge hydrological drought under varied conditions in the Mun watershed. Notably, Nakhon Ratchasima and Sisaket provinces are most susceptible to intense and frequent droughts in the upcoming 3.70–19.13 years. These insights are vital for strategic agricultural water allocation and gauging the success of water management initiatives, especially during Mun watershed drought episodes. [ABSTRACT FROM AUTHOR]

Rice is not only central to Thailand's economy and dietary consumption but also plays a significant role in global food security. Northeast Thailand, in particular, is a principal region for rice cultivation. However, with the mounting concerns of climate change, it becomes paramount to understand the interplay between regional weather patterns and rice yields, aiming to develop effective adaptive agricultural strategies. The current study aimed to fill the research gap by investigating an optimal copula for the spatial dependence of rice production and related meteorological variables in this area. The objective of this study is to understand how rice production in different areas relates to each other in order to improve farming practices and address challenges such as suitable weather. To achieve this goal, we apply three families of copulas—elliptical, Archimedean, and extreme—to analyze crop and meteorological variables across the watershed in the northeastern region of Thailand. With a data foundation extending from 1981 to 2021 from the Regional Office of Agricultural Economics Sector 4, Thailand, this study offers a comprehensive analysis of the spatial dynamics driving rice production across twenty provinces in Northeast Thailand. Using a piecewise linear model, we dissected rice yield trends, revealing distinct slopes in production and yield across various periods. The analysis leaned on elliptical, Archimedean, and extreme copula families, using the maximum likelihood estimation to discern marginal distribution residuals. Through rigorous bootstrap goodness-of-fit tests and cross-validation, the most appropriate copula for each province was identified. Key findings demonstrate pronounced spatial interdependencies in rice yields, with the Frank copula prominently capturing the product relationship between provinces such as Maha Sarakham and Roi-Et. Conversely, the Clayton copula better characterized regions such as Srisaket and Ubon Ratchathani. Moreover, the results underscore the considerable influence of meteorological factors, notably rainfall and temperature, on rice production, especially in regions like Ubon Ratchathani. In distilling these multifaceted relationships, the study charts a pathway for crafting sustainable, localized agricultural strategies. As the world grapples with climate change's ramifications, the insights from this research stand crucial, offering direction for fostering resilience, adaptation, and optimizing rice productivity across Thailand's diverse agrarian landscapes. [ABSTRACT FROM AUTHOR]

To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other load-influencing factors such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm (GA) to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are turbomachines that have wide industrial applications and could perform in different ways such as pump and turbine mode. The maintenance of this equipment is mostly carried out using invasive methods that are expensive, time-consuming, and even complicated. The application of non-invasive methods is sought since they offer the advantage of real-time monitoring without stopping the process, reducing component assembly and disassembly times and providing a faster response. The aim of this work is done an experimental investigation that shows evidence about how the information on the hydraulic variables can be obtained if the electrical variables are monitored for the modes of operation such as pump and turbine. This work is divided into two parts, the first part is based on a statistical analysis to perform a multivariate adjustment through copulas and probability distributions. The second part focuses on the graphical analysis of the power density spectra for the hydraulic variables, the torque, and the defined electrical variables. The amplitude peaks of each variable and which peaks are common between them are determined. A statistically significant fit for Tawn type 2 copula is obtained with the indicator variable of pressure fluctuation and a multivariate transformation of the three-phase network currents. In the spectra analysis, common amplitude peaks are observed between the spectra that indicate the information flow on the phenomena between the hydraulic variables and the electrical variables.

Frequency analysis is a widely used method in hydrology; however, the conventional approaches to frequency analysis do not consider the spatial dependence between stations. This study proposes a novel approach for multisite frequency analysis using the copula method. This approach uses the framework of pair copula construction to extended bivariate copula functions for multivariate analysis, allowing for non-homogeneous marginal distribution and spatial dependence. Our approach was applied to analyze hourly rainfall in Taiwan's Lanyang watershed from 1996 to 2011, constructing the multisite rainfall distribution to estimate the frequency of extreme rainfall events. The results showed the nonlinear relationships and distinct frequency distributions of rainfall across the study area due to changes in elevations. Based on the copula-based multisite rainfall distribution, the study simulated extreme rainfall events at multiple sites with predetermined frequencies, preserving the spatial characteristics of rainfall more accurately than previous methods. This approach avoids potential underestimation and overestimation of hourly rainfall intensity at specified return periods resulting from assumptions of homogeneity and spatial independence. This study also evaluated possible multisite rainfall combinations at a given frequency using Monte-Carlo simulation. These simulations characterized spatial uncertainties in design multisite rainfall, providing critical information for water resources management and hydrological design. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are turbomachines that have wide industrial applications and could perform in different ways such as pump and turbine mode. The maintenance of this equipment is mostly carried out using invasive methods that are expensive, time-consuming, and even complicated. The application of non-invasive methods is sought since they offer the advantage of real-time monitoring without stopping the process, reducing component assembly and disassembly times and providing a faster response. The aim of this work is done an experimental investigation that shows evidence about how the information on the hydraulic variables can be obtained if the electrical variables are monitored for the modes of operation such as pump and turbine. This work is divided into two parts, the first part is based on a statistical analysis to perform a multivariate adjustment through copulas and probability distributions. The second part focuses on the graphical analysis of the power density spectra for the hydraulic variables, the torque, and the defined electrical variables. The amplitude peaks of each variable and which peaks are common between them are determined. A statistically significant fit for Tawn type 2 copula is obtained with the indicator variable of pressure fluctuation and a multivariate transformation of the three-phase network currents. In the spectra analysis, common amplitude peaks are observed between the spectra that indicate the information flow on the phenomena between the hydraulic variables and the electrical variables. [ABSTRACT FROM AUTHOR]

Water pollution incidents pose a significant threat to the safety of drinking water supplies and directly impact the quality of life of the residents when multiple pollutants contaminate drinking water sources. The majority of drinking water sources in China are derived from rivers and lakes that are often significantly impacted by water pollution incidents. To tackle the internal mechanisms between water quality and quantity, in this study, a Copula-based spatiotemporal probabilistic model for drinking water sources at the watershed scale is proposed. A spatiotemporal distribution simulation model was constructed to predict the spatiotemporal variations for water discharge and pollution to each drinking water source. This method was then applied to the joint probabilistic assessment for the entire Yangtze River downstream watershed in Nanjing City. The results demonstrated a significant negative correlation between water discharge and pollutant concentration following a water emergency. The water quantity-quality joint probability distribution reached the highest value (0.8523) after 14 hours of exposure during the flood season, much higher than it was (0.4460) during the dry season. As for the Yangtze River downstream watershed, five key risk sources (N1–N5) and two high-exposure drinking water sources (W3–W4; AEI=1) should be paid more attention. Overall, this research highlights a comprehensive mode between water quantity and quality for drinking water sources to cope with accidental water pollution. [Display omitted] • A Copula-based spatiotemporal probabilistic model is proposed for drinking water sources. • Joint probability distributions between water quality and quantity can be estimated using Copula functions. • N1-N5 are the five key risk sources and W3-W4 are the two high exposure drinking water sources. • Copula models provide a comprehensive mode for the joint behavior of multiple variables. [ABSTRACT FROM AUTHOR]

The performance of uncertainty estimation methods, namely bootstrapping and Markov chain Monte Carlo (MCMC), in univariate frequency analysis of hydrometeorological extremes has been well tested in the literature. However, the two methods have not been thoroughly compared for multivariate frequency analysis of such events. In this study, we compare the performance of bootstrapping and MCMC in estimating the uncertainty of bivariate quantiles of extremes as defined by the return period quantiles of hydrologic drought duration and severity, and concurrent meteorological drought and heat wave. Using a copula framework, we analyse the accuracy and size of confidence intervals of the bivariate quantiles, and bias in point estimates of them. We also investigate the performance of the two methods in estimating the uncertainty of univariate quantiles of the marginal distributions of the resulting bivariate copulas. This is to evaluate if any advantage of one method over the other is consistent, whether in estimating the univariate or bivariate quantiles. We conduct this study with synthetic datasets of various sample sizes and predefined distributions derived from a set of empirical data. The results show MCMC to be superior when estimating the uncertainty of bivariate quantiles where the sample size is small (~50). Where the sample size is large (~100 and ~200), the results show bootstrapping to be the better option for estimating uncertainties of bivariate quantiles. For estimating uncertainties of univariate quantiles, bootstrapping is performing better under all investigated sample sizes. Results and conclusions in this study will be beneficial for hydrometeorological risk assessment, hydrologic infrastructure design, and water resources assessment. [ABSTRACT FROM AUTHOR]

We perform the first global analysis of the spatial footprints of storm surges, using observed and simulated storm surge data. Three different techniques are applied to quantify the spatial footprints: clustering analysis, percentage of co‐occurrence, and joint probability analysis. The capability of the simulated data to represent the observed storm surge footprints is demonstrated. Results lead to the identification of coastline stretches prone to be impacted simultaneously by the same storm surge events. The spatial footprint sizes differ around the globe, partially conditioned by the geography of the coastline, that is, more irregular coastlines consist of a larger number of different storm surge clusters with varying footprint sizes. For the northwestern Atlantic, spatial footprints of storm surges vary when specifically accounting for tropical cyclones, using storm track information in the storm surge simulations. Our results provide important new insights into the spatial footprints of storm surges at the global scale and will help to facilitate improvements in how coastal flood risk is identified, assessed, and managed, by taking these spatial features into account. Plain Language Summary: When an extreme storm surge event impacts a particular site on the coast, other coastal locations are expected to also experience an extreme storm surge. Thus, a single extreme event can affect multiple critical service locations, populations, interconnected infrastructure systems, and diverse industrial sectors simultaneously, increasing the impact level of the event. Understanding the spatial dependence of surges on coasts is crucial for accurate risk analyses and the development of efficient emergency management plans. In the present paper, we identified the coastal stretches prone to be impacted simultaneously by a storm surge event. Our results show that not only contiguous but also unconnected coastlines are often affected by the same storm surge events and highlight that the spatial footprints of storm surges are not biased toward individual extreme events. Instead, we find that many other events, smaller in height and intensity (while still being extremes), have similar spatial footprints. Key Points: Contiguous and unconnected coastal stretches are often affected by the same storm surge event.Storm surge reanalysis data is able to correctly represent the spatial patterns of storm surge.A set of reference storm surge time series can be used as indicators of storm surge behavior for spatially coherent coastlines. [ABSTRACT FROM AUTHOR]

Abstract Within specific cities or regions, water and energy are intimately and highly interwoven, forming water-energy nexus (WEN) systems. Such a nexus system is complicated, leading to the generation of coupled risks of water and energy resources. In this research, an integrated approach of systems dynamics, orthogonal design and copula analysis (IA-SOC) was developed for supporting risk analysis of WEN systems. Innovations of this approach includes: 1) the development of a method through coupling system dynamics and orthogonal design, and 2) the combination of Copula analysis for supporting interactive risk assessment of both water and energy resources. The proposed approach was applied in Jing-Jin-Ji (J-J-J) region to deal with risk analysis of WEN and promote coordinated development. The results showed that: 1) the established system dynamics models can be employed to predict the water and energy demands; 2) the orthogonal table L 27 ( 3 13 ) can be adopted to obtain the representative scenario combinations, which could be introduced into system dynamic models to obtain the water and energy demands over the planning period; 3) it was appropriate to employ Lognormal distribution to establish the marginal distribution function of water and energy resources, meanwhile the Bivariate Frank Copula function was adopted to construct the joint distribution function of WEN to quantify the inherent relationship between water and energy resources; 4) the demands for water and energy resources in J-J-J region over the planning period were [252.06, 290.7] billion m3 and [433.67, 477.02] million tons of standard coal equivalent (S.C.E.), respectively. Correspondingly, the shortage risks of water and energy resources were [0.938, 0.981] and [0.835, 0.936]; and 5) different scenario combinations were set to identify the controlled amount of water and energy demands. The results could provide reasonable policy recommendations on the risk analysis of water and energy resources to promote regional coordinated development. Highlights • An integrated approach was developed. • The approach is new for assessing coupled risks of water-energy nexus systems. • Systems dynamics, orthogonal design and copula analysis were employed for the developed approach. • The developed approach was applied in Jing-Jin-Ji region of China. [ABSTRACT FROM AUTHOR]

In this paper, a new Groundwater Quantity-Quality-based Drought Index (GQQI) is developed based on multivariate Copula analysis of groundwater quantity and quality indicators. For evaluating the developed index, its temporal and spatial distribution is studied and compared to those of some other indices, such as Standardized Salinity Index (SSI), Standardized Groundwater Index (SGI), and Standardized Water level Index (SWI). The proposed index is applied for the temporal and spatial evaluation of the quantity and quality of groundwater in theLake Urmia basin, having 1084 piezometric and 935 groundwater quality monitoring wells. For a more comprehensive analysis of drought, 24 marginal and 26 joint probability distribution functions are driven. Based on the results, the developed GQQI is correlated with drought indices of SSI, SGI, and SWI by 88%, 86%, and 61%, respectively. Moreover, the values of the developed drought index indicates the occurrence of more severe droughts, compared to those detected by other drought indices. This can be resulted from considering the combined effects of groundwater quantity and quality using the proposed index. In addition, the multivariate GQQI can spatially and temporally represent the drought severity over the basin, and identify severe and extreme drought conditions better than other univariate drought indices. [ABSTRACT FROM AUTHOR]

The quantitative environmental management of reservoir inflows is challenging due to complex coexistence relationships between water quantity and water quality variables. Taking discharge as a representative water quantity indicator, as well as total nitrogen (TN) and total phosphorus (TP) as water quality indicators for the twin rivers (i.e., the Chaohe and Baihe rivers) which run into the Miyun Reservoir in North China, this study calculated marginal probability distributions of these indicators, and analyzed the joint probability distribution of discharge and TN/TP concentration by applying the Frank copula function. According to an analysis of various scenario combinations of discharge and TN/TP concentration, the quantitative management intervals including the priority control interval, the key attention interval and the daily maintenance interval, were identified. The results were as follows: (a) a fitting degree evaluation indicated that the Pearson-III distribution for the marginal probability distribution of discharge and the lognormal distribution for that of TN/TP concentration were feasible. Additionally, the Frank copula theory was applicable for their joint probability analysis according to the applicability analysis and goodness-of-fit test; (b) regarding to the water quality of the Miyun Reservoir inflows, it is more important to enhance the control of the Chaohe River and the monitoring of TP concentration; and (c) the TN concentration within division values of discharge (i.e., 16.59, 24.14 m 3 /s) was tend to exceed the class III limitation of the Environmental Quality Standard for Surface Water in China, and the concentrations of TN and TP increased as the discharge increased for the two rivers. The quantitative management intervals based on copula analysis is an intuitive and effective solution for comprehensive risk management of reservoir inflows. [ABSTRACT FROM AUTHOR]

[Display omitted] • Critical climate factors involved in nitrogen loss were identified. • High and normal precipitation dominated variation of Org-N and nitrate loss. • SWAT and copula were used to identify risk areas for N loss under climate extremes. • The N loss risk caused by high precipitation was higher than that by temperature. Climate change is a key factor that profoundly affects aquatic environments. Because of climate warming, the increase in the intensity and frequency of extreme climate events has aggravated the uncertainty of nitrogen pollution. However, the risk of nitrogen loss under different climatic conditions has not been well assessed, which is of great significance for controlling diffuse pollution. In this study, we used the upper and middle Wei River Basin (UMWB) as the study area, and selected organic nitrogen (Org-N) and nitrate (NO 3 -N) as the two forms of nitrogen pollution. Then, we quantified the contributions of 10 climate factors and combined the Soil and Water Assessment Tool (SWAT) and copula to analyze the risk of pollution when extreme weather occurs. Our results showed that during periods of high precipitation and temperature, Org-N loss accounted for 96% and 83% of the total loss, and nitrate loss accounted for 74% and 67%, respectively. Org-N loss responded more strongly to high precipitation than nitrate loss because Org-N was transported with soil particles. The attribution analysis indicated that high precipitation amount (R95P) contributed to the largest Org-N loss. As for the nitrate loss, R95P, normal precipitation amount, and consecutive days with no precipitation were the most important climatic drivers, accounting for 35%, 32%, and 13% of the watershed area, respectively. After selecting critical source areas by identification method, an optimized copula model for nitrogen loss and the main climatic factors was proposed. The risk of nitrogen pollution under the defined climate severity was then quantified. The probabilities of Org-N and nitrate loss exceeding the top 1%–20% were 0.2%–15% and 0.8%–10% when the precipitation exceeded the top 20%. The pollution risk caused by high temperatures is lower than that caused by precipitation. This study emphasized the dominant role of extreme climate in driving nitrogen loss and proposed a method for quantifying the risk of nitrogen pollution under specific climate conditions, which enabled managers to identify high-risk pollution areas and optimize management measures to prevent diffuse nitrogen pollution. [ABSTRACT FROM AUTHOR]

Hydrology in many agricultural landscapes around the world is changing in unprecedented ways due to the development of extensive surface and subsurface drainage systems that optimize productivity. This plumbing of the landscape alters water pathways, timings, and storage, creating new regimes of hydrologic response and driving a chain of environmental changes in sediment dynamics, nutrient cycling, and river ecology. In this work, we nonparametrically quantify the nature of hydrologic change in the Minnesota River Basin, an intensively managed agricultural landscape, and study how this change might modulate ecological transitions. During the growing season when climate effects are shown to be minimal, daily streamflow hydrographs exhibit sharper rising limbs and stronger dependence on the previous-day precipitation. We also find a changed storage-discharge relationship and show that the artificial landscape connectivity has most drastically affected the rainfall-runoff relationship at intermediate quantiles. Considering the whole year, we show that the combined climate and land use change effects reduce the inherent nonlinearity in the dynamics of daily streamflow, perhaps reflecting a more linearized engineered hydrologic system. Using a simplified dynamic interaction model that couples hydrology to river ecology, we demonstrate how the observed hydrologic change and/or the discharge-driven sediment generation dynamics may have modulated a regime shift in river ecology, namely extirpation of native mussel populations. We posit that such nonparametric analyses and reduced complexity modeling can provide more insight than highly parameterized models and can guide development of vulnerability assessments and integrated watershed management frameworks. [ABSTRACT FROM AUTHOR]

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The water-energy nexus (WEN) system is a large-scale complex system that comes with diverse forms of risks owing to many challenges in the process of maintaining economic-resource-environmental sustainability. First, the rapidly increasing demand for water and energy subjects many regions to the high risk of water and energy shortages. Second, decision makers face difficulties in weighing system benefits and loss risks under a series of stricter water-energy policies. To handle the aforementioned dual risks of WEN, in this study we propose copula-based stochastic downside risk-aversion programming (CSDP) for regional water-energy management. CSDP integrates the superiority of the copula analysis method and downside risk-aversion programming into a framework, which can not only reveal the risk interactions between water resources and energy demand by using copula functions under different probability distributions, even previously unknown correlations, but also control economic risk, tackle systemic uncertainties, and provide an effective linkage between system stability and conflicting economic benefits. The proposed model was applied to a water-energy system case study in Tianjin City, China. Optimal solutions for various water resources and energy demand copulas associated with different scenarios and hierarchical risk levels were examined in the CSDP model. The results showed that water resources have a greater influence than energy on industrial structure adjustment in Tianjin, with consequent effects on system benefits, optimal output value schemes, and environmental protection strategies. In addition, the tertiary industry provides a new opportunity for economic growth based on a large amount of water-energy consumption, and its potential resources and water-air pollution risks also deserve extensive attention. [Display omitted] • A dual risk-aversion programming model for water-energy nexus was established. • Water-energy demand joint risk and systemic economic risk were addressed. • Optimization solutions of various joint-interaction scenarios were obtained. • The proposed model was applied to Tianjin City, China for industrial structure adjustment. [ABSTRACT FROM AUTHOR]

• A Copula-based interval two-stage stochastic mixed-integer programming was built. • It may provide optimal resource allocation plan for agricultural irrigation system. • It can quantify water-energy joint risks in food production. • The application of water-saving irrigation for irrigation system are considered. The interactive water-energy relationship is a major restriction on food production in agricultural irrigation systems. Applying water-saving irrigation systems can further intensify the interrelationship between water and electricity and trigger a water-energy joint risk. Currently, there are no approaches capable of effectively assessing the various uncertainties and water-energy joint risks in irrigation districts. In this study, a novel mathematical programming method termed copula-based interval two-stage stochastic mixed-integer programming (CITSMIP) is proposed. CITSMIP quantifies water-energy joint risks in food production and provides an optimal resource allocation plan and water-saving irrigation application schemes under different joint risk levels. CITSMIP was applied to solve an irrigation resource management problem in northwest China. The results show that under a certain water-energy joint risk, planting sunflowers would be the first choice for water-saving irrigation applications. As water-saving applications have become increasingly common over time, the water-saving volume is expected to increase to [74.65, 84.46] × 107 m3 by 2035. Moreover, under a certain joint risk, compared with the water risk, fluctuations in energy risk would have a greater impact on the total benefit of the system and the total consumption of resources. Compared with single water risk or energy risk management, the joint risk management results would have a lower degree of uncertainty and higher lower-bound benefits. Establishing CITSMIP can provide valuable insights into informing stakeholders to allocate resources and maximize benefits under water-energy joint risk. [ABSTRACT FROM AUTHOR]

Growing demands for water, energy, and food put many systems at a composite risk of resource shortages. There was a lack of approaches capable of effectively assessing tridimensional composite risk and describing non-linear correlations among subsystem risks within a Water-Energy-Food (WEF) nexus system. In this study, an integrated approach was developed to assess the composite risk of WEF nexus systems and generate risk-based plans. Specifically, a composite risk assessment model that could capture the interdependence among the risk for water, energy, and food shortage was proposed. Furthermore, a generalized Copula-based chance-constrained programming model and its solving algorithm were developed. The proposed approach has been applied to an agricultural WEF nexus system in northern China, where the shortage of water, energy and / or land affected agricultural outputs. Results show that the composite risk of the entire system would be higher than the maximum value among subsystem risks, but less than their sum. Higher composite risks could bring higher benefits. Under a certain composite risk, the overall system benefit would vary with different combinations of subsystem risks; and it could be promoted through coordinating resources supplied by different subsystems. A risk - benefit frontier consisting of optimal solutions corresponding to different combinations of composite and subsystem risks was identified through Monte Carlo simulation. The scheme that could generate 12.76 billion Yuan under a moderate composite risk level of 0.22 was recommended for the study problem. Accordingly, net irrigation water, energy for agriculture, and effective irrigation area should be no less than 1.66 × 109 m3, 81.08 × 103 tce, and 509.12 × 103 hm2, respectively. • A novel risk management approach was proposed under Water-Energy-Food nexus. • It can assess composite risk induced by random input resources. • Composite risk-based planning schemes are provided. • System benefits are promoted through coordinating the supply of multiple resources. [ABSTRACT FROM AUTHOR]

Evaluating the eutrophication risk of a receiving reservoir is crucial for scientific water transfer schemes. However mega water transfer projects would greatly affect both water quantity and water quality processes, making the eutrophication evaluation more difficult. This study assessed the eutrophication risk of a receiving reservoir (i.e., Miyun Reservoir) after the implementation of the world's largest water transfer project, namely, the South-to-North Water Transfer Project (SNWTP) in China. A new perspective of considering joint effects of water quantity indicator (i.e., water storage) and water quality indicator (i.e., Chlorophyll a concentration or Chla concentration for short) was proposed to assess the eutrophication risk. The GMM model was first introduced into the copula model to adaptively describe the marginal distribution of hydrological variables and to improve the accuracy of the marginal probability distribution for water storage. Besides, the Frank copula model was selected to establish the joint probability distribution function of water storage and Chla concentration from four candidate copula models. The eutrophication risk of the receiving reservoir was then assessed under ten water transfer scenarios concerning six amounts of water transfer in four certain periods. Results showed: 1) there was little eutrophication risk (<0.0005) in Miyun Reservoir under the selected water transfer scenarios; 2) the probability of higher Chla concentration in Miyun Reservoir would increase with larger water storage after the implementation of the SNWTP, and 3) the probability (0.00027) of water quality deterioration in the reservoir under uniform water transfer was about half (0.00045) of the centralized water transfer. These findings can contribute to the eutrophication risk assessment and adaptive management of the world's largest water transfer project. [ABSTRACT FROM AUTHOR]

• Drought propagation is assessed in varies different climatic areas and seasons across China. • Drought propagation probability is interpreted by a copula-based model. • Shorter drought propagation time is found in summer across China. • Humid areas present a higher correlation between MD and AD. • The probability of AD is affected by the severity of MD and climatic conditions. Agricultural drought has become a serious threat to the world food security and sustainable development. Although various characteristics have been investigated for agricultural drought assessment and early warning, the propagation of meteorological drought to agricultural drought is less than clear. This study investigated the spatiotemporal variations of characteristics of the propagation from meteorological drought to agricultural drought over China during the period of 1953–2012. The Standardized Precipitation Index (SPI) and the Standardized Soil moisture Index (SSI) were used to characterize meteorological drought and agricultural drought, respectively. The SSI-1 and SPI-m (1–12) were chosen as drought propagation series by correlation analysis to evaluate the correlation and propagation time between these two droughts. The probability of propagation in different regions of China was quantified using copula-based models. Results indicated that the strongest and the weakest correlations between meteorological drought and agricultural drought were found in summer (0.8–0.9) and in winter (0.5–0.8), respectively, while the propagation time increased from 1 to 2 months in summer to 2–7 months in the next spring. Spatially, correlation and propagation sensitivity between meteorological drought and agricultural drought were greater in humid areas than in other climatic zones. Further, the probability of agricultural drought occurrence was found to synchronously increase with the severity of meteorological drought. This study will help improve drought warning and forecasting systems. [ABSTRACT FROM AUTHOR]

Most heavy metal exposure and pollution results from multiple industrial activities, including metal processing in refineries, and microelectronics. These issues pose a great threat to human health, ecological balance, and even societal stability. During 2012–2017, China, in particular, faced the challenge of 23 heavy metals accidents, six of which were extraordinarily serious accidents. Accidental environmental pollution is rarely caused by a single heavy metal, but rather by heavy metal mixtures. To address the need for a joint exposure risk assessment for heavy metal mixed pollution accidents at the watershed scale, a Copula-based exposure risk dynamic simulation model was proposed. A coupled hydrodynamic and accidental heavy metal exposure model is constructed for an hourly simulation of the exposure fate of heavy metals from each risk source once accidental leakage has occurred. The Copula analysis was introduced to calculate the dual heavy metal joint exposure probability in real time. This method was applied to an acute Cr6+-Hg2+ joint exposure risk assessment for 43 electroplating plants in nine sub-watersheds within the Dongjiang River downstream basin. The results indicated seven risk sources (i.e., S1, S4, H18, H23, H27–H28, and H34) that presented relatively high exposure risk to their surrounding sub-watersheds. Spatially, the acute exposure risk level was highest in the tributary basin (sub-watershed XW) than in the mainstream (sub-watershed DW2) and the river network (sub-watershed RW) of the lower reaches of the Dongjiang River. This research highlights an effective probabilistic approach for performing a joint exposure risk analysis of heavy metal mixed pollution accidents at the watershed scale. • A Copula-based exposure risk dynamic simulation model is proposed. • Dual heavy metal join exposure probabilities can be estimated in real time using Copula analysis. • Acute Cr6+-Hg2+ exposure risk level of the tributary is the highest in the Dongjiang River downstream watershed. • This method proves to be useful for the water pollution exposure risk control and management. [ABSTRACT FROM AUTHOR]

• We examine the static and time varying patterns between Qatar and other stock markets. • We test for changes in the dependence between Qatar and other stock markets. • There are significant changes in the dependence between the studied stock markets due to the oil price changes and to the blockade as well. • The 2008 global financial crisis has a stronger impact than the price shocks and GCC political crisis. This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers. [ABSTRACT FROM AUTHOR]

• A copula-based probabilistic model is proposed for quantifying drought impacts on vegetation vigor. • Northern mainland China has a faster vegetation response to water stress than southern part in growing season. • Non-growing season exhibits a reverse spatial pattern compared with growing season. • Water deficits are more likely to cause vegetation decline than water surplus across 80% of mainland China. • North China is identified as drought-vulnerable region from a probabilistic perspective. Frequent droughts in a warming climate may exert more negative influences on ecosystems. Unlike previous studies that have investigated the vegetation response to drought generally in a deterministic way, a copula-based model is developed for quantifying drought impacts on terrestrial vegetation and identifying drought-vulnerable regions for mainland China from a probabilistic perspective in this study. The Normalized Difference Vegetation Index (NDVI) is firstly correlated with the Standardized Precipitation Evapotranspiration Index (SPEI) at varying timescales from 1 month to 24 months to determine the response time of vegetation to water variability. Then, the dependence structure of vegetation vigor and water availability is modeled through the bivariate copula analysis. Furthermore, conditional probabilities of vegetation decline under moderate, severe and extreme drought scenarios are systematically estimated using copula-based conditional probability distributions. Results indicate that spatial patterns of vegetation response time present distinct seasonality, with faster response to water variation in the southern part than in the northern proportion of mainland China during the non-growing season while the inverse pattern is observed for the growing season. The higher conditional probabilities of the below-average vegetation status in the dry condition evidence that water deficits overwhelming water surplus play a more profound role in diminishing vegetation vigor across more than 80% of mainland China. Specifically, when moderate droughts develop into extreme ones, the average probability of vegetation status below the 50th percentile escalates by 6.9%. Moreover, extreme droughts are noted to exaggerate probabilities of vegetation activity falling below the increasingly lower (40th, 30th, 20th and 10th) percentiles by 8.8%, 10.8%, 12.7% and 13.7% in comparison with moderate counterparts, thereby suggesting higher likelihood of the deteriorating drought conditions inducing vegetation losses, especially major vegetation decline. As for the drought-vulnerable region, North China, particularly the central Inner Mongolia, is recognized with vegetation decline probabilities being 28.1% and 68.8% greater than the mainland average given drought conditions (quantified as SPEI ≤ −1), respectively. Results of the study may improve our understanding of climatic extreme influence on vegetation status and benefit the effective drought preparedness and mitigation. [ABSTRACT FROM AUTHOR]