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The design flood for a coastal basin based on model simulations can be impacted by various conditions other than design storm, such as tide level. However, the design storm and tide level are usually investigated separately, and the relation between them has not been sufficiently studied, which may have unexpected impacts on model simulations. This paper presents a bivariate assessment framework that aims to evaluate the coordination between design storm and tide level using a copula-based joint distribution to derive the conditional probability of coincidences between design storm and tide level intervals. We apply this framework to the Tai Lake Basin (TLB) in the eastern coast of China and investigate what tide level along its coastline is appropriate for guiding flood control planning. Our findings reveal that the currently-used tide level along the southern coast of TLB is too low and should be raised by approximately 0.35 m and 0.12 m under maximum 1-day and 3-day storms, respectively. Meanwhile, the tide levels for the north and east coasts are appropriate. This paper provides a comprehensive understanding of the interplay between design storm and tide level in a large coastal basin, offering insights into the design of flood protection systems. [ABSTRACT FROM AUTHOR]

Knowledge of how to define and estimate the dependence among multivariate hydrological series is essential for detecting abrupt changes in the dependence. In this paper, a new method (BMCTC) is proposed to detect all possible abrupt change points in the dependence among multivariate hydrological series. The total correlation estimated by the matrix-based Renyi's alpha-order entropy functional is firstly introduced to define and measure the dependence strength among multivariate hydrological series. Then, the moving cut total correlation (MCTC) sequence is built by the moving window technique, which is used to measure changes in the dependence strength among multivariate hydrological series. Finally, the Bernaola-Galvan algorithm is used to detect all change points of the MCTC sequence. Simulations are performed to compare the effectiveness of BMCTC with Pearson correlation (BMCPC) and Spearman correlation (BMCSC), Cramer-von Mises (CvM) and copula-based likelihood-ratio (CLR). The results show that all change points are detected by BMCTC regardless of the samples size, but wrong change points or no change points are detected by other methods in most cases. BMCTC is applied to detect change points in the dependence among annual runoff, precipitation and sediment discharge series in the Xiliugou and the Kuyehe River, China. It is found that the dependence among runoff, precipitation and sediment discharge changed abruptly in 1980 and 1996 in the Kuyehe River and in 1999 in the Xiliugou River. These changes are mainly caused by human activities such as construction of water conservancy projects and coal mining. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

The complex topology of river networks and the numerous factors influencing streamflow make it challenging to forecast streamflow in large river basins. Improving the accuracy of streamflow forecasting in these basins is of great importance for water resource planning and management. In this paper, considering the spatial variability of impacts of human activities on streamflow, a streamflow forecasting method with a hybrid physical process-mathematical statistic is proposed to obtain better results by realizing the complementary advantages of conventional methods and the streamflow into Hongze Lake in China is forecasted by the proposed method. Firstly, the physical process-based Soil and Water Assessment Tool (SWAT) is used to forecast the streamflow of tributaries with few water storage projects such as reservoirs in the catchment area. Secondly, considering the correlation between mainstream streamflow and tributaries streamflow as well as the correlation between mainstream streamflow and its previous streamflow, streamflow forecasting models based on the vine copula function are constructed to forecast the mainstream streamflow and its confidence intervals, where there are many water storage projects in the catchment area. Then, the total streamflow into Hongze Lake and its confidence intervals are obtained by coupling the outputs of the above two parts. Finally, statistical indicators are chosen to evaluate the forecasting effects in terms of the credibility of deterministic forecasting as well as the reliability and acuity of probabilistic forecasting. The results demonstrate that the proposed method outperforms existing SWAT and long and short-term memory (LSTM) neural network methods in terms of forecasting performance. Consequently, it presents an effective alternative for addressing complex hydrological forecasting tasks in large river basins. [ABSTRACT FROM AUTHOR]

The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60–65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China's carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial–temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003–2017. Our analysis shows that: (1) China's carbon intensity showed the spatial agglomeration and temporal "inertia" from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China. [ABSTRACT FROM AUTHOR]

The ecological health of water quality is an essential factor in the survival and development of human beings. By setting up multiple sensors in the water body, people observe the factual state of water quality, and collect a large number of sequence data containing time information. In order to effectively utilize water quality time series data and predict future water quality changes, this paper proposes an improved deep learning method based on spatiotemporal feature correlated, called the convolution recurrent basis expansion analysis architecture. The model improves the original model by using the ability of convolutional neural network structure to extract spatial features and the continuous memory ability of recurrent neural network structure. Then, the physical prior knowledge is integrated into the proposed network to limit the unreasonable results predicted in the feasible solution space to further improve the learning efficiency of the model. For specific research objects, the model can mine multi-dimensional features in water quality sequences at different depths, and learn the temporal features of the sequences hierarchically. In order to verify the performance of the proposed model, we apply the proposed model to the water quality dataset collected in Tanghe Reservoir for simulation. The results of the study demonstrate that our model is suitable for water quality prediction and has distinct advantages over traditional neural networks. Our model will make accurate predictions of future changes in water quality and provide technical support in the refined water quality automatic monitoring system. [ABSTRACT FROM AUTHOR]

The weekend effect, i.e., the concentration of air pollutants is different from weekends to weekdays, has been explored since the 1970 s. In most studies, the weekend effect is referred to the change of O 3 , i.e., lower emission of NO x on weekends leads to a higher concentration of O 3 . Determining whether it is true can provide insight into the strategy of air pollution controlling. In this study, we explore the weekly cycle of cities of China based on the weekly cycle anomaly (WCA), which is proposed in this paper. The advantage of using WCA is that we can avoid the influence of other change components such as the daily cycle and the seasonal cycle. The p values of the significant tests from all cities are analyzed for obtaining a whole picture of the weekly cycle of air pollution. The result shows that the concept of the weekend effect is not proper for cities in China, as many cities reach the valley phase of the emission on weekdays but not weekends. Thus, researches should not pre-assume that the weekend is the low emission scenario. We focus on the anomaly of O 3 on the peak and the valley of the emission scenario estimated by the NO 2 concentration. Through the analysis of the distribution of the p values of all cities, we show that almost all cities in China have weekly cycle of O 3 corresponding to the weekly cycle emission of NO x , i.e., O 3 of the peak time of NO 2 is less than valley time. The cities of the strong weekly cycle are located in four regions, i.e., the Beijing-Tianjing-Hebei region, the Shandong Peninsula Delta, the Yangtze River Delta, and the Pearl River Delta, and these regions are also the regions with relatively severe pollution levels. [ABSTRACT FROM AUTHOR]

Effective flood forecasting can significantly prevent losses related to severe floods. However, nonlinear and dynamic flooding processes are difficult to simulate using traditional static networks, necessitating the use of dynamic networks with feedback connections and time delays. In this paper, we introduced a general framework to uniformly describe different dynamic networks, including the focused time delay network (FTDN), layered recurrent network (LRN), and nonlinear autoregressive network with exogenous inputs (NARX) network, which we used to construct 1080 models with six different lead times, ten neuron numbers, and six different time delays to forecast the reservoir inflow in east China. Three hybrid algorithms were utilized for training the models: steepest descent (SD), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Levenberg–Marquardt (LM) mixing with real-time recurrent learning (RTRL), respectively, which is an online implementation algorithm suitable for real-time flood forecasting. Early stopping was applied to improve model generalizations. In addition to the number of hidden layer neurons, we analyzed the impact of input time delays on the model error. The numerical and experimental results indicated that the training errors of the models exhibited significant change trends with the number of neurons and time delays. The BFGS algorithm demonstrated better stability and convergence than the SD and LM algorithms. For 1–6 h lead times, the NARX-based real-time flood forecasting model achieved the highest accuracy though this advantage is not obvious. [ABSTRACT FROM AUTHOR]

A hybrid framework by integrating stacking ensemble with two deep learning methods of convolutional neural network (CNN) and recurrent neural network (RNN) is introduced in this paper for landslide spatial prediction in the Three Gorges Reservoir area, China. The proposed framework is summarized in following steps. First, a spatial database consists of 20 landslide conditioning factors and 196 landslide polygons was established. Then, landslide and non-landslide pixels were randomly divided into training (70% of the total) and test (30%) sets. Next, a stacking ensemble method that integrates CNN and RNN was constructed using the training set. Finally, the proposed stacking framework was applied for landslide susceptibility mapping and evaluated. Experimental results demonstrated that the proposed framework can obtain the best predictive capability (0.918) than CNN (0.904), RNN (0.900) and logistic regression (0.877) in terms of area under the receiver operating characteristic curve (AUC). Therefore, it can be useful for landslide disaster management and assessment. [ABSTRACT FROM AUTHOR]

Drought awareness promotes people to be more sensitive to water shortage and more likely to support water-saving policies. Studying the spatiotemporal patterns and determinants of drought awareness is necessary for developing drought-resistant measures that can adapt to regional social responses. This paper employs the Baidu Index and sc-PDSI, in conjunction with the Principal Component Analysis, to explore the spatiotemporal patterns and influencing factors of drought awareness in China. The results indicate that the first two principal component modes can explain 75% of the total variance of drought awareness in China. The drought awareness of the people is more sensitive to summer droughts and less to winter droughts. The population and education level are the two most important influencing factors at the current stage. It is found that people are most concerned about the drought in Northeast China, while people in central and eastern China are concerned about the drought events in local and other regions. The responses of drought awareness to actual meteorological drought events can be synchronous or asynchronous for different regions. The results demonstrate the usability of big data in drought awareness research and provide crucial insights for the formulation of drought mitigation policies in China. [ABSTRACT FROM AUTHOR]

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes. [ABSTRACT FROM AUTHOR]

Drought is a major natural hazard that can have devastating impacts on regional agriculture, water supply, ecological environment and social economy. Extreme events, including droughts, are expected to be severer and more frequent because of climate warming. This paper presents an analysis of observed changes and future projections of meteorological drought in the Daqing River Basin (DRB), North China, based on the modified Standardized Precipitation Index (SPImod). Observed changes of meteorological drought are analyzed based on the precipitation series of 26 meteorological stations from 1971 to 2011, and future projections are made under two representative concentration pathways (RCPs 4.5 and 8.5) from 2021 to 2050. The result shows a change from wet to dry detected in most areas of the DRB in summer, winter and annual series. The drought risk is higher under the condition of "OR" and "AND" return period during the observation period, and the high-risk regions cover a large area. The drought risk from 2021 to 2050 will increase across the basin under RCP 4.5 and RCP 8.5, compared with which in the base period from 1971 to 2000. Most regions will be at a higher drought risk under the condition of "OR" return period, and the area with high-risk under RCP 4.5 is slightly larger than that under RCP 8.5. Under the condition of "AND" return period, the high-risk regions will cover the area from the northwest to the northeast, while the area of high-risk regions under RCP 8.5 will be larger than that under RCP 4.5. The result of this paper will be helpful for performing an efficient water resource management in the DRB under the impacts of global warming and climate change. [ABSTRACT FROM AUTHOR]

Air pollution has been an environmental problem exerting serious impact on human health. An accurate prediction of air pollutant concentrations in space and time is essential to the mitigation and minimization of exposure to air pollution, particularly in cities. To take advantage of data collected at monitoring stations and the effect of secondary information such as meteorological factors, this paper proposes a flexible hierarchical Bayesian model (HBM) which can predict air pollutant concentrations in space and time. Within the framework, spatial temporal Kriging (STK) is employed for interpolation and the obtained results are used as priors. Because of the undesirable performance of the STK, the likelihood in the form of a nonlinear regression (NLR) with skewed normal residuals is employed to take into account the effect of meteorological factors to derive the posterior distribution of air pollutant concentrations. Due to high dimensionality and complexity, the formulation of the posterior distribution is non-analytic. We thus need to draw samples from the estimated parameters of the posterior distribution with Markov chain Monte Carlo (MCMC) method, and approximate the population characteristics with the sample characteristics. We evaluate the HBM with the concentrations of air pollutants and the meteorological variables from Northern China. For all pollutants, in the cross-validation (CV) experiments, the HBM reduces the root mean square error (RMSE) of NLR and STK by at least 0.08 and 0.4, and R 2 by at least 0.02 and 0.4. The empirical results show that the proposed HBM generally outperforms the NLR and STK in terms of efficiency, accuracy and robustness. The proposed framework is general enough for the analysis of spatial-temporal data of all kinds. [ABSTRACT FROM AUTHOR]

This paper combines a risk-based two-stage stochastic robust programming (RTSRP) model into an agricultural-ecological system dynamics (AESD) to conduct agricultural-ecological water resources management. It integrates the chance- constrained programming, conditional risk-at-value, two-stage stochastic robust programming with the AESD model. The RTSRP model improves upon the TSRP model by introducing the risk control measurements. The agricultural-ecological dynamic system (AEDS) model can build water connections amid wheat, field corn, seed corn, economic crops, woodland, meadow, and city greening and farmland shelterbelts. Besides, the ecological service value is transformed into the irrigation benefit of ecological sector to guide water allocation amid sub-ecological sectors at a more applicable detailed scale. It can tradeoff relationships between economic benefit and ecological recovery by formulating two enlarging ecological areas scenarios. The combinations of the RTSRP model and the AEDS model can get optimal water allocation schemes, and simulate future sustainable degree (SD) of optimal water allocation schemes and explore relationships amid water allocation, economic benefit, and SD and risk and robustness of model. It is used to the middle reaches of the Heihe River Basin in China to verify its application. The results show that water allocation schemes have different responses on various risk scenarios. Higher water allocation corresponds to bigger water shortage risk and higher robustness of the model. The economic benefit positive with water allocation is contradictory with SD. Enlarging ecological areas contributes to improvements of economic benefit and ecological recovery, but not the SD under the condition of enough water resources. [ABSTRACT FROM AUTHOR]

The landslide caused a huge disaster to the living environment and seriously threatened the lives and property safety of nearby residents. Assess or predict landslide-susceptible the landslide displacement through monitoring are great beneficial to guide landslide control and mitigate these hazards by taking appropriate preparatory measures. In this paper, a new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN) was established to make up for the shortcoming of BPNN convergence speed. A typical step-wise landslide——Langshuwan Landslide happened in the Three Gorges Reservoir area of China is taken as a case, and the displacement monitoring data of 4 years was used for time series analysis and modeling. The long-term creep effect of the landslide and the short-term acceleration effect of the climate are considered in the model, and the accumulative displacement is divided into two kinds of trend displacement and periodic displacement. The key influencing factors of landslide periodic displacement were screened by gray relational grade analysis method, and then used as learning data. In addition to comparing the predicted value of the model with the measured value, it also compares the accuracy of the three models of BPNN, support vector machine, extreme learning machine under the training conditions of the same learning data set. The results show that the WAC-BPNN model has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models. [ABSTRACT FROM AUTHOR]

PM2.5 (Fine particulate matter in the atmosphere) pollution has become a major problem affecting human health and social development, especially in developing countries like China. The interaction between PM2.5 and the underlying factors across different regions or spatial scales are very complex, so it is helpful to study the spatial distribution and evolution of PM2.5, which is the foundation for carrying out regional collaborative governances. However, there is a paucity of this kind of research in China. This paper tries to put forward a new multi-scalar framework based on the spatiotemporal synthesis. After the spatial distribution and evolution of PM2.5 data is integrated with the methods of remote sensing, geographic information system and social ecological system analysis, we can more properly describe the evolving trajectory and characteristics of PM2.5 at different time–space scales. The paper defines and divides the space influenced by PM2.5 into four categories, which indicate the paths of spatial diffusion and finally puts forward the methodology of regional collaboration and environmental governance. [ABSTRACT FROM AUTHOR]

Time series land cover maps are important materials for the work related to land use and land cover change. Satellite remote sensing images prove advantageous in fast mapping with low cost. In most time series land cover products yielded by the satellite remote sensing images, a number of illogical transitions exist between different time phases. The time series land cover products cannot exactly reflect the real land cover types and land cover changes for each pixel. The accuracy evaluation based on the limited ground truth cannot well guide the users because the reliability of different pixels of the land cover products is unknown. A generic model for the reliability evaluation of time series land cover products should be developed based on a strong theoretical frame. In order to better guide the use of the land cover products, this paper proposed an approach to evaluate the reliability of time series land cover products by exploiting the joint probability of hidden Markov model (HMM), in which the classification performance and the spatio-temporal relationships were taken into account. We applied the proposed evaluation method on the time series land cover maps of Poyang Lake Eco-economic Region in China. The reliability of the land cover products was presented by the grading of the joint probability of HMM. The results effectively reflected the classification performance, the spatio-temporal relationships and even the quality of the data source. [ABSTRACT FROM AUTHOR]

With the economic and social development and population growth, as three basic interconnected resources, water, energy and food are gradually deficient and inharmonious. This research selected 31 provinces of China as the study area and collected data of 16 years' time series. The temporal and spatial synergetic change of water, energy and food was quantitatively described by wavelet analysis and MK test methods. The results show that water consumption for energy in 2001–2016 has the first main period of 12 years and the temporal synergetic change of water and energy would not be obvious in the future. According to the results, the resources consumption of water energy and food can be optimized to implement the sustainable utilization. Also, this paper proposed the adjustment strategy for the country. [ABSTRACT FROM AUTHOR]

Demands for water, food, and energy are rising owing to increasing population and growing economy. Agriculture is the main source of food and is the largest consumer of water and has therefore the greatest impact on water security. It also greatly affects energy security and is also the main source of non-point source pollution. Changes in the availability of water, energy, and land are driven by uncertainties in nature and strongly affect food production, with severe implications for the security of water–food–energy nexus (WFEN). Thus, a coordinated and effective management of WFEN in the agricultural sector is needed. This paper therefore proposes an integrated modelling approach for the optimization of agricultural WFEN in a random environment. The approach quantifies the interactions and feedbacks within agricultural water, food and energy subsystems, making tradeoffs between agricultural benefit and environmental impacts, which will serve as sustainability indicators for agricultural systems, including crop farming and livestock farming. The incorporation of stochastic mathematical programming in the modeling framework aids in understanding how strategies and comprehensive benefits change under different scenarios. The approach was applied in a real-world case study in an irrigation district in northeast China. The development and implementation of such an integrated approach are anticipated to be applied in other agriculture-centered regions to guide policies of sustainable water, food, energy and land resources management. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal (ST) characterization of precipitation is one key component in evaluating the climatic environment, which provides executable and effective suggestions for the implementers of climatic strategy. In this paper, the ST ordinary kriging method was used to obtain the ST distribution of annual precipitation (AP) based on the data collected from the 961 meteorological monitoring stations of the Huanghuaihai basin in China during 1956–2016. Then, stochastic site indicators were applied to quantitatively assess the ST uncertainties, and the results indicated the probability that higher precipitation (beyond 1000 mm) occurred simultaneously at any two regions was extremely low. In terms of the AP intensity analysis, the annual rainfall in 50% of the regions exceeded 500 mm, and the largest fragmentation of AP was calculated from this threshold. Furthermore, the ST characterization and the trend of AP were analyzed in different climatic environments, indicating that (1) the AP increased from west to east, exhibiting a significant gradient distribution; (2) overall, the temperature of the study area increased by approximately 0.027 °C per year, while the AP decreased by 0.2 mm per year; (3) the temperature in three subregions [high-altitude region (HAR), mid-altitude region (MAR), low-altitude region (LAR)] exhibited an increasing trend, and the significant increasing trend of temperature occurred primarily in the LAR; (4) the precipitation in the MAR and LAR exhibited a nonsignificant decreasing trend, while the precipitation in the HAR showed a significant increasing trend. [ABSTRACT FROM AUTHOR]

Climate transition at different scales complicates the dynamics of catchment water cycle. Examination of meteorological pattern shift enables us to have a deeper insight of the mechanism in hydrological extremes. Lake Hulun, the largest freshwater lake in Northern China, plays an important role in maintaining the regional ecological sustainability. The abrupt decrease of water level in the lake after 2000 has resulted in serious impacts on the lake's ecosystem. This paper presents a systematic analysis by linking meteorological dynamics with hydrological extremes based on the long-term precipitation, river discharge, and evapotranspiration (ET) data in the Lake Hulun watershed, which is a transition zone from desert to wetland and to permafrost along the Mongolia–China borderline. The results indicate that signals of large-scale precipitation variations can be well identified by using the Empirical Orthogonal Function (EOF). Spatially, the precipitation in the Lake Hulun watershed is dominated by two leading EOF patterns at the basin scale. The two leading patterns present complementary features of the high value centers in space. The amplitudes evolution of the leading patterns of EOF reveal that the redistribution of precipitation that occurred more in the middle flat plain and less in the mountain regions of the watershed triggers a big effect on the increasing ET and weakening runoff production due to the spatial heterogeneity in hydrological processes. Such effects are echoed by the trend patterns in precipitation and ET. The mechanism of the precipitation pattern shift is also confirmed by the large scale meteorological variations in geopotential and water vapor transport. Therefore, the change in the leading patterns of precipitation and the effects in ET triggered by the pattern shift in precipitation are the major causes of decrease in watershed runoff and the hydrological extremes in the terminal lake. [ABSTRACT FROM AUTHOR]

A dam-break flood can cause catastrophic life losses due to the uncontrolled large amount of water. This paper aims to improve the estimation of loss of life (LOL) from dam failure in China. For this purpose, we collect fifteen dam failure cases in China. Each failure case is further divided into subcases, if possible, based on flood severity and distance between the flooded area and the dam site. As a result, there are totally twenty-two subcases. Using the subcases as the basic data, an improved method is proposed for determining LOL that occurred from dam break by dividing the flood severity into two conditions: low flood severity, and medium and high flood severity. Correspondingly, two empirical equations are developed based on the multi-variable regression analysis for low flood severity, and medium and high flood severity, respectively. The proposed equations can predict a range for fatality rates from 0.0 to 1.0, and thus it can be applied for any case with any fatality rate. A comparison between the proposed equations and other methods is carried out, which shows that the proposed equations are much closer to the actual values with a coefficient of determination (R2) equals 0.9848, and 0.9872 for low flood severity equation and medium and high flood severity equation, respectively. Furthermore, the proposed empirical equations are applied to predict the life losses of three cases, two cases of low flood severity and one of medium flood severity. The results show a good agreement between the estimated life losses and actual life losses. [ABSTRACT FROM AUTHOR]

Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets. [ABSTRACT FROM AUTHOR]

It is expected that climate warming will be experienced through increases in the magnitude and frequency of extreme events, including droughts. This paper presents an analysis of observed changes and future projections for meteorological drought for four different time scales (1 month, and 3, 6 and 12 months) in the Beijiang River basin, South China, on the basis of the standardized precipitation evapotranspiration index (SPEI). Observed changes in meteorological drought were analysed at 24 meteorological stations from 1969 to 2011. Future meteorological drought was projected based on the representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, as projected by the regional climate model RegCM4.0. The statistical significance of the meteorological drought trends was checked with the Mann-Kendall method. The results show that drought has become more intense and more frequent in most parts of the study region during the past 43 years, mainly owing to a decrease in precipitation. Furthermore, long-term dryness is expected to be more pronounced than short-term dryness. Validation of the model simulation indicates that RegCM4.0 provides a good simulation of the characteristic values of SPEIs. During the twenty first century, significant drying trends are projected for most parts of the study region, especially in the southern part of the basin. Furthermore, the drying trends for RCP8.5 (or for long time scales) are more pronounced than for RCP4.5 (or for short time scales). Compared to the baseline period 1971-2000, the frequency of drought for RCP4.5 (RCP8.5) tends to increase (decrease) in 2021-2050 and decrease (increase) in 2051-2080. The results of this paper will be helpful for efficient water resources management in the Beijiang River basin under climate warming. [ABSTRACT FROM AUTHOR]

As lake ecosystem assessment is the foundation to achieve lake monitoring, environmental management and ecological restoration, a new concept of lake ecosystem health and driving force-pressure-state-impact-response-management framework was proposed to find out the causal relationship of the system and health distance model was taken to represent the health level of ecosystem. An assessment indicator system comprised of water quality, ecological and socio-economic criteria was established. The evaluation models were applied for the assessment of the ecosystem health level of a typical lake, Nansi Lake, China. Depends on the values of health distance, the heath level was described as 5°: very healthy, healthy, general healthy, sub-healthy and diseased. Using field investigation data and statistic data within the theory and applied models, the results of comprehensive assessment show that: (1) the health distances of water quality indicators, ecological indicators, socio-economic indicators and comprehensive health distance were 0.3989, 0.2495, 0.4983 and 0.4362, respectively. The health level was in general healthy condition. Ecological indicators were in healthy condition, which indicate that the stability was high. The distance of water quality had shown a tendency to approach general healthy level. As the health distance of socio-economic indicators have shown a bad impact form human beings, more effective measures need to be developed. (2) The results of a case study demonstrated that the methods in this paper provide a similar result corresponding with the actual lake health condition. Therefore, this paper shows that the proposed method is efficient and worths generalization. [ABSTRACT FROM AUTHOR]

A recent Chinese policy requires that all enterprises that store and use hazardous chemicals must become part of a chemical industry park. Although this reduces the environmental risk caused by the establishment of uncontrolled sites, it cannot eliminate the risk within the industrial park; on the contrary, it may exacerbate the risk. The storage and use of a wide range of hazardous chemicals in the industrial park increases both the complexity and the diversity of the environmental risk. Identification of the sources of risk will significantly affect the results of regional risk assessments. In this paper, we selected China's Nanjing Chemical Industry Park as a case study. Based on an environmental risk assessment for every enterprise within the park, we calculated the maximum environmental risk values of each individual risk source and obtained the overall regional environmental risk values by using information-diffusion theory. The regional environmental risk value can be clustered according to the classification criteria described in this paper and depicted using a spatial partitioning map. The result can help administrative managers optimize the distribution of industrial areas and support the development of risk-prevention and -management measures. [ABSTRACT FROM AUTHOR]

Flood mitigation should deal with those most sensitive flooding elements to very efficiently release risks and reduce losses. Present the most concerns of flood control are peak level or peak discharge which, however, may not always be the most sensitive flooding element. Actually, along with human activities and climate change, floods bring threats to bear on human beings appear in not only peak level and peak discharge, but also other elements like maximum 24-h volume and maximum 72-h volume. In this paper, by collecting six key flooding intensity indices (elements), a catastrophe progression approach based sensitivity analysis algorithm model is developed to identify the indices that mostly control over the flood intensity. The indices sensitivity is determined through a selected case study in the Wujiang River, South China, based on half a century of flow record. The model results indicate that there is no evident relationship of interplay among the index sensitivities, but the variability of the index sensitivity is closely related to the index variability and the index sensitivity increases with the decrease of index value. It is found that peak discharge is not the most influential flooding factor as is generally thought in this case. The sensitivity value of the maximum 24-h volume is the greatest influential factor among all the other indices, indicating that this index plays a leading role in the flood threat of the Wujiang River, South China. It is inferred that, for the purpose of flood warning and mitigation, the peak flood discharge is not always the most sensitive and dominant index as opposed to the others, depending on the sensitivity. [ABSTRACT FROM AUTHOR]

Salinization threatens the viability of water resources and is common in many important inland freshwater lakes worldwide, especially in arid and semi-arid areas. Bosten Lake is a typical inland freshwater lake that has evolved into a subsaline lake and is located in the arid region of Northwest China. The water resources of Bosten Lake are important for supplying regional drinking water and agricultural irrigation and for economic development. In this study, changes in salinity with time and space were analyzed in Bosten Lake. Overall, the salinity increased from 0.39 g/L in 1958 to 1.87 g/L in 1987, reaching its highest value in 1987. After 1987, the salinity decreased to 1.17 g/L in 2003 and increased to 1.45 g/L in 2010. Increased salinity adversely affects aquatic lake systems, regional eco-environments and water resource use, and has become a serious environmental problem in Bosten Lake. Thus, the causes of increasing salinity are discussed in this paper. Overall, the influences of climate variations and human activities resulted in the salinization of the lake. Understanding the salinization processes in Bosten Lake can be useful for implementing actions that improve water quality and water resource use in the lake. [ABSTRACT FROM AUTHOR]

China's urban environments are particularly vulnerable to flooding due to climate change and rapid urbanization. Study of the urban flood risk analysis has significantly increased over the past decade, and this paper therefore reviews the main results (i.e. theoretical basis, methods, techniques, case studies) obtained in the literature from China. We focus on the following topics: (1) urban flood hazard analysis, (2) exposure and vulnerability analysis, and (3) urban flood risk assessment. Recent advances made in the research area are presented with suggestions for further research to improve the availability and reliability of urban flood risk analysis. [ABSTRACT FROM AUTHOR]

This paper presents a cloud model (CM) based approach with step-by-step procedures for risk assessment of existing pipelines in tunneling environments (RAEPTE), where CM provides a basis for uncertainty transforming between qualitative concepts and their quantitative expressions. An evaluation index system of multiple layers and attributes is established for RAEPTE based upon the tunnel-induced pipeline failure mechanism analysis. The evaluation result is assessed by the correlation with CMs of each risk level. A confidence indicator is proposed to illustrate the reliability of evaluating results. Risk analysis for ten underground buried pipelines adjacent to the construction of Wuhan Metro Line Two in China is shown in a case study. Comparisons between different evaluation methods are further discussed according to results. The proposed approach is verified to be a more competitive solution, where the uncertainties of fuzziness and randomness are incorporated in the risk assessment system. This approach can serve as a decision tool for the safety risk assessment in other similar projects, and to increase the likelihood of a successful project in an uncertain environment. [ABSTRACT FROM AUTHOR]

Precipitation extremes could cause a series of social, environmental and ecological problems. This paper, taking Heihe River basin, the second largest inland river basin in China, as the study area, focused on the frequency analysis of precipitation extremes based on the historical daily precipitation records (1960-2010) at nine stations. Generalized Pareto distribution (GPD) was employed for fitting the peaks over threshold (POT) series, in which Hill plot, percentile method and the average annual occurrence number were used to select the threshold in GPD. Maximum likelihood estimate and L-moment were used to estimate the parameters. The inherent assumptions for POT series were investigated by auto-correlation coefficient, Mann-Kendall test, Spearman's ρ test, cumulative deviation test and Worsley likelihood ratio test. 10, 20, 50 and 100 year precipitation extremes for Heihe River basin were calculated and analyzed as well. It was found the POT series derived from several methods involved were approximately independent and stationary, and GPD could give a satisfactory fit to the POT series for each station. For the upper and lower reaches, the frequency of precipitation extremes at long return periods (20, 50 year or longer) presented increasing in recent years, and the intensity of the highest precipitation were getting stronger as well. The intensity of the highest precipitation extremes for the lower reach (21 and 35 %) increased higher than those for the upper reach (10 and 11 %). For the middle reach, the frequency of precipitation extremes (over 20 year return level) was not found to be increased. The uneven spatial and temporal distribution of precipitation extremes for the basin especially for the upper and lower reaches were getting more and more serious, which would bring great challenges for the local water allocation and management. [ABSTRACT FROM AUTHOR]

The Northwest Pearl River Delta is one of the most developed areas in China and has faced serious water problems because of its fast economic growth, urbanisation and other developments. It is widely believed that an integrated management of the socio-economic factors cross individual administrative cities is an effective way to solve the water problems. To serve this purpose, this paper aims to develop an integrated model for the optimal allocation of water quantity and waste load. In order to consider the interaction between water quantity and waste load allocation, the Saint-Venant equations were used to simulate dynamic water flow for the water quantity allocation, whereas the one dimensional advection-dispersion mass transport equation was used to simulate water quality for the waste load allocation. In addition to the maximisation of the economic benefits, which is often considered as an objective of optimal water resource allocation models, the minimisation of water shortages and maximisation of waste load were also introduced as objectives of the model. To solve the multi-objective allocation model, a second generation non-dominated sorting genetic algorithm was employed because of its computational efficiency and running time. The results indicate that it is a serious task to reduce the COD in the Northwest Pearl River Delta since the maximum waste load allocations under water quality targets is less than the present amount of waste discharged into rivers in the study area. [ABSTRACT FROM AUTHOR]

In recent years, a large number of bank destruction occur in the reservoir area under the effect of water fluctuation, which may be lead to reservoir accumulative landslide geological hazards finally. The paper conducted the bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach. A representative scenario of Jinle landslide is then taken for analysis purposes. On the basis of the existing data sets of bank destruction cases, the BP neural network forecasting model and the corresponding programs for bank destruction are both presented, whose forecasting result is validated by two independent approaches, namely empirical method and numerical modeling method. Furthermore, the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience. According to the bank destruction forecasting scale presented above, the corresponding revetment measures can be proposed to prevent the occurring of the bank destruction, whose effectiveness has been further validated by the actual engineering practice. [ABSTRACT FROM AUTHOR]

Chinese cities are plagued by the rise in resource and energy input and output over the last decade. At the same time, the scale and pace of economic development sweeping across Chinese cities have revived the debate about urban metabolisms, which could be simply seen as the ratio of output to resource and energy input in urban systems. In this study, an emergy (meaning the equivalent solar energy) accounting, sustainable indices of urban metabolisms, and an urban metabolic system dynamics model, are developed in support of the research task on Chinese cities 'metabolisms and their related policies. The dynamic simulation model used in the paper is capable of synthesizing component-level knowledge into system behavior simulation at an integrated level, which is directly useful for simulating and evaluating a variety of decision actions and their dynamic consequences. For the study case, interactions among a number of Beijing's urban emergy components within a time frame of 20 years (from 2010 to 2030) are examined dynamically. Six alternative policy scenarios are implemented into the system simulation. Our results indicate that Beijing's current model of urban metabolism-tertiary industry oriented development mode-would deliver prosperity to the city. However, the analysis also shows that this mode of urban metabolism would weaken urban self-support capacity due primarily to the large share of imported and exported emergy in the urban metabolic system. The keys of improving the efficiency of urban metabolism include the priority on the renewable resource and energy, increase in environmental investment and encouragement on innovative technologies of resource and energy utilization, et al. [ABSTRACT FROM AUTHOR]