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Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (5): 521-536    DOI: 10.1007/s40333-022-0094-9
Research article     
Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios
YAO Linlin1,2,3, ZHOU Hongfei1,2,*(), YAN Yingjie1,2,3, LI Lanhai1,3,4,5,6, SU Yuan1,2,3
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Fukang National Field Scientific Observation and Research Station for Desert Ecosystems, Chinese Academy of Sciences, Fukang 831505, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Ili Station for Watershed Ecosystem Research, Chinese Academy of Sciences, Xinyuan 835800, China
5CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
6Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone, Urumqi 830011, China
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Abstract  

Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015-2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046-2075, but degenerates during 2015-2044 and 2076-2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.

Key words： hydrothermal condition      water-thermal product index      bias correction and spatial disaggregation      SSP-RCP scenarios      Central Asia     
Received: 22 December 2021      Published: 31 May 2022
Fund:  the Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) of China(XDA2004030202);Shanghai Cooperation and the Organization Science and Technology Partnership of China(2021E01019)
Corresponding Authors: *: ZHOU Hongfei (E-mail: zhouhongfei_ucas@163.com)
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YAO Linlin
ZHOU Hongfei
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LI Lanhai
SU Yuan
Cite this article:

YAO Linlin, ZHOU Hongfei, YAN Yingjie, LI Lanhai, SU Yuan. Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios. Journal of Arid Land, 2022, 14(5): 521-536.

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http://jal.xjegi.com/10.1007/s40333-022-0094-9     OR     http://jal.xjegi.com/Y2022/V14/I5/521

Fig. 1 Overview of the study area. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Index Description Equation Optimum value
Bias Bias between the historical MMEs and reference data $Bias=\frac{\sum^{i=1}_{n}MME_{hist,i}-obs_{hist,i}}{n}$ 0.0
Relative bias
(RB)		 The ratio of bias to historical reference data $RB=\frac{Bias}{obs_{hist}}$ 0.0
Time correlation (TC) Time correlation between the historical MMEs and reference data $TC=\frac{\sum^{n}_{i=1}(MME_{hist,i}-\bar{MME}_{hist})×\sum^{n}_{i=1}(obs_{hist,i}-\bar{obs}_{hist})}
{\sqrt{\sum^{n}_{i=1}(MME_{hist,i}-\bar{MME}_{hist})^{2}}×\sqrt{\sum^{n}_{i=1}(obs_{hist,i}-\bar{obs}_{hist})^{2}}}$		 1.0
Spatial correlation (SC) Spatial correlation between the historical MMEs and reference data $SC=\frac{\sum^{m}_{j=1}(MME_{hist,j}-\bar{MME}_{hist})×\sum^{m}_{j=1}(obs_{hist,j}-\bar{obs}_{hist})}
{\sum^{m}_{j=1}(MME_{hist,j}-\bar{MME}_{hist})^{2}×\sqrt{\sum^{m}_{j=1}(obs_{hist}-\bar{obs}_{hist})^{2}}}$		 1.0
Normalized standard
deviation (NSTD)		 Normalized standard deviation between the historical MMEs and reference data $NSTD=\frac{\sum^{i=1}_{n}(MME_{hist,i}-\bar{MME}_{hist,i})^{2}}
{\sum^{i=1}_{n}(obs_{hist,i}-\bar{obs}_{hist,i})^{2}}$		 0.8-1.2
Mean absolute error (MAE) Mean absolute error of the historical MMEs $MAE=\frac{\sum^{i=1}_{n}|MME_{hist,i}-obs_{hist,i}|}{n}$ 0.0
Table 1 Evaluation indices of bias correction
Fig. 2 Spatial distribution of relative bias (RB) of precipitation based on five Global Circulation Models (GCMs) (CanESM5 (a), IPSL-CM6A-LR (b), MIROC6 (c), MRI-ESM2-0 (d), and FGOALS-g3 (e)) and multi-model ensembles (MMEs, f) in Central Asia during 1975-2014. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 3 Spatial distribution of RB of temperature based on five GCMs (CanESM5 (a), IPSL-CM6A-LR (b), MIROC6 (c), MRI-ESM2-0 (d), and FGOALS-g3 (e)) and MMEs (f) in Central Asia during 1975-2014. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 4 Spatial distribution of RB of potential evapotranspiration (PET) based on five GCMs (CanESM5 (a), IPSL-CM6A-LR (b), MIROC6 (c), MRI-ESM2-0 (d), and FGOALS-g3 (e)) and MMEs (f) in Central Asia during 1975-2014. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 5 Taylor diagram of precipitation
(a), temperature (b), and PET (c) in Central Asia during 1975-2014. The green lines represent the reference of mean square error.
Index Precipitation Temperature PET Tmax Tmin
BC AC BC AC BC AC BC AC BC AC
Bias 0.27 0.01 1.20 0.08 -0.37 0.01 0.44 0.00 1.00 0.04
RB 0.39 0.01 0.18 0.01 -0.15 0.00 0.03 0.00 0.63 0.02
TC 0.36 0.66 0.96 0.96 0.96 0.97 0.97 0.97 0.97 0.96
SC 0.62 0.63 0.88 0.96 0.85 0.91 0.91 0.96 0.88 0.96
NSTD 0.86 0.98 1.09 1.00 1.28 1.01 1.15 1.01 1.10 1.01
MAE 0.47 0.39 2.97 3.15 0.74 0.41 2.99 2.32 2.87 2.03
Table 2 Statistical evaluation of bias correction for climate variables
Fig. 6 Annual RB of precipitation (a), temperature (b), PET (c), maximum temperature (Tmax, d), and minimum temperature (Tmin, e) before and after correction in Central Asia during 1975-2014
Fig. 7 Temporal variation of water-thermal product index (k index) at annual (a) and seasonal (b-e) scales during different periods. The boxes represent the range from the lower quantile (Q25) to the upper quantile (Q75). The black horizontal lines represent the means. The upper and lower whiskers extent to the maximum and minimum value within the 1.5 interquartile range of the upper and lower quartile, respectively. SSP126, SSP1-RCP2.6; SSP245, SSP2-RCP4.5; SSP460, SSP4-RCP6.0; SSP585, SSP5-RCP8.5. SSP, Shared Socioeconomic Pathway; RCP, Representative Concentration Pathway.
Fig. 8 Projected spatial distribution of k index at annual (a) and seasonal (b-e) scales under SSP126 scenario in Central Asia during 2015-2100. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 9 Projected spatial distribution of the difference value of k index at annual (a1-a3) and seasonal (b1-e3) scales under SSP245, SSP460, and SSP585 scenarios in Central Asia during 2015-2100. (b1-b3), spring; (c1-c3), summer; (d1-d3), autumn; (e1-e3), winter. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 10 Spatial distribution of dominant climate variable at annual (a1-a4) and seasonal (b1-e4) scales under SSP126, SSP245, SSP460, and SSP585 scenarios in Central Asia during 2015-2100. (b1-b4), spring; (c1-c4), summer; (d1-d4), autumn; (e1-e4), winter. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
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