Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2022
Research article |
| 22 Sep 2022
| 22 Sep 2022
Spatiotemporal responses of the crop water footprint and its associated benchmarks under different irrigation regimes to climate change scenarios in China
Zhiwei Yue
College of Water Resources and Architectural Engineering, Northwest
A&F University, Yangling 712100, China
Institute of Water-saving Agriculture in Arid Regions of China,
Northwest A&F University, Yangling 712100, China
Xiangxiang Ji
College of Water Resources and Architectural Engineering, Northwest
A&F University, Yangling 712100, China
Institute of Water-saving Agriculture in Arid Regions of China,
Northwest A&F University, Yangling 712100, China
La Zhuo
CORRESPONDING AUTHOR
Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
Institute of Water-saving Agriculture in Arid Regions of China,
Northwest A&F University, Yangling 712100, China
Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resource, Yangling 712100, China
Graduate School, University of Chinese Academy of Sciences, Beijing 100049, China
Wei Wang
Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resource, Yangling 712100, China
Graduate School, University of Chinese Academy of Sciences, Beijing 100049, China
Zhibin Li
Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resource, Yangling 712100, China
Graduate School, University of Chinese Academy of Sciences, Beijing 100049, China
Pute Wu
CORRESPONDING AUTHOR
Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
Institute of Water-saving Agriculture in Arid Regions of China,
Northwest A&F University, Yangling 712100, China
Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resource, Yangling 712100, China
Graduate School, University of Chinese Academy of Sciences, Beijing 100049, China
Related authors
Wei Wang, La Zhuo, Xiangxiang Ji, Zhiwei Yue, Zhibin Li, Meng Li, Huimin Zhang, Rong Gao, Chenjian Yan, Ping Zhang, and Pute Wu
Earth Syst. Sci. Data, 15, 4803–4827, https://doi.org/10.5194/essd-15-4803-2023, https://doi.org/10.5194/essd-15-4803-2023, 2023
Short summary
Short summary
The consumptive water footprint of crop production (WFCP) measures blue and green evapotranspiration of either irrigated or rainfed crops in time and space. A gridded monthly WFCP dataset for China is established. There are four improvements from existing datasets: (i) distinguishing water supply modes and irrigation techniques, (ii) distinguishing evaporation and transpiration, (iii) consisting of both total and unit WFCP, and (iv) providing benchmarks for unit WFCP by climatic zones.
Wei Wang, La Zhuo, Xiangxiang Ji, Zhiwei Yue, Zhibin Li, Meng Li, Huimin Zhang, Rong Gao, Chenjian Yan, Ping Zhang, and Pute Wu
Earth Syst. Sci. Data, 15, 4803–4827, https://doi.org/10.5194/essd-15-4803-2023, https://doi.org/10.5194/essd-15-4803-2023, 2023
Short summary
Short summary
The consumptive water footprint of crop production (WFCP) measures blue and green evapotranspiration of either irrigated or rainfed crops in time and space. A gridded monthly WFCP dataset for China is established. There are four improvements from existing datasets: (i) distinguishing water supply modes and irrigation techniques, (ii) distinguishing evaporation and transpiration, (iii) consisting of both total and unit WFCP, and (iv) providing benchmarks for unit WFCP by climatic zones.
Lei Tian, Baoqing Zhang, and Pute Wu
Earth Syst. Sci. Data, 14, 2259–2278, https://doi.org/10.5194/essd-14-2259-2022, https://doi.org/10.5194/essd-14-2259-2022, 2022
Short summary
Short summary
We propose a global monthly drought dataset with a resolution of 0.25° from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index adding snow processes (SZIsnow). The consideration of snow processes improved its capability, and the improvement is prominent over snow-covered high-latitude and high-altitude areas. This new dataset is well suited to monitoring, assessing, and characterizing drought and is a valuable resource for drought studies.
Xi Yang, La Zhuo, Pengxuan Xie, Hongrong Huang, Bianbian Feng, and Pute Wu
Hydrol. Earth Syst. Sci., 25, 169–191, https://doi.org/10.5194/hess-25-169-2021, https://doi.org/10.5194/hess-25-169-2021, 2021
Short summary
Short summary
Maximizing economic benefits with higher water productivity or lower water footprint is the core sustainable goal of agricultural water resources management. Here we look at spatial and temporal variations and developments in both production-based (PWF) and economic value-based (EWF) water footprints of crops, by taking a case study for China. A synergy evaluation index is proposed to further quantitatively evaluate the synergies and trade-offs between PWF and EWF.
Pute Wu, La Zhuo, Guoping Zhang, Mesfin M. Mekonnen, Arjen Y. Hoekstra, Yoshihide Wada, Xuerui Gao, Xining Zhao, Yubao Wang, and Shikun Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-436, https://doi.org/10.5194/hess-2018-436, 2018
Manuscript not accepted for further review
Short summary
Short summary
This study estimates the concomitant economic benefits and values to the crop-related (physical and virtual) water flows at a basin level. The net benefit of blue water was 13–42 % lower than that of green water in the case for the Yellow River Basin. The basin got a net income through the virtual water exports. It is necessary to manage the internal trade-offs between the water consumption and economic returns, for maximizing both the water use efficiency and water economic productivities.
La Zhuo, Mesfin M. Mekonnen, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 20, 4547–4559, https://doi.org/10.5194/hess-20-4547-2016, https://doi.org/10.5194/hess-20-4547-2016, 2016
Short summary
Short summary
Benchmarks for the water footprint (WF) of crop production can serve as a reference and be helpful in setting WF reduction targets. The study explores which environmental factors should be distinguished when determining benchmarks for the consumptive (green and blue) WF of crops. Through a case study for winter wheat in China over 1961–2008, we find that when determining benchmark levels for the consumptive WF of a crop, it is most useful to distinguish between different climate zones.
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Stochastic approaches
Controls on flood managed aquifer recharge through a heterogeneous vadose zone: hydrologic modeling at a site characterized with surface geophysics
Bridging the scale gap: obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate
3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures
News media coverage of conflict and cooperation dynamics of water events in the Lancang–Mekong River basin
Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management
Using paleoclimate reconstructions to analyse hydrological epochs associated with Pacific decadal variability
Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves
Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India
Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods
A risk assessment methodology to evaluate the risk failure of managed aquifer recharge in the Mediterranean Basin
A coupled stochastic rainfall–evapotranspiration model for hydrological impact analysis
Real-time updating of the flood frequency distribution through data assimilation
Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors
The cost of ending groundwater overdraft on the North China Plain
Definition of efficient scarcity-based water pricing policies through stochastic programming
A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties
Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology
Determining spatial variability of dry spells: a Markov-based method, applied to the Makanya catchment, Tanzania
Streamflow droughts in the Iberian Peninsula between 1945 and 2005: spatial and temporal patterns
Estimating the flood frequency distribution at seasonal and annual time scales
Domestic wells have high probability of pumping septic tank leachate
Record extension for short-gauged water quality parameters using a newly proposed robust version of the Line of Organic Correlation technique
Calibration of the modified Bartlett-Lewis model using global optimization techniques and alternative objective functions
Trend analysis of extreme precipitation in the Northwestern Highlands of Ethiopia with a case study of Debre Markos
Zach Perzan, Gordon Osterman, and Kate Maher
Hydrol. Earth Syst. Sci., 27, 969–990, https://doi.org/10.5194/hess-27-969-2023, https://doi.org/10.5194/hess-27-969-2023, 2023
Short summary
Short summary
In this study, we simulate flood managed aquifer recharge – the process of intentionally inundating land to replenish depleted aquifers – at a site imaged with geophysical equipment. Results show that layers of clay and silt trap recharge water above the water table, where it is inaccessible to both plants and groundwater wells. Sensitivity analyses also identify the main sources of uncertainty when simulating managed aquifer recharge, helping to improve future forecasts of site performance.
Qifen Yuan, Thordis L. Thorarinsdottir, Stein Beldring, Wai Kwok Wong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 25, 5259–5275, https://doi.org/10.5194/hess-25-5259-2021, https://doi.org/10.5194/hess-25-5259-2021, 2021
Short summary
Short summary
Localized impacts of changing precipitation patterns on surface hydrology are often assessed at a high spatial resolution. Here we introduce a stochastic method that efficiently generates gridded daily precipitation in a future climate. The method works out a stochastic model that can describe a high-resolution data product in a reference period and form a realistic precipitation generator under a projected future climate. A case study of nine catchments in Norway shows that it works well.
Rasmus Bødker Madsen, Hyojin Kim, Anders Juhl Kallesøe, Peter B. E. Sandersen, Troels Norvin Vilhelmsen, Thomas Mejer Hansen, Anders Vest Christiansen, Ingelise Møller, and Birgitte Hansen
Hydrol. Earth Syst. Sci., 25, 2759–2787, https://doi.org/10.5194/hess-25-2759-2021, https://doi.org/10.5194/hess-25-2759-2021, 2021
Short summary
Short summary
The protection of subsurface aquifers from contamination is an ongoing environmental challenge. Some areas of the underground have a natural capacity for reducing contaminants. In this research these areas are mapped in 3D along with information about, e.g., sand and clay, which indicates whether contaminated water from the surface will travel through these areas. This mapping technique will be fundamental for more reliable risk assessment in water quality protection.
Jing Wei, Yongping Wei, Fuqiang Tian, Natalie Nott, Claire de Wit, Liying Guo, and You Lu
Hydrol. Earth Syst. Sci., 25, 1603–1615, https://doi.org/10.5194/hess-25-1603-2021, https://doi.org/10.5194/hess-25-1603-2021, 2021
Richard Arsenault and Pascal Côté
Hydrol. Earth Syst. Sci., 23, 2735–2750, https://doi.org/10.5194/hess-23-2735-2019, https://doi.org/10.5194/hess-23-2735-2019, 2019
Short summary
Short summary
Hydrological forecasting allows hydropower system operators to make the most efficient use of the available water as possible. Accordingly, hydrologists have been aiming at improving the quality of these forecasts. This work looks at the impacts of improving systematic errors in a forecasting scheme on the hydropower generation using a few decision-aiding tools that are used operationally by hydropower utilities. We find that the impacts differ according to the hydropower system characteristics.
Lanying Zhang, George Kuczera, Anthony S. Kiem, and Garry Willgoose
Hydrol. Earth Syst. Sci., 22, 6399–6414, https://doi.org/10.5194/hess-22-6399-2018, https://doi.org/10.5194/hess-22-6399-2018, 2018
Short summary
Short summary
Analyses of run lengths of Pacific decadal variability (PDV) suggest that there is no significant difference between run lengths in positive and negative phases of PDV and that it is more likely than not that the PDV run length has been non-stationary in the past millennium. This raises concerns about whether variability seen in the instrumental record (the last ~100 years), or even in the shorter 300–400 year paleoclimate reconstructions, is representative of the full range of variability.
William H. Farmer, Thomas M. Over, and Julie E. Kiang
Hydrol. Earth Syst. Sci., 22, 5741–5758, https://doi.org/10.5194/hess-22-5741-2018, https://doi.org/10.5194/hess-22-5741-2018, 2018
Short summary
Short summary
This work observes that the result of streamflow simulation is often biased, especially with regards to extreme events, and proposes a novel technique to reduce this bias. By using parallel simulations of relative streamflow timing (sequencing) and the distribution of streamflow (magnitude), severe biases can be mitigated. Reducing this bias allows for improved utility of streamflow simulation for water resources management.
Arun Ravindranath, Naresh Devineni, Upmanu Lall, and Paulina Concha Larrauri
Hydrol. Earth Syst. Sci., 22, 5125–5141, https://doi.org/10.5194/hess-22-5125-2018, https://doi.org/10.5194/hess-22-5125-2018, 2018
Short summary
Short summary
We present a framework for forecasting water storage requirements in the agricultural sector and an application of this framework to water risk assessment in India. Our framework involves defining a crop-specific water stress index and applying a particular statistical forecasting model to predict seasonal water stress for the crop of interest. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra.
Adrian A. S. Barfod, Ingelise Møller, Anders V. Christiansen, Anne-Sophie Høyer, Júlio Hoffimann, Julien Straubhaar, and Jef Caers
Hydrol. Earth Syst. Sci., 22, 3351–3373, https://doi.org/10.5194/hess-22-3351-2018, https://doi.org/10.5194/hess-22-3351-2018, 2018
Short summary
Short summary
Three-dimensional geological models are important to securing and managing groundwater. Such models describe the geological architecture, which is used for modeling the flow of groundwater. Common geological modeling approaches result in one model, which does not quantify the architectural uncertainty of the geology.
We present a comparison of three different state-of-the-art stochastic multiple-point statistical methods for quantifying the geological uncertainty using real-world datasets.
Paula Rodríguez-Escales, Arnau Canelles, Xavier Sanchez-Vila, Albert Folch, Daniel Kurtzman, Rudy Rossetto, Enrique Fernández-Escalante, João-Paulo Lobo-Ferreira, Manuel Sapiano, Jon San-Sebastián, and Christoph Schüth
Hydrol. Earth Syst. Sci., 22, 3213–3227, https://doi.org/10.5194/hess-22-3213-2018, https://doi.org/10.5194/hess-22-3213-2018, 2018
Short summary
Short summary
In this work, we have developed a methodology to evaluate the failure risk of managed aquifer recharge, and we have applied it to six different facilities located in the Mediterranean Basin. The methodology was based on the development of a probabilistic risk assessment based on fault trees. We evaluated both technical and non-technical issues, the latter being more responsible for failure risk.
Minh Tu Pham, Hilde Vernieuwe, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 22, 1263–1283, https://doi.org/10.5194/hess-22-1263-2018, https://doi.org/10.5194/hess-22-1263-2018, 2018
Short summary
Short summary
In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Still, the developed model has great potential for hydrological impact analysis.
Cristina Aguilar, Alberto Montanari, and María-José Polo
Hydrol. Earth Syst. Sci., 21, 3687–3700, https://doi.org/10.5194/hess-21-3687-2017, https://doi.org/10.5194/hess-21-3687-2017, 2017
Short summary
Short summary
Assuming that floods are driven by both short- (meteorological forcing) and long-term perturbations (higher-than-usual moisture), we propose a technique for updating a season in advance the flood frequency distribution. Its application in the Po and Danube rivers helped to reduce the uncertainty in the estimation of floods and thus constitutes a promising tool for real-time management of flood risk mitigation. This study is the result of the stay of the first author at the University of Bologna.
Veit Blauhut, Kerstin Stahl, James Howard Stagge, Lena M. Tallaksen, Lucia De Stefano, and Jürgen Vogt
Hydrol. Earth Syst. Sci., 20, 2779–2800, https://doi.org/10.5194/hess-20-2779-2016, https://doi.org/10.5194/hess-20-2779-2016, 2016
Claus Davidsen, Suxia Liu, Xingguo Mo, Dan Rosbjerg, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 20, 771–785, https://doi.org/10.5194/hess-20-771-2016, https://doi.org/10.5194/hess-20-771-2016, 2016
Short summary
Short summary
In northern China, rivers run dry and groundwater tables drop, causing economic losses for all water use sectors. We present a groundwater-surface water allocation decision support tool for cost-effective long-term recovery of an overpumped aquifer. The tool is demonstrated for a part of the North China Plain and can support the implementation of the recent China No. 1 Document in a rational and economically efficient way.
H. Macian-Sorribes, M. Pulido-Velazquez, and A. Tilmant
Hydrol. Earth Syst. Sci., 19, 3925–3935, https://doi.org/10.5194/hess-19-3925-2015, https://doi.org/10.5194/hess-19-3925-2015, 2015
Short summary
Short summary
One of the most promising alternatives to improve the efficiency in water usage is the implementation of scarcity-based pricing policies based on the opportunity cost of water at the basin scale. Time series of the marginal value of water at selected locations (reservoirs) are obtained using a stochastic hydro-economic model and then post-processed to define step water pricing policies.
C. Dong, Q. Tan, G.-H. Huang, and Y.-P. Cai
Hydrol. Earth Syst. Sci., 18, 1793–1803, https://doi.org/10.5194/hess-18-1793-2014, https://doi.org/10.5194/hess-18-1793-2014, 2014
F. Lombardo, E. Volpi, D. Koutsoyiannis, and S. M. Papalexiou
Hydrol. Earth Syst. Sci., 18, 243–255, https://doi.org/10.5194/hess-18-243-2014, https://doi.org/10.5194/hess-18-243-2014, 2014
B. M. C. Fischer, M. L. Mul, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 17, 2161–2170, https://doi.org/10.5194/hess-17-2161-2013, https://doi.org/10.5194/hess-17-2161-2013, 2013
J. Lorenzo-Lacruz, E. Morán-Tejeda, S. M. Vicente-Serrano, and J. I. López-Moreno
Hydrol. Earth Syst. Sci., 17, 119–134, https://doi.org/10.5194/hess-17-119-2013, https://doi.org/10.5194/hess-17-119-2013, 2013
E. Baratti, A. Montanari, A. Castellarin, J. L. Salinas, A. Viglione, and A. Bezzi
Hydrol. Earth Syst. Sci., 16, 4651–4660, https://doi.org/10.5194/hess-16-4651-2012, https://doi.org/10.5194/hess-16-4651-2012, 2012
J. E. Bremer and T. Harter
Hydrol. Earth Syst. Sci., 16, 2453–2467, https://doi.org/10.5194/hess-16-2453-2012, https://doi.org/10.5194/hess-16-2453-2012, 2012
B. Khalil and J. Adamowski
Hydrol. Earth Syst. Sci., 16, 2253–2266, https://doi.org/10.5194/hess-16-2253-2012, https://doi.org/10.5194/hess-16-2253-2012, 2012
W. J. Vanhaute, S. Vandenberghe, K. Scheerlinck, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 16, 873–891, https://doi.org/10.5194/hess-16-873-2012, https://doi.org/10.5194/hess-16-873-2012, 2012
H. Shang, J. Yan, M. Gebremichael, and S. M. Ayalew
Hydrol. Earth Syst. Sci., 15, 1937–1944, https://doi.org/10.5194/hess-15-1937-2011, https://doi.org/10.5194/hess-15-1937-2011, 2011
Cited articles
Ahmadi, M., Etedali, H. R., and Elbeltagi, A.: Evaluation of the effect of climate change on maize water footprint under RCPs scenarios in Qazvin plain, Iran, Agr. Water Manage., 254, 106969, https://doi.org/10.1016/j.agwat.2021.106969, 2021.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration-Guidelines for computing crop water requirements-FAO
Irrigation and drainage paper 56, 300, FAO, Rome, Italy, ISBN 9251042195, 1998.
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L.,
Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon
emission limits required to satisfy future representative concentration
pathways of greenhouse gases, Geophys. Res. Lett., 38, 387–404, https://doi.org/10.1029/2010GL046270, 2011.
Arunrat, N., Pumijumnong, N., Sereenonchai, S., Chareonwong, U., and Wang,
C.: Assessment of climate change impact on rice yield and water footprint of
large-scale and individual farming in Thailand, Sci. Total Environ., 726,
137864, https://doi.org/10.1016/j.scitotenv.2020.137864, 2020.
Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane,
A. C., Boote, K. J., Thorburn, P. J., Rötter, R. P., Cammarano, D.,
Brisson, N., Basso, B., Martre, P., Aggarwal, P. K., Angulo, C., Bertuzzi,
P., Biernath, C., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R.,
Grant, R., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R.
C., Kersebaum, K. C., Müller, C., Naresh Kumar, C., Nendel, C., O'Leary,
G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D.,
Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C., Stratonovitch,
P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., Wallach, D.,
White, J. W., Williams, J. R., and Wolf, J.: Uncertainty in simulating wheat
yields under climate change, Nat. Clim. Chang., 3, 827–832, https://doi.org/10.1038/nclimate1916, 2013.
Bai, T. and Gao, J.: Optimization of the nitrogen fertilizer schedule of
maize under drip irrigation in Jilin, China, based on DSSAT and GA, Agr.
Water Manage., 244, 106555, https://doi.org/10.1016/j.agwat.2020.106555, 2021.
Batjes, N.: ISRIC-WISE Derived Soil Properties on a 5 by 5 Arc-Minutes
Global Grid (Ver. 1.2), ISRIC, Wageningen, the Netherlands, https://www.isric.org (last access: 19 August 2022), 2012.
Bowes, G.: Facing the Inevitable: Plants and Increasing Atmospheric CO2,
Annu. Rev. Plant Phys. Plant Mol. Biol., 44, 309–332, https://doi.org/10.1146/annurev.pp.44.060193.001521, 1993.
CCAFS: CCAFS-Climate Statistically Downscaled Delta Method data, Climate
Change, Agriculture and Food Security, http://www.ccafs-climate.org (last access: 10 August 2022), 2015.
CEDA: Climatic Research Unit (CRU) time-series datasets of variations in
climate with variations in other phenomena, NCAS British Atmospheric Data
Centre, https://data.ceda.ac.uk/ (last access: 25 June 2022), 2018.
Chukalla, A. D., Krol, M. S., and Hoekstra, A. Y.: Green and blue water footprint reduction in irrigated agriculture: effect of irrigation techniques, irrigation strategies and mulching, Hydrol. Earth Syst. Sci., 19, 4877–4891, https://doi.org/10.5194/hess-19-4877-2015, 2015.
CIDDC: China Irrigation and Drainage Development Center, China, http://www.jsgg.com.cn/temp/Index/Display.asp?NewsID=12313,
last access: 14 April 2022.
Dai, C., Qin, X. S., Lu, W. T., and Huang, Y.: Assessing adaptation measures
on agricultural water productivity under climate change: A case study of
Huai River Basin, China, Sci. Total Environ., 721, 137777, https://doi.org/10.1016/j.scitotenv.2020.137777, 2020.
Delworth, T. L., Broccoli, A. J., Rosati, A., Stouffer, R. J., Balaji, V.,
Beesley, J. A., Cooke, W. F., Dixon, K. W., Dunne, J., Dunne, K. A.,
Durachta, J. W., Findell, K. L., Ginoux, P., Gnanadesikan, A., Gordon, C.
T., Griffies, S. M., Gudgel, R., Harrison, M. J., Held, I. M., Hemler, R.
S., Horowitz, L. W., Klein, S. A., Knutson, T. R., Kushner, P. J.,
Langenhorst, A. R., Lee, H. C., Lin, S. J., Lu, J., Malyshev, S. L., Milly,
P. C. D., Ramaswamy, V., Russell, J., Schwarzkopf, M. D., Shevliakova, E.,
Sirutis, J. J., Spelman, M. J., Stern, W. F., Winton, M., Wittenberg, A. T.,
Wyman, B., Zeng, F., and Zhang, R.: GFDL's CM2 Global Coupled Climate
Models. Part I: Formulation and Simulation Characteristics, J. Climate, 19,
643–674, https://doi.org/10.1175/JCLI3629.1, 2006.
Dijkshoorn, J. A., Engelen, V. W. P. V., and Huting, J. R. M.: Soil and
landform properties for LADA partner countries (Argentina, China, Cuba,
Senegal, South Africa and Tunisia), ISRIC–World Soil Information and FAO,
Wageningen, the Netherlands, https://doi.org/10.13031/2013.42676, 2008.
Donner, L. J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao,
M., Golaz, J. C., Ginoux, P., Lin, S. J., Schwarzkopf, M. D., Austin, J.,
Alaka, G., Cooke, W. F., Delworth, T. L., Freidenreich, S. M., Gordon, C.
T., Griffies, S. M., Held, I. M., Hurlin, W. J., Klein, S. A., Knutson, T.
R., Langenhorst, A. R., Lee, H. C., Lin, Y., Magi, B. I., Malyshev, S. L.,
Milly, P. C. D., Naik, V., Nath, M. J., Pincus, R., Ploshay, J. J.,
Ramaswamy, V., Seman, C. J., Shevliakova, E., Sirutis, J. J., Stern, W. F.,
Stouffer, R. J., Wilson, R. J., Winton, M., Wittenberg, A. T., and Zeng, F.:
The Dynamical Core, Physical Parameterizations, and Basic Simulation
Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled
Model CM3, J. Climate, 24, 3484–3519, https://doi.org/10.1175/2011JCLI3955.1, 2011.
Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., Noblet, N. D., Duvel, J. P., Ethé, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J. Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M. P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1, 2013.
Fader, M., Rost, S., Müller, C., Bondeau, A., and Gerten, D.: Virtual
water content of temperate cereals and maize: Present and potential future
patterns, J. Hydrol., 384, 218–231, https://doi.org/10.1016/j.jhydrol.2009.12.011, 2010.
FAO: FAOSTAT on-line database, Food and Agriculture Organization of the
United Nation, Rome, Italy, http://www.fao.org/faostat/en/#data/QC (last access: 1 August 2022), 2021.
Garofalo, P., Ventrella, D., Kersebaum, K. C., Gobin, A., Trnka, M., Giglio,
L., Dubrovský, M., and Castellini, M.: Water footprint of winter wheat
under climate change: Trends and uncertainties associated to the ensemble of
crop models, Sci. Total Environ., 658, 1186–1208, https://doi.org/10.1016/j.scitotenv.2018.12.279, 2019.
Guo, H., Li, S., Wong, F. L., Qin, S., Wang, Y., Yang, D., and Lam, H. M.:
Drivers of carbon flux in drip irrigation maize fields in northwest China,
Carbon Balance Manag., 16, 12, https://doi.org/10.1186/s13021-021-00176-5, 2021.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hatfield, J. L. and Dold, C.: Water-Use Efficiency: Advances and Challenges
in a Changing Climate, Front. Plant Sci., 10, 103, https://doi.org/10.3389/fpls.2019.00103, 2019.
Hoekstra, A. Y. (Ed.): Virtual water trade: Proceedings of the International
Expert Meeting on Virtual Water Trade, 12–13 December 2002, Delft, the Netherlands, Value of Water Research Report Series No. 12, UNESCO-IHE,
Delft, the Netherlands, 244 pp., ISSN 02731223, 2003.
Hoekstra, A. Y.: The water footprint of modern consumer society, Routledge,
London, UK, 208 pp., ISBN 9781849713030, 2013.
Hoekstra, A. Y.: Sustainable, efficient, and equitable water use: the three
pillars under wise freshwater allocation, WIREs Water, 1, 31–40, https://doi.org/10.1002/wat2.1000, 2014.
Hoekstra, A. Y., Chapagain, A. K., Aldaya, M. M., and Mekonnen, M. M.: The
Water Footprint Assessment Manual: Setting the Global Standard, Earthscan,
London, UK, ISBN 9781849712798, 2011.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb,
W. H., Long, M. C., Mahowald, M., Marsh, D. R., Neale, R. B., Rasch, P.,
Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl,
J., and Marshall, S.: The Community Earth System Model: A Framework for
Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013.
IPCC: Summary for Policymakers, in: Climate Change 2021: The Physical
Science Basis. Contribution of Working Group I to the Sixth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by:
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C.,
Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. L., Huang, M.,
Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield,
T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 3–32, https://doi.org/10.1017/9781009157896, 2021.
Jans, Y., von Bloh, W., Schaphoff, S., and Müller, C.: Global cotton production under climate change – Implications for yield and water consumption, Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, 2021.
Kappelle, M.: WMO Statement on the State of the Global Climate in 2019,
World Meteorological Organization, Geneva, Switzerland, ISBN 9789262112485, 2020.
Karandish, F., Nouri, H., and Schyns, J. F.: Agricultural adaptation to
reconcile food security and water sustainability under climate change: the
case of cereals in Iran, Earths Future, 10, e2021EF002095,
https://doi.org/10.1029/2021EF002095, 2022.
Konapala, G., Mishra, A. K., Wada, Y., and Mann, M. E.: Climate change will
affect global water availability through compounding changes in seasonal
precipitation and evaporation, Nat. Commun., 11, 3044, https://doi.org/10.1038/s41467-020-16757-w, 2020.
Li, H., Mei, X., Nangia, V., Guo, R., Liu, Y., Hao, W., and Wang, J.:
Effects of different nitrogen fertilizers on the yield, water- and
nitrogen-use efficiencies of drip-fertigated wheat and maize in the North
China Plain, Agr. Water Manage., 243, 106474, https://doi.org/10.1016/j.agwat.2020.106474, 2021.
Liu, X., Li, C., Zhao, T., and Han, L.: Future changes of global potential
evapotranspiration simulated from CMIP5 to CMIP6 models, Atmos. Ocean. Sci.
Lett., 13, 568–575, https://doi.org/10.1080/16742834.2020.1824983, 2020.
Lobell, D. B. and Gourdji, S. M.: The influence of climate change on global
crop productivity, Plant Physiol., 160, 1686–1697, https://doi.org/10.1104/pp.112.208298, 2012.
Mali, S. S., Shirsath, P. B., and Islam, A.: A high-resolution assessment of
climate change impact on water footprints of cereal production in India,
Sci. Rep., 11, 8715, https://doi.org/10.1038/s41598-021-88223-6, 2021.
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.:
Intercomparison makes for a better climate model, Eos. Trans. Amer. Geophys.
Union, 78, 445–451, https://doi.org/10.1029/97EO00276, 1997.
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: The
Coupled Model Intercomparison Project (CMIP), B. Am. Meteorol. Soc., 81,
313–318, https://doi.org/10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2, 2000.
Mekonnen, M. M. and Hoekstra, A. Y.: A global and high-resolution assessment of the green, blue and grey water footprint of wheat, Hydrol. Earth Syst. Sci., 14, 1259–1276, https://doi.org/10.5194/hess-14-1259-2010, 2010.
Mekonnen, M. M. and Hoekstra, A. Y.: The green, blue and grey water footprint of crops and derived crop products, Hydrol. Earth Syst. Sci., 15, 1577–1600, https://doi.org/10.5194/hess-15-1577-2011, 2011.
Mekonnen, M. M. and Hoekstra, A. Y.: Water footprint benchmarks for crop
production: A first global assessment, Ecol. Indic., 46, 214–223,
https://doi.org/10.1016/j.ecolind.2014.06.013, 2014.
Mialyk, O., Schyns, J. F., Booij, M. J., and Hogeboom, R. J.: Historical simulation of maize water footprints with a new global gridded crop model ACEA, Hydrol. Earth Syst. Sci., 26, 923–940, https://doi.org/10.5194/hess-26-923-2022, 2022.
Middleton, N. and Thomas, D. S. G.: World atlas of desertification, Arnold,
London, UK, ISBN 0340691662, 1997.
Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J.,
Elgizouli, I., Emori, S., Erda, L., Hibbard, K., Jones, R., Kainuma, M.,
Kelleher, J., Lamarque, J. F., Manning, M., Matthews, B., Meehl, J., Meyer,
L., Mitchell, J., Nakicenovic, N., O'Neill, B., Pichs, R., Riahi, K., Rose,
S., Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks, T., van
Ypersele, J. P., and Zurek, M.: Towards New Scenarios for Analysis of
Emissions, Climate Change, Impacts, and Response Strategies, IPCC Expert
Meeting Report, 19–21 September, 2007, Noordwijkerhout, the Netherlands,
Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 132 pp., 2008.
Müller, C., Franke, J., Jägermeyr, J., Ruane, A. C., Elliott, J.,
Moyer, E., Heinke, J., Falloon, P. D., Folberth, C., Francois, L., Hank, T.,
César Izaurralde, R., Jacquemin, I., Liu, W., Olin, S., Pugh, T. A. M.,
Williams, K., and Zabel, F.: Exploring uncertainties in global crop yield
projections in a large ensemble of crop models and CMIP5 and CMIP6 climate
scenarios, Environ. Res. Lett., 16, 034040, https://doi.org/10.1088/1748-9326/abd8fc, 2021.
Myers, S. S., Smith, M. R., Guth, S., Golden, C. D., Vaitla, B., Mueller, N.
D., Dangour, A. D., and Huybers, P.: Climate Change and Global Food Systems:
Potential Impacts on Food Security and Undernutrition, Annu. Rev. Publ.
Health, 38, 259–277, https://doi.org/10.1146/annurev-publhealth-031816-044356, 2017.
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., and
Ramirez-Villegas, J.: High-resolution and bias-corrected CMIP5 projections
for climate change impact assessments, Sci. Data, 7, 7, https://doi.org/10.1038/s41597-019-0343-8, 2020.
NBSC: National Data, China, National Bureau of Statistics, Beijing, China,
https://data.stats.gov.cn/ (last access: 12 July 2022), 2021.
NOAA: National Oceanic and Atmospheric Administration, U.S., https://www.esrl.noaa.gov (last access: 10 April 2022), 2018.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Pastor, A. V., Palazzo, A., Havlik, P., Biemans, H., Wada, Y., Obersteiner,
M., Kabat, P., and Ludwig, F.: The global nexus of food–trade–water
sustaining environmental flows by 2050, Nat. Sustain., 2, 499–507,
https://doi.org/10.1038/s41893-019-0287-1, 2019.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global monthly
irrigated and rainfed crop areas around the year 2000: A new high-resolution
data set for agricultural and hydrological modelling, Global Biogeochem.
Cy., 24, GB1011, https://doi.org/10.1029/2008gb003435, 2010.
Qiao, F., Song, Z., Bao, Y., Song, Y., Shu, Q., Huang, C., and Zhao, W.:
Development and evaluation of an Earth System Model with surface gravity
waves, J. Geophys. Res.-Oceans, 118, 4514–4524, https://doi.org/10.1002/jgrc.20327, 2013.
Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: Reference manual,
Chapter 2, AquaCrop model, Version 6.0, Food and Agriculture Organization of
the United Nations, Rome, Italy, 314 pp., https://www.fao.org/aquacrop/resources/referencemanuals/en/ (last access: 20 August 2022), 2017.
Rallison, R. E.: Origin and evolution of the SCS runoff equation, in:
Symposium on Watershed Management, 21–23 July 1980, Boise, Idaho, United States, 912–924, ISBN 9780872622500, 1980.
Riahi, K., Gruebler, A., and Nakicenovic, N.: Scenarios of long-term
socio-economic and environmental development under climate stabilization,
Technol. Forecast. Soc. Chang., 74, 887–935, https://doi.org/10.1016/j.techfore.2006.05.026, 2007.
Rosa, L., Chiarelli, D. D., Sangiorgio, M., Beltran-Peña, A. A., Rulli,
M. C., D'Odorico, P., and Fung, I.: Potential for sustainable irrigation
expansion in a 3 ∘C warmer climate, P. Natl. Acad. Sci. USA, 117, 29526–29534, https://doi.org/10.1073/pnas.2017796117, 2020.
Schmidt, G. A., Ruedy, R., Hansen, J. E., Aleinov, I., Bell, N., Bauer, M.,
Bauer, S., Cairns, B., Canuto, V., Cheng, Y., Delgenio, A. D., Faluvegi, G.,
Friend, A. D., Hall, T. M., Hu, Y., Kelley, M., Kiang, N. Y., Koch, D.,
Lacis, A. A., Lerner, J., Lo, K. K., Miller, R. L., Nazarenko, L., Oinas,
V., Perlwitz, J., Perlwitz, J., Rind, D., Romanou, A., Russell, G. L., Sato,
M., Shindell, D. T., Stone, P. H., Sun, S., Tausnev, N., Thresher, D., and
Yao, M. S.: Present-Day Atmospheric Simulations Using GISS ModelE:
Comparison to In Situ, Satellite, and Reanalysis Data, J. Climate, 19,
153–192, https://doi.org/10.1175/JCLI3612.1, 2006.
Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L.,
Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., Canuto, V.,
Chen, Y. H., Cheng, Y., Clune, T. L., Genio, A. D., Fainchtein, R. D.,
Faluvegi, G., Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A.
A., LeGrande, A. N., Lerner, J., Lo, K. K., Matthews, E. E., Menon, S.,
Miller, R. L., Oinas, V., Oloso, A. O., Perlwitz, J. P., Puma, M. J.,
Putman, W. M., Rind, D., Romanou, A., Sato, M., Shindell, D. T., Sun, S.,
Syed, R. A., Tausnev, N., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao,
M. S., and Zhang, J.: Configuration and assessment of the GISS ModelE2
contributions to the CMIP5 archive, J. Adv. Model. Earth Syst., 6, 141–184,
https://doi.org/10.1002/2013MS000265, 2014.
Schyns, J. F., Hogeboom, R. J., and Krol, M. S.: 4 - Water Footprint
Assessment: towards water-wise food systems, in: Food Systems Modelling,
edited by: Peters, C. and Thilmany, D., Academic Press, Salt Lake City,
USA, 63–88, https://doi.org/10.1016/B978-0-12-822112-9.00006-0, 2022.
Semenov, M. A. and Stratonovitch, P.: Use of multi-model ensembles from
global climate models for assessment of climate change impacts, Clim. Res.,
41, 1–14, https://doi.org/10.3354/cr00836, 2010.
Tian, Y., Ruth, M., Zhu, D., Ding, J., and Morris, N.: A Sustainability
Assessment of Five Major Food Crops' Water Footprints in China from 1978 to
2010, Sustainability, 11, 1–20, https://doi.org/10.3390/su11216179, 2019.
Trnka, M., Feng, S., Semenov, M. A., Olesen, J. E., Kersebaum, K. C.,
Rötter, R. P., Semerádová, D., Klem, K., Huang, W., Ruiz-Ramos,
M., Hlavinka, P., Meitner, J., Balek, J., Havlík, P., and Büntgen,
U.: Mitigation efforts will not fully alleviate the increase in water
scarcity occurrence probability in wheat-producing areas, Sci. Adv., 5,
eaau2406, https://doi.org/10.1126/sciadv.aau2406, 2019.
USDA: Estimation of direct runoff from storm rainfall, Section 4 Hydrology,
chap. 4, National Engineering Handbook, Washington DC, USA, 1–24, ISBN 109993057231, 1964.
van Vuuren, D. P., den Elzen, M. G. J., Lucas, P. L., Eickhout, B.,
Strengers, B. J., van Ruijven, B., Wonink, S., and van Houdt, R.:
Stabilizing greenhouse gas concentrations at low levels: an assessment of
reduction strategies and costs, Clim. Change, 81, 119–159, https://doi.org/10.1007/s10584-006-9172-9, 2007.
von Salzen, K., Scinocca, J. F., McFarlane, N. A., Li, J., Cole, J. N. S.,
Plummer, D., Verseghy, D., Reader, M. C., Ma, X., Lazare, M., and Solheim,
L.: The Canadian Fourth Generation Atmospheric Global Climate Model
(CanAM4). Part I: Representation of Physical Processes, Atmos.-Ocean,
51, 104–125, https://doi.org/10.1080/07055900.2012.755610, 2013.
Wang, B., Feng, P., Liu, D. L., O'Leary, G. J., Macadam, I., Waters, C.,
Asseng, S., Cowie, A., Jiang, T., Xiao, D., Ruan, H., He, J., and Yu, Q.:
Sources of uncertainty for wheat yield projections under future climate are
site-specific, Nat. Food., 1, 720–728, https://doi.org/10.1038/s43016-020-00181-w, 2020.
Wang, C., Guo, L., Li, Y., and Wang, Z.: Systematic Comparison of C3 and C4 Plants Based on Metabolic Network Analysis, BMC Syst Biol., 6, S9,
https://doi.org/10.1186/1752-0509-6-S2-S9, 2012.
Wang, J., Gao, S., Xu, Y., and Wang, H.: Application and Existing Problems
of Drip Irrigation for Wheat in Xinjiang, in: 2011 International Conference
on Agricultural and Natural Resources Engineering (ANRE 2011), Intelligent
Information Technology Application Association, 3 July 2011, Singapore,
25–29, 2011.
Wang, W., Zhuo, L., Li, M., Liu, Y., and Wu, P.: The Effect of Development
in Water-Saving Irrigation Techniques on Spatial-Temporal Variations in Crop
Water Footprint and Benchmarking, J. Hydrol., 577, 123916, https://doi.org/10.1016/j.jhydrol.2019.123916, 2019.
Xiao, D., Liu, D. L., Wang, B., Feng, P., Bai, H., and Tang, J.: Climate
change impact on yields and water use of wheat and maize in the North China
Plain under future climate change scenarios, Agr. Water Manage., 238,
106238, https://doi.org/10.1016/j.agwat.2020.106238, 2020.
Xu, Z., Chen, X., Wu, S. R., Gong, M., Du, Y., Wang, J., Li, Y., and Liu,
J.: Spatial-temporal assessment of water footprint, water scarcity and crop
water productivity in a major crop production region, J. Clean. Prod., 224,
375–383, https://doi.org/10.1016/j.jclepro.2019.03.108, 2019.
Yoon, P. R. and Choi, J. Y.: Efects of shift in growing season due to
climate change on rice yield and crop water requirements, Paddy Water
Environ., 18, 291–307, https://doi.org/10.1007/s10333-019-00782-7, 2020.
Zain, M., Si, Z., Chen, J., Mehmood, F., Rahman, S. U., Shah, A. N., Li, S.,
Gao, Y., and Duan, A.: Suitable nitrogen application mode and lateral
spacing for drip-irrigated winter wheat in North China Plain, PLoS One, 16, e0260008, https://doi.org/10.1371/journal.pone.0260008, 2021.
Zheng, J., Wang, W., Ding, Y., Liu, G., Xing, W., Cao, X., and Chen, D.:
Assessment of climate change impact on the water footprint in rice
production: Historical simulation and future projections at two
representative rice cropping sites of China, Sci. Total Environ., 709,
136190, https://doi.org/10.1016/j.scitotenv.2019.136190, 2020.
Zhuo, L., Mekonnen, M. M., and Hoekstra, A. Y.: Sensitivity and uncertainty in crop water footprint accounting: a case study for the Yellow River basin, Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, 2014.
Zhuo, L., Mekonnen, M. M., and Hoekstra, A. Y.: Benchmark levels for the consumptive water footprint of crop production for different environmental conditions: a case study for winter wheat in China, Hydrol. Earth Syst. Sci., 20, 4547–4559, https://doi.org/10.5194/hess-20-4547-2016, 2016a.
Zhuo, L., Mekonnen, M. M., Hoekstra, A. Y., and Wada, Y.: Inter- and
intra-annual variation of water footprint of crops and blue water scarcity
in the Yellow River basin (1961–2009), Adv. Water Resour., 87, 29–41,
https://doi.org/10.1016/j.advwatres.2015.11.002, 2016b.
Zhuo, L., Mekonnen, M. M., and Hoekstra, A. Y.: The effect of inter-annual
variability of consumption, production, trade and climate on crop-related
green and blue water footprints and inter-regional virtual water trade: A
study for China (1978–2008), Water Res., 94, 73–85, https://doi.org/10.1016/j.watres.2016.02.037, 2016c.
Zhuo, L., Mekonnen, M. M., and Hoekstra, A. Y.: Consumptive water footprint
and virtual water trade scenarios for China — With a focus on crop
production, consumption and trade, Environ. Int., 94, 211–223, https://doi.org/10.1016/j.envint.2016.05.019, 2016d.
Zhuo, L., Liu, Y., Yang, H., Hoekstra, A. Y., Liu, W., Cao, X., Wang, M.,
and Wu, P.: Water for maize for pigs for pork: An analysis of
inter-provincial trade in China, Water Res., 166, 115074, https://doi.org/10.1016/j.watres.2019.115074, 2019.
Short summary
Facing the increasing challenge of sustainable crop supply with limited water resources due to climate change, large-scale responses in the water footprint (WF) and WF benchmarks of crop production remain unclear. Here, we quantify the effects of future climate change scenarios on the WF and WF benchmarks of maize and wheat in time and space in China. Differences in crop growth between rain-fed and irrigated farms and among furrow-, sprinkler-, and micro-irrigated regimes are identified.
Facing the increasing challenge of sustainable crop supply with limited water resources due to...
