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Groundwater level (GWL) prediction is important for ecological protection and resource utilization; it helps in formulating policies for artificial groundwater recharge, modifying the number of extraction wells, etc., and can support sustainable human development as well as inform water resource management decisions. However, climate change, anthropogenic impacts, and the complex coupling between surface water and groundwater increase the difficulty of predicting groundwater levels. The model proposed in this paper combines external data as well as multiple models. The method leverages long and short-term memory (LSTM) and convolutional neural network (CNN) models, combined with secondary modal decomposition and slime mould algorithm (SMA), together with an adaptive weight module (AWM). The study applies this method to predict GWL for three different hydrological conditions in China, specifically for the Jinan Baotu Spring, Heihu Spring, and Zhongtianshe watershed of Taihu Lake. A comparison of metrics such as mean absolute error and Nash efficiency coefficient for single and hybrid models shows that the model in this paper is more advantageous than the single model and other hybrid models. The interpretability of the model is enhanced by SHAP values that demonstrate the degree of contribution of the input variables. This paper uses SHAP analyses to identify the key drivers affecting groundwater levels. These factors must be detected in order to develop groundwater resource protection measures. [Display omitted] • Multivariate fusion data including hydrology and meteorology are used as model input. • A secondary modal decomposition module for historical groundwater level data was utilized. • The neural network hyperparameters are optimized using the slime mould algorithm. • Aggregate subsets of prediction with adaptive modules rather than linear summation. • The interpretable SHAP model measures the degree of influence of external variables. [ABSTRACT FROM AUTHOR]

Reliable probabilistic hydrological prediction requires appropriate handling of residual errors, which can pose considerable complexity. This paper proposes a nonparametric residual error (NRE) model that effectively captures the statistical characteristics of raw residuals. The NRE model employs a local linear estimator with a robust bandwidth selector to estimate the regression and conditional volatility functions of raw residuals. Additionally, the AR(1) model and location-mixture Gaussian distribution are used to estimate the temporal correlation structure and innovation distribution. Through two case studies in South-East China, this research demonstrates the superiority of the NRE model over the benchmark Box-Cox transformation approach in terms of prediction reliability, precision, and bias correction capabilities. Simulation experiments further reveal that the NRE model can effectively fit the residual regression function, conditional volatility function, and innovation distribution under varying scenarios. The proposed residual error model is anticipated to promote the adoption of probabilistic predictions in hydrological modeling applications. • Residual Error Handling: This paper tackles complex residual errors in hydrological modeling. • Accuracy Improvement: The NRE model boosts accuracy with a local linear estimator. • Predictive Superiority: The NRE model excels in predicting reliability, precision, and bias correction. • Data-Driven Flexibility: The NRE model reduces reliance on assumptions for hydrological modelers. [ABSTRACT FROM AUTHOR]

Earthquake-stricken areas are characterized by frequent and long-lasting geological disasters. Therefore, a scientific and reasonable study on the spatial prediction of post-earthquake landslide hazards is beneficial to the sustainable development of these areas. Firstly, the spatial effects of landslide hazards are analyzed using KDE, and the singularity index is used to delineate the area where negative samples can be selected to reduce the impact of uncertainty on the model. Secondly, eight landslide influencing factors are selected, and the weights of each influencing factor are calculated by using a geodetector. Finally, the traditional binary dependent variable approach is changed, and a KDE-MDBN landslide hazard spatial susceptibility assessment model is constructed. The research indicates that the assessment model based on KDE-MDBN can accurately reflect the characteristics of the spatial distribution of landslide hazards in the area. The results of presented in this paper can provide reference information for subsequent susceptibility assessments. • A quantification method to delineate the selection area of negative samples is proposed. • The landslide hazard effect is considered in the construction of the landslide susceptibility assessment model. • The KDE-MDBN susceptibility assessment model can improve the spatial characteristic of landslide hazards. • The most important factors for spatial landslide hazards were determined. [ABSTRACT FROM AUTHOR]

Flood Detection and Service (FD&S) is of great significance to flood management and decision-making. But most of the state-of-art FD&S methods are weak in timeliness and extensibility, and could not reflect flood processes. To resolve the abovementioned problems, this paper proposed the process-based FD&S (PFD&S) method based on Sensor Web. PFD&S is a four-layer architecture the core components of which are the access adapter and process-based detection rules and it is capable of heterogeneous sensor access, flood detection, and flood phase adaptive services. A prototype was developed based on the PFD&S method. Two floods occurring in the Huanghan basin (Hubei, China, with the area of 5.04 × 104 km2) during July 2016, was selected as the case studies to validate the PFD&S method and prototype. The results demonstrated that the proposed PFD&S method and prototype could achieve the instant flood process detection and services in 2.7 min. • The PFD&S method was proposed to achieve the rapid flood process detection and flood phase adaptive services. • The PFD&S prototype was designed and implemented based on the PFD&S method. • The method and prototype were sensor web - based, with universality and extensibility. [ABSTRACT FROM AUTHOR]

Abstract Increasingly, composite indicators and multi-criteria approaches are applied in environmental assessment and decision-making, including the EU Water Framework Directive. For example, integrated evaluation of aquatic ecosystem conditions and functioning usually involves a group of criteria, such as biological organisms and communities, physicochemical and hydromorphological variables, which are measured individually and combined by a weighted linear function into an overall 'score'. We argue that the network interactions of evaluation components are useful information for expert judgments, which have not been sufficiently considered in existing multi-criteria combination strategies in environmental assessment and management. Built upon the Analytic Network Process and demonstrated with the Chishui River Basin in China, this paper introduces a network-based expert judgment approach to construct ecological water quality indicators, and to determine and adjust their variable weight settings with information of interaction networks. This approach has potential to construct composite indicators for a broad environmental context. Highlights • Networks are key features of ecosystems, providing useful information for decision-making. • Interactions are seldom used in setting weights for ecological composite indicators. • We develop a method to adjust indicator weights using ecological network information. • We show an extensible and more justified indicator building strategy. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system. Highlights • Proposing a dynamic Bayesian network for risk analysis of a multi-reservoir system. • Performing prediction of risks under uncertainties using Bayesian networks. • Performing diagnosis of reasons under given risk events using Bayesian networks. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces the development of a dynamic parallel algorithm for conducting hydrological model simulations. This new algorithm consists of a river network decomposition method and an enhanced master–slave paradigm. The decomposition method is used to divide a basin river network into a large number of subbasins, and the enhanced master–slave paradigm is adopted to realize the function of this new dynamic basin decomposition method through using the Message-Passing Interface (MPI) and C++ language. This new algorithm aims to balance computation load and then to achieve a higher speedup and efficiency of parallel computing in hydrological simulation for the river basins which are delineated by high-resolution drainage networks. This paper uses a modified binary-tree codification method developed by to code drainage networks, and the basin width function to estimate the possible maximum parallel speedup and the associated efficiency. As a case study, with a hydrological model, the Digital Yellow River Model, this new dynamic parallel algorithm is applied to the Chabagou basin in northern China. The application results reveal that the new algorithm is efficient in the dynamic dispatching of simulation tasks to computing processes, and that the parallel speedup and efficiency are comparable with the estimations made by using the basin width function. [Copyright &y& Elsevier]

Serious transboundary conflicts over water pollution commonly arise from the environmental management of Chinese river basins adopting the current model of proportional share of pollution reduction (MPSPR). This paper discusses problems with the current MPSPR and then proposes a new model. Based on four assumptions, including the assumption that pollutant discharge or transfer within the river basin is highly controlled by individual administrative regions, a descriptive model of transboundary water pollution was developed for a typical Chinese river basin. Based on the above assumptions and the descriptive model, the author developed a model of collective cooperation and reallocation of benefits (MCCRB) to solve transboundary conflicts. MCCRB comprises two interacting parts. First, the administrator of the river basin calculates an optimal pollutant reduction allocation for the whole basin that meets water quality standards at the minimum cost to the environment. Second, the administrator finds an agreeable mechanism for benefit allocation that encourages each region to cooperate in reducing pollution. The first part is the basis for the MCCRB; the second encourages effective implementation of the optimal share of pollutant reduction by each region. MCCRB seeks an optimal solution for the whole basin rather than a particular region. Theoretical analysis and a case study for Chinese Huaihe River Basin suggest that the MCCRB is superior to the MPSPR: it decreases the total environmental cost (1995–1997) by USD$ 9,479,000, and increases resource utilization efficiency (1995–1997) by 33.4%. Findings of the paper will be helpful in designing an environmental policy that solves conflicts over water pollution across regional boundaries in Chinese river basin. However, some factors that affect the MCCRB must be considered by the administrator of the river basin. [Copyright &y& Elsevier]

Abstract: This paper introduces a model and corresponding software for modeling China''s Energy Requirements and the CO2 Emissions Analysis System (CErCmA). Based on the input–output approach, CErCmA was designed for scenario analysis of energy requirements and CO2 emissions to support policymakers, planners and others strategically plan for energy demands and environmental protection in China. In the system, major drivers of energy consumption are identified as technology, population, economy and urbanization; scenarios are based on the major driving forces that represent various growth paths. The input–output approach is employed to compute energy requirements and CO2 emissions under each scenario. The development of CErCmA is described in a case study: China''s energy requirements and CO2 emissions in 2010 and 2020 are computed based on the input–output table of 1997. The results show that China''s energy needs and related CO2 emissions will grow exponentially even with many energy efficiency improvements, and that it will be hard for China to maintain its advantage of low per capita emissions in the next 20 years. China''s manufacturing and transportation sectors should be the two major sectors to implement energy efficiency improvements. Options for improving this model are also presented in this paper. [Copyright &y& Elsevier]

Global climate change has led to large fluctuations in lake levels in recent years as meteorological and hydrological parameters have changed and water use has been intense. Water scientists use various computer models to analyze the hydrological variables recorded in the past and make projections for all future scenarios. Based on the technological progress, six different types of algorithms were studied in this review to predict the water level in lakes. The prediction results show that Deep Learning (DL) has the highest accuracy in terms of the evaluation metrics. Since the Artificial Intelligence (AI) field is still emerging and continue to improve, this study highlights better comprehension of current applications and the problems that need to be investigated more for LWL forecasting techniques. It reveals that the studies mainly focused on lakes either in USA or China and there is room for improvement for other locations that are scarcely investigated. • This paper presents a systematic literature review for lake and reservoir water level studies for the first time. • Researchers favored input parameters as lake water level, precipitation and temperature among others. • The studies can be extended towards the direction of least studied areas such as deep learning based algorithms. • Increasing water level prediction areas recently indicates it became a hot topic because of visible climate change effects. [ABSTRACT FROM AUTHOR]

Modeling is an essential tool for studying environmental systems. Due to the interdisciplinary nature, several models should be integrated to consider the nexus between different natural processes. However, the computational cost and accuracy of physics-based numerical models are always limited, even contradictory. In this study, a novel paradigm for integrating numerical models and machine learning models is proposed based on a previous study which integrates WOFOST, HYDRUS and MODFLOW. In the paradigm, the MODFLOW is replaced by Radial Basis Function neural network to reduce the computational costs with comparable accuracy. Object Modeling System is used as modeling framework which provides a run-time environment for the components. A synthetic case is implemented with data collected from Linze Inland River Basin Research Station in northwestern of China. Furthermore, the computation efficiency and generalization ability are discussed by refining the MODFLOW grids and changing the driving forces (precipitation). The proposed paradigm shows significant better performance on flexibility and computational efficiency with similar accuracy and acceptable generalization ability which would be further improved by larger dataset. This paper explores a computational efficiency paradigm by integrating physics-based and machine learning models which also provides a reference for physics-informed/guided model integration. • A new paradigm is proposed to integrate machine learning models and physics-based numerical models. • WOFOST, HYDRUS and MODFLOW (later replaced by RBF neural network) are integrated in OMS. • Two discretization schemes for the aquifer are implemented to demonstrate the computational efficiency. • Several experiments on the driving forces are implemented to demonstrate the generalization ability. • The new paradigm performs better in computational efficiency with comparable accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

Abstract: Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology. [Copyright &y& Elsevier]

Abstract: China''s demand for energy has grown to fuel its rapidly expanding industrial, commercial and consumer sectors. At the same time, China has become the second largest consumer of petroleum products having surpassed Japan for the first time in 2003. The environmental consequences of a continuation of these trends will have global implications. Government policies and consumers have become more environmentally aware, but the ability of governments to formulate policies has been hindered by the lack of data on inter-factor and inter-fuel substitution possibilities. In this paper Allen partial elasticities of factor and energy substitution, and price elasticities of energy demand are calculated for China''s industrial economy using a two-stage translog cost function approach for the period 1995–2004. The results suggest that energy is substitutable with both capital and labor. Coal is significantly substitutable with electricity and slightly complementary with oil, while oil and electricity are slightly substitutable. China''s energy intensity is increasing during the study period and the major driver appears to be due to the increased use of energy-intensive technology. [Copyright &y& Elsevier]

Crop yield is a key element in rural development and an indicator of national food security. A method that could estimate crop yield over large hilly areas would be highly desirable. Methods including high spatial resolution satellite imagery have the potential to achieve this objective. This paper describes a method of integrating QuickBird imagery with a production efficiency model (PEM) to estimate crop yield in Zhonglianchuan, a hilly area on Loess Plateau, China. In the PEM model, crop yield is a function of the photosynthetic active radiation (PAR), fraction of absorbed photosynthetically active radiation (fAPAR) and light-use efficiency (LUE). Based on the high spatial resolution QuickBird imagery, a land cover classification is used to attribute a class-specific LUE. The fAPAR is related to spectral vegetation indices (SVI), which can be derived from the satellite images. The LUE, fAPAR and incident PAR data were combined to estimate the crop yield. Farmer-reported crop yield data in 80 representative plots were used to validate the model output. The results indicated QuickBird imagery can improve the accuracy of predicted results relative to the Landsat TM image. The predicted yield approximated well with the data reported by the farmers (r 2 =0.86; n =80). The spatial distributions of crop yield derived here also offers valuable information to manage agricultural production and understand ecosystem functioning. [Copyright &y& Elsevier]

Abstract: Based on the work of Jensen [Jensen, S.S., 1998. Mapping human exposure to traffic air pollution using GIS. Journal of Hazardous Materials 61(1–3), 385–392; Jensen, S.S., 1999. A geographic approach to modelling human exposure to traffic air pollution using GIS. Ph.D. Thesis. National Environmental Research Institute, Roskilde], a prototype system for modelling noise and air pollution is developed for the Macao Peninsula. The system integrates a road traffic noise model, an operational air pollution model, digital maps, an urban landscape model and a Geographic Information System (GIS). Compared with mesoscale model systems with input/output resolution in kilometres, the present one has a higher spatial resolution down to individual buildings along both sides of the street. Applying the developed model system, a preliminary study investigates the ways that four urban forms existing nowadays on the Macao Peninsula influence vehicle transport and street environment. This paper shows that the urban forms in historical areas with narrower roads, complex road networks and a higher density of intersections lead to lower traffic volumes and thus lower noise pollution. However, the greater street canyon effects in these historical urban areas lead to higher carbon monoxide (CO) concentrations. [Copyright &y& Elsevier]

Abstract: Pollution loads from land sources and their impact on the receiving waters can be predicted by using land-use and surface water quality models, respectively. In this paper details are given of the development of an integrated modelling system for managing water quality in coastal basins. The system includes a model linking the pollution input loads to land-use and a model for simulating surface flow and chemical and biological processes. The land-use model consists of two main components: a database and a model base. It uses a GIS system, namely ArcGIS, as the model platform. The database, built in the Geo-database format, includes six data sets containing information on social, economical, bathymetrical and hydrological aspects of a study area. The model base includes a series of transfer functions that link the pollution loads to key sources, including those from: industrial, domestic and agricultural inputs, and fish and animal farming. The water quality model is a numerical model which solves the governing equations representing the physical, chemical and biological processes in coastal waters. The modelling system was set up for Bohai Bay in China, with the land-use model being used to provide nutrient loadings from individual inputs and the water quality model used to predict the impact of these inputs on the receiving waters. Model simulations were also undertaken to predict the water quality conditions for various development and management scenarios. [Copyright &y& Elsevier]

Wind erosion occurs in many arid, semiarid and agricultural areas of the world. The desert areas of China, which occupy approximately 13% of China’s total surface area, are major sources of Asian dust. The major wind-erosion areas are the sandy lands in western and northwestern China together with the extensive regions of the Gobi desert in northern and northeastern China, especially along the basin of the Yellow River. In this paper, dust storms which occurred in China in the spring of 2002 were simulated using an integrated numerical modeling system.The purpose of the simulation is to produce quantitative predictions of wind erosion on regional scales. The integrated wind erosion modeling system used in this study coupled the following three major components: (1) An atmospheric prediction model, together with a land-surface model; (2) a wind-erosion model and (3) a geographic information database. The atmospheric model provides the necessary input data for the wind erosion scheme, including wind speed and precipitation. It also provides input data for the land-surface model that produces predictions for soil moisture. Dust transport and deposition are also considered in the atmospheric model. The wind-erosion model predicts streamwise saltation and dust emission rate for given atmospheric, soil and land surface conditions. The geographic information database provides spatially distributed parameters, such as soil type and vegetation coverage, for the atmospheric, land surface and wind erosion models.Dust storms in China occur mainly in spring and winter, but most frequently in April. In spring, surface soils frozen in the previous winter become especially loose, creating a favorable condition for wind erosion. As an example, the severe dust storms of 15–20 March were simulated. The results show the integrated modeling system can simulate the main characteristics of the dust storms. The system produced estimates of wind erosion intensity and patterns that are in agreement with observations. Such a system offers the possibility of determining wind erosion patterns on broad scales with high spatial resolution, as well as dust transport and deposition. [Copyright &y& Elsevier]

Storm Water Management Model (SWMM), a hydrodynamic rainfall-runoff and urban drainage simulation model, is widely applied in planning, analysis, and design. It is worth mentioning that the hydrological and hydrodynamic simulation functions of SWMM can also provide decision support for real-time urban stormwater management. However, it remains challenging to directly apply traditional SWMM to real-time urban stormwater management based on web technology. Here we designed and implemented a web service framework based on SWMM (WEB-SWMM), which can provide real-time computing services for urban water management. To test the functionality, efficiency, and stability of the WEB-SWMM, WEB-SWMM was applied to an urban area in China. Test results show that WEB-SWMM could provide real-time computing services stably, quickly, and accurately. In general, the implementation of WEB-SWMM enables traditional SWMM to be quickly and efficiently applied in real-time urban stormwater management. What is more, the web-based hydrological model framework proposed in this paper also applicable to most existing hydrological models. • The real-time simulation of SWMM is implemented. • WEB-SWMM can provide real-time computing services for urban stormwater management. • WEB-SWMM can provide web services and exchange data with other applications. • The core four tasks and four modules guarantee the regular running of WEB-SWMM. • The framework can be installed and deployed across platforms. [ABSTRACT FROM AUTHOR]

In Shanxi Province, China, groundwater is a major problem and exploration of groundwater potential zones (GWPZs) is a great necessity. This paper contributed to integrate RS-GIS to delineate GWPZ and applied Fuzzy-AHP method in a single platform. The main objective includes delineation GWPZs with RS and geo-environmental factors using Fuzzy-AHP method. Fuzzy-AHP method was employed to calculate weight of factors. RS-GIS was used to create maps and discover groundwater availability. GWPZs were classified in five separate classes. Results indicated that 13.26%, 27.02%, 26.35%, 23.64%, and 9.71% area classified as very good, good, moderate, poor, and very poor GWPZs. The validated analytical results revealed 82.5%, 12.3%, 3.5%, and 1.7% existing water wells exhibited in very good, good, moderate, and poor/very poor GWPZs. This indicates Fuzzy-AHP model generated findings were in very good agreement with ground-truth data. This RS-GIS based Fuzzy-AHP method is proficient and efficient in identification and delineation of GWPZs. Image 1 • Groundwater potential zones (GWPZs) in semi-arid area delineated with RS-GIS. • Seven geo-environmental factors are used for GWPZ. • RS-GIS for GWPZ and Fuzzy-AHP model are used in a single platform. • Fuzzy-AHP method was employed to calculate the weight of the factors. • GWPZs are classified as very good, good, moderate, poor, and very poor GWPZ. [ABSTRACT FROM AUTHOR]

Environmental models have a key role to play in understanding complex environmental phenomena in space and time. Although their inherent uncertainty and non-identifiability are being increasingly recognized with the development and application of various methods, a more holistic analysis of all sources of model uncertainty is warranted. This paper addresses sources of uncertainty from various types of input datasets and model parameters, including those related to model structure assumptions, using a Soil and Water Assessment Tool (SWAT) application for the Minjiang River watershed, China. The holistic uncertainty sources in the SWAT application are summarized, and a sensitivity analysis (SA) is applied to examine the relative importance of the uncertainty sources influencing average streamflow and the load of nitrate. The analysis reveals that uncertainties related to the stream network precision and certain SWAT parameters are the most critical factors. Furthermore, building upon our SA framework to consider uncertainty sources more holistically would provide a good starting point for subsequent SA of spatially distributed environmental models in general. • A holistic sensitivity analysis, including model structure uncertainty, is conducted for a spatially distributed model. • A sensitivity analysis examines the relative importance of the uncertainty sources using a SWAT application. • The uncertainties in the stream network precision and certain SWAT parameters are critical. [ABSTRACT FROM AUTHOR]

It is necessary to develop a flexible and extensible watershed modeling framework with the support of parallel computing to conduct long-term high-resolution simulations over large areas with diverse watershed characteristics. This paper introduced an open-source, modular, and parallelized watershed modeling framework called SEIMS (short for Spatially Explicit Integrated Modeling System) to meet this need. First, a flexible modular structure with standard interfaces was designed, in which each module corresponds to one simulation algorithm for a watershed subprocess. Then, a parallel-computing middleware based on an improved two-level parallel computing approach was constructed to speed up the computational efficiency. With SEIMS, users can add their own algorithms in a nearly serial programming manner and construct parallelized watershed models. SEIMS also supports model level parallel computation for applications which need numerous model runs. The effectiveness and efficiency of SEIMS were illustrated through the simulation of streamflow in the Youwuzhen watershed, Southeastern China. • A modular and parallelized watershed modeling framework was developed. • The modular structure of watershed processes modeling is easy to learn and extend. • Develop new parallelized watershed models in a nearly serial programming manner. • Compatible with common operating systems and parallel computing platforms. [ABSTRACT FROM AUTHOR]