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Effective management of an urban solid waste system (USWS) is crucial for balancing the tradeoff between economic development and environment protection. A factorial ecological-extended physical input-output model (FE-PIOM) was developed for identifying an optimal urban solid waste path in an USWS. The FE-PIOM integrates physical input-output model (PIOM), ecological network analysis (ENA), and fractional factorial analysis (FFA) into a general framework. The FE-PIOM can analyze waste production flows and ecological relationships among sectors, quantify key factor interactions on USWS performance, and finally provide a sound waste production control path. The FE-PIOM is applied to managing the USWS of Fujian Province in China. The major findings are: (i) waste is mainly generated from primary manufacturing (PM) and advanced manufacturing (AM), accounting for 30% and 38% of the total amount; (ii) AM is the biggest sector that controls the productions of other sectors (weight is from 35% to 50%); (iii) the USWS is mutualistic, where direct consumption coefficients of AM and PM are key factors that have negative effects on solid waste production intensity; (iv) the commodity consumption of AM and PM from other sectors, as well as economic activities of CON, TRA and OTH, should both decrease by 20%, which would be beneficial to the sustainability of the USWS.
Jing Liu; Yongping Li; Gordon Huang; Yujin Yang; Xiaojie Wu. A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China. Sustainability 2021, 13, 8341 .
AMA StyleJing Liu, Yongping Li, Gordon Huang, Yujin Yang, Xiaojie Wu. A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China. Sustainability. 2021; 13 (15):8341.
Chicago/Turabian StyleJing Liu; Yongping Li; Gordon Huang; Yujin Yang; Xiaojie Wu. 2021. "A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China." Sustainability 13, no. 15: 8341.
Virtual water is an important indicator measuring the amount of water needed from the perspective of consumption, which can help decision makers to identify desired system design and optimal management strategy against water resources shortage. In this study, a novel model named as factorial ecologically-extended input-output model (abbreviated as FEIOM) is developed for virtual water management. FEIOM integrates techniques of input-output model (IOM), ecological network analysis (ENA) and factorial analysis (FA) into a general framework. It is effective to evaluate the virtual water flows, reveal ecological inter-connections in virtual water system (VWS), and identify key water consumption sectors that have significant individual and interactive effects on VWS's performance. FEIOM is then applied to identifying optimal virtual water management strategies for Kazakhstan in Central Asia. The main findings are: (i) Kazakhstan is a net importer of virtual water (reaching up to 46.0 × 109 m3), demonstrating that the national economic structure is reasonable, which can abate the national water scarcity and improve its eco-environmental protection; (ii) the virtual water of agricultural sector is net exporter, where vegetables, fruits and nuts occupy 86% of the total agricultural exports; the massive export of water-intensive products further squeezes the water for other users; (iii) the key factors affecting the national VWS are agriculture > primary manufacturing > advanced manufacturing > services. Therefore, from solving water resources shortage and facilitating sustainable development perspectives, Kazakhstan should stimulate the domestic primary manufacturing productions and improve agriculture and advanced manufacturing water-use efficiencies.
X.J. Wu; Y.P. Li; J. Liu; G.H. Huang; Y.K. Ding; J. Sun; H. Zhang. Identifying optimal virtual water management strategy for Kazakhstan: A factorial ecologically-extended input-output model. Journal of Environmental Management 2021, 297, 113303 .
AMA StyleX.J. Wu, Y.P. Li, J. Liu, G.H. Huang, Y.K. Ding, J. Sun, H. Zhang. Identifying optimal virtual water management strategy for Kazakhstan: A factorial ecologically-extended input-output model. Journal of Environmental Management. 2021; 297 ():113303.
Chicago/Turabian StyleX.J. Wu; Y.P. Li; J. Liu; G.H. Huang; Y.K. Ding; J. Sun; H. Zhang. 2021. "Identifying optimal virtual water management strategy for Kazakhstan: A factorial ecologically-extended input-output model." Journal of Environmental Management 297, no. : 113303.
Synergistic management of economy-energy-environment nexus (EEEN) system is essential for balancing the tradeoff among economic development, energy supply, and environment conservation. In this study, a multi-scenario input-output economy-energy-environment nexus management (abbreviated as MIO-ENM) model is developed through incorporating input-output model (IOM), scenario analysis, and simulation-optimization techniques within a general framework. MIO-ENM is capable of identifying the main factors affecting the EEEN system as well as generating optimal strategies supporting regional sustainability. The MIO-ENM model is applied to planning EEEN of Pearl River Delta (abbreviated as PRD) urban agglomeration over a long-term planning horizon (2021–2035). Results for planning economic and energy-related activities have been obtained, which can be used for further generating decision alternatives. Results disclose that: (i) development of advanced manufacturing and service industry can improve the regional economic efficiency; (ii) under all scenarios, the proportion of electricity in energy consumption would exceed 35% mainly due to the environmental requirement; (iii) the annual economic growth rate is suggested around 6.0% during 2021–2035; (iv) compared to the conventional approach, the integrated modeling of EEEN can generate optimal joint-management strategies for economy development, energy production, and environmental protection, which are beneficial to the sustainability of the PRD urban agglomeration.
X. Li; Y.P. Li; G.H. Huang; J. Lv; Y. Ma. A multi-scenario input-output economy-energy-environment nexus management model for Pearl River Delta urban agglomeration. Journal of Cleaner Production 2021, 317, 128402 .
AMA StyleX. Li, Y.P. Li, G.H. Huang, J. Lv, Y. Ma. A multi-scenario input-output economy-energy-environment nexus management model for Pearl River Delta urban agglomeration. Journal of Cleaner Production. 2021; 317 ():128402.
Chicago/Turabian StyleX. Li; Y.P. Li; G.H. Huang; J. Lv; Y. Ma. 2021. "A multi-scenario input-output economy-energy-environment nexus management model for Pearl River Delta urban agglomeration." Journal of Cleaner Production 317, no. : 128402.
Synergetic development of water, energy and food is prerequisite for coping with issues of increment of global population, deterioration of ecological environment and aggravation of climate change. This study aims to develop a scenario-based type-2 fuzzy interval programming (STFIP) approach for planning agricultural water, energy and food (WEF) as well as crop area management. Uncertainties presented as interval numbers, scenarios and fuzzy sets as well as the dual uncertainties (i.e. interval-scenario and type-2 fuzzy interval) can be effectively tackled by the STFIP method. Then, a STFIP-WEFN model is developed and applied to maximize net agricultural profit with integrated management of productive resources for Henan Province, China. Solutions of different water resources, diverse energy resources and multiple agricultural crops in association with various water supply structures between current situation and future policy orientation are examined. Results disclose that: over the entire planning horizon, a) the total planting area of crops can increase from [129.3, 133.6] × 103 km2 to [132.0, 135.6] × 103 km2 by optimizing resources allocation; b) uncertainties existing in the WEFN system can lead to a change rate of the system benefit by 16.93%; c) the total planting area can increase by [4.00, 6.05] % when the groundwater ratio changes from 40% to 55%. These findings can help effectively optimize the existing planting structure and coordinate the development of Henan Province among water, energy, food, economy, society and environment.
Qiting Zuo; Qingsong Wu; Lei Yu; Yongping Li; Yurui Fan. Optimization of uncertain agricultural management considering the framework of water, energy and food. Agricultural Water Management 2021, 253, 106907 .
AMA StyleQiting Zuo, Qingsong Wu, Lei Yu, Yongping Li, Yurui Fan. Optimization of uncertain agricultural management considering the framework of water, energy and food. Agricultural Water Management. 2021; 253 ():106907.
Chicago/Turabian StyleQiting Zuo; Qingsong Wu; Lei Yu; Yongping Li; Yurui Fan. 2021. "Optimization of uncertain agricultural management considering the framework of water, energy and food." Agricultural Water Management 253, no. : 106907.
A bi-level decentralized chance-constrained programming (BDCP) method is developed for planning water-food-ecology-energy (WFEE) nexus system. The BDCP method has advantages in balancing the tradeoff between two-level stakeholders in hierarchical structure and reflecting the synergy effect among multiple divisions under random uncertainty. Then, a BDCP-WFEE model is formulated for the Aral Sea Basin, where the upper-level model aims to maximize system benefit, and the multiple divisions at the lower-level model aim to maximize food production, ecological water allocation, and electricity generation. Compared with the conventional single-level model, results obtained from the BDCP-WFEE model under multiple scenarios reveal that (i) the food production would increase by 2.0%–3.6% , implying that the food demand of additional 0.7 million people can be met; (ii) the ecological water allocation would increase by 0.9%–3.0%, denoting that the amount of water to the Aral Sea would reach 23.4 km3 at the end of planning period; (iii) the electricity generation would increase by 5.4%–8.5%. Besides, under the premise of ensuring food security, the proportion of agricultural water allocation in the Aral Sea Basin would reduce by 17.0%, indicating that the BDCP-WFEE model can effectively optimize the water allocation pattern and alleviate the conflict of water resources allocation among competetive users. These findings can provide policy support for managers to solve the problems of water shortage, food crisis, ecological degradation, and electricity insecurity.
Y. Ma; Y.P. Li; Y.F. Zhang; G.H. Huang. Mathematical modeling for planning water-food-ecology-energy nexus system under uncertainty: A case study of the Aral Sea Basin. Journal of Cleaner Production 2021, 308, 127368 .
AMA StyleY. Ma, Y.P. Li, Y.F. Zhang, G.H. Huang. Mathematical modeling for planning water-food-ecology-energy nexus system under uncertainty: A case study of the Aral Sea Basin. Journal of Cleaner Production. 2021; 308 ():127368.
Chicago/Turabian StyleY. Ma; Y.P. Li; Y.F. Zhang; G.H. Huang. 2021. "Mathematical modeling for planning water-food-ecology-energy nexus system under uncertainty: A case study of the Aral Sea Basin." Journal of Cleaner Production 308, no. : 127368.
In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.
Huawei Li; Guohe Huang; Yongping Li; Jie Sun; Pangpang Gao. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability 2021, 13, 4627 .
AMA StyleHuawei Li, Guohe Huang, Yongping Li, Jie Sun, Pangpang Gao. A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China. Sustainability. 2021; 13 (9):4627.
Chicago/Turabian StyleHuawei Li; Guohe Huang; Yongping Li; Jie Sun; Pangpang Gao. 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China." Sustainability 13, no. 9: 4627.
In this study, a two-stage factorial-analysis-based input-output model (TFA-IOM) is advanced for virtual water assessment, which integrates techniques of factorial analysis (FA) and ecological network analysis (ENA) into input-output model (IOM). TFA-IOM can not only identify the crucial water transaction sectors and the associated integral utility relationships, but also investigate the individual and interactive effects of multiple factors on virtual water metabolic network (VWMN) through measuring water consumptions of different sectors. The developed TFA-IOM is applied to Kyrgyzstan in Central Asia to quantify its virtual water and identify its metabolic network, where agricultural and animal husbandry sectors are the main water consumers. Our major findings are: (i) Kyrgyzstan is a country relying on net virtual water import (reaching up to 3,242 × 106 m3); (ii) WHT (wheat) is the main virtual water supplier (with 349 × 106 m3 virtual water to others); (iii) WHT, VGF (vegetables, fruit, and nuts), PFB (plant-based fibers), CTL (bovine cattle) and RMK (raw milk) are the main sectors affecting the VWMN (e.g., utility relationship and integral virtual water recycling index). From a long-term and sustainable development point view, stimulating Kyrgyzstan’s domestic WHT and RMK productions and improving VGF, PFB and CTL water-use efficiencies can facilitate reducing net virtual water import and promoting the mutualism degree of the national VWMN.
H. Zhang; Y.P. Li; J. Sun; J. Liu; G.H. Huang; Y.K. Ding; X.J. Wu. A two-stage factorial-analysis-based input-output model for virtual-water quantification and metabolic-network identification in Kyrgyzstan. Journal of Cleaner Production 2021, 301, 126960 .
AMA StyleH. Zhang, Y.P. Li, J. Sun, J. Liu, G.H. Huang, Y.K. Ding, X.J. Wu. A two-stage factorial-analysis-based input-output model for virtual-water quantification and metabolic-network identification in Kyrgyzstan. Journal of Cleaner Production. 2021; 301 ():126960.
Chicago/Turabian StyleH. Zhang; Y.P. Li; J. Sun; J. Liu; G.H. Huang; Y.K. Ding; X.J. Wu. 2021. "A two-stage factorial-analysis-based input-output model for virtual-water quantification and metabolic-network identification in Kyrgyzstan." Journal of Cleaner Production 301, no. : 126960.
In this study, a multi-scenario factorial analysis and multi-regional input-output (MFA-MRIO) model is developed, which is capable of evaluating carbon dioxide (CO2) emission and simulating CO2 emission reduction path, as well as disclosing individual and interactive effects of multi-factor, multi-sector and multi-city for urban agglomeration. A case study of Jing-Jin-Ji region that is one of the most strategic core regions for China’s economic development is conducted to prove the applicability of the MFA-MRIO model. Multiple scenarios based on direct CO2 reduction and final demand mitigation on various industries are examined. The major findings are: (i) for the whole region in the future, metallurgical industry, electric heating industry, and transportation would be the main CO2 emission sectors; (ii) among all CO2 emission transfers, CO2 flow from Hebei to Beijing would be the highest, especially for metallurgical industry; (iii) for the whole region in the future, the annual growth rate of CO2 emission from the tertiary industry would be higher than that of the secondary industry; (iv) with a high GDP growth rate, loose direct CO2 reduction policy on sectors would also achieve effective CO2 mitigation; (v) with a high GDP growth rate, appropriate final demand reduction policy on high-carbon industries in Tianjin and Hebei would have a positive effect on carbon intensity reduction. These findings can provide desired decision support for CO2 mitigation of urban agglomeration within a multi-sector and multi-city context.
P.P. Wang; Y.P. Li; G.H. Huang; S.G. Wang; C. Suo; Y. Ma. A multi-scenario factorial analysis and multi-regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region. Journal of Cleaner Production 2021, 300, 126782 .
AMA StyleP.P. Wang, Y.P. Li, G.H. Huang, S.G. Wang, C. Suo, Y. Ma. A multi-scenario factorial analysis and multi-regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region. Journal of Cleaner Production. 2021; 300 ():126782.
Chicago/Turabian StyleP.P. Wang; Y.P. Li; G.H. Huang; S.G. Wang; C. Suo; Y. Ma. 2021. "A multi-scenario factorial analysis and multi-regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region." Journal of Cleaner Production 300, no. : 126782.
Land-use and climate changes have impacts on hydrological processes for river basin. In this study, a multi-scenario ensemble streamflow forecast (MESF) method is developed for analyzing the streamflow variation under considering climate and land-use changes, through incorporating CA-Markov model, global climate model (GCM) and Soil and Water Assessment Tool (SWAT) model within a general framework. The advantages of MESF are as follows: (i) it can simultaneously assess the impacts of land-use and climate changes on streamflow; (ii) it can obtain the possible trend and the range of future streamflows through ensemble forecast under multiple scenarios; (iii) based on analysis of streamflow processes under extreme scenarios, it can examine the effects of key factors on streamflow. The MESF method is applied to the upper reaches of the Amu Darya River Basin in Central Asia. Totally 72 scenarios, under different land-use patterns, GCMs and Representative Concentration Pathways (RCPs), are analyzed. Ensemble forecast results reveal that (i) during 2021–2050, the average annual precipitation and the average annual temperature would both increase, but the mean annual streamflow would decrease; (ii) compared to the impact of land-use change, climate change has more obvious effects on the streamflow (with contribution of 78.8%–98.7%); (iii) among all factors of land-use change, glacier melting triggered by climate warming is the most prominent factor; (iv) the peak flow in one year would have a tendency to shift from summer to spring due to the rising temperature and the speeding up snow melt.
Z.P. Xu; Y.P. Li; G.H. Huang; S.G. Wang; Y.R. Liu. A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes. Journal of Hydrology 2021, 598, 126276 .
AMA StyleZ.P. Xu, Y.P. Li, G.H. Huang, S.G. Wang, Y.R. Liu. A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes. Journal of Hydrology. 2021; 598 ():126276.
Chicago/Turabian StyleZ.P. Xu; Y.P. Li; G.H. Huang; S.G. Wang; Y.R. Liu. 2021. "A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes." Journal of Hydrology 598, no. : 126276.
Frequent drought events under climate change are endangering food security and sustainable agricultural development. Quantitative assessment of crop yield anomalies under drought conditions is essential for effective water resources management and adaptative drought risk mitigation strategies. In this study, a copula‐based bivariate probabilistic framework model is developed to assess the impacts of drought events on crop yield, where the correlation of crop yield anomalies and standardized precipitation evapotranspiration index (SPEI) at multi‐month lags are quantified. This model has advantages in quantifying the impacts of drought scales on crop yield through the joint probability of the corresponding series. Then, the model is applied to Xinjiang Province in northwestern China, an arid region with extensive agricultural activities. SPEI and yield anomalies of wheat, maize and cotton during 1984‐2015 are firstly identified, and then copula‐based framework is constructed to quantify probabilistic crop yield losses caused by drought events. Finally, crop water and irrigation requirements of the crops under different drought severity conditions are compared and analyzed. Our findings are (i) wheat and maize yield anomalies are vulnerable to long drought time‐scales, with exceedance probabilities of required yield anomalies returning to normal are 19.3% for wheat and 21.1% for maize, while cotton is susceptible to short drought time‐scales, with exceedance probability of 42.3% for drought recovery; (ii) response of wheat crop yield anomalies to the SPEI at 1‐, 3‐, 6‐ and 12‐month scales is the most sensitive, followed by maize, and cotton is the least; (iii) under extreme drought conditions, the probability of cotton yield reduction is lower than that of wheat and maize. Results may enhance our understanding of the impacts of drought scales on crops during the growing season, thus providing general guidance for rational irrigation management of crops and incentives for irrigation to mitigate drought risk, ultimately promoting sustainable agricultural development.
Huawei Li; Yongping Li; Guohe Huang; Jie Sun. Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China. International Journal of Climatology 2021, 41, 4077 -4094.
AMA StyleHuawei Li, Yongping Li, Guohe Huang, Jie Sun. Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China. International Journal of Climatology. 2021; 41 (8):4077-4094.
Chicago/Turabian StyleHuawei Li; Yongping Li; Guohe Huang; Jie Sun. 2021. "Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China." International Journal of Climatology 41, no. 8: 4077-4094.
Temperature and precipitation are the two most critical climate variables and their extreme states have more severe impacts than average states on both human society and natural ecosystem. In this study, an integrated multivariate trend-frequency analysis (IMTFA) approach is developed for the risk assessment of climate extremes under the global warming. Through incorporating multiple time series analysis techniques (i.e., M-K test, Sen's slope estimator and Pettitt test) and copula function into a general framework, IMTFA is capable not only of analyzing the temporal trends and change points of extreme temperatures and precipitations, but also of quantifying their univariate and multivariate risks. IMTFA is applied to the Central Asia with considering a long-term (1881–2018) observation data. Our findings are: (i) significant wetting and warming trends were occurred in the Central Asia over past one hundred years, where 42.5%, 59.4% and 79.2% stations have change points for extreme precipitations, maximum and minimum temperatures, respectively; (ii) the occurrences of extreme climate events show obviously spatial heterogeneity, where the highest risks of meteorological drought, flood and frost events are occurred in the southwest, southeast and northeast regions, respectively; (iii) global warming significantly affects the intensities and frequencies of extreme precipitations and temperatures, and their univariate and multivariate risks are intensified in the most regions of Central Asia. The above findings can provide more valuable information for risk assessment and disaster adaptation of climate extremes in Central Asia.
Y.R. Liu; Y.P. Li; X. Yang; G.H. Huang. Development of an integrated multivariate trend-frequency analysis method: Spatial-temporal characteristics of climate extremes under global warming for Central Asia. Environmental Research 2021, 195, 110859 .
AMA StyleY.R. Liu, Y.P. Li, X. Yang, G.H. Huang. Development of an integrated multivariate trend-frequency analysis method: Spatial-temporal characteristics of climate extremes under global warming for Central Asia. Environmental Research. 2021; 195 ():110859.
Chicago/Turabian StyleY.R. Liu; Y.P. Li; X. Yang; G.H. Huang. 2021. "Development of an integrated multivariate trend-frequency analysis method: Spatial-temporal characteristics of climate extremes under global warming for Central Asia." Environmental Research 195, no. : 110859.
In this study, an integrated CCA-RF-FA framework (abbreviated as CRFF) is developed for analyzing the streamflow variation in the mountainous watershed. CRFF incorporates cross-correlation analysis (CCA), random forest (RF), and factorial analysis (FA) within a general framework. CRFF can identify the time lag effect in the runoff mechanism (both rainfall and glacier/snow meltwater), tackle the problem of the simulation performance degradation caused by the time lag effect, as well as investigate the individual and interactive effects of meteorological and physical factors on runoff simulation. CRFF is applied to the Amu Darya River Basin (ADRB), a typical mountainous watershed in Central Asia. Bayesian neural network (BNN) and stepwise cluster analysis (SCA) are used for illustrating the advantage of RF in streamflow simulation. The main findings reveal that (i) compared with BNN and SCA, RF has the better simulation capacity; (ii) the time lag effect of heat conditions such as temperature (T) and shortwave radiation (SWR) is weak, with the lag time being less than 30 days; the time lag effect of the precipitation conditions such as snow precipitation rate (SPR) is strong, with a lag time ranging from 30 days to 90 days; (iii) in snow-melting, non-melting and entire periods, T has the dominant impact on the variation of the runoff in ADRB; (iv) the interaction of T and SWR has important effect on the streamflow; SPR can still considerably affect the runoff generation in non-melting period.
H. Wang; Y.P. Li; Y.R. Liu; G.H. Huang; Q.M. Jia. Analyzing streamflow variation in the data-sparse mountainous regions: An integrated CCA-RF-FA framework. Journal of Hydrology 2021, 596, 126056 .
AMA StyleH. Wang, Y.P. Li, Y.R. Liu, G.H. Huang, Q.M. Jia. Analyzing streamflow variation in the data-sparse mountainous regions: An integrated CCA-RF-FA framework. Journal of Hydrology. 2021; 596 ():126056.
Chicago/Turabian StyleH. Wang; Y.P. Li; Y.R. Liu; G.H. Huang; Q.M. Jia. 2021. "Analyzing streamflow variation in the data-sparse mountainous regions: An integrated CCA-RF-FA framework." Journal of Hydrology 596, no. : 126056.
As one of the most pressing issues in the world, climate change has already caused evident impacts on natural and human systems (e.g., hydrological cycle, eco‐environment and socio‐economy) in recent decades. In this study, an integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis (MBHA) method is developed for quantifying climate change impacts on runoff. MBHA incorporates multiple global climate models (multi‐GCMs), hydrological model (HBV‐light), and Bayesian neural network (BNN) within a general framework. MBHA can provide the reliable prediction for runoff as well as reflect the impact of climate change on data scarcity catchments. MBHA is applied to the Amu Darya River basin in Central Asia. Climate data are derived from multiple GCMs (i.e., GFDL‐ESM2G, HadGEM2‐AO and NorESM1‐M) under RCP4.5 and RCP8.5. Several findings can be summarized: (1) during 2021–2100, both precipitation and temperature would increase, with more precipitation falling as rain instead of snow; (2) by 2100, glacier areas are predicted to reduce by 62.3% (RCP4.5) and 71.9% (RCP8.5); (3) under RCP8.5, monthly runoff would increase by 11.2% in 2021–2060 and reduce by 5.0% in 2061–2100; this is because the glaciers would rapidly disappear with the rising temperature after 2060. The findings suggest that the shrinked glacier and the reduced runoff threaten the water availability especially in summer seasons as well as affect the agricultural irrigation in the downstream of the Amu Darya River.
Yuanyuan Su; Yongping Li; Yuanrui Liu; Guohe Huang; Qimeng Jia; Yanfeng Li; Yuanyuan Su. An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin. International Journal of Climatology 2021, 1 .
AMA StyleYuanyuan Su, Yongping Li, Yuanrui Liu, Guohe Huang, Qimeng Jia, Yanfeng Li, Yuanyuan Su. An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin. International Journal of Climatology. 2021; ():1.
Chicago/Turabian StyleYuanyuan Su; Yongping Li; Yuanrui Liu; Guohe Huang; Qimeng Jia; Yanfeng Li; Yuanyuan Su. 2021. "An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin." International Journal of Climatology , no. : 1.
A copula-based stochastic fractional programming (CSFP) method is developed for optimizing water-food-energy nexus (WFEN) system, which can tackle random variables under conflicting objectives and analyze the interrelationship between marginal benefit and system-failure risk. Then, a CSFP-WFEN model is formulated for the Aral Sea basin and multiple scenarios with individual- and joint-probabilistic risks related to water resources, arable land, and hydropower generation are examined. Synthesizing the results under all scenarios, hydropower generation would decrease by 11.9% and crop area would increase by 12.4% by 2035 under severe water scarcity. Agricultural water would decrease with time (from 69.1% in 2021 to 53.9% in 2035), and the share of wheat farmland would increase by 16.4% for food security. Compared with the traditional stochastic fractional programming (SFP) and copula-based stochastic programming (CSP) methods, the marginal benefit from CSFP would be improved 1.7% and water would be saved 9.2%. To support sustainable development of the study basin, some suggestions can be derived from the results: (i) since agriculture is still the largest water user in the future, it is desired to reduce its water consumption through improving irrigation efficiency and adjusting crop planting structure; (ii) effectively reducing Uzbekistan’s water consumption can improve water allocations to other countries and relieve regional conflicts caused by water shortage; (iii) compared to the single management pattern, the integrated optimization of water, food and energy nexus not only has higher efficiency of resources allocation and utilization, but also allows an increased robustness in controlling system-failure risk under joint probabilistic constraints.
Y.F. Zhang; Y.P. Li; G.H. Huang; Y. Ma. A copula-based stochastic fractional programming method for optimizing water-food-energy nexus system under uncertainty in the Aral Sea basin. Journal of Cleaner Production 2021, 292, 126037 .
AMA StyleY.F. Zhang, Y.P. Li, G.H. Huang, Y. Ma. A copula-based stochastic fractional programming method for optimizing water-food-energy nexus system under uncertainty in the Aral Sea basin. Journal of Cleaner Production. 2021; 292 ():126037.
Chicago/Turabian StyleY.F. Zhang; Y.P. Li; G.H. Huang; Y. Ma. 2021. "A copula-based stochastic fractional programming method for optimizing water-food-energy nexus system under uncertainty in the Aral Sea basin." Journal of Cleaner Production 292, no. : 126037.
Synergic management of energy-water nexus (EWN) system is essential for coping with the dilemma of joint shortage of energy and water and supporting socio-economic sustainable development. The system is full of multiple uncertainties, making deterministic analysis methods infeasible. In this study, an interval bi-level joint-probabilistic programming (IBJP) approach is first developed through incorporating bi-level programming (BP) and interval joint-probabilistic programming (IJP) within a framework. IBJP has advantages in balancing the tradeoff between two-level decision makers under uncertainty, tackling uncertainties expressed as joint probabilities and interval values, and examining the risk of violating joint-probabilistic constraints. Then, the developed method is applied to planning China’s EWN system over a long-term planning horizon (2021–2050). Multiple scenarios related to different groups of constraint-violation levels for violating electricity demand and/or water availability constraints are examined. Results reveal that uncertainties associated with joint and individual probabilities have effects on the synergic management of EWN system. Results also disclose that limited water resource can promote electricity generation structure toward a low water-intensity, clean and sustainable pattern, in which the share of clean energy would increase to 66.25% by 2050 and the corresponding water withdrawal would save 41.20%.
J. Lv; Y.P. Li; G.H. Huang; S. Nie; J.W. Gong; Y. Ma. Synergetic management of energy-water nexus system under uncertainty: An interval bi-level joint-probabilistic programming method. Journal of Cleaner Production 2021, 292, 125942 .
AMA StyleJ. Lv, Y.P. Li, G.H. Huang, S. Nie, J.W. Gong, Y. Ma. Synergetic management of energy-water nexus system under uncertainty: An interval bi-level joint-probabilistic programming method. Journal of Cleaner Production. 2021; 292 ():125942.
Chicago/Turabian StyleJ. Lv; Y.P. Li; G.H. Huang; S. Nie; J.W. Gong; Y. Ma. 2021. "Synergetic management of energy-water nexus system under uncertainty: An interval bi-level joint-probabilistic programming method." Journal of Cleaner Production 292, no. : 125942.
In this study,a structural adjustment optimization model for electric-power system management under fuzzy-random environment was developed for regional electric power system management in Urumqi, China. The emission control policy andstructural adjustment are considered and quantified in the developed model to tackle the electric power problems of a regional electric-power system. The objective of this study was to develop a power structural adjustment optimization model with fuzzy-random parameters for discussing the effects, merits and defects of structural adjustment and policy adjustment to manage the electric power system of the city Urumqi. Finally, the results and comparisons analysis of the case study demonstrated the practicality and efficiency of the optimization method. The results indicated that the model can provide an effective linkage between conflicting economic cost and the system stability, and different power demand levels correspond to different electricity generation schemes with varied energy policy and power structural adjustment. The power generation schemes, pollution emission and CO2 emission were analyzed. The modeling results are valuable for supporting the adjustment or justification of the energy policies and structures within a complicated power system under uncertainty.
S. Wang; Y.L. Xie; G.H. Huang; Y. Yao; Y.F. Li. A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China. Energy Policy 2021, 149, 112056 .
AMA StyleS. Wang, Y.L. Xie, G.H. Huang, Y. Yao, Y.F. Li. A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China. Energy Policy. 2021; 149 ():112056.
Chicago/Turabian StyleS. Wang; Y.L. Xie; G.H. Huang; Y. Yao; Y.F. Li. 2021. "A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China." Energy Policy 149, no. : 112056.
Water scarcity causes a series of eco-environmental problems, such as land salinization, biodiversity reduction and food crisis, which seriously restricts the sustainable development of the Aral Sea basin. In this study, a stepwise-cluster factorial analysis (SCFA) approach is proposed for assessing the effects of natural condition and human activity on the outflow of Syr Darya River (abbreviated as OSR) that has significant effects on the eco-environmental restoration of the Aral Sea. SCFA coupled stepwise cluster analysis and factorial analysis cannot only reflect the variability of outflow, but also identify the driving factors quantitatively. The results disclose that, in 1960–1991, the dominant factors (affecting the OSR) are upstream inflow (25.77%) > agricultural water use of Uzbekistan (7.21%) > industrial water use of Uzbekistan (4.53%) > agricultural water use of Kazakhstan (3.81%) > Precipitation (3.66%); interactions between upstream inflow and agricultural water use of Uzbekistan, Kazakhstan and Tajikistan and interactions between reservoir and evapotranspiration have the significant effects on the OSR. Results also indicate that, in 1992–2015, the dominant factors that affect the OSR are agricultural water use of Uzbekistan (23.31%) > agricultural water use of Kazakhstan (22.15%) > industrial water use of Uzbekistan (8.31%) > domestic water use of Kazakhstan (4.68%) > agricultural water use of Tajikistan (4.54%) > domestic water use of Uzbekistan (4.41%); the interactions between industrial water use and agricultural water use of Uzbekistan, Kazakhstan and Tajikistan and the interactions between reservoir and upstream inflow have the pivotal effects on OSR. In the future, when the agricultural water use of the basin decrease as 4% and the industry water use of Uzbekistan decrease as 2%, the OSR may recover to the middle level of 1970s. The results help identify the major factors affecting the outflow of Syr Darya River as well as seek an effective approach to restore the eco-environment of Aral Sea basin.
X.B. Zhai; Y.P. Li; Y.R. Liu; G.H. Huang. Assessment of the effects of human activity and natural condition on the outflow of Syr Darya River: A stepwise-cluster factorial analysis method. Environmental Research 2020, 194, 110634 .
AMA StyleX.B. Zhai, Y.P. Li, Y.R. Liu, G.H. Huang. Assessment of the effects of human activity and natural condition on the outflow of Syr Darya River: A stepwise-cluster factorial analysis method. Environmental Research. 2020; 194 ():110634.
Chicago/Turabian StyleX.B. Zhai; Y.P. Li; Y.R. Liu; G.H. Huang. 2020. "Assessment of the effects of human activity and natural condition on the outflow of Syr Darya River: A stepwise-cluster factorial analysis method." Environmental Research 194, no. : 110634.
An integrated Bayesian least-squares-support-vector-machine factorial-analysis (B-LSVM-FA) method is developed through integrating techniques of Bayesian inference, least squares support vector machine (LSVM), and factorial analysis (FA) into a general framework. B-LSVM-FA has advantages in: (i) capturing the complicated nonlinear relationship between input factors and streamflow, (ii) optimizing the key parameters of LSVM through a maximum posterior density estimation, and (iii) quantifying the contributions of individual and interactive effects of multiple factors to streamflow variation. B-LSVM-FA is then applied to inferring the changes in inflow from the Amu Darya to the Aral Sea (named as IATA). Results obtained cannot only identify the key impact factors reducing IATA during the period of 1960–2015, but also predict future trends in IATA for 2020–2050. Comparing to the conventional ANN, SVM and LSVM, the proposed method performs better in describing the IATA changes with anthropogenic, hydrometeorological, and ecological factors in terms of NSE, RMSE, and PBS. Results disclose that the major factors affecting IATA at annual/seasonal scale are upstream streamflow, agricultural water use in Uzbekistan, reservoir water storage, and evapotranspiration. The significant differences in the contributions of main factors to IATA at seasonal scale are observed because each season has unique characteristics of human activities, meteorological conditions, and vegetation coverages. In order to seek the feasible strategies of recovering the IATA level in the future, 162 scenarios based on ensemble prediction are analyzed. Results indicate that the IATA would restore to its 1970s condition if the drip irrigation rate reaches 50% at the end of 2050, and the reservoir water storage level reduces to the average value of 1960–1970. The findings can provide valuable suggestions for decision makers to increase IATA, and thus ameliorating the ecological crisis within the Aral Sea Basin.
P.P. Gao; Y.P. Li; G.H. Huang; Y.Y. Su. An integrated Bayesian least-squares-support-vector-machine factorial-analysis (B-LSVM-FA) method for inferring inflow from the Amu Darya to the Aral Sea under ensemble prediction. Journal of Hydrology 2020, 594, 125909 .
AMA StyleP.P. Gao, Y.P. Li, G.H. Huang, Y.Y. Su. An integrated Bayesian least-squares-support-vector-machine factorial-analysis (B-LSVM-FA) method for inferring inflow from the Amu Darya to the Aral Sea under ensemble prediction. Journal of Hydrology. 2020; 594 ():125909.
Chicago/Turabian StyleP.P. Gao; Y.P. Li; G.H. Huang; Y.Y. Su. 2020. "An integrated Bayesian least-squares-support-vector-machine factorial-analysis (B-LSVM-FA) method for inferring inflow from the Amu Darya to the Aral Sea under ensemble prediction." Journal of Hydrology 594, no. : 125909.
Effective land-use planning considering ecosystem service value (ESV) is indispensable in facilitating economic development and eco-environment sustainability. In this study, a Monte-Carlo-based interval fuzzy De Novo programming (MC-IFDP) method is developed for land-use planning under uncertainty. MC-IFDP can achieve optimal system design to maximize multiple conflicting objectives simultaneously, and provide a number of alternatives for decision-makers. It can also address both stipulation uncertainty and parameter uncertainty in constraint and objective function. MC-IFDP is then applied to a real case for land-use planning of Guangzhou (China), where six scenarios related to different decision-makers’ preferences are examined. Results reveal that (i) different decision-makers’ preferences (denoted as S1 to S6) result in different land-use planning schemes, leading to varied satisfactory degrees, system benefits, ESVs and pollutant emissions; (ii) system benefit would reduce as satisfactory degree λ decreases, which would be RMB¥ [4287.5, 11025.7] × 109 with the degree of λ being [0.43, 0.76]; (iii) under advantage condition (S1), the area of ecological land would increase from [455.1, 494.1] × 103 ha to [503.9, 537.8] × 103 ha from period 1 to period 2; the total ESV would expect to have an average annual growth rate of [1.3, 1.5] %; (iv) pollutant emissions (e.g., solid waste, sulfur dioxide, dust, COD, and NH3-N) would be mitigated over the planning horizon under all scenarios. The findings are rewarding for decision-makers to identify desired land-use planning strategies and coordinate the conflicts among satisfactory degree, economic benefit, ESV, and pollutant mitigation under multi-uncertainty.
P.P. Gao; Y.P. Li; J.W. Gong; G.H. Huang. Urban land-use planning under multi-uncertainty and multiobjective considering ecosystem service value and economic benefit - A case study of Guangzhou, China. Ecological Complexity 2020, 45, 100886 .
AMA StyleP.P. Gao, Y.P. Li, J.W. Gong, G.H. Huang. Urban land-use planning under multi-uncertainty and multiobjective considering ecosystem service value and economic benefit - A case study of Guangzhou, China. Ecological Complexity. 2020; 45 ():100886.
Chicago/Turabian StyleP.P. Gao; Y.P. Li; J.W. Gong; G.H. Huang. 2020. "Urban land-use planning under multi-uncertainty and multiobjective considering ecosystem service value and economic benefit - A case study of Guangzhou, China." Ecological Complexity 45, no. : 100886.
An integrated PCA-SCA-ANOVA framework (abbreviated as PSAF) is advanced to analyze the impacts of multiple factors on water-flow variation. PSAF incorporates techniques of principle component analysis (PCA), stepwise-cluster analysis (SCA), and analysis of variance (ANOVA) within a general framework. PSAF cannot only quantify the sensitivity of water flow to individual and interactive factors, but also simulate water flow effectively under different scenarios. A case study of the Aral Sea is conducted to demonstrate the effectiveness and practicability of PSAF. The Nash-Sutcliffe coefficients for calibration and validation are 0.96 and 0.84, respectively. Results reveal that the key impact factors that cause the variability of water flow from the Amu Darya into the Aral Sea are runoff in the upper reach (contributing 53%), agricultural water use (occupying 23%) and water storage in reservoirs (occupying 3%). Results also disclose that runoff in the upper reach and agricultural water use have obvious interaction; the high level of runoff in the upper reach can enhance the negative effects of agricultural water use on water flow. In the future, to recover the water flow from the Amu Darya into the Aral Sea to the level of that in the 1990s (about 8 km3), the decreasing rates of annual agricultural water use and water storage in reservoirs should be controlled at 1.0% and 0.5%, respectively. The findings are helpful for supporting water resources management and restoring the ecological environment of the Aral Sea.
Y.Y. Su; Y.P. Li; Y.R. Liu; Y.R. Fan; P.P. Gao. Development of an integrated PCA-SCA-ANOVA framework for assessing multi-factor effects on water flow: A case study of the Aral Sea. CATENA 2020, 197, 104954 .
AMA StyleY.Y. Su, Y.P. Li, Y.R. Liu, Y.R. Fan, P.P. Gao. Development of an integrated PCA-SCA-ANOVA framework for assessing multi-factor effects on water flow: A case study of the Aral Sea. CATENA. 2020; 197 ():104954.
Chicago/Turabian StyleY.Y. Su; Y.P. Li; Y.R. Liu; Y.R. Fan; P.P. Gao. 2020. "Development of an integrated PCA-SCA-ANOVA framework for assessing multi-factor effects on water flow: A case study of the Aral Sea." CATENA 197, no. : 104954.