Please wait a minute...
Journal of Arid Land  2020, Vol. 12 Issue (2): 303-317    DOI: 10.1007/s40333-020-0121-7
Research article     
Assessing the collapse risk of Stipa bungeana grassland in China based on its distribution changes
QIAO Xianguo1,2, GUO Ke1,2,*(), LI Guoqing3,4, ZHAO Liqing5, LI Frank Yonghong5, GAO Chenguang1
1 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
2 University of Chinese Academy of Sciences, Beijing 100049, China
3 State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
4 Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
5 Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Download: HTML     PDF(588KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The criteria used by International Union for Conservation of Nature (IUCN) for its Red List of Ecosystems (RLE) are the global standards for ecosystem-level risk assessment, and they have been increasingly used for biodiversity conservation. The changed distribution area of an ecosystem is one of the key criteria in such assessments. The Stipa bungeana grassland is one of the most widely distributed grasslands in the warm-temperate semi-arid regions of China. However, the total distribution area of this grassland was noted to have shrunk and become fragmented because of its conversion to cropland and grazing-induced degradation. Following the IUCN-RLE standards, here we analyzed changes in the geographical distribution of this degraded grassland, to evaluate its degradation and risk of collapse. Past (1950-1980) distribution areas were extracted from the Vegetation Map of China (1:1,000,000). Present realizable distribution areas were equated to these past areas minus any habitat area losses. We then predicted the grassland's present and future (under the Representative Concentration Pathway 8.5 scenario) potential distribution areas using maximum entropy algorithm (MaxEnt), based on field survey data and nine environmental layers. Our results showed that the S. bungeana grassland was mainly distributed in the Loess Plateau, Hexi Corridor, and low altitudes of the Qilian Mountains and Longshou Mountain. This ecosystem occurred mainly on loess soils, kastanozems, steppe aeolian soils and sierozems. Thermal and edaphic factors were the most important factors limiting the distribution of S. bungeana grassland across China. Since 56.1% of its past distribution area (4.9×104 km2) disappeared in the last 50 a, the present realizable distribution area only amounts to 2.2×104 km2. But only 15.7% of its present potential distribution area (14.0×104 km2) is actually occupied by the S. bungeana grassland. The future potential distribution of S. bungeana grassland was predicted to shift towards northwest, and the total area of this ecosystem will shrink by 12.4% over the next 50 a under the most pessimistic climate change scenario. Accordingly, following the IUCN-RLE criteria, we deemed the S. bungeana grassland ecosystem in China to be endangered (EN). Revegetation projects and the establishment of protected areas are recommended as effective ways to avert this looming crisis. This empirical modeling study provides an example of how IUCN-RLE categories and criteria may be valuably used for ecosystem assessments in China and abroad.



Key wordsclimate change      limiting factors      maximum entropy algorithm      potential distribution      realizable distribution      Red List of Ecosystems      Loess Plateau     
Received: 13 June 2018      Published: 10 March 2020
Corresponding Authors:
About author: *Corresponding author: GUO Ke (E-mail: guoke@ibcas.ac.cn)
Cite this article:

QIAO Xianguo, GUO Ke, LI Guoqing, ZHAO Liqing, LI Frank Yonghong, GAO Chenguang. Assessing the collapse risk of Stipa bungeana grassland in China based on its distribution changes. Journal of Arid Land, 2020, 12(2): 303-317.

URL:

http://jal.xjegi.com/10.1007/s40333-020-0121-7     OR     http://jal.xjegi.com/Y2020/V12/I2/303

Fig. 1 Framework used to assess the collapse risk of Stipa bungeana grassland based on changes in its spatial distribution. Vegetation Map of China (VMC; 1:1,000,000) was used to extract the pertinent vegetation information for the period 1950-1980. This map was originally generated from field survey data for this period. ENMs is the abbreviation for ecological niche models.
Fig. 2 Locations of the 111 vegetation sites dominated by S. bungeana grassland that were used for predicting its distribution in MaxEnt (a), the current status of S. bungeana grassland in abandoned cropland (b) and artificial Caragana microphylla shrubland where S. bungeana is growing between shrubs (c)
Fig. 3 The past and present realizable distributions (a), limiting factors of the distribution (b), present potential distribution (c) and future potential distribution under Representative Concentration Pathway (RCP) 8.5 (d) of Stipa bungeana grassland in China. ST, soil type; PWM, precipitation of the wettest month; PDM, precipitation of the driest month; PS, precipitation seasonality; MDR, mean diurnal range; ISO, isothermality; TS, temperature seasonality; MTWM, mean temperature of the warmest month; MTCM, mean temperature of the coldest month.
Variable Contribution (%) Present potential suitable habitat category
Core area Medium area Marginal area Unsuitable area
PWM (mm) 22.7 45.0-135.0 41.0-140.0 32.0-159.0 3.0-1244.0
PDM (mm) 0.0 0.0-6.0 0.0-18.0 0.0-18.0 0.0-206.0
PS 0.1 78.3-121.1 37.7-122.5 35.2-141.5 19.1-155.7
MDR (°C) 11.6 10.2-16.1 9.2-16.2 8.7-16.2 1.1-20.1
ISO (%) 2.9 24.2-39.6 23.4-41.8 21.7-45.4 4.4-76.3
TS 2.2 8.0-13.3 6.9-13.5 6.4-14.4 0.0-18.3
MTWM (°C) 9.9 18.0-31.2 17.0-31.8 14.5-32.1 -15.7-40.3
MTCM (°C) 12.8 -22.3- -9.2 -23.5- -7.2 -24.1- -4.3 -48.1- -14.5
ST 37.8 - - - -
Table 1 MaxEnt model-predicted climatic ranges for the present suitable distribution area of Stipa bungeana grassland across China, and the respective contribution of nine environmental variables to these predictions
[1]   Akiyama T, Kawamura K. 2007. Grassland degradation in China: Methods of monitoring, management and restoration. Grassland Science, 53(1): 1-17.
[2]   Anderson R P, Lew D, Peterson A T. 2003. Evaluating predictive models of species' distributions: Criteria for selecting optimal models. Ecological Modelling, 162(3): 211-232.
[3]   Bland L M, Keith D A, Miller R M, et al. 2015. Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 1.0. Gland, Switzerland: The International Union for Conservation of Nature, 1-50.
doi: 10.1098/rstb.2014.0003 pmid: 25561664
[4]   Brites-Neto J, Duarte K. 2015. Modeling of spatial distribution for scorpions of medical importance in the São Paulo State, Brazil. Veterinary World, 8(7): 823-830.
doi: 10.14202/vetworld.2015.823-830 pmid: 27047160
[5]   Burns E L, Lindenmayer D B, Stein J, et al. 2015. Ecosystem assessment of mountain ash forest in the central highlands of Victoria, south-eastern Australia. Austral Ecology, 40(4): 386-399.
[6]   Byrne K M, Adler P B, Lauenroth W K. 2017. Contrasting effects of precipitation manipulations in two Great Plains plant communities. Journal of Vegetation Science, 28(2): 238-249.
doi: 10.1111/jvs.2017.28.issue-2
[7]   Chen L Z, Sun H, Guo K. 2014. Flora and Vegetation Geography of China. Beijing: Science Press, 304-312. (in Chinese)
[8]   Chen Y, Liang Y, Cheng J. 2002. The zonal character of vegetation construction on Loess Plateau. Acta Phytoecologica Sinica, 26(3): 339-345. (in Chinese)
doi: 10.1111/j.1745-7254.2005.00040.x pmid: 15715931
[9]   Cheng J, Hu T M, Cheng J M, et al. 2010. Distribution of biomass and diversity of Stipa bungeana community to climatic factors in the Loess Plateau of northwestern China. African Journal of Biotechnology, 9(40): 6733-6739.
[10]   Cheng M, An S S. 2015. Response of soil nitrogen, phosphorous and organic matter to vegetation succession on the Loess Plateau of China. Journal of Arid Land, 7(2): 216-223.
[11]   Costanza R, de Groot R, Sutton P, et al. 2014. Changes in the global value of ecosystem services. Global Environmental Change-Human and Policy Dimensions, 26: 152-158.
[12]   Curtis J T, Mclntosh R P. 1951. An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology, 32(3): 476-496.
[13]   ECVC (The Editorial Committee of Vegetation of China). 1980. Vegetation of China. Beijing: Science Press, 505-546. (in Chinese)
[14]   ECVIM (The Editorial Committee of Vegetation of Inner Mongolia). 1985. Vegetation of Inner Mongolia. Beijing: Science Press, 547-560. (in Chinese)
[15]   ECVMC (The Editorial Committee of Vegetation Map of China). 2007. Vegetation Map of China. Xi'an: Geological Publishing House, 330-350. (in Chinese)
[16]   Elith J, Graham C H, Anderson R P, et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29(2): 129-151.
[17]   Elith J, Kearney M, Phillips S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution, 1(4): 330-342.
[18]   Elith J, Phillips S J, Hastie T, et al. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1): 43-57.
[19]   Fang J Y, Lechowicz M J. 2006. Climatic limits for the present distribution of beech (Fagus L.) species in the world. Journal of Biogeography, 33(10): 1804-1819.
doi: 10.1111/jbi.2006.33.issue-10
[20]   Feng X M, Fu B J, Piao S L, et al. 2016. Revegetation in China's Loess Plateau is approaching sustainable water resource limits. Nature Climate Change, 6: 1019-1022.
[21]   Fick S E, Hijmans R J. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12): 4302-4315.
[22]   Franklin J. 2009. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge: Cambridge University Press, 21-160.
[23]   Fu B J. 1989. Soil erosion and its control in the Loess Plateau of China. Soil Use and Management, 5(2): 76-82.
[24]   Gaston K J. 2003. The Structure and Dynamics of Geographic Ranges. Oxford: Oxford University Press, 8-32.
[25]   Guisan A, Thuiller W. 2005. Predicting species distribution: Offering more than simple habitat models. Ecology Letters, 8(9): 993-1009.
[26]   Guisan A, Tingley R, Baumgartner J B, et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters, 16(12): 1424-1435.
doi: 10.1111/ele.12189 pmid: 24134332
[27]   Guo K. 2000. Cyclic succession of Artemisia ordosica krash community in the Mu Us sandy grassland. Acta Phytoecologica Sinica, 24(2): 243-247. (in Chinese)
[28]   Guo K, Liu C C, Xie Z Q, et al. 2018. China vegetation classification: Concept, approach and applications. Phytocoenologia, 48(2): 113-120.
[29]   Han F P, Dong L N, Luo W L, et al. 2008. Effects of Stipa bungeana on soil water contents and nutrients of sloping lands in Loess Plateau of China. Acta Agrestia Sinica, 16(4): 403-407. (in Chinese)
[30]   Hao W, Liang Z, Chen C, et al. 2005. Study of the different succession stage community dynamic and the evolution of soil characteristics of the old-field in Loess Hills gully. Chinese Agricultural Science Bulletin, 21(8): 226-232. (in Chinese)
[31]   Harpole W S, Tilman D. 2007. Grassland species loss resulting from reduced niche dimension. Nature, 446: 791-793.
doi: 10.1038/nature05684 pmid: 17384633
[32]   Harris R B. 2010. Rangeland degradation on the Qinghai-Tibetan Plateau: A review of the evidence of its magnitude and causes. Journal of Arid Environments, 74(1): 1-12.
[33]   Hoekstra J M, Boucher T M, Ricketts T H, et al. 2005. Confronting a biome crisis: Global disparities of habitat loss and protection. Ecology Letters, 8(1): 23-29.
[34]   Hutchinson G E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22: 415-427.
[35]   IPCC (The Intergovernmental Panel on Climate Change). 2013. Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 1-29.
[36]   Jiao J Y, Tzanopoulos J, Xofis P, et al. 2008. Factors affecting distribution of vegetation types on abandoned cropland in the hilly-gullied Loess Plateau region of China. Pedosphere, 18(1): 24-33.
[37]   Keith D A, Rodriguez J P, Rodriguez-Clark K M, et al. 2013. Scientific foundations for an IUCN red list of ecosystems. PLoS ONE, 8(5): e62111, doi: 10.1371/journal.pone.0062111.
doi: 10.1371/journal.pone.0062111 pmid: 23667454
[38]   Keith D A, Rodriguez J P, Brooks T M, et al. 2015. The IUCN red list of ecosystems: Motivations, challenges, and applications. Conservation Letters, 8(3): 214-226.
doi: 10.1111/conl.2015.8.issue-3
[39]   Kira T. 1976. Terrestrial Ecosystem: A General Survey. Tokyo: Kyorisu Shuppan, 100-166.
[40]   Li G Q, Du S, Wen Z M. 2016a. Mapping the climatic suitable habitat of oriental arborvitae (Platycladus orientalis) for introduction and cultivation at a global scale. Scientific Reports, 6: 30009, doi: 10.1038/srep30009.
doi: 10.1038/srep30009 pmid: 27443221
[41]   Li G Q, Xu G H, Guo K, et al. 2016b. Geographical boundary and climatic analysis of Pinus tabulaeformis in China: Insights on its afforestation. Ecological Engineering, 86: 75-84.
[42]   Liu X D, Chen B D. 2000. Climatic warming in the Tibetan Plateau during recent decades. International Journal of Climatology, 20(14): 1729-1742.
doi: 10.1371/journal.pone.0088178 pmid: 24505418
[43]   Liu X D, Cheng Z G, Yan L B, et al. 2009. Elevation dependency of recent and future minimum surface air temperature trends in the Tibetan Plateau and its surroundings. Global and Planetary Change, 68(3): 164-174.
doi: 10.1016/j.gloplacha.2009.03.017
[44]   Ma K P. 2017. Red List of Ecosystems (RLE): Progress and challenges. Biodiversity Science, 25(5): 451-452. (in Chinese)
[45]   Mclendon T, Redente E F. 1990. Succession patterns following soil disturbance in a sagebrush steppe community. Oecologia, 85(2): 293-300.
doi: 10.1007/BF00319415 pmid: 28312569
[46]   Miles L, Newton A, Defries R, et al. 2006. A global overview of the conservation status of tropical dry forests. Journal of Biogeography, 33(3): 491-505.
[47]   Mirzabaev A, Ahmed M, Werner J, et al. 2016. Rangelands of Central Asia: challenges and opportunities. Journal of Arid Land, 8(1): 93-108.
[48]   Moisen G G, Frescino T S. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157(2-3): 209-225.
[49]   Moss R H, Edmonds J A, Hibbard K A, et al. 2010. The next generation of scenarios for climate change research and assessment. Nature, 463: 747-756.
doi: 10.1038/nature08823 pmid: 20148028
[50]   Nature Reserve Management Office of Yunwu Mountain in Ningxia. 2001. Collected works of scientific investigation and management of nature reserve on Yunwu Mountain in Ningxia. Yinchuan: Ningxia People Press, 50-263. (in Chinese)
[51]   Pan Y, Li X, Gong P, et al. 2003. An integrative classification of vegetation in China based on NOAA AVHRR and vegetation-climate indices of the Holdridge life zone. International Journal of Remote Sensing, 24(5): 1009-1027.
[52]   Parmesan C, Yohe G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421: 37-42.
doi: 10.1038/nature01286 pmid: 12511946
[53]   Peterson A T, Soberon J, Pearson R G, et al. 2011. Ecological Niches and Geographic Distributions. Monographs in Population Biology No. 49. Princeton: Princeton University Press, 56-86.
[54]   Phillips S J, Dudik M, Schapire R E. 2004. A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning, 83: 655-662.
[55]   Phillips S J, Anderson R P, Schapire R E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4): 231-259.
doi: 10.1016/j.ecolmodel.2005.03.026
[56]   Phillips S J, Elith J. 2013. On estimating probability of presence from use-availability or presence-background data. Ecology, 94(6): 1409-1419.
[57]   Phillips S J. 2017. A brief tutorial on MaxEnt. [2018-06-13]. http://biodiversityinformatics.amnh.org/open_source/maxent/.
[58]   Phillips S J, Anderson R P, Dudík M, et al. 2017. Opening the black box: An open-source release of Maxent. Ecography, 40(7): 887-893.
[59]   Prieto-Torres D A, Navarro-Sigüenza A G, Santiago-Alarcon D, et al. 2016. Response of the endangered tropical dry forests to climate change and the role of Mexican protected areas for their conservation. Global Change Biology, 22(1): 364-379.
doi: 10.1111/gcb.13090 pmid: 26367278
[60]   Pulliam H R. 2000. On the relationship between niche and distribution. Ecology Letters, 3(4): 349-361.
[61]   Riahi K, Rao S, Krey V, et al. 2011. RCP 8.5: A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1-2): 33-57.
[62]   Rodríguez J P, Keith D A, Rodríguez-Clark K M, et al. 2015. A practical guide to the application of the IUCN Red List of Ecosystems criteria. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 370(1662): 20140003.
doi: 10.1098/rstb.2014.0003 pmid: 25561664
[63]   Root T L, Price J T, Hall K R, et al. 2003. Fingerprints of global warming on wild animals and plants. Nature, 421: 57-60.
doi: 10.1038/nature01333 pmid: 12511952
[64]   Segurado P, Araujo M B. 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography, 31(10): 1555-1568.
doi: 10.1111/jbi.2004.31.issue-10
[65]   Shi Y H, Zhou G S, Jiang Y L, et al. 2018. Sensitive indicators of Stipa bungeana response to precipitation under ambient and elevated CO2 concentration. International Journal of Biometeorology, 62(2): 141-151.
doi: 10.1007/s00484-017-1434-x pmid: 28864884
[66]   Song B, Niu S L, Wan S Q. 2016. Precipitation regulates plant gas exchange and its long-term response to climate change in a temperate grassland. Journal of Plant Ecology, 9(5): 531-541.
[67]   Stephenson N L. 1990. Climatic control of vegetation distribution: The role of the water balance. American Naturalist, 135(5): 649-670.
doi: 10.1086/285067
[68]   Stephenson N L. 1998. Actual evapotranspiration and deficit: Biologically meaningful correlates of vegetation distribution across spatial scales. Journal of Biogeography, 25(5): 855-870.
[69]   Svenning J C, Skov F. 2004. Limited filling of the potential range in European tree species. Ecology Letters, 7(7): 565-573.
doi: 10.1111/ele.2004.7.issue-7
[70]   Walther G-R, Post E, Convey P, et al. 2002. Ecological responses to recent climate change. Nature, 416: 389-395.
doi: 10.1038/416389a pmid: 11919621
[71]   Wang D. 1989. A Synthesis of Forage. Nanjing: Jiangsu Science and Technology Press, 130-145. (in Chinese)
[72]   Wang H, Zhou G S, Jiang Y L, et al. 2017. Photosynthetic acclimation and leaf traits of Stipa bungeana in response to elevated CO2 under five different watering conditions. Photosynthetica, 55(1): 164-175.
[73]   Wen Z M, He X H, Jiao F, et al. 2008. The predictive distribution of Stipa bungeana in Yanhe River catchment: GAM model and its application. Acta Ecological Sinica, 28(1): 192-201. (in Chinese)
[74]   Wesche K, Ambarlı D, Kamp J, et al. 2016. The Palaearctic steppe biome: A new synthesis. Biodiversity and Conservation, 25(12): 2197-2231.
doi: 10.1007/s10531-016-1214-7
[75]   Wu Z Y, Raven P H. 2013. Flora of China: Volume 22. Beijing: Science Press, 196-203. (in Chinese)
[76]   Yost A C, Petersen S L, Gregg M, et al. 2008. Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from southern Oregon. Ecological Informatics, 3(6): 375-386.
doi: 10.1016/j.ecoinf.2008.08.004
[77]   Young T P, Chase J M, Huddleston R T. 2001. Community succession and assembly: Comparing, contrasting and combining paradigms in the context of ecological restoration. Ecological Restoration, 19(1): 5-18.
doi: 10.3368/er.19.1.5
[78]   Zhao H W, Guo K, Yang Y, et al. 2018. Stipa steppes in scantily explored regions of the Tibetan Plateau: Classification, community characteristics and climatic distribution patterns. Journal of Plant Ecology, 11(4): 585-594.
doi: 10.1093/jpe/rtx029
[79]   Zhou Q P, Cheng J M, Wan H, et al. 2009. Study on the diurnal variations of photosynthetic characteristics and water use efficiency of Stipa bungeana Trin. under drought stress. Acta Agrestia Sinica, 17(4): 510-514. (in Chinese)
[1] WANG Jing, WEI Yulu, PENG Biao, LIU Siqi, LI Jianfeng. Spatiotemporal variations in ecosystem services and their trade-offs and synergies against the background of the gully control and land consolidation project on the Loess Plateau, China[J]. Journal of Arid Land, 2024, 16(1): 131-145.
[2] ZHAO Xuqin, LUO Min, MENG Fanhao, SA Chula, BAO Shanhu, BAO Yuhai. Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change[J]. Journal of Arid Land, 2024, 16(1): 46-70.
[3] Mitiku A WORKU, Gudina L FEYISA, Kassahun T BEKETIE, Emmanuel GARBOLINO. Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia[J]. Journal of Arid Land, 2023, 15(9): 1023-1036.
[4] QIN Guoqiang, WU Bin, DONG Xinguang, DU Mingliang, WANG Bo. Evolution of groundwater recharge-discharge balance in the Turpan Basin of China during 1959-2021[J]. Journal of Arid Land, 2023, 15(9): 1037-1051.
[5] MA Jinpeng, PANG Danbo, HE Wenqiang, ZHANG Yaqi, WU Mengyao, LI Xuebin, CHEN Lin. Response of soil respiration to short-term changes in precipitation and nitrogen addition in a desert steppe[J]. Journal of Arid Land, 2023, 15(9): 1084-1106.
[6] MA Xinxin, ZHAO Yunge, YANG Kai, MING Jiao, QIAO Yu, XU Mingxiang, PAN Xinghui. Long-term light grazing does not change soil organic carbon stability and stock in biocrust layer in the hilly regions of drylands[J]. Journal of Arid Land, 2023, 15(8): 940-959.
[7] ZHANG Hui, Giri R KATTEL, WANG Guojie, CHUAI Xiaowei, ZHANG Yuyang, MIAO Lijuan. Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China[J]. Journal of Arid Land, 2023, 15(7): 871-885.
[8] ZHANG Zhen, XU Yangyang, LIU Shiyin, DING Jing, ZHAO Jinbiao. Seasonal variations in glacier velocity in the High Mountain Asia region during 2015-2020[J]. Journal of Arid Land, 2023, 15(6): 637-648.
[9] GAO Xiang, WEN Ruiyang, Kevin LO, LI Jie, YAN An. Heterogeneity and non-linearity of ecosystem responses to climate change in the Qilian Mountains National Park, China[J]. Journal of Arid Land, 2023, 15(5): 508-522.
[10] Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments[J]. Journal of Arid Land, 2023, 15(5): 545-561.
[11] Sakine KOOHI, Hadi RAMEZANI ETEDALI. Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset[J]. Journal of Arid Land, 2023, 15(4): 377-392.
[12] Mehri SHAMS GHAHFAROKHI, Sogol MORADIAN. Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors?[J]. Journal of Arid Land, 2023, 15(4): 424-438.
[13] ZHANG Yixin, LI Peng, XU Guoce, MIN Zhiqiang, LI Qingshun, LI Zhanbin, WANG Bin, CHEN Yiting. Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process[J]. Journal of Arid Land, 2023, 15(4): 439-459.
[14] Adnan ABBAS, Asher S BHATTI, Safi ULLAH, Waheed ULLAH, Muhammad WASEEM, ZHAO Chengyi, DOU Xin, Gohar ALI. Projection of precipitation extremes over South Asia from CMIP6 GCMs[J]. Journal of Arid Land, 2023, 15(3): 274-296.
[15] ZHAO Lili, LI Lusheng, LI Yanbin, ZHONG Huayu, ZHANG Fang, ZHU Junzhen, DING Yibo. Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index[J]. Journal of Arid Land, 2023, 15(12): 1421-1438.