Historic and Simulated Desert-Oasis Ecotone Changes in the Arid Tarim River Basin, China

The desert-oasis ecotone, as a crucial natural barrier, maintains the stability of oasis agricultural production and protects oasis habitat security. This paper investigates the dynamic evolution of the desert-oasis ecotone in the Tarim River Basin and predicts the near-future landuse change in the desert-oasis ecotone using the cellular automata–Markov (CA-Markov) model. Results indicate that the overall area of the desert-oasis ecotone shows a shrinking trend (from 67,642 km2 in 1990 to 46,613 km2 in 2015) and the land-use change within the desert-oasis ecotone is mainly manifested by the conversion of a large amount of forest and grass area into arable land. The increasing demand for arable land for groundwater has led to a decline in the groundwater level, which is an important reason for the habitat deterioration in the desert-oasis ecotone. The rising temperature and drought have further exacerbated this trend. Assuming the current trend in development without intervention, the CA-Markov model predicts that by 2030, there will be an additional 1566 km2 of arable land and a reduction of 1151 km2 in forested area and grassland within the desert-oasis ecotone, which will inevitably further weaken the ecological barrier role of the desert-oasis ecotone and trigger a growing ecological crisis.

[1]  Yaning Chen,et al.  Potential risks and challenges of climate change in the arid region of northwestern China , 2020, Regional Sustainability.

[2]  J. Y. Li,et al.  Impact of Climate Change on Water Resources in the Tarim River Basin , 2004 .

[3]  Yaning Chen,et al.  Dry/wet pattern changes in global dryland areas over the past six decades , 2019, Global and Planetary Change.

[4]  Q. Shen,et al.  A system dynamics model for the sustainable land use planning and development , 2009 .

[5]  F. Veroustraete,et al.  Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China , 2006 .

[6]  Yaning Chen,et al.  How Hydrologic Processes Differ Spatially in a Large Basin: Multisite and Multiobjective Modeling in the Tarim River Basin , 2018, Journal of Geophysical Research: Atmospheres.

[7]  Ruifeng Zhao,et al.  Land use and land cover change and driving mechanism in the arid inland river basin: a case study of Tarim River, Xinjiang, China , 2012, Environmental Earth Sciences.

[8]  Yaning Chen,et al.  Desiccation of the Tarim River, Xinjiang, China, and mitigation strategy , 2011 .

[9]  J. Gosz Ecological Functions in a Biome Transition Zone: Translating Local Responses to Broad-Scale Dynamics , 1992 .

[10]  Yinon Rudich,et al.  Desert dust suppressing precipitation: A possible desertification feedback loop , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Yaning Chen,et al.  Quantifying the effects of climate variability and human activities on runoff for Kaidu River Basin in arid region of northwest China , 2013, Theoretical and Applied Climatology.

[12]  Jianyu Yang,et al.  Simulation of land use spatial pattern of towns and villages based on CA-Markov model , 2011, Math. Comput. Model..

[13]  Lukas Gudmundsson,et al.  Candidate Distributions for Climatological Drought Indices (SPI and SPEI) , 2013 .

[14]  L. Xiaobing,et al.  Modelling Land Use Change Dynamics under Different Aridification Scenarios in Northern China , 2006 .

[15]  Robert Gilmore Pontius,et al.  Useful techniques of validation for spatially explicit land-change models , 2004 .

[16]  Xuelian Bai,et al.  Width identification of transition zone between desert and oasis based on NDVI and TCI , 2020, Scientific Reports.

[17]  L. Du,et al.  Characteristics of vegetation activity and its responses to climate change in desert/grassland biome transition zones in the last 30 years based on GIMMS3g , 2019, Theoretical and Applied Climatology.

[18]  Jun Li,et al.  Effect of plant species on shrub fertile island at an oasis–desert ecotone in the South Junggar Basin, China , 2007 .

[19]  Guojia Fang,et al.  Variation in agricultural water demand and its attributions in the arid Tarim River Basin , 2018, The Journal of Agricultural Science.

[20]  Yanzhao Zhou,et al.  Progress in the study of oasis-desert interactions , 2016 .

[21]  Hadi Memarian,et al.  Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia , 2012 .

[22]  J. Thompson,et al.  Carbon emissions from land-use change and management in China between 1990 and 2010 , 2016, Science Advances.

[23]  Yuan-Ming Zhang,et al.  Divergent Responses of Plant Communities under Increased Land-Use Intensity in Oasis-Desert Ecotones of Tarim Basin☆ , 2020, Rangeland Ecology and Management.

[24]  Yaning Chen,et al.  Global perspective on hydrology, water balance, and water resources management in arid basins , 2009 .

[25]  H. Long,et al.  Effects of land use transitions due to rapid urbanization on ecosystem services: Implications for urban planning in the new developing area of China , 2014 .

[26]  P. Ciais,et al.  Characteristics, drivers and feedbacks of global greening , 2019, Nature Reviews Earth & Environment.

[27]  T. Yue,et al.  Land-cover changes of biome transition zones in Loess Plateau of China , 2013 .

[28]  David M Levinson,et al.  Predicting Land Use Change , 2009 .

[29]  M. Aniya,et al.  Rural sustainability under threat in Zimbabwe - Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model , 2009 .

[30]  Günther Fischer,et al.  Model based analysis of future land-use development in China , 2001 .

[31]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[32]  S. Vicente‐Serrano,et al.  Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring , 2014 .

[33]  Jinfeng Wang,et al.  Land Use/Cover Change Impacts on Water Table Change over 25 Years in a Desert-Oasis Transition Zone of the Heihe River Basin, China , 2015 .

[34]  Zhu Chenggang,et al.  Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China , 2009 .

[35]  Bibit Halliday Traut,et al.  The role of coastal ecotones: a case study of the salt marsh/upland transition zone in California , 2005 .

[36]  Zhi Li,et al.  Assessment of the Irrigation Water Requirement and Water Supply Risk in the Tarim River Basin, Northwest China , 2019, Sustainability.

[37]  Roger White,et al.  An Activity-Based Cellular Automaton Model to Simulate Land-Use Dynamics , 2012 .

[38]  PETER H. VERBURG,et al.  Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model , 2002, Environmental management.

[39]  Yuanhong Deng,et al.  Vegetation greening intensified soil drying in some semi-arid and arid areas of the world , 2020 .

[40]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[41]  Sergio M. Vicente-Serrano,et al.  Analysis of the atmospheric circulation pattern effects over SPEI drought index in Spain , 2019 .

[42]  Conghe Song,et al.  Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening? , 2017 .

[43]  Daniel L. Civco,et al.  Artificial Neural Networks for Land-Cover Classification and Mapping , 1993, Int. J. Geogr. Inf. Sci..

[44]  Manchun Li,et al.  Impacts of LUCC on soil properties in the riparian zones of desert oasis with remote sensing data: a case study of the middle Heihe River basin, China. , 2015, The Science of the total environment.

[45]  D. G. Thomas,et al.  Using artificial neural networks to predict future dryland responses to human and climate disturbances , 2019, Scientific Reports.

[46]  Xia Li,et al.  Defining agents' behaviors to simulate complex residential development using multicriteria evaluation. , 2007, Journal of environmental management.

[47]  Alireza Gharagozlou,et al.  Predicting Urban Land Use Changes Using a CA–Markov Model , 2014 .

[48]  Wei Zhang,et al.  Multidecadal, county-level analysis of the effects of land use, Bt cotton, and weather on cotton pests in China , 2018, Proceedings of the National Academy of Sciences.