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Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios

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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.

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References

  • Abd-Elmabod S K, Muñoz-Rojas M, Jordán A, et al. 2020. Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region. Geoderma, 374: 114453, doi: https://doi.org/10.1016/j.geoderma.2020.114453.

    Article  Google Scholar 

  • Ahmed K F, Wang G L, Silander J, et al. 2013. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast. Global and Planetary Change, 100: 320–332.

    Article  Google Scholar 

  • Alley W M. 1984. The Palmer drought severity index: limitations and assumptions. Journal of Applied Meteorology and Climatology, 23(7): 1100–1109.

    Article  Google Scholar 

  • Bannayan M, Sanjani S, Alizadeh A, et al. 2010. Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research, 118(2): 105–114.

    Article  Google Scholar 

  • Beven K. 1979. A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology, 44(3–4): 169–190.

    Article  Google Scholar 

  • Cardoso A S, Alonso J, Rodrigues A S, et al. 2019. Agro-ecological terroir units in the North West Iberian Peninsula wine regions. Applied Geography, 107: 51–62.

    Article  Google Scholar 

  • Carli C, Yuldashev F, Khalikov D, et al. 2014. Effect of different irrigation regimes on yield, water use efficiency and quality of potato (Solanum tuberosum L.) in the lowlands of Tashkent, Uzbekistan: A field and modeling perspective. Field Crops Research, 163: 90–99.

    Article  Google Scholar 

  • Chernozhukov V, Galichon A, Hallin M, et al. 2017. Monge-kantorovich depth, quantiles, ranks and signs. The Annals of Statistics, 45(1): 223–256.

    Google Scholar 

  • Christensen J H, Boberg F, Christensen O B, et al. 2008. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophysical Research Letters, 35(20): 6.

    Article  Google Scholar 

  • Deng H Y, Yin Y H, Han X. 2020. Vulnerability of vegetation activities to drought in Central Asia. Environmental Research Letters, 15(8): 12.

    Article  Google Scholar 

  • Dubovyk O, Ghazaryan G, Gonzalez J, et al. 2019. Drought hazard in Kazakhstan in 2000–2016: a remote sensing perspective. Environmental Monitoring and Assessment, 191(8): 1–17.

    Article  Google Scholar 

  • Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5): 1937–1958.

    Article  Google Scholar 

  • Fang W, Huang S, Huang Q, et al. 2019. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China. Remote Sensing of Environment, 232: 111290, doi: https://doi.org/10.1016/j.rse.2019.111290.

    Article  Google Scholar 

  • Ge F, Zhu S P, Luo H L, et al. 2021. Future changes in precipitation extremes over Southeast Asia: insights from CMIP6 multi-model ensemble. Environmental Research Letters, 16(2): 024013, doi: https://doi.org/10.1088/1748-9326/abd7ad.

    Article  Google Scholar 

  • Geng H, Pan B, Huang B, et al. 2017. The spatial distribution of precipitation and topography in the Qilian Shan Mountains, northeastern Tibetan Plateau. Geomorphology, 297: 43–54.

    Article  Google Scholar 

  • Gidden M J, Riahi K, Smith S J, et al. 2019. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific Model Development, 12(4): 1443–1475.

    Article  Google Scholar 

  • Guo H, Bao A M, Chen T, et al. 2021. Assessment of CMIP6 in simulating precipitation over arid Central Asia. Atmospheric Research, 252: 105451, doi: https://doi.org/10.1016/j.atmosres.2021.105451.

    Article  Google Scholar 

  • Harris I, Jones P D, Osborn T J, et al. 2014. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology, 34(3): 623–642.

    Article  Google Scholar 

  • Horion S, Prishchepov A V, Verbesselt J, et al. 2016. Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier. Global Change Biology, 22(8): 2801–2817.

    Article  Google Scholar 

  • Ji X, Li Y, Luo X, et al. 2020. Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin. Atmospheric Research, 242: 104964, doi: https://doi.org/10.1016/j.atmosres.2020.104964.

    Article  Google Scholar 

  • Jiang L L, Jiapaer G, Bao A M, et al. 2019. Monitoring the long-term desertification process and assessing the relative roles of its drivers in Central Asia. Ecological Indicators, 104: 195–208.

    Article  Google Scholar 

  • Jiang L L, Bao A M, Jiapaer G, et al. 2022. Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia. Science of the Total Environment, 807: 150868, doi: https://doi.org/10.1016/j.scitotenv.2021.150868.

    Article  Google Scholar 

  • Kienzler K M, Lamers J P A, McDonald A, et al. 2012. Conservation agriculture in Central Asia-What do we know and where do we go from here? Field Crops Research, 132: 95–105.

    Article  Google Scholar 

  • Konapala G, Mishra A K, Wada Y, et al. 2020. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature Communications, 11(1): 3044, doi: https://doi.org/10.1038/s41467-020-16757-w.

    Article  Google Scholar 

  • Lacombe G, Hoanh C T, Smakhtin V. 2012. Multi-year variability or unidirectional trends? Mapping long-term precipitation and temperature changes in continental Southeast Asia using PRECIS regional climate model. Climatic Change, 113: 285–299.

    Article  Google Scholar 

  • Li J, Fei L, Li S, et al. 2020. Development of “water-suitable” agriculture based on a statistical analysis of factors affecting irrigation water demand. Science of the Total Environment, 744: 140986, doi: https://doi.org/10.1016/j.scitotenv.2020.140986.

    Article  Google Scholar 

  • Li M X, Ma Z G. 2018. Decadal changes in summer precipitation over arid northwest China and associated atmospheric circulations. International Journal of Climatology, 38(12): 4496–4508.

    Article  Google Scholar 

  • Li W, Li C, Liu X, et al. 2018. Analysis of spatial-temporal variation in NPP based on hydrothermal conditions in the Lancang-Mekong River Basin from 2000 to 2014. Environmental Monitoring and Assessment, 190(6): 321, doi: https://doi.org/10.1007/s10661-018-6690-7.

    Article  Google Scholar 

  • Li Z, Fang G, Chen Y, et al. 2020. Agricultural water demands in Central Asia under 1.5 degrees C and 2.0 degrees C global warming. Agricultural Water Management, 231: 106020, doi: https://doi.org/10.1016/j.agwat.2020.106020.

    Article  Google Scholar 

  • Lioubimtseva E, Henebry G M. 2009. Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. Journal of Arid Environments, 73(11): 963–977.

    Article  Google Scholar 

  • Luo M, Liu T, Meng F H, et al. 2019. Spatiotemporal characteristics of future changes in precipitation and temperature in Central Asia. International Journal of Climatology, 39(3): 1571–1588.

    Article  Google Scholar 

  • Mannig B, Muller M, Starke E, et al. 2013. Dynamical downscaling of climate change in Central Asia. Global and Planetary Change, 110: 26–39.

    Article  Google Scholar 

  • Martonne E D. 1926. A new ciimatological function: the aridity index. La Météorologie, 2: 449–458. (in French)

    Google Scholar 

  • McCain C M, Colwell R K. 2011. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecology Letters, 14(12): 1236–1245.

    Article  Google Scholar 

  • Meng M, Ni J, Zhang Z G. 2004. Aridity index and its applications in geo-ecological study. Acta Phytoecologica Sinica, 28: 853–861. (in Chinese)

    Google Scholar 

  • Mondal S K, Huang J, Wang Y, et al. 2021. Doubling of the population exposed to drought over South Asia: CMIP6 multi-model-based analysis. Science of the Total Environment, 771: 145186, doi: https://doi.org/10.1016/j.scitotenv.2021.145186.

    Article  Google Scholar 

  • Ni J, Zhang X S. 1997. Estimation of water and thermal product index and its application to the study of vegetation-climate interaction in China. Acta Botanica Sinica, 12: 1147–1159. (in Chinese)

    Google Scholar 

  • Reshmidevi T V, Eldho T I, Jana R. 2009. A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds. Agricultural Systems, 101(1–2): 101–109.

    Article  Google Scholar 

  • Rivera J A, Arnould G. 2020. Evaluation of the ability of CMIP6 models to simulate precipitation over Southwestern South America: Climatic features and long-term trends (1901–2014). Atmospheric Research, 241: 104953, doi: https://doi.org/10.1016/j.atmosres.2020.104953.

    Article  Google Scholar 

  • Schierhorn F, Hofmann M, Adrian I, et al. 2020. Spatially varying impacts of climate change on wheat and barley yields in Kazakhstan. Journal of Arid Environments, 178: 104164, doi: https://doi.org/10.1016/j.jaridenv.2020.104164.

    Article  Google Scholar 

  • Seljaninov G T. 1966. Agroclimatic Map of the World. Leningrad: Hydrometeoizdat Publishing House.

    Google Scholar 

  • Seo K H, Ok J. 2013. Assessing future changes in the East Asian summer monsoon using CMIP3 models: results from the best model ensemble. Journal of Climate, 26(5): 1807–1817.

    Article  Google Scholar 

  • Su B, Huang J, Mondal S K, et al. 2021. Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China. Atmospheric Research, 250: 105375, doi: https://doi.org/10.1016/j.atmosres.2020.105375.

    Article  Google Scholar 

  • Sun F Y, Mejia A, Zeng P, et al. 2019. Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin. Science of the Total Environment, 696: 134076, doi: https://doi.org/10.1016/j.scitotenv.2019.134076.

    Article  Google Scholar 

  • Taylor K E. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research-Atmospheres, 106(D7): 7183–7192.

    Article  Google Scholar 

  • Teutschbein C, Seibert J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456: 12–29.

    Article  Google Scholar 

  • Vicente-Serrano S M, Begueria S, Lopez-Moreno J I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7): 1696–1718.

    Article  Google Scholar 

  • Wang H, Zang F, Zhao C, et al. 2022. A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China. Science of the Total Environment, 810: 1522066, doi: https://doi.org/10.1016/j.scitotenv.2021.152066.

    Google Scholar 

  • Wang J S, Chen F H, Jin L Y, et al. 2010. Characteristics of the dry/wet trend over arid central Asia over the past 100 years. Climate Research, 41: 51–59.

    Article  Google Scholar 

  • Wang T, Tu X, Singh V P, et al. 2021. Global data assessment and analysis of drought characteristics based on CMIP6. Journal of Hydrology, 596: 126091, doi: https://doi.org/10.1016/j.jhydrol.2021.126091.

    Article  Google Scholar 

  • Weiland F C S, van Beek L P H, Weerts A H, et al. 2012. Extracting information from an ensemble of GCMs to reliably assess future global runoff change. Journal of Hydrology, 412: 66–75.

    Article  Google Scholar 

  • Weltzin J F, Loik M E, Schwinning S, et al. 2003. Assessing the response of terrestrial ecosystems to potential changes in precipitation. Bioscience, 53(10): 941–952.

    Article  Google Scholar 

  • Wood A W, Maurer E P, Kumar A, et al. 2002. Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres, 107(D20): 15, doi: https://doi.org/10.1029/2001jd000659.

    Article  Google Scholar 

  • Wood A W, Leung L R, Sridhar V, et al. 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62(1–3): 189–216.

    Article  Google Scholar 

  • Wu H, Huang A, He Q, et al. 2013. Projection of the spatial and temporal variation characteristics of precipitation over Central Asia of 10 CMIP5 models in the next 50 years. Arid Land Geography, 36(4): 669–679. (in Chinese)

    Google Scholar 

  • Wu Z T, Dijkstra P, Koch G W, et al. 2011. Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation. Global Change Biology, 17(2): 927–942.

    Article  Google Scholar 

  • Xu H j, Wang X P, Zhang X X. 2016. Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012. International Journal of Applied Earth Observation and Geoinformation, 52: 390–402.

    Article  Google Scholar 

  • Yu Y, Chen X, Disse M, et al. 2020. Climate change in Central Asia: Sino-German cooperative research findings. Science Bulletin, 65(9): 689–692.

    Article  Google Scholar 

  • Yuan Y, Bao A, Jiang P, et al. 2022. Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia. Journal of Environmental Management, 310: 114504, doi: https://doi.org/10.1016/j.jenvman.2022.114504.

    Article  Google Scholar 

  • Zeng P, Sun F, Liu Y, et al. 2021. Mapping future droughts under global warming across China: A combined multi-timescale meteorological drought index and SOM-Kmeans approach. Weather and Climate Extremes, 31: 100304, doi: https://doi.org/10.1016/j.wace.2021.100304.

    Article  Google Scholar 

  • Zhan Y J, Ren G Y, Yang S. 2018. Change in precipitation over the Asian continent from 1901–2016 based on a new multi-source dataset. Climate Research, 76(1): 41–57.

    Article  Google Scholar 

  • Zhang J, Su Y, Wu J, et al. 2015. GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Computers and Electronics in Agriculture, 114: 202–211.

    Article  Google Scholar 

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Acknowledgements

This research was supported by 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) and Shanghai Cooperation and the Organization Science and Technology Partnership of China (2021E01019).

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Authors and Affiliations

  1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Linlin Yao, Hongfei Zhou, Yingjie Yan, Lanhai Li & Yuan Su

  2. Fukang National Field Scientific Observation and Research Station for Desert Ecosystems, Chinese Academy of Sciences, Fukang, 831505, China

    Linlin Yao, Hongfei Zhou, Yingjie Yan & Yuan Su

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Linlin Yao, Yingjie Yan, Lanhai Li & Yuan Su

  4. Ili Station for Watershed Ecosystem Research, Chinese Academy of Sciences, Xinyuan, 835800, China

    Lanhai Li

  5. CAS Research Center for Ecology and Environment of Central Asia, Urumqi, 830011, China

    Lanhai Li

  6. Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone, Urumqi, 830011, China

    Lanhai Li

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  1. Linlin Yao
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  2. Hongfei Zhou
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  3. Yingjie Yan
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Correspondence to Hongfei Zhou.

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Yao, L., Zhou, H., Yan, Y. et al. Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios. J. Arid Land 14, 521–536 (2022). https://doi.org/10.1007/s40333-022-0094-9

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