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Increased risks of economic policy uncertainty and overexploitation of natural resources exist in China. At the same time, the growth rate of urban residents' consumption has generally declined. The paper analyses the role of economic policy uncertainty (EPU) and natural resource exploitation on the urban residents' consumption in China. Based on the data from the first quarter of 2002 to the third quarter of 2021, the paper uses a nonlinear autoregressive distributed lag model to verify the asymmetric effects. Then the paper constructs a time-varying parameter vector autoregressive model with stochastic volatility term to analyze the nonlinear responses. Impulse response analysis was used to further explain the relationship between the three. The negative impact of rising EPU on urban residents' consumption is larger than its reduction. Negative shocks to natural resource development increase the urban residents' consumption. Positive shocks reduce the urban residents' consumption. There is a time-varying non-linear effect of EPU and natural resource development on urban residents' consumption in China. The negative impact of EPU on urban consumption has been further exacerbated by major crises such as the financial crisis, COVID-19 and the post-crisis period. The negative impact of natural resource development diminished after the government introduced industrial upgrading policies and environmental regulations. This study provides constructive suggestions for the optimization of economic policies and the improvement of urban consumption. This study also enriches consumer theory and provides new evidence for the resource curse hypothesis. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning. [ABSTRACT FROM AUTHOR]

A modified version of a seismic risk assessment method is presented that generalises original risk analytical processes, constructs interrelationships among hazard variables in pre-assign weight-based formats, and establishes priorities to address potentially vulnerable dams. The concerned parameter values contained in each component are labelled based on adjusted grading standards, respecting the regional difference in hydraulic structure attributes and safety acceptability ranges. The cascade dams in Dadu River, China, are selected to test the applicability of the proposed method. We systematically investigate potential seismic hazards to demonstrate the implications of earthquake on dam safety. The results indicate that the adapted parameter ranges correspond with the physical aspects of the dams and potential social-economic consequences in seismic-prone regions. Correspondingly, the hazard recognition levels accommodate their local near-source earthquake conditions. Moreover, the investigation guides local emergency services with warnings of potential dam damages and ensuing floods when encountering critical earthquake shaking. [ABSTRACT FROM AUTHOR]

The global pandemic caused by the outbreak of COVID-19 has posed significant risks to our health. Preventive measures such as closed management have greatly affected the economies, environments and societies of various countries. Economy, air pollution and income are three important interconnected aspects of sustainable development. However, current research lacks systematic quantitative analysis of their relationships. To fill the gap, this study adopts monthly data from January 2016 to April 2022 and constructs both a Simultaneous Equation Model (SEM) and a Time Varying Parameter Stochastic Volatility Vector Autoregressive (TVP-SV-VAR) model to empirically analyze the impact of COVID-19 on China's economy, air pollution and income. This study finds that the COVID-19 has a negative impact on China's economy and income, and a positive impact on air pollution, and the impact of the COVID-19 is systematic. In addition, there is an inverted-U shaped relationship between air pollution and economics, and a positive correlation between economic and income. The impact of COVID-19 on the economy, air pollution and income show a process of sharp fluctuations to gradual stabilization that gradually stabilized over time. This process is time-varying in the short-term, medium-term and long-term. The impacts are persistent at three different time points (before, during and after the outbreak of COVID-19), but the negative impact on the economy and income is persistent, while the positive impact on air pollution is limited. This study provides a more systematic and dynamic understanding of the COVID-19 preventive and mitigation measures in China and even the world, which helps to provide insights into the formulation of more comprehensive planning strategies in the future. [ABSTRACT FROM AUTHOR]

Based on China's policy of double control of energy consumption, this study adopts the Logarithmic Mean Divisia Index model to explore the potential factors driving the changes in industrial energy consumption in the Yangtze River Delta during 2005–2019. Moreover, the convergence in energy intensity is also tested with sigma, beta, and stochastic convergence methods. The results are as follows: (1) The economic output effect was the largest contributor to the increase in energy consumption, followed by the population size effect, whereas the industrial structure effect was the dominant factor in reducing energy consumption over the same period. For the four effects, the contribution of the energy intensity effect to the reduction of the total effect was greatest, occupying the overwhelming proportion of 117%. (2) The industrial energy intensity in the Yangtze River Delta exhibits varying degrees of convergence in its distribution. (3) The foreign direct investment and industrialization level make no contribution to the convergence of energy intensity. Based on obtained results, this study then provides targeted policy recommendations to effectively manage energy consumption. [ABSTRACT FROM AUTHOR]

On September 5, 2022 (local time), a magnitude 6.6 earthquake was reported to have occurred in Luding County, Sichuan Province, Southwest China. In this simulation, a widely used stochastic finite-fault model was used to analyze how the source models affect the near-fault earthquake ground motion simulations of the 2022 Mw 6.6 Luding earthquake in China. Seven different slip models, one of them obtained from common fault parameters and random distributed slip amount, were used to yield the best match with the recordings. The simulated earthquake ground motions calculated in the frequency band of 0.05–20 Hz were compared with the observed values in both the time and frequency domains. Twelve acceleration observation stations located near the fault plane were selected in our simulation for comparison. The average H/V curves were estimated using the available acceleration records to consider the local site effect at each selected station. The research results indicate that none of the source models adopted in this study fully estimate the observed values at all the selected ground-motion stations. The simulated values of some slip models underestimate the level of the Fourier amplitude spectrum at frequencies above 6 Hz. The underestimation may be attributed to the directivity effect, which may produce a higher amplitude of observed ground motion in the high-frequency band. All the slip models show similar average model deviations except for the random slip model. Finally, the peak ground accelerations and peak ground velocities were predicted at these selected near-fault observation stations. The results indicate that the peak accelerations and velocities obtained from seven slip models correlate well with each other, but are slightly lower than the recorded values at most stations. In addition, the synthetized results calculated from the random and inverted slip models can be the same level only if a greater stress drop is adopted in the random model. [ABSTRACT FROM AUTHOR]

The Huaihe River Basin (HRB) is located in the north-south climate transition zone of China and is highly susceptible to climate change. Analysis of the evolution pattern and potential causes of extreme hydrometeorological events is of great significance for flood prevention, mitigation and water resource planning in the basin. Based on the measured meteorological data in the HRB for 1963–2018, statistical analysis methods were used to test variations on extreme hydrometeorological events (precipitation and drought). Possible effects of global warming, atmospheric circulation patterns and sunspots on the spatiotemporal variability of hydrological events were also explored, effectively identifying the common influencing factors of extreme precipitation and drought events. The results show that (1) Extreme hydrometeorological events in the upper reaches are serious, but extreme precipitation tends to slow with time; the increase in extreme precipitation indices in the middle reaches deserves attention, while drought conditions are relatively mild; the drought trend in the lower reaches is significant, and extreme precipitation has improved. (2) Each inactive image for different periods is loaded onto the canvas, and a dynamic graph is used to display the temporal and spatial evolution pattern of extreme hydrometeorological events. (3) Global mean near-surface temperature anomalies (GTEM) are positively correlated with most extreme precipitation indices but negatively correlated with drought indices. The increase in the number of sunspots eased extreme precipitation and worsened drought. [ABSTRACT FROM AUTHOR]

To systematically study the environmental problems of heavy metal pollution in soils caused by antimony (Sb) smeltery in central and southern China, the pollution levels, sources and risks of heavy metals were explored based on the GIS, pollution indices, multivariate statistical methods combined with positive matrix factorization (PMF) model, and risk assessment model. The results showed the average contents of Hg, Pb, Cd, Cr, As, and Sb in soils were 0.06, 47.77, 2.88, 53.12, 21.22 and 20.53 mg/kg, respectively. Except for Hg and Cr, the contents of other metals all exceeded the background values of Hunan Mountain soil (BV). Spatial distribution of heavy metal contents and pollution indices (Igeo, PLI, and RI) indicated that above 50% of the studied area were polluted moderately by Cd and Sb, and 77% of sampling sites were under a severe ecological risk level. Multivariate statistics and PMF analysis suggested that Cd (76.9%) and As (57.7%) were mainly from agricultural activities, followed by Sb smelting (12.7% for Cd and 25% for As). The atmosphere deposition of transportation and industrial activities, antimony smelting and nature origin respectively contributed 40.6%, 20.5% and 30.7% of Hg in soils. Sb (73.9%) and Pb (54.5%) were from the Sb smelting, followed by the high geological background (35.6% for Pb and 25% for Sb). Cr (69.7%) was natural origin, but agricultural activities had 21.6% of contribution to its accumulation in soils. Significantly, the values of non-carcinogenic risk (HI) for children under three exposure pathways in different soil depths was bigger than 1 and exceeded the "acceptable level" of risk, and the total carcinogenic risks (TCR) for adults and children were exceeded 1 × 10–4 with an unacceptable carcinogenic risk for human body. Oral ingestion of As was the remarkable characteristic of risk, which should be paid attention to. [ABSTRACT FROM AUTHOR]

Global warming has exacerbated the instability of the climate and has led to the frequent occurrence of extreme cold and warm events. The complex geography of the high-latitude region of China's monsoon-continental transition zone makes it extremely sensitive to climate response, understanding temperature changes over long periods of time is crucial to revealing trends in climate change trends. In this study, we constructed a standardized tree-ring width chronology of Korean spruce growing in this area from 1845 to 2016 and used it to analyzed the response of the radial growth of Korean spruce to climatic factors such as temperature and precipitation. The results show that the annual mean temperature was the dominant climatic factor affecting the growth of Korean spruce. Hence, we reconstructed the annual mean temperature series of this region spanning the past 172 years. The analysis results show that the study area experienced five warm periods and five cold periods in the past 172 years. Cold years were dominant before 1960s, while temperature continuously rose and changed drastically in the early twenty-first century. The reconstructed annual mean temperature series has variability cycles of 3a, 7a, 10–12a, 15–22a and 30–40a. The results of this reconstruction enrich the tree-ring database in the representative regions of the monsoon-continental climate transition zone in China and provide a reference for systematically understanding the climate change patterns in the representative regions of the transition zone. [ABSTRACT FROM AUTHOR]

In addition to common synoptic wind system, the mountainous terrain forms a local thermally driven wind system, which makes the mountain wind system have strong terrain dependence. Therefore, in order to estimate the reliable design wind speeds for structural safety, the samples for extreme wind speeds for certain return periods at mountainous areas can only come from field measurements at construction site. However, wind speeds measuring duration is usually short in real practice. This work proposes a novel method for calculating extreme wind speeds in mountainous areas by using short-term field measurement data and long-term nearby meteorological observatory data. Extreme wind speeds in mountainous area are affected by mixed climates composed by local-scale wind and large-scale synoptic wind. The local winds can be recorded at construction site with short observatory time, while the extreme wind speeds samples from synoptic wind climate from nearby meteorological station with long observatory time is extracted for data augmentation. The bridge construction site at Hengduan Mountains in southwestern China is taken as an example in this study. A 10-month dataset of field measurement wind speeds is recorded at this location. This study firstly provides a new method to extract wind speed time series of windstorms. Based on the different windstorm features, the local and synoptic winds are separated. Next, the synoptic wind speeds from nearby meteorological stations are converted and combined with local winds to derive the extreme wind speeds probability distribution function. The calculation results shows that the extreme wind speed in the short return period is controlled by the local wind system, and the long-period extreme wind speed is determined by the synoptic wind system in the mountain area. The descending slope of the synoptic wind speed exceeding probability exceeds the local wind by about 20%. If the influence of mixed climate is not considered, and the wind speed samples are not divided by category, the decline slope will be 7% lower. [ABSTRACT FROM AUTHOR]

Rapid urbanization has produced many metropolitan areas in China, resulting in regional human-land-ecological environment contradiction phenomenon. In recent years, Chinese government has launched a series of new national ecological civilization construction and ecological protection projects. Monitoring the changes in land-use/land cove (LULC) and exploring its impact on net primary productivity (NPP) is a key hot issue to support sustainable development. This study took Wuhan metropolitan area as the research area to monitor the distribution and relationship between LULC and vegetation NPP changes is helpful to strengthen carbon balance and improve the quality of human settlements. The results showed that, from 2000 to 2020, land use types in the study area changed dramatically, mainly cultivated land, urban land, and other construction land. The vegetation NPP showed a continuous growth trend in time and space, with growth rate of 114.48%. With the passage of time, LULC spatial distribution had significant effects on NPP and chaotic urban sprawl has seriously affected NPP losses, while the ecological forest and grassland in remote areas can significantly increase NPP yield. This study also revealed the correlation between LULC and NPP, and opened up research approaches for land-use optimization and vegetation NPP based on different scenarios in the future. Our analysis not only contributes urban land-use planners, but also provides important insights into improving the competitiveness of green development among urban agglomeration systems. [ABSTRACT FROM AUTHOR]

Establishing an accurate and dependable displacement prediction model is essential to building an early warning system for landslide hazards. This study proposes an adaptive hybrid machine learning model for forecasting the step-like displacements of reservoir colluvial landslides. Firstly, candidate factors are determined based on the landslide deformation response. Then, the cumulative displacement and candidate factors are decomposed using the optimized variational mode decomposition algorithm. Second, the sensitivity analysis of the gray wolf optimizer-based kernel extreme learning machine (GWO-KELM) models to each factor component is analyzed using the PAWN method. Then, the factors are optimized based on the analysis results. Third, based on the optimized factors, GWO-KELM models of different displacement components are established and integrated to predict the cumulative displacement. The Baishuihe landslide was taken as an example. The raw data of its three monitoring sites were employed to verify the performance of the proposed model. The results indicate that the model can decompose the cumulative displacement and factors with the adaptively determined parameters. In addition, the model performed well over a three-year prediction of the landslide displacement. [ABSTRACT FROM AUTHOR]

Tailings dam failures may cause catastrophic losses of personnel and property as well as environmental pollution. Therefore, the accurate assessment of the risks of tailings dams and the implementation of effective control measures are critical to ensuring the safety and stability of tailings dams. In this study, first, based on the Decision-Making Trial and Evaluation Laboratory and interpretive structural modeling (DEMATEL-ISM), a causal coupling hierarchical structure model of the disaster-causing factors of tailings dams was established. The importance and causal relationships among the disaster-causing factors were analyzed, and the key disaster-causing factors and total degree were identified. Second, according to the hierarchical structure model, the Pythagorean fuzzy analytic hierarchy process was utilized to calculate the hierarchical weights of the factors. According to DEMATEL and complex network analysis, the centrality and clustering coefficient of each disaster-causing factor are obtained. A model for determining the weights of disaster-causing factors is proposed with centrality (α), hierarchical weight (β) and clustering coefficient (θ) as the core. This model reflects not only the importance of each disaster-causing factor to the risk of tailings dam failure but also the causal coupling relationship between the factors and the structural importance of each factor in the tailings dam system. Finally, combined with the cloud model and matter-element extension model, a risk assessment model for tailings dam failure was proposed. An example of a tailings dam in China was analyzed. The results show that the case risk assessment results are consistent with the actual project assessment results. The weight determination and risk assessment method reduce the impact on the accuracy of risk assessment results due to the ambiguity, randomness and correlation of disaster-causing factors. [ABSTRACT FROM AUTHOR]

Several hands are now working worldwide to reduce exposure to air pollution, especially in developing nations. Future steps should be determined and classified as possible research solutions and gaps from the massive bulk of research output. Therefore, a scientometric approach has been applied using VOSviewer to show an accurate picture and trend in the mentioned area "Air pollution exposure and health," and its signify issues. According to the proposed study, complete 26,859 documents were retrieved from the database (ISI Web of Science) related to air pollution exposure and health effects during 2018–2022. The mapping analysis is been conducted on the country's collaboration, co-authorship, institutional collaboration, and co-occurrence of keywords. The data collected shows the information about published articles (upward trend) over the years. Based on the citations and publication database, countries like China and the USA play a prominent role in air pollution exposure and health-related research. The study clearly defines the 3 domains of research and 4 major themes that have been currently focused. The case studies related to pollution and its impact on climate and health, studies involving chemical characteristics and management practices, also Hazardous health effects, theme like association of air pollutants, chemical composition and characterization of aerosols, health impacts due to exposure and modelling and analytical approach have been the most researched topics in the past 5 years. The developing and developed countries might potentially change the research network and work structure in order to obtain advancement in the field of Air pollution and enhance measures on exposure and health. The following research attempts to provide insights to the researchers and health sectors by straightening out developments up to date and raveling the research gaps that are needed to be addressed regarding Air pollution health and exposure. [ABSTRACT FROM AUTHOR]

Monthly streamflow forecasts have important practical applications in short-term water resources management, e.g., water allocation for different users, flooding prevention, and drought mitigation. This study focuses on developing categorical streamflow forecasts for flooding mitigation purpose, which can be used as a critical component in an early flood warning system. A Bayesian logistic regression approach is proposed to use antecedent streamflow and forecasted precipitation from General Circulation Models (GCMs) and derive the probability of streamflow greater than threshold streamflow. The logistic regression model is Hierarchical Bayesian Modeling that assumes Bernoulli distribution for monthly and Normal distribution for the parameters in the logistic function. To accommodate outliers in the modeling dataset, an additional parameter is added to the Bayesian modeling framework to make it a more robust approach. The Bayesian Logistic Regression is implemented in JAGS and posterior distributions of model parameters are estimated from Markov Chain Monte Carlo (MCM) chains. The proposed method is applied to a watershed in Hunan Province located in the middle south of China. Precipitation and streamflow data in the years 1960–2012 were used to estimate the model parameters' posterior distributions. The model's performance is tested for monthly streamflow data in the years 2013–2017, using one-month-ahead precipitation forecasts from GCMs. The model is superior to climatology, the reference model, in terms of the accuracy of hit rates. Potential improvement to the model is also discussed. Although the proposed method is demonstrated for the study area, it can be applied to other regions with similar applications. [ABSTRACT FROM AUTHOR]

The concentrations of As, Cd, Co, Cu, Mn, Ni, Pb and Zn in 78 urban soil samples from Zhengzhou, Henan Province, China were determined in the study. The mean values of the eight heavy metals were lower than the natural background reference values of soils from Henan Province, with the exception of Cd and Pb. However, the peak values for six species of the elements exceed the corresponding reference values, indicating enrichment of these metals in urban soils in Zhengzhou City, especially for Cd and Pb. The spatial distribution patterns showed that levels of As, Co, Mn and Ni decreased gently from northwest to southeast without hot-pot area, whereas Cd, Cu, Pb and Zn were with significant spatial variability. PMF model coupled with Pearson's correlation analysis was performed to apportion and quantify possible sources of the eight elements. Four Factors (natural source, industrial production, agricultural activities and traffic discharges) were distinguished and their respective contribution rates were 18.73%, 33.22%, 23.91% and 24.14%, respectively. Results of uncertainty analysis confirmed that the all four source Factors based on the PMF model were reliable. Furthermore, the probability distribution of health risk assessed by means of Monte Carlo simulation revealed that non-carcinogenic risks associated with heavy metals were acceptable for both adults and children, while the total carcinogenic risk values remained relatively high. Additionally, the children were more susceptible to health risks in comparison to adults, and the ingestion way was considered to be the major exposure pathway. The results of source-oriented health risk demonstrated that the agricultural source was the major contributor to health risks, which was characterized by As, Ni and Co. [ABSTRACT FROM AUTHOR]

The Approximate Bayesian Computation (ABC) provides a powerful tool for signature-domain calibration of hydrological models where hydrological signatures are incorporated into calibration objectives. However, the efficiency of ABC relies strongly on the use of a vector of sufficient signatures that can fully represent relevant information in raw data. The application of ABC with randomly chosen signatures can result in inaccurate calibration results. To fill this gap, a hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models is proposed. In this framework, a set of approximately sufficient signatures is pursued through simultaneous consideration of the information redundancy analysis (IRA) and discriminatory power analysis (DPA) procedures. While the IRA deals with the information redundancy inherent in the pool of available signatures, DPA quantifies the discriminatory power of a given signature as the reliability and sharpness of the associated probabilistic predictions generated by ABC. The verified residual error scheme in time-domain inference is approximated as the probabilistic model in the acceptance test of ABC. The proposed framework is then tested on the Xin'anjiang rainfall-runoff model applied to the Ren River basin (RRB) of China. The use of IRA and DPA provides a probabilistic model prediction statistically equivalent to that of classical time-domain inference in terms of the reliability and sharpness. The comparison to signature-domain inference using the complete set of hydrological signatures further demonstrates the importance of IRA and DPA in improving the quality of Bayesian model calibration in the signature domain and reducing the total predictive uncertainty. The framework makes it practically possible to maintain adequate accuracy of model predictions produced by signature-domain inference, improving the efficiency of ABC in solving the model calibration problems and consequently promotes the use of ABC in signature-domain model calibration. [ABSTRACT FROM AUTHOR]

A compound perspective on hydrological extreme events is of paramount significance as it may lead to damages with larger losses. In this study, an integrated framework, based on downscaled climate variables and hydrological model, i.e. the Soil and Water Assessment Tool, was applied to generate extreme precipitation (Rx1day) and extreme streamflow (Sx1day) series under historical and future climate conditions. Then the potential impacts of climate change for univariate and bivariate joint frequency of extreme precipitation and flood in Xitiaoxi River Basin (XRB), a representative watershed of the Yangtze River Delta, are detected. The compound risk of extreme precipitation and flood under different levels of joint return period for historical and projected periods is estimated by copula‐based two-dimensional approaches. The Rx1day and Sx1day under future scenarios changed by − 0.4% to 11.7% and 0.7% to 20.4%, respectively, compared to historical period based on univariate frequency analysis, indicating the increasing magnitude of the flood in the future. Climate change with different emission scenarios all have a driving effect on the rising coactivity of extreme precipitation and flood under compound flooding frequency analysis. In addition, the enhancement of climate change to extreme events is more apparent for extremes with higher return period and under the periods of 2080s. Moreover, the flood frequency designs are deduced by bivariate joint distribution are safer than that by univariate distribution. This study may provide actionable insights to formulate the planning scheme of flood control and disaster reduction under the changing environment. [ABSTRACT FROM AUTHOR]

With the increasing development of economics in China, the performance evaluation and obstacle diagnosis for urban water ecological civilization construction (UWECC) have attracted more and more attention, this study analyzes the coupling relationship between social economy and urban water ecology by applying framework model of "Driving force-Pressure-State-Influence-Response-Management", establishing the performance evaluation index system of UWECC. The group fuzzy analytic hierarchy process is developed to determine indicator weights and to find out the key influencing factors. A combination of fuzzy synthetic evaluation (FSE) and cloud model is introduced for measuring UWECC performance level. The integrated model includes two parts. One component is that fuzzy set theory is combined with evaluation methods, proposing a FSE method. The other part is that cloud model is utilized to make up for this limitation of ignoring the internal randomness and ambiguity for the FSE method. Subsequently, the UWECC of Shizuishan City in China is selected from 2010 to 2019 as a case study, the results show that its UWECC performance scores gradually increased in the past ten years, the growth speed of which can be divided into three development stages: bad and slow-growth (2010–2014), intermediate but slightly fast-growth (2015–2017) and good and fast-growth (2018–2019). Besides, through making sensitivity analysis of indicators to each subsystem, it is found that their sensitivities show obvious differences. In terms of the main obstacle indicators affecting UWECC performance, they also present obvious temporal heterogeneity characteristics, that is, the obstacle degrees of indicators in the same stage have slightly smaller differences, and those of indicators in different stages exist significant differences. The demonstration conducted proves that the proposed method can be used not only to accurately understand the changes of UWECC through time but also to diagnose the crucial obstacle indicators, providing theoretical basis for improving UWECC performance of this urban in the future. [ABSTRACT FROM AUTHOR]

Food safety is influenced by soil heavy metal pollution, since it is of great significance to study soil heavy metal pollution of agricultural products. In this work, 5 vertical and 20 horizontal sampling points were collected from apple orchards in Qixia City, Shandong Province to study soil pollution. Geo-accumulation index and potential ecological risk were used to assess the level of soil pollution. Positive matrix decomposition (PMF) receptor model was used to identify the source contribution of the heavy metal in soil. A geostatistical analysis method was used to study the spatial distribution patterns of heavy metals. Vertical concentration analysis of heavy metals showed that the concentration of heavy metals, excluding Cr, was higher in the soil surface than in the bottom soil. The potential ecological risk level of ordinary orchards was the highest. The horizontal concentration analysis showed that the average value of Hg was over twice the background value in Shandong Province. The potential ecological risk index values showed that the overall risk level of the soil was at moderate. No heavy metal was detected in the apple sample, but the residue of carbendazim requires attention. The spatial distribution of heavy metals indicated that the use of fertilizers and pesticides and industrial activities may be the main sources of heavy metals in orchards, which was similar to the results of PMF. The sustainable development of Qixia City orchards clearly requires focus on soil acidification, low soil organic matter, and older fruit growers. [ABSTRACT FROM AUTHOR]

Soil heavy metal contaminated sites with multiple sources of pollution have caused worldwide public concern. However, the lack of correlation of risk assessment between source identification of heavy metal leads to unclear direction of source governance. An integrated methodology is established in this study by combining source apportionment and human health risks model to characterize source-identified risks of heavy metals based on spatial interplotation. Principal component analysis and positive matrix factorization model are used to identify and classify potential sources of heavy metals synthetically. The integrated results are incorporated into the health risk model to evaluate potential non-carcinogenic and carcinogenic risk of soil heavy metals. A case study was conducted in Suzhou city of China. The results indicate that concentrations of Cd and Hg were highly above the background values, accounting for percentages of 239.6% and 415.9% above background values, respectively. The source-contributed human health risk index of As contributed 76.9% to non-carcinogenic risk by pollutant sources of agriculture activities. The non-carcinogenic health risk index for adults and children are 0.308 and 1.00 respectively, indicating health threat to the children. Overall, the non-carcinogenic and carcinogenic risks of heavy metals are at an unacceptable level of risk and require environmental management. Furthermore, the health risk evaluation based on source contribution suggests that industrial processes contribute more to the health risk of heavy metals in soils. This study will provide a new insight for the treatment of mutil-sources of soil heavy metal pollution and also some reference value for the improvement of the risk assessment system. [ABSTRACT FROM AUTHOR]

Hydro-morphological processes (HMPs) are some of the most destructive natural disasters. Understanding the spatiotemporal characteristics of HMPs across China is important for enabling better disaster estimation and prevention at the national scale. However, few studies have focused on the spatiotemporal HMP characterization under various geomorphic settings in China, which may shed light on their regional/national evolution. To bridge this research gap, we analysed the longest HMP time series available in China, which including 47,483 HMP records, to detect spatiotemporal patterns. Specifically, we run several tests namely, Mann–Kendall test, wavelet analysis, Monthly Frequency, and Index of Dispersion, with the objective of detecting the temporal evolution, trends, period, and clustering of HMPs in six geomorphological regions (geomorphic-regions): Eastern Plain, South Eastern Mountain (SEM), North Central Plateau, North Western Basin, South Western Mountain (SWM), and Tibetan Plateau. Our results show that in the last decades have been associated with a marked increase in HMPs and this should be accounted for, especially in those areas where we have retrieved high spatiotemporal clustering (e.g., SEM, SWM). Besides, the main periodicity of HMPs is approximately 12–25 years for most of China since the 1980s, which showed analogous patterns with precipitation anomalies. This study provides a preliminary reference for revealing the spatiotemporal characteristics of HMPs in the context of climate change; therefore, the information provided can be crucial to plan engineering applications with specific return period. [ABSTRACT FROM AUTHOR]

Landslide displacement prediction is essential to establish the early warning system (EWS). According to the dynamic characteristics of landslide evolution and the shortcomings of the traditional static prediction model, a dynamic prediction model of landslide displacement based on long short-term memory (LSTM) neural networks was proposed. Meanwhile, the Variational mode decomposition (VMD) theory was used to decompose the cumulative displacement and triggering factors, which not only give clear physical meaning to each displacement subsequence, but also closely connect the rock and soil conditions with the influence of external factors. Besides, the maximum information coefficient (MIC) was used to sort the redundant features. The LSTM is a dynamic model that can remember historical information and apply it to the current output. The hyperparameters of the LSTM model was optimized by the Grey wolf optimizer (GWO), and the dynamic one-step prediction was carried out for each displacement. All the predicted values were superimposed to complete the displacement prediction based on the time series model. The Tangjiao landslide in the Three Gorges Reservoir area (TGRA), China, was taken as a case study. The displacement data of monitoring sites GPS03 and GPS06 had step-like characteristics. Measured data from March 2007 to December 2016 were selected for analysis. The results indicate that the displacement prediction model based on MIC-GWO-LSTM model effectively improves the prediction accuracy and generalization ability, and is better than other prediction models. This model provides a new idea and exploration for the displacement prediction of step-like characteristics landslide in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay. Nonpoint source pollution management for multi-watershed aiming to alleviate environmental contamination as well as yield considerable economic and environmental benefits can be under additional challenges. In this study, a Bayesian simulation-based multi-watershed effluent trading designing model (BS-METM) is established for multi-watershed nonpoint source pollution management through incorporating techniques of water quality simulation, uncertainty analysis with Bayesian inference, optimal design for effluent trading, as well as mechanism analysis. BS-METM is capable of reflecting parameter uncertainties in nutrient simulation, disclosing the detailed optimal trading schemes under the impact of uncertainties and vital factors, and identifying optimal effluent trading mechanisms through revealing interaction among trading processes of multiple watersheds. BS-METM is applied to a real case of adjacent coastal watersheds (i.e. Daguhe and Moshuihe watersheds), which are identified as major sources of total phosphorus and ammonia nitrogen loadings to Jiaozhou Bay, China. Effluent trading optimization under multiple mechanisms, including intra-watershed trading, cross-watershed trading and non-trading, are conducted. The optimized industry scales and trading processes are obtained. The effects of vital factors on the trading process (i.e. environmental allowance-violation risk level and water availability level) are investigated. The interactions between water availability level and trading mechanism are also analyzed. It is proved that non-trading mechanism would be recommended under low water availability level and cross-watershed trading mechanism would be recommended under medium and high water availability level. The results provide a solid scientific basis for nonpoint source pollution management as well as effective sustainable development for multi-watershed region. [ABSTRACT FROM AUTHOR]

It is critical to recognize and reproduce aquifer heterogeneity properly in groundwater modeling and management due to its impacts in groundwater flow, transport, mixing and reaction. It is widely reported that attributes of geologic formations often exhibit statistical scaling with probability distribution of non-Gaussian type. To capture those features in a unified and self-consistent manner, a general geostatistical model termed Generalized sub-Gaussian (GSG) model was proposed by Riva et al. (Water Resour Res 51:4623–4634, 2015. https://doi.org/10.1002/2015WR016998). One may wonder whether GSG also works on a broader variety of environmental systems having attributes of different types. Geophysical data are playing growingly significant role in aquifer characterization mainly due to its ease of implementation and low cost. This study focuses on assessing the applicability and transferability of GSG model via a dataset of electrical resistivity collected in three deep boreholes in the northern Ordos Basin in China. By analyzing statistical scaling behaviors, frequency distributions and model fittings of resistivity data, it is found that resistivity data from each borehole indeed can be characterized by GSG model. Though three neighboring boreholes are in a same formation and bear similar geological texture, their corresponding GSG models seem distinct. Cross-validation results further suggest that GSG model calibrated using two boreholes are different from that based on the remaining borehole, though large-lag increments exhibit similar frequency distributions. It is found that GSG is able to characterize well log resistivity in three boreholes but with distinct parameters. Attention should be paid when transferring a calibrated GSG model to the vicinity even within a single sedimentary structure. [ABSTRACT FROM AUTHOR]

The Lowest navigable water level (LNWL) is an important indicator used for navigation design to balance the relationship between navigation safety and economic benefits of a waterway. However, it is a challenge of accurately estimating LNWLs due to the nonstationary characteristics of observed water level data series. In this study, a comprehensive framework was developed for handling this issue. In this framework, inter-annual variabilities in both the mean and variance of water level series were described by decomposing original series and were eliminated by composing new series. Intra-annual variability was determined by detecting indicators describing intra-annual water level distributions. Considerations of inter- and intra-annual variabilities were combined by designing annual water level processes for the past and current environments. Shipping risks during both annual and multi-annual periods were considered in the framework as well. The framework was demonstrated in estimating LNWLs at the Gaodao and Shijiao stations in the North River basin, southern China. The recommended LNWLs at the Gaodao station were 22.32 m for 95% guaranteed rate and 21.84 m for 98% guaranteed rate; LNWLs at the Shijiao station were 0.27 m for 95% guaranteed rate and 0.15 m for 98% guaranteed rate. The impact of variance variability on estimations of LNWLs was also evaluated. Results indicated that the recommended LNWLs would have errors of 0.11 ~ 0.48 m at the Gaodao station and 0.03 ~ 0.04 m at the Shijiao station if the variance variability was not considered. The proposed framework was then compared with Nonstationary synthetic duration curve (NSDC) method, and results illustrated that the duration curves plotted by NSDC method were unreasonable, leading to inaccurate design values. Overall, the developed framework is more reasonable and suitable for designing LNWLs of waterways where the variabilities of the water levels at different time scales are different or where the historical water level data contain various variations. [ABSTRACT FROM AUTHOR]

Evaluating the hazards of debris flows is necessary for risk assessment. The May 12, 2008 earthquake in Wenchuan County, China caused many landslides and loose slopes. Under continuous or heavy rainfall, the landslides and loose slopes are easily destabilized and provide huge amounts of material source for debris flows, significantly increasing the hazard level of associated disasters. Moreover, the hazard of debris flows is not always constant, but variable over time because of the changes in influencing factors (e.g., geomorphology, rainfall conditions, and earthquakes). This study proposes a coupled model based on the TRIGRS (Transient Rainfall Infiltration and Grid based Regional Slope-stability Model) and Flow-R (Flow path assessment of gravitational hazards at a Regional scale) models for a dynamic hazard assessment of debris flows applied in a small watershed, Bayi Gully. This coupled model considers the impact of the actual rainfall process, potential debris sources on slope surfaces, and existing loose sources in the debris flow channel. The results indicate that (1) the hazard assessment by the coupled model is superior to that by the static Flow-R model; (2) the spatial distribution of landslides, debris flow sources, debris flow hazard areas, hazard probability, and debris flow energy vary with rainfall duration; and (3) the debris flow hazard area is strongly affected by topographic and geomorphic factors. Thus, the coupled model provides a powerful and robust tool for the hazard assessment and disaster prevention and mitigation of debris flows. [ABSTRACT FROM AUTHOR]

China is among the countries most severely affected by storm surge disasters, with substantial economic losses and human casualties inflicted on its coastal areas. Computing inundation from storm surges for various typical return periods (TRPs) can serve as the scientific basis for preparing evacuation maps kin the case of storm surge disasters, conducting spatial planning of coastal cities, and formulating emergency plans. Zhoushan City in Zhejiang Province, China, was selected as the representative case for this study. A high-precision storm surge numerical model was established and historical observation data were used to calculate the probability distribution curves of extreme tidal levels at two representative tide stations within the study area. First, based on historical typhoon and extratropical-cyclone events affecting the study area, a dataset including weather events was used, and extreme tidal levels for the two representative tide stations were deployed to identify weather events that could cause a storm surge for different return periods. Numerical modeling was then used to generate the resulting inundation range and water depth distribution, establishing a method for calculating inundation from storm surge for various TRPs. The proposed method could easily be adopted in various coastal counties and serve as an effective tool for decision-making in storm surge disaster risk mitigation efforts. [ABSTRACT FROM AUTHOR]

This study explores the moderating mechanism of fiscal decentralization on the relationship between environmental regulations and carbon emissions. Using longitudinal data of 30 provinces in China from 2002 to 2017, a spatial econometric model was constructed to empirically test the moderating effect of fiscal decentralization, as well as its spatial and temporal differences. The highlights of the results are as follows: (1) Environmental regulations has significantly curbed regional carbon emissions, and the suppression effect remains consistent over spatial and temporal stages, which indicates it exerts a positive effect in carbon control. (2) Fiscal decentralization exacerbates carbon emissions, although this negative effect is not significant in general. However, there are significant spatial regional differences in its impact on carbon emissions. Fiscal decentralization significantly suppresses carbon emissions in the eastern region, exacerbates carbon emissions in the western region, and has no significant impact on carbon emissions in the central region. This shows that local governments in China prioritize economic development over environmental protection and carbon control. (3) The moderating effect of fiscal decentralization on the relationship between environmental regulations and carbon emissions is not significant for the entire country as a whole. However, there are significant spatial regional differences in this moderating effect, and the relationship was found to be significant in the Western region. (4) There are no significant time-phased differences in the effects of environmental regulations, fiscal decentralization, and its cross item on carbon emissions. [ABSTRACT FROM AUTHOR]

The rivers-connected lake involved in the "River–Lake-Reservoir" hydrological complex system and it's water level fluctuations are more severe than those of other lakes, which challenges the scientific management of lakes. Therefore, to improve the accuracy of water level prediction for the rivers-connected lake, taking Hongze Lake as an example, we used the BFAST algorithm to analyze the inconsistency of the lake's inter-annual water level and selected a stable stage for water level prediction research. Next, considering the lake basin shape, based on the Stage-discharge relationship curve, the fluctuation process of the lake's inter-annual water level was divided into four periods: the discharge period, the early period of storage, the later period of storage, and the balance period. Then, the NARX model was used to build the water level prediction model for different periods. Finally, the wavelet analysis and KNN algorithm were introduced into the water level prediction model for input data pre-process and result post-processing, respectively. The result shows that: (1) There are significant differences in the mechanism of water level regime modification in different periods. The outflowing runoff is the main driving factor for the water level regime modification in most times; (2) Coupling multiple machine learning methods is an effective way to improve the accuracy of the lake water level prediction; (3) The combination of the staged-divided water level prediction method and the hybrid machine learning models can further improve the accuracy of the water level prediction. [ABSTRACT FROM AUTHOR]