Assessing Socioeconomic Drought Based on a Standardized Supply and Demand Water Index

Socioeconomic drought is a phenomenon of water shortage caused by an imbalance between the supply and demand of water resources in natural and human socioeconomic systems. Occurrence of these droughts is closely related to sustainable socioeconomic development. However, compared with meteorological drought, hydrological drought and agricultural drought, socioeconomic drought has received relatively little attention. Therefore, this paper proposes a universal and relatively simple socioeconomic drought assessment index, the Standardized Supply and Demand Water Index (SSDWI). Taking the Jianjiang River Basin (JJRB) in Guangdong Province, China as an example, socioeconomic drought characteristics and trends during 1985-2019 were analyzed. The return period of different levels of drought were calculated using a copula function to estimate the risk of socioeconomic drought in the basin, and the relationship between socioeconomic, meteorological, and hydrological droughts and their potential drivers were discussed. The results showed that: (1) SSDWI was a better index for characterizing socioeconomic drought in the JJRB. 29 socioeconomic droughts occurred in the basin during the past 35 years, with an average duration of 6.16 months and an average severity of 5.82 per events. Socioeconomic droughts mainly occurred in autumn and winter, which also had more severe droughts than other seasons. (2) In the JJRB, the joint return periods of ‘∪’ and ‘∩’ for moderate drought, severe drought and extreme drought were 8.81a and 10.81a, 16.49a and 26.44a, and 41.68a and 91.13a, respectively; (3) Due to the increasing outflow from Gaozhou Reservoir, the risk of socioeconomic drought and hydrological drought in the JJRB has significantly declined since 2008. The reasonable operation of the reservoir has played an important role in alleviating the hydrological and socioeconomic drought in the basin.

EXPRESS: Handling Endogenous Regressors using Copulas: A Generalization to Linear Panel Models with Fixed Effects and Correlated Regressors

This paper proposes a panel data generalization for a recently suggested IVfree estimation method that builds on joint estimation. The author shows how the method can be extended to linear panel models by combining fixed-effects transformations with the common GLS transformation to allow for heterogeneous intercepts. To account for between-regressor dependence, the author proposes determining the joint distribution of the error term and all explanatory variables using a Gaussian copula function, with the distinction that some variables are endogenous and the others are exogenous. The identification does not require any instrumental variables if the regressor-error relation is nonlinear. With a normally distributed error, nonnormally distributed endogenous regressors are therefore required. Monte Carlo simulations assess the finite sample performance of the proposed estimator and demonstrate its superiority to conventional instrumental variable estimation. A specific advantage of the proposed method is that the estimator is unbiased in dynamic panel models with small time dimensions and serially correlated errors; therefore, it is a useful alternative to GMM-style instrumentation. The practical applicability of the proposed method is demonstrated via an empirical example.

Comparative study of flood coincidence risk estimation methods in the mainstream and its tributaries

The coincidence of floods in the mainstream and its tributaries may lead to a large flooding in the downstream confluence area, and the flood coincidence risk analysis is very important for flood prevention and disaster reduction. In this study, the multiple regression model was used to establish the functional relationship among flood magnitudes in the mainstream and its tributaries. The mixed von Mises distribution and Pearson Type III distribution were selected to fit the probability distribution of the annual maximum flood occurrence dates and magnitudes, respectively. The joint distributions of the annual maximum flood occurrence dates and magnitudes were established using copula function, respectively. Fuhe River in the Poyang Lake region was selected as a study case. The joint probability, co-occurrence probability and conditional probability of flood magnitudes were quantitatively estimated and compared with the predicted flood coincidence risks. The results show that the selected marginal and joint distributions can fit observed flood dataset very well. The coincidence probabilities of flood occurrence dates in the upper mainstream and its tributaries mainly occur from May to early July. It is found that the conditional probability is the most consistent with the predicted flood coincidence risks in the mainstream and its tributaries, and is more reliable and rational in practice.

Stochastic Dynamic Response Analysis of the 3D Slopes of Rockfill Dams Based on the Coupling Randomness of Strength Parameters and Seismic Ground Motion

Because rockfill strength and seismic ground motion are dominant factors affecting the slope stability of rockfill dams, it is very important to accurately characterize the distribution of rockfill strength parameters, develop a stochastic ground motion model suitable for rockfill dam engineering, and effectively couple strength parameters and seismic ground motion to precisely evaluate the dynamic reliability of the three-dimensional (3D) slope stability of rockfill dams. In this study, a joint probability distribution model for rockfill strength based on the copula function and a stochastic ground motion model based on the improved Clough-Penzien spectral model were built; the strength parameters and the seismic ground motion were coupled using the GF-discrepancy method, a method for the analysis of dynamic reliability of the 3D slope stability of rockfill dams was proposed based on the generalized probability density evolution method (GPDEM), and the effectiveness of the proposed method was verified. Moreover, the effect of different joint distribution models on the dynamic reliability of the slope stability of rockfill dams was revealed, the effect of the copula function type on the dynamic reliability of the slope stability was analysed, and the differences in the dynamic reliability of the slope stability under parameter randomness, seismic ground motion randomness, and coupling randomness of parameters and seismic ground motion were systematically determined. The results were as follows: the traditional joint distribution models ignored related nonnormal distribution characteristics of rockfill strength parameters, which led to excessively low calculated failure probabilities and overestimations of the reliability of the slope stability; in practice, we found that the optimal copula function should be selected to build the joint probability distribution model, and seismic ground motion randomness must be addressed in addition to parameter randomness.

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

In order to accurately describe the risk dependence structure and correlation between financial variables, carry out scientific financial risk assessment, and provide the basis for accurate financial decision-making, first the basic theory of Copula function is established and the mixed Copula model is constructed. Then the hybrid Copula model is nested in a hidden Markov model (HMM), the risk dependences among banking, insurance, securities and trust industries are analysed, and the Copula–Garch model is constructed for empirical analysis of investment portfolio. Finally, the deep learning Markov model is adopted to predict the financial index. The results show that the mixed Copula model based on HMM is more effective than the single Copula and the mixed Copula models. The empirical structure shows that among the four major financial industries in China, the banking and insurance industries have strong interdependence and high probability of risk contagion. The investment failure rate under 95%, 97.5% and 99% confidence intervals calculated by Copula–Garch model are 4.53%, 2.17% and 1.08%, respectively. Moreover, the errors of deep learning Markov model in stock price prediction of Shanghai Pudong Development Bank (sh600000), Guizhou Moutai (sh600519) and China Ping An Insurance (sh601318) are 2.56%, 2.98% and 3.56% respectively, which indicates that the four major financial industries in China have strong interdependence and risk contagion, so that the macro or systemic risks may arise, and the deep-learning Markov model can be adopted to predict the stock prices.

Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach

Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and reservoir regulation. This study proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape parameters (GAMLSS) method, the Copula function and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods. The reliability and applicability of the proposed approach were assessed by flood datasets collected from two hydrological monitoring stations located in the Hanjiang River of China. The precipitation and the reservoir index were selected as the explanatory variables for modeling the time-varying parameters of marginal and joint distributions using long-term (1954–2018) observed datasets. First, the GAMLSS method was employed to model and fit the time-varying characteristics of parameters in marginal and joint distributions. Second, the Copula function was employed to execute the point estimations of non-stationary design floods. Finally, the BUP technique was employed to perform the interval estimations of design floods based on the point estimations obtained from the Copula function. The results demonstrated that the proposed approach can provide reliable probabilistic interval estimations of design floods meanwhile reducing the uncertainty of non-stationary flood frequency analysis. Consequently, the integrated approach is a promising way to offer an indication on how design values can be estimated in a high-dimensional problem.

Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function

The momentum wheel is a key component of the satellite attitude control system and has a direct impact on the reliability and overall life of the satellite. The momentum wheel has the characteristics of a high reliability, long life, and complex failure mechanics, which leads to expensive maintenance and a low reliability of the test sample. Therefore, it is challenge to implement an accelerated life test. The traditional life data statistical method has great difficulty in solving the reliability analysis of the momentum wheel. A reliability calculation method based on copula function for multi-degradation is proposed. Firstly, the key factors affecting the reliability of the momentum wheel are analyzed, and the lubricant residual quantity and current are selected as the degradation quantity. Secondly, the wiener process is used to model the degradation of a single degradation quantity, and the edge distribution function of the momentum wheel reliability is obtained. Considering that the correlation between multiple degradation quantities has a non-negligible influence on the reliability analysis result, the copula function is introduced to describe the correlation, and the edge distributions are fused to obtain the joint distribution function of the momentum wheel reliability.

Assessing tropical cyclone compound flood risk using hydrodynamic modelling: a case study in Haikou City, China

The co-occurrence of storm tide and rainstorm during tropical cyclones (TCs) can lead to compound flooding in low-lying coastal regions. The assessment of TC compound flood risk can provide vital insight for research on coastal flooding prevention. This study investigates TC compound flooding by constructing a storm surge model and overland flooding model using Delft3D Flexible Mesh (DFM), illustrating the serious consequences from the perspective of storm tide. Based on the probability distribution of storm tide, this study regards TC1415 as the 100-year event, TC6311 as the 50-year event, TC8616 as the 25-year event, TC8007 as the 10-year event, and TC7109 as the 5-year event. The results indicate that the coastal area is a major floodplain, primarily due to storm tide, with the inundation severity positively correlated with the height of the storm tide. For 100-year TC event, the inundation area with a depth above 1.0 m increases by approximately 2.5 times when compared with 5-year TC event. The comparison of single-driven flood (storm tide flooding and rainstorm inundation) and compound flood hazards shows that simply accumulating every single-driven flood hazard to define the compound flood hazard may cause underestimation. For future research on compound flooding, copula function can be adopted to investigate the joint occurrence of storm tide and rainstorm to reveal the severity of extreme TC flood hazards.

Study on the streamflow compensation characteristics of reservoirs in Luanhe River Basin based on Copula function

With the rapid development of economy and society, the contradiction between supply and demand of water resources is increasing. Efficient utilization and allocation of limited water resources are one of the main means to solve the above contradictions. In this paper, the multidimensional joint distribution of natural streamflow series in reservoirs is constructed by introducing the mixed Copula function, and the probability of wet and dry encounters between natural streamflow is analyzed. Luan River is located in the northeastern part of Hebei Province, China, taking the group of Panjiakou Reservoir, Douhe Reservoir and Yuqiao Reservoir in the downstream of Luan River Basin as an example, the probabilities of synchronous and asynchronous abundance and depletion of inflow from the reservoirs are calculated. The results show that the probability of natural streamflow series between reservoirs is 61.14% for wetness and dryness asynchronous, which has certain mutual compensation ability. Therefore, it is necessary to minimize the risk of water supply security in Tianjin, Tangshan and other cities, and strengthen the optimal joint water supply scheduling of reservoirs. The research results are reasonable and reliable, which can provide reference for water supply operation of other basins.