Articles | Volume 26, issue 15
https://doi.org/10.5194/hess-26-3965-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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https://doi.org/10.5194/hess-26-3965-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Aug 2022
Research article |
| 02 Aug 2022
| 02 Aug 2022
A system dynamic model to quantify the impacts of water resources allocation on water–energy–food–society (WEFS) nexus
Yujie Zeng
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Dedi Liu
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Province Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan 430072, China
Shenglian Guo
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Lihua Xiong
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Jiabo Yin
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Province Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan 430072, China
Zhenhui Wu
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
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Hydrol. Earth Syst. Sci., 29, 3315–3339, https://doi.org/10.5194/hess-29-3315-2025, https://doi.org/10.5194/hess-29-3315-2025, 2025
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Jiayu Zhang, Dedi Liu, Jiaoyang Wang, Feng Yue, Hanxu Liang, Zhengbo Peng, and Wei Guan
EGUsphere, https://doi.org/10.5194/egusphere-2025-2734, https://doi.org/10.5194/egusphere-2025-2734, 2025
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Water use is often estimated with coarse data that overlook spatial heterogeneity, limiting effective water planning. This study proposes a framework to simulate water use at multiple spatial scales across China, combining a grid-based approach and uncertainty analysis. It finds that both the model structure and spatial scale affect. The framework reveals detailed patterns in water use and can guide smarter water resources management.
Chao Ma, Weifeng Hao, Qing Cheng, Fan Ye, Ying Qu, Jiabo Yin, Fang Xu, Haojian Wu, and Fei Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-79, https://doi.org/10.5194/essd-2025-79, 2025
Revised manuscript accepted for ESSD
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Antarctic sea ice albedo is a key factor influencing the energy balance of the cryosphere. Here we present a daily 1 km shortwave albedo product for Antarctic sea ice from 2012 to 2021, based on VIIRS reflectance data. Additionally, we reconstructed the albedo for missing pixels due to cloud cover. This dataset can be used to assess changes in Antarctic sea ice, radiation budget, and the strength of sea ice albedo feedback mechanisms, as well as their potential interconnections.
Qiumei Ma, Chengyu Xie, Zheng Duan, Yanke Zhang, Lihua Xiong, and Chong-Yu Xu
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We propose a method to estimate the reservoir WLS curve based on the capacity loss induced by sediment accumulation and further assess the potential negative impact caused by outdated design WLS curve on flood regulation risks. The findings highlight that when storage capacity is considerably reduced, continued use of the existing design WLS curve may significantly underestimate, thus posing potential safety hazards to the reservoir itself and downstream flood protection objects.
Xin Xiang, Shenglian Guo, Chenglong Li, and Yun Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-279, https://doi.org/10.5194/egusphere-2025-279, 2025
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Deep learning models excel in hydrological simulations but lack physical foundations. We combine the Xinanjiang model’s principles into recurrent neural network units, forming a physical-based XAJRNN layer. Combined with LSTM layers, we propose an explainable deep learning model. The model shows low peak errors and minimal timing differences. The model improves accuracy and interpretability, offering new insights for combining deep learning and hydrological models in flood forecasting.
Ruikang Zhang, Dedi Liu, Lihua Xiong, Jie Chen, Hua Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 28, 5229–5247, https://doi.org/10.5194/hess-28-5229-2024, https://doi.org/10.5194/hess-28-5229-2024, 2024
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Flash flood warnings cannot be effective without people’s responses to them. We propose a method to determine the threshold of issuing warnings based on a people’s response process simulation. The results show that adjusting the warning threshold according to people’s tolerance levels to the failed warnings can improve warning effectiveness, but the prerequisite is to increase forecasting accuracy and decrease forecasting variance.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo
Hydrol. Earth Syst. Sci., 28, 1873–1895, https://doi.org/10.5194/hess-28-1873-2024, https://doi.org/10.5194/hess-28-1873-2024, 2024
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Temporal variability and spatial heterogeneity of climate systems challenge accurate estimation of probable maximum precipitation (PMP) in China. We use high-resolution precipitation data and climate models to explore the variability, trends, and shifts of PMP under climate change. Validated with multi-source estimations, our observations and simulations show significant spatiotemporal divergence of PMP over the country, which is projected to amplify in future due to land–atmosphere coupling.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar, James S. Famiglietti, Chong Zhang, Gionata Ghiggi, Shenglian Guo, Yun Pan, and Bramha Dutt Vishwakarma
Earth Syst. Sci. Data, 15, 4571–4597, https://doi.org/10.5194/essd-15-4571-2023, https://doi.org/10.5194/essd-15-4571-2023, 2023
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To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
Youjiang Shen, Karina Nielsen, Menaka Revel, Dedi Liu, and Dai Yamazaki
Earth Syst. Sci. Data, 15, 2781–2808, https://doi.org/10.5194/essd-15-2781-2023, https://doi.org/10.5194/essd-15-2781-2023, 2023
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Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
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A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Jinghua Xiong, Shenglian Guo, Abhishek, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 26, 6457–6476, https://doi.org/10.5194/hess-26-6457-2022, https://doi.org/10.5194/hess-26-6457-2022, 2022
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Although the "dry gets drier, and wet gets wetter (DDWW)" paradigm is prevalent in summarizing wetting and drying trends, we show that only 11.01 %–40.84 % of the global land confirms and 10.21 %–35.43 % contradicts the paradigm during 1985–2014 from a terrestrial water storage change perspective. Similar proportions that intensify with the increasing emission scenarios persist until the end of the 21st century. Findings benefit understanding of global hydrological responses to climate change.
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, and Yanghe Liu
Hydrol. Earth Syst. Sci., 26, 6379–6397, https://doi.org/10.5194/hess-26-6379-2022, https://doi.org/10.5194/hess-26-6379-2022, 2022
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Multiyear drought has been demonstrated to cause non-stationary rainfall–runoff relationship. But whether changes can invalidate the most fundamental method (i.e., paired-catchment method (PCM)) for separating vegetation change impacts is still unknown. Using paired-catchment data with 10-year drought, PCM is shown to still be reliable even in catchments with non-stationarity. A new framework is further proposed to separate impacts of two non-stationary drivers, using paired-catchment data.
Jing Tian, Zhengke Pan, Shenglian Guo, Jiabo Yin, Yanlai Zhou, and Jun Wang
Hydrol. Earth Syst. Sci., 26, 4853–4874, https://doi.org/10.5194/hess-26-4853-2022, https://doi.org/10.5194/hess-26-4853-2022, 2022
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Most of the literature has focused on the runoff response to climate change, while neglecting the impacts of the potential variation in the active catchment water storage capacity (ACWSC) that plays an essential role in the transfer of climate inputs to the catchment runoff. This study aims to systematically identify the response of the ACWSC to a long-term meteorological drought and asymptotic climate change.
Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-217, https://doi.org/10.5194/essd-2022-217, 2022
Revised manuscript not accepted
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There are currently no available common datasets of the Soil moisture storage capacity (SMSC) on a global scale, especially for hydrological models. Here, we produce a dataset of the SMSC parameter for global hydrological models. The global SMSC is constructed based on the deep residual network at 0.5° resolution. SMSC products are validated on global grids and typical catchments from different climatic regions.
Jinghua Xiong, Shenglian Guo, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-645, https://doi.org/10.5194/hess-2021-645, 2022
Manuscript not accepted for further review
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Although the “dry gets drier and wet gets wetter” (DDWW) paradigm is widely used to describe the trends in wetting and drying globally, we show that 27.1 % of global land agrees with the paradigm, while 22.4 % shows the opposite pattern during the period 1985–2014 from the perspective of terrestrial water storage change. Similar percentages are discovered under different scenarios during the future period. Our findings will benefit the understanding of hydrological responses under climate change.
Ren Wang, Pierre Gentine, Jiabo Yin, Lijuan Chen, Jianyao Chen, and Longhui Li
Hydrol. Earth Syst. Sci., 25, 3805–3818, https://doi.org/10.5194/hess-25-3805-2021, https://doi.org/10.5194/hess-25-3805-2021, 2021
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Assessment of changes in the global water cycle has been a challenge. This study estimated long-term global latent heat and sensible heat fluxes for recent decades using machine learning and ground observations. The results found that the decline in evaporative fraction was typically accompanied by an increase in long-term runoff in over 27.06 % of the global land areas. The observation-driven findings emphasized that surface vegetation has great impacts in regulating water and energy cycles.
Xiaojing Zhang and Pan Liu
Hydrol. Earth Syst. Sci., 25, 711–733, https://doi.org/10.5194/hess-25-711-2021, https://doi.org/10.5194/hess-25-711-2021, 2021
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Rainfall–runoff models are useful tools for streamflow simulation. However, efforts are needed to investigate how their parameters vary in response to climate changes and human activities. Thus, this study proposes a new method for estimating time-varying parameters, by considering both simulation accuracy and parameter continuity. The results show the proposed method is effective for identifying temporal variations of parameters and can simultaneously provide good streamflow simulation.
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, Yanghe Liu, and Jun Xia
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-5, https://doi.org/10.5194/hess-2021-5, 2021
Manuscript not accepted for further review
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We use statistical methods and data assimilation method with physical model to verify that prolonged drought can induce non-stationarity in the control catchment rainfall-runoff relationship, which causes three inconsistent results at the Red Hill paired-catchment site. The findings are fundamental to correctly use long-term historical data and effectively assess ecohydrological impacts of vegetation change given that extreme climate events are projected to occur more frequently in the future.
Zhengke Pan, Pan Liu, Chong-Yu Xu, Lei Cheng, Jing Tian, Shujie Cheng, and Kang Xie
Hydrol. Earth Syst. Sci., 24, 4369–4387, https://doi.org/10.5194/hess-24-4369-2020, https://doi.org/10.5194/hess-24-4369-2020, 2020
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This study aims to identify the response of catchment water storage capacity (CWSC) to meteorological drought by examining the changes of hydrological-model parameters after drought events. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.
Cited articles
Alexandratos, N. and Bruinsma, J.: World agriculture towards 2030/2050,
FAO, http://water2return.eu/wp-content/uploads/2017/11/FAO_world-agriculture-towards-2030-2050.pdf
(last access: 1 August 2021), 2012.
Bertalanffy, L. V.: General System Theory: Foundations, Development,
Applications, 3, George Braziller, New York, USA, https://doi.org/10.1109/TSMC.1974.4309376, 1976.
Blanke, A., Rozelle, S., Lohmar, B., Wang, J., and Huang, J.: Water saving
technology and saving water in China, Agr. Water Manage., 87, 139–150,
https://doi.org/10.1016/j.agwat.2006.06.025, 2007.
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating
human systems, P. Natl. Acad. Sci. USA, 99, 7280–7287, https://doi.org/10.1073/pnas.082080899, 2002.
Brekke, L., Larsen, M. D., Ausburn, M., and Takaichi, L.: Suburban water
demand modeling using stepwise regression, J. Am. Water Works Assoc., 94, 65–75, 2002.
Chen, J., Brissette, F. P., and Leconte, R.: Uncertainty of downscaling
method in quantifying the impact of climate change on hydrology, J. Hydrol., 401, 190–202, https://doi.org/10.1016/j.jhydrol.2011.02.020, 2011.
Chen, X., Wang, D., Tian, F., and Sivapalan, M.: From channelization to
restoration: Sociohydrologic modeling with changing community preferences in
the Kissimmee River Basin, Florida, Water Resour. Res., 52, 1227–1244,
https://doi.org/10.1002/2015wr018194, 2016.
Chiang, Y. M., Chang, L. C., and Chang, F. J.: Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff
modeling, J. Hydrol., 290, 297–311, https://doi.org/10.1016/j.jhydrol.2003.12.033,
2004.
CWRC – Changjiang Water Resources Commission: Integrated Water Resources
Planning of Hanjiang River Basin, Wuhan, China, 2016.
Davies, E. G. R. and Simonovic, S. P.: ANEMI: a new model for integrated
assessment of global change, Interdisciplin. Environ. Rev., 11, 127–161, https://doi.org/10.1504/ier.2010.037903, 2010.
Dawson, R. J., Peppe, R., and Wang, M.: An agent-based model for risk-based
flood incident management, Nat, Hazards, 59, 167–189,
https://doi.org/10.1007/s11069-011-9745-4, 2011.
Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Yan, K., Brandimarte, L., and Bloeschl, G.: DebatesPerspectives on socio-hydrology: Capturing
feedbacks between physical and social processes, Water Resour. Res., 51,
4770–4781, https://doi.org/10.1002/2014wr016416, 2015.
Di Baldassarre, G., Sivapalan, M., Rusca, M., Cudennec, C., Garcia, M.,
Kreibich, H., Konar, M., Mondino, E., Mard, J., Pande, S., Sanderson, M. R.,
Tian, F., Viglione, A., Wei, J., Wei, Y., Yu, D. J., Srinivasan, V., and
Bloeschl, G.: Sociohydrology: Scientific Challenges in Addressing the
Sustainable Development Goals, Water Resour. Res., 55, 6327–6355,
https://doi.org/10.1029/2018wr023901, 2019.
El Gafy, I., Grigg, N., and Reagan, W.: Dynamic Behaviour of the Water-Food-Energy Nexus: Focus on Crop Production and Consumption, Irrig. Drain., 66, 19–33, https://doi.org/10.1002/ird.2060, 2017.
El Gafy, I. K.: System Dynamic Model for Crop Production, Water Footprint,
and Virtual Water Nexus, Water Resour. Manage., 28, 4467–4490,
https://doi.org/10.1007/s11269-014-0667-2, 2014.
Elshafei, Y., Sivapalan, M., Tonts, M., and Hipsey, M. R.: A prototype
framework for models of socio-hydrology: identification of key feedback
loops and parameterisation approach, Hydrol. Earth Syst. Sci., 18, 2141–2166, https://doi.org/10.5194/hess-18-2141-2014, 2014.
Eusgeld, I., Nan, C., and Dietz, S.: “System-of-systems” approach for
interdependent critical infrastructures, Reliabil. Eng. Syst. Safe., 96, 679–686, https://doi.org/10.1016/j.ress.2010.12.010, 2011.
Feng, M., Liu, P., Li, Z., Zhang, J., Liu, D., and Xiong, L.: Modeling the
nexus across water supply, power generation and environment systems using
the system dynamics approach: Hehuang Region, China, J. Hydrol., 543, 344–359, https://doi.org/10.1016/j.jhydrol.2016.10.011, 2016.
Feng, M., Liu, P., Guo, S., Yu, D. J., Cheng, L., Yang, G., and Xie, A.:
Adapting reservoir operations to the nexus across water supply, power generation, and environment systems: An explanatory tool for policy makers, J. Hydrol., 574, 257–275, https://doi.org/10.1016/j.jhydrol.2019.04.048, 2019.
French, R. J. and Schultz, J. E.: Water-use efficiency of wheat in a
mediterranean-type environment. 1. The relation between yield, water-use and
climate, Aust. J. Agric. Res., 35, 743–764, https://doi.org/10.1071/ar9840743, 1984.
He, S., Guo, S., Yin, J., Liao, Z., Li, H., and Liu, Z.: A novel impoundment
framework for a mega reservoir system in the upper Yangtze River basin, Appl. Energy, 305, 117792, https://doi.org/10.1016/j.apenergy.2021.117792, 2022.
He, S. Y., Lee, J., Zhou, T., and Wu, D.: Shrinking cities and resource-based economy: The economic restructuring in China's mining cities, Cities, 60, 75–83, https://doi.org/10.1016/j.cities.2016.07.009, 2017.
Hepburn, C., Duncan, S., and Papachristodoulou, A.: Behavioural Economics,
Hyperbolic Discounting and Environmental Policy, Environ. Resour. Econ., 46, 189–206, https://doi.org/10.1007/s10640-010-9354-9, 2010.
Hoff, H.: Understanding the nexus, in: Background Paper for the Bonn 2011
Conference, The Water, Energy and Food Security Nexus, Stockholm Environment Institute, Stockholm, https://mediamanager.sei.org/documents/Publications/SEI-Paper-Hoff-UnderstandingTheNexus-2011.pdf
(last access: 1 August 2021), 2011.
Housh, M., Cai, X., Ng, T. L., McIsaac, G. F., Ouyang, Y., Khanna, M.,
Sivapalan, M., Jain, A. K., Eckhoff, S., Gasteyer, S., Al-Qadi, I., Bai, Y.,
Yaeger, M. A., Ma, S., and Song, Y.: System of Systems Model for Analysis of
Biofuel Development, J. Infrastruct. Syst., 21, 04014050, https://doi.org/10.1061/(asce)is.1943-555x.0000238, 2015.
HPDWR – Hubei Provincial Department of Water Resources: Dispatching schedules of Hubei provincial large reservoirs, Wuhan, China, 2014.
Hritonenko, N. and Yatsenko, Y.: Mathematical Modeling in Economics, Ecology
and the Environment, Kluwer Academic Publishers, Dordrecht, Boston, London, https://doi.org/10.1007/978-1-4614-9311-2, 1999.
Hsiao, T. C., Steduto, P., and Fereres, E.: A systematic and quantitative
approach to improve water use efficiency in agriculture, Irrig. Sci., 25,
209–231, https://doi.org/10.1007/s00271-007-0063-2, 2007.
Hubei Provincial Bureau of Statistics: Hubei Statistical Yearbook, http://data.cnki.net/, last access: 1 August 2021.
International Energy Agency: World Energy Outlook 2012, International Energy
Agency, Paris, France, https://iea.blob.core.windows.net/assets/4ed140c1-c3f3-4fd9-acae-789a4e14a23c/WorldEnergyOutlook2021.pdf
(last access: 1 August 2021), 2012.
Khare, D., Jat, M. K., and Sunder, J. D.: Assessment of water resources
allocation options: Conjunctive use planning in a link canal command, Resour. Conserv. Recycl., 51, 487–506, https://doi.org/10.1016/j.resconrec.2006.09.011, 2007.
Kleinmuntz, D. N.: Information-processing and misperceptions of the implications of feedback in dynamic decision-making, Syst. Dynam. Rev., 9, 223–237, https://doi.org/10.1002/sdr.4260090302, 1993.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Laspidou, C. S., Mellios, N. K., Spyropoulou, A. E., Kofinas, D. T., and
Papadopoulou, M. P.: Systems thinking on the resource nexus: Modeling and
visualisation tools to identify critical interlinkages for resilient and
sustainable societies and institutions, Sci. Total Environ., 717, 137264,
https://doi.org/10.1016/j.scitotenv.2020.137264, 2020.
Law, R., Murrell, D. J., and Dieckmann, U.: Population growth in space and
time: spatial logistic equations (vol 84, pg 252, 2003), Ecology, 84,
535–535, 2003.
Li, B., Sivapalan, M., and Xu, X.: An Urban Sociohydrologic Model for
Exploration of Beijing's Water Sustainability Challenges and Solution Spaces, Water Resour. Res., 55, 5918–5940, https://doi.org/10.1029/2018wr023816, 2019.
Li, X. Y., Zhang, D. Y., Zhang, T., Ji, Q., and Lucey, B.: Awareness, energy
consumption and pro-environmental choices of Chinese households, J. Clean. Product., 279, 123734, https://doi.org/10.1016/j.jclepro.2020.123734, 2021.
Lian, X. B., Gong, Q., and Wang, L. F. S.: Consumer awareness and ex-ante
versus ex-post environmental policies revisited, Int. Rev. Econ. Financ., 55, 68–77, https://doi.org/10.1016/j.iref.2018.01.014, 2018.
Lin, J. Y., Wan, G., and Morgan, P. J.: Prospects for a re-acceleration of
economic growth in the PRC, J. Comp. Econ., 44, 842–853,
https://doi.org/10.1016/j.jce.2016.08.006, 2016.
Linderhof, V., Dekkers, K., and Polman, N.: The Role of Mitigation Options
for Achieving a Low-Carbon Economy in the Netherlands in 2050 Using a System
Dynamics Modelling Approach, Climate, 8, 132, https://doi.org/10.3390/cli8110132, 2020.
Liu, D.: Evaluating the dynamic resilience process of a regional water
resource system through the nexus approach and resilience routing analysis,
J. Hydrol., 578, 124028, https://doi.org/10.1016/j.jhydrol.2019.124028, 2019.
Liu, D., Guo, S., Liu, P., Xiong, L., Zou, H., Tian, J., Zeng, Y., Shen, Y.,
and Zhang, J.: Optimisation of water-energy nexus based on its diagram in
cascade reservoir system, J. Hydrol., 569, 347–358,
https://doi.org/10.1016/j.jhydrol.2018.12.010, 2019.
Lobell, D. B., Cassman, K. G., and Field, C. B.: Crop Yield Gaps: Their
Importance, Magnitudes, and Causes, Annu. Rev. Environ. Resour., 34, 179–204, https://doi.org/10.1146/annurev.environ.041008.093740, 2009.
Loucks, D. P.: Interactive River-Aquifer Simulation and Stochastic Analyses
for Predicting and Evaluating the Ecologic Impacts of Alternative Land and
Water Management Policies, Kluwer Academic Publishers, Dordrecht, the
Netherlands, ISBN 9781402009112, 2002.
Makindeodusola, B. A. and Marino, M. A.: Optimal-control of groundwater by the feedback method of control, Water Resour. Res., 25, 1341–1352,
https://doi.org/10.1029/WR025i006p01341, 1989.
Malthus, T.: An Essay on the Principle of Population, Penguin, Harmondsworth, UK, ISBN 9781351291521, 1798.
Matrosov, E. S., Harou, J. J., and Loucks, D. P.: A computationally efficient open-source water resource system simulator – Application to London and the Thames Basin, Environ. Model. Softw., 26, 1599–1610, https://doi.org/10.1016/j.envsoft.2011.07.013, 2011.
McKinsey & Company: Charting our water future: economic frameworks to inform decision-making, 2030 Water Resources Group, http://www.indiaenvironmentportal.org.in/files/Charting_Our_Water_Future_Full_Report_001.pdf
(last access: 1 August 2021), 2009.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Purwanto, A., Susnik, J., Suryadi, F. X., and de Fraiture, C.: Quantitative
simulation of the water-energy-food (WEF) security nexus in a local planning
context in indonesia, Sustain. Product. Consump., 25, 198–216,
https://doi.org/10.1016/j.spc.2020.08.009, 2021.
Ravar, Z., Zahraie, B., Sharifinejad, A., Gozini, H., and Jafari, S.: System
dynamics modeling for assessment of water-food-energy resources security and
nexus in Gavkhuni basin in Iran, Ecol. Indic., 108, 105682, https://doi.org/10.1016/j.ecolind.2019.105682, 2020.
Rockson, G., Bennett, R., and Groenendijk, L.: Land administration for food
security: A research synthesis, Land Use Policy, 32, 337–342,
https://doi.org/10.1016/j.landusepol.2012.11.005, 2013.
Roobavannan, M., van Emmerik, T. H. M., Elshafei, Y., Kandasamy, J.,
Sanderson, M. R., Vigneswaran, S., Pande, S., and Sivapalan, M.: Norms and
values in sociohydrological models, Hydrol. Earth Syst. Sci., 22, 1337–1349, https://doi.org/10.5194/hess-22-1337-2018, 2018.
Si, Y., Li, X., Yin, D., Li, T., Cai, X., Wei, J., and Wang, G.: Revealing
the water-energy-food nexus in the Upper Yellow River Basin through multi-objective optimization for reservoir system, Sci. Total Environ., 682,
1–18, https://doi.org/10.1016/j.scitotenv.2019.04.427, 2019.
Simonovic, S. P.: World water dynamics: global modeling of water resources,
J. Environ. Manage., 66, 249–267, https://doi.org/10.1006/jema.2002.0585, 2002.
Smith, K., Liu, S., Liu, Y., Savic, D., Olsson, G., Chang, T., and Wu, X.:
Impact of urban water supply on energy use in China: a provincial and national comparison, Mitig. Adapt. Strat. Global Change, 21, 1213–1233, https://doi.org/10.1007/s11027-015-9648-x, 2016.
Susnik, J.: Data-driven quantification of the global water-energy-food system, Resour. Conserv. Recycl., 133, 179–190, https://doi.org/10.1016/j.resconrec.2018.02.023, 2018.
Swanson, J.: Business dynamics – Systems thinking and modeling for a complex
world, J. Oper. Res. Soc., 53, 472–473, https://doi.org/10.1057/palgrave.jors.2601336, 2002.
Tennant, D. L.: Instream flow regimens for fish, wildlife, recreation and
related environmental resources, Fisheries, 1, 6–10,
https://doi.org/10.1577/1548-8446(1976)001<0006:ifrffw>2.0.co;2, 1976.
Van Emmerik, T. H. M., Li, Z., Sivapalan, M., Pande, S., Kandasamy, J.,
Savenije, H. H. G., Chanan, A., and Vigneswaran, S.: Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River basin,
Australia, Hydrol. Earth Syst. Sci., 18, 4239–4259, https://doi.org/10.5194/hess-18-4239-2014, 2014.
Vörösmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B.:
Global water resources: Vulnerability from climate change and population
growth, Science, 289, 284–288, https://doi.org/10.1126/science.289.5477.284, 2000.
Wolstenholme, E. F. and Coyle, R. G.: The development of system dynamics as
a methodology for system description and qualitative analysis, J. Oper. Res. Soc., 34, 569–581, https://doi.org/10.1057/jors.1983.137, 1983.
Wu, Z., Liu, D., Mei, Y., Guo, S., Xiong, L., Liu, P., Yin, J., and Zeng, Y.: Delayed feedback between adaptive reservoir operation and environmental
awareness within water supply-hydropower generation-environment nexus, J. Clean. Product., 345, 131181, https://doi.org/10.1016/j.jclepro.2022.131181, 2022.
Xiong, Y. L., Wei, Y. P., Zhang, Z. Q., and Wei, J.: Evolution of China's
water issues as framed in Chinese mainstream newspaper, Ambio, 45, 241–253,
https://doi.org/10.1007/s13280-015-0716-y, 2016.
Xu, X. B., Hu, H. Z., Tan, Y., Yang, G. S., Zhu, P., and Jiang, B.: Quantifying the impacts of climate variability and human interventions on
crop production and food security in the Yangtze River Basin, China,
1990–2015, Sci. Total Environ., 665, 379–389, https://doi.org/10.1016/j.scitotenv.2019.02.118, 2019.
Zeng, Y., Liu, D., Guo, S., Xiong, L., Liu, P., Yin, J., Tian, J., Deng, L.,
and Zhang, J.: Impacts of Water Resources Allocation on Water Environmental
Capacity under Climate Change, Water, 13, 1187, https://doi.org/10.3390/w13091187, 2021.
Zhang, P., Zhang, Y. Y., Ren, S. C., Chen, B., Luo, D., Shao, J. A., Zhang,
S. H., and Li, J. S.: Trade reshapes the regional energy related mercury
emissions: A case study on Hubei Province based on a multi-scale input-output analysis, J. Clean. Product., 185, 75–85, https://doi.org/10.1016/j.jclepro.2018.03.013, 2018.
Zhao, S., Liu, Y., Liang, S., Wang, C., Smith, K., Jia, N., and Arora, M.:
Effects of urban forms on energy consumption of water supply in China, J. Clean. Product., 253, 119960, https://doi.org/10.1016/j.jclepro.2020.119960, 2020.
Zhou, Y., Chang, L., Uen, T., Guo, S., Xu, C., and Chang, F.: Prospect for
small-hydropower installation settled upon optimal water allocation: An
action to stimulate synergies of water-food-energy nexus, Appl. Energy, 238,
668–682, https://doi.org/10.1016/j.apenergy.2019.01.069, 2019.
Short summary
The sustainability of the water–energy–food (WEF) nexus remains challenge, as interactions between WEF and human sensitivity and water resource allocation in water systems are often neglected. We incorporated human sensitivity and water resource allocation into a WEF nexus and assessed their impacts on the integrated system. This study can contribute to understanding the interactions across the water–energy–food–society nexus and improving the efficiency of resource management.
The sustainability of the water–energy–food (WEF) nexus remains challenge, as interactions...
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