An integrated simulation-optimization approach for combined allocation of water quantity and quality under multiple uncertainties.

Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

Land-use simulation for synergistic pollution and carbon reduction: Scenario analysis and policy implications.

Simulations of sustainable land use and management are required to achieve targets to reduce pollution and carbon emissions. Limited research has been conducted on synergistic pollution and carbon reduction (SPCR) in land-use simulations. This study proposed a framework for land-use simulation focused on SPCR. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the entropy weight-based technique for order of preference by similarity to an ideal solution (TOPSIS) were used to optimize the land-use structure according to minimum net carbon, nitrogen, and phosphorus emissions. The cellular automata (CA) Markov model was then utilized to simulate the land-use spatial pattern according to the optimal conditions. The proposed framework was applied to the Dongjiang River Basin, South China, and three other scenarios (natural development (ND), carbon minimization (CM), and pollution minimization (PM)) were designed to validate the effectiveness of pollution and carbon emissions reduction under the SPCR scenario. The land-use structure and the pollution and carbon emissions in the scenarios were compared. The results showed the following. (1) The proportions of cultivated land, woodland, grassland, water, and construction land In the SPCR scenario accounted for 14%, 72%, 4%, 3%, and 7% of the total area, respectively. The carbon, nitrogen, and phosphorus emissions were 42.4%, 6.6%, and 7.8% lower, respectively, in the SPCR scenario than in the ND scenario, demonstrating the advantages of simultaneous pollution and carbon reduction. (2) The kappa coefficient of the CA-Markov model was 0.8729, indicating high simulation accuracy. (3) The simulated land-use spatial patterns exhibited low spatial heterogeneity under the CM, PM, and SPCR scenarios. However, there were significant disparities between the ND and SPCR scenarios. The cultivated and construction land areas were significantly smaller in the SPCR scenario than in the ND scenario. In contrast, the woodland and grassland areas were larger, with most differences in the central and southwestern regions of the Dongjiang River Basin. The results of the current study can be used to formulate effective land use policies and strategies in the Dongjiang Basin and similar areas to achieve the Coupling coordination between pollution reduction and carbon reduction. Policy recommendations include increasing the proportion of woodland and grassland, implementing reasonable constraints on expanding cultivated and construction lands, and establishing farmland red lines to promote synergistic pollution and carbon reduction.

The structural transformation reversibility of biogas slurry derived dissolved organic matter and its binding properties with norfloxacin under temperature fluctuation.

The widespread use of biogas slurry could potentially raise the environmental risk of antibiotics. Dissolved organic matter (DOM), as the most active part of biogas slurry, was able to interact with antibiotics and play a crucial role in the structure and function of soil and aquatic ecosystems. The recent shifts in global climate patterns have garnered significant attention due to their substantial impact on temperature, thereby exerting a direct influence on the characteristics of DOM and subsequently on the environmental behavior of antibiotics. However, there is limited research concerning the impact of temperature on the binding of DOM and antibiotics. Thus, this study aimed to explore the temperature-dependent structural transformation and driving factors of biogas slurry-derived DOM (BSDOM). Additionally, the binding characteristics between BSDOM and the commonly used antibiotic norfloxacin (NOR) at different temperatures were studied by using multi spectroscopic methods and two-dimensional correlation spectroscopy (2D-COS) analysis. The results suggested that the temperature-dependent structural transformation of BSDOM was reversible, with a slight lag in the transition temperature under cooling (13 °C for heating and 17 °C for cooling). Heating promoted the conversion of protein-like to humic-like substances while cooling favored the decomposition of humic-like substances. BSDOM and NOR were static quenching, with oxygen-containing functional groups such as C-O and -OH playing an important role. Temperature influenced the order of binding, the activity of the protein fraction, and its associated functional groups. At temperatures of 25 °C and 40 °C, the fluorescent components were observed to exhibit consistent binding preferences, whereby the humic-like component demonstrated a greater affinity for NOR compared to the protein-like component. However, the functional group binding order exhibited an opposite trend. At 10 °C, a new protein-like component appeared and bound preferentially to NOR, when no C-O stretch corresponding to the amide was observed. The finding will contribute to a comprehensive understanding of the interaction mechanisms between DOM and antibiotics under climate change, as well as providing a theoretical basis to reduce the environmental risks of biogas slurry and antibiotics.

Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework.

A novel newsvendor model-based framework for regional industrial water resources allocation that considers uncertainties in water supply and demand was proposed in this study. This framework generates optimal water allocation schemes while minimizing total costs. The total cost of water allocation consists of the allocated water cost, the opportunity loss for not meeting water demand, and the loss of the penalty for exceeding water demand. The uncertainties in water demand and supply are expressed by cumulative distribution functions. The optimal water allocation for each water use sector is determined by the water price, the unit loss of the penalty and opportunity loss, and the cumulative distribution functions. The model was then applied to monthly water allocation for domestic, industrial, and agricultural water use in two counties of Huizhou City, China, whose water supply mainly depends on Baipenzhu Reservoir. The water demand for each water use sector and the monthly reservoir inflow showed good fits with the uniform and P-III distributions, respectively. The water demand satisfied ratio for each water use sector was stable and increased for the optimal water allocation scheme from the newsvendor model-based framework, and the costs were lower compared with the actual water allocation scheme. The novel framework is characterized by less severe water shortages, lower costs, and greater similarity to actual water use compared with the traditional deterministic multi-objective analysis model, and demonstrates strong robustness in the advantages of lower released surplus water and higher water demand satisfied ratio. This novel framework yields the optimal water allocation for each water use sector by integrating the properties of the market (i.e., determining the opportunity loss for not meeting water demand) with the government (i.e., determining the water price and the loss of the penalty for exceeding water demand) under the strictest water resources management systems.

Improvement of sediment yield index model through incorporating rainfall erosivity.

Soil erosion and sediment yield in watersheds are comprehensively affected by land use/cover changes and climatic factors. The current sediment yield index (SYI) model incorporates parameters of area (A), delivery ratio (DR), and curve number (CN), which reflect the character of underlying surface conditions, while the impact of rainfall intensity on sediment yield could not be properly considered. This study aims to improve the current SYI model by introducing rainfall-related factors such as rainfall erosivity (R) and applying it to estimate the sediment yield of river basin. Taking the Dongjiang River basin, South China, as a case study, the performances of the improved simplified SYI model (SYI-CN + R) were compared and demonstrated at multi-spatiotemporal scales. The results showed that (1) compared with the SYI model which only has the parameter CN (SYI-CN), the model SYI-CN + R achieves better simulation performances at yearly (the efficiency coefficient (CE) is 81% in the whole basin and 62% in the sub-basin) and half-month (CE is 69% in the whole basin and 57% in the sub-basin) time scales. (2) On the basin scale, the simulation performance in the whole basin is better overall compared to that in the sub-basin, and the model SYI-CN + R at the half-month time scale is more suitable for the sediment yield simulation in the Dongjiang River basin, with higher value of correlation coefficient (CC) of 87% and 83% for the whole basin and the sub-basin, respectively. And (3) the values of CN and R have an obvious spatial gradient in the whole basin, showing an increasing trend from northeast to southwest as a whole, with larger values concentrated in the lower reaches and smaller values in the middle and upper reaches. This study extends the application and improves the performance of the SYI model, and provides a basis for soil and water conservation in a river basin with fewer observation data.

Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China.

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds.

Nitrogen and phosphorus emissions to water in agricultural crop-animal systems and driving forces in Hainan Island, China.

The NUFER (Nutrient Flow in food chains, Environment and Resources) model has been used to reliably quantify nitrogen (N) and phosphorus (P) emissions from agriculture land to water bodies. However, factors impacting agricultural N and P emissions at the island scale have rarely been studied due to the lack of high-resolution spatialization tools, which are critical for exploring mitigation options. Here, a high-resolution NUFER model was constructed based on geology, meteorology, land-use data, statistical data, and field investigation. The spatial characteristics of N and P emissions in Hainan Island, China, were quantified, and the driving forces were analyzed. We also explored effective measures to reduce emissions by 2035 using scenario analysis. Overall, 98 Gg N from agriculture entered water bodies in 2018, of which crop system contributed 70%; 15 Gg P entered water bodies, of which, animal system contributed 78%. Nitrate (NO3-) leaching (65%) and direct discharge of animal manure (69%) accounted for most of the N and P emissions, respectively. Plains contributed 89% of N and 92% of P emissions. Spatial overlay analysis showed that high N and P emissions were mainly concentrated in the western and northeastern plain areas. At the sub-basin scale, the Nandu River basin had the largest agricultural N and P emissions, accounting for more than 20% of all emissions. Scenario analysis showed that N and P emissions were significantly correlated with natural (e.g., elevation, slope, and soil texture) and anthropogenic (e.g., rural income, population density, planting structure, and livestock density) factors. We further analyzed the emissions of N and P can be reduced by 71 Gg and 14 Gg by 2035, respectively, via reducing food chain waste and consumption, importing more food, and improving production efficiency, but especially prohibiting the direct discharge of livestock manure. This high-resolution quantification of agricultural N and P emissions to the water bodies provides an exploration of the most effective options for reducing agricultural non-point source (ANPS) pollution at the island scale.

A bi-level multi-objective programming model for water resources management under compound uncertainties in Dongjiang River Basin, Greater Bay Area of China.

To facilitate regional water resources allocation, an integrated bi-level multi-objective programming (IBMP) model with dual random fuzzy variables was developed in this research The proposed model was derived through incorporating dual random fuzzy variables, multi-objective programming, and interval parameter programming within a bi-level optimization framework. This approach improved upon the previous bi-level programming methods and had two advantages. Firstly, it was capable of reflecting tradeoffs among multiple conflict preferences for water related bi-level hierarchical decision-making processes. Secondly, random fuzzy variables were used to tackle the dual uncertainties in both sides of the constraints, which were characterize as probability density functions and discrete intervals. Then, a real-world water resources planning problem was employed for illustrating feasibility of the application of IBMP model in Dongjiang river watershed of south China. Results reflected the alternative decisions for water allocation schemes under a set of probability levels and fuzzy α - cut levels, which can support in-depth analysis of tradeoffs among multiple levels and objective values. Moreover, modeling comparison analysis was undertaken to illustrate the performances of the proposed model.

An optimization model for water resources allocation in Dongjiang River Basin of Guangdong-Hong Kong-Macao Greater Bay Area under multiple complexities.

In this research, an interval two-stage stochastic fuzzy-interval credibility constraint programming (ISFICP) method was developed for water resources allocation among multiple water users under complexities and uncertainties. The method could reflect the multiple complexities of water resources management, also trade-offs between the system benefits and violation risks. Dongjiang River (DJR) Basin, which supplies water to several core cities in south China such as Guangzhou, Shenzhen, and Hong Kong, was applied as the real demonstrative case. The water resources system of DJR Basin is particularly complex due to it is the primary source water for Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Through considering multiple complexities and uncertainties of the water resources system, such as natural, economic, and social conditions, ISFICP was developed to obtain potential water-allocation schemes. Probabilistic distribution, fuzzy-interval sets (FIS), and discrete intervals were introduced to represent the multiple uncertainties associated with the multiple complexities. The results indicated that the model could provide practical schemes for local decision-makers under multiple scenarios such as flow levels, credibility levels, and recycling rates.

Comprehensive assessment of water environmental carrying capacity for sustainable watershed development.

Due to insufficient understanding of human-water interaction, many water-related problems arise in watersheds, posing severe threats to the sustainability of watershed development. Although water environmental carrying capacity (WECC) is a powerful tool to support sustainable development of watersheds, few studies considered aquatic ecological factors and uncertainty in indicator values, leading to losses of sample information in the evaluation of WECC. This paper developed a systematic framework for comprehensive WECC assessment that included the indicator system and a novel variable fuzzy pattern recognition (VFPR) approach. The WECC index system incorporated aquatic ecological factors, and addressed uncertainties associated with the indicator values. The proposed VFPR-based assessment model could realize successive evaluation to retain more original information of the sample and distinguish similar result values by treating the sample as having a continuous degree of membership instead of the traditional point form. In addition, it could be more adaptable to various circumstances including extreme cases, and closely reflect the impacts of indicator changes on the results. The established evaluation framework has been applied to Dongjiang River Basin in Guangdong Province. The spatial differences and main influencing factors of WECC in the study area were analyzed. Results show that 50% and 16.7% of the sub-regions in the study area would be subject to a poor level of WECC under pessimistic and optimistic circumstances, respectively. WECCs in the upper and lower reaches are the best and worst, respectively, which is in line with the levels of economic development in the Dongjiang River Basin. The proposed method can also be applicable to many other problems involving numerous indicators.

Spatiotemporal characteristics of agricultural nitrogen and phosphorus emissions to water and its source identification: A case in Bamen Bay，China.

The spatiotemporal characteristics and sources identification of agricultural nitrogen (N) and phosphorus (P) emissions to the gulf are rarely reported in tropical regions of China, mainly due to the lack of local reliable data and quantitative tools for spatiotemporal changes. In this study, we constructed a high-resolution NUFER (NUtrient Flow in food chains, Environment and Resources use) model based on geology, meteorology, land use data, statistical data, and field investigation to quantify the spatiotemporal characteristics and sources of N and P emissions. Bamen Bay (BMB), a bay with a mangrove national wetland Park in the Hainan Island, China, was chosen as a case study. The results showed that agricultural N emission to water in 2018 increased fivefold compared to 1990. Leaching was the main method of agricultural N emission and was mainly distributed in farms in the west and north. The contribution of N emission from crop system to water increased 20.3% in 28 years. Poultry and fruits have contributed the most to N output, and the trend is continuing. P emission to water increased sevenfold compared 1990. The contribution of P emission from animal system to water increased from 86.8% in 1990 to 90.1% in 2018 due to low removal rate of livestock manure. P emission was mainly via direct discharge of manure, mainly distributed in livestock breeding sites near the bay. Poultry has consistently contributed the most to P output in 28 years, accounting for 49.1% in 2018. Fertilizers and fodder were the largest sources of N and P. The average N and P loss rates of BMB were 5.32 t km2 yr-1 and 0.26 t km2 yr-1. The future agricultural transformation is essential, and it is necessary to reduce the application of N fertilizer and increase the removal rate of livestock manure. These results can provide reference for other typical agricultural pollution bays in exploring the spatiotemporal characteristics of N and P emissions to water and the identification of agricultural sources.

Spatial patterns and influencing factors of sewage treatment plants in the Guangdong-Hong Kong-Macau Greater Bay Area, China.

Over the past few years the discharge of waste and sewage in the Guangdong-Hong Kong-Macau Greater Bay Area (GHMB) of China has increased, exerting a great amount of pressure on ecological protection. In this study, we focus on achieving a balanced spatial layout of sewage treatment plants in order to reveal the regional differences and spatial patterns of sewage treatment plants in the GHMB and identify the key factors influencing the spatial patterns. In particular, we employ POI (point of interest) geographical data to evaluate the spatial patterns and agglomeration status of sewage treatment plants in the GHMB using Exploratory Spatial Data Analysis (ESDA). We then explore the principle influencing factors of the determined spatial patterns using the geographical weighted regression model (GWR). Results demonstrate that: (1) the absolute number of sewage treatment plants is highest in the central and western regions, while the per capita of sewage treatment plants is clearly clustered in the northwest and southwest regions; (2) the absolute number of sewage treatment plants exhibits larger spatial dissimilarity than that of the per capita values, with High-High cluster types principally distributed in the conjunction areas of Jiangmen, Foshan and Zhaoqing in western GHMB and Low-Low clusters in the western Pearl River Delta (PRD) estuary; and (3) the key influencing factors are identified as GDP per capita, the output value of the primary and secondary industries and industrial water consumption. Regions with high regression coefficient fluctuations of the four influencing factors are centralized within the PRD estuary and adjacent regions. Policy recommendations including optimizing the sewage treatment plant layout, enhancing the service capacity potential of the existing sewage treatment plants and upgrading the industrial structure are proposed to promote a spatial equilibrium configuration of sewage treatment plants in the GHMB.

A dual-randomness bi-level interval multi-objective programming model for regional water resources management.

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions. It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 108 CNY to [22,290.84, 27,492.57] × 108 CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios.

Coordination degree of the exploitation of water resources and its spatial differences in China.

Coordinated relationships between socioeconomic development and water utilization, as well as environmental protection measures, are crucial for achieving sustainable water resource exploitation (WRE) under changing environmental conditions. Moreover, a balanced condition of regional WRE is essential to guaranteeing the water security of the entire nation. In this study, an evaluation model of the coordination degree (CD) of WRE is built based on 10 selected indicators and is then applied to 31 provincial zones in China during the period 2004-2014. The cluster analysis method is introduced to classify the relative rank (RK) of the CD at the provincial scale in the pre- (2004-2010) and post- (2011-2014) phases; these phases are divided by the first year of the implementation of the "Three Red Lines Policy" in China (2011), and the spatial differences of the CD of the entire nation were decomposed. The results showed the following. 1) The CD of WRE increases significantly in 26 provincial zones after the implementation of the "Three Red Lines Policy", and the average CD of WRE for 28 provincial zones during the post-phase was greater than that during the pre-phase. 2) Provincial zones with the RK of coordinated development model (CDM) are mainly located in southern and southwestern China. Provincial zones with the RK of the uncoordinated development model (UDM) were mainly found in northern inland China. Eight provincial zones with dense populations and intensive water use, but with only scarce per capita water resources, have the RK of the severely uncoordinated development model (SUDM). 3) The spatial differences in CD diminished in the post-phase, and the differences in CD at the inter-regional scale accounted for a predominant proportion of the difference in CD at the national scale. In addition to improving the coordination status of WRE, the implementation of the "Three Red Lines Policy" also decreased the disparity among the CD of WRE, and this policy continued to be effectively implemented and followed to make a lasting difference. Cross-regional cooperation and coordinated relationships between socioeconomic development and water resource protection are needed to achieve a more coordinated and balanced WRE for long-term development. The results of this study have scientific and practical implications for sustainable WRE and water security in rapidly developing countries such as China.