Assessing the Spatial Pattern of Irrigation Demand under Climate Change in Arid Area

Studying the pattern of agricultural water demand under climate change has great significance for the regional water resources management, especially in arid areas. In this study, the future pattern of the irrigation demand in Hotan Oasis in Xinjiang Uygur Autonomous Region in Northwest China, including Hotan City, Hotan County, Moyu County and Luopu County, was assessed based on the general circulation models (GCMs) and the Surface Energy Balance System model (SEBS). Six different scenarios were used based on the GCMs of BCC_CSM1.1, HadGEM2-ES and MIROC-ESM-CHEM under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. The results showed that the method integrating the GCMs and SEBS to predict the spatial pattern was useful. The irrigation demand of Hotan Oasis will increase in 2021–2040. The annual irrigation demand of Hotan City is higher, with 923.2 and 936.2 mm/a in 2021–2030 and 2031–2040, respectively. The other three regions (Hotan County, Moyu County and Luopu County) are lower in the six scenarios. The annual irrigation demand showed a spatial pattern of high in the middle, low in the northwest and southeast under the six scenarios in 2021–2040. The study can provide useful suggestions on the water resources allocation in different regions to protect water resources security in arid areas.

[1]  Chunlin Huang,et al.  Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China , 2017 .

[2]  Chong Xu,et al.  Preliminary Assessment of Simulations of Climate Changes over China by CMIP5 Multi-Models , 2012 .

[3]  Haowen Yan,et al.  Daily Evapotranspiration Estimation at the Field Scale: Using the Modified SEBS Model and HJ-1 Data in a Desert-Oasis Area, Northwestern China , 2018 .

[4]  Ga Zhuo,et al.  Study on daily surface evapotranspiration with SEBS in Tibet Autonomous Region , 2014, Journal of Geographical Sciences.

[5]  E. Takle,et al.  The Relationships between Climatic and Hydrological Changes in the Upper Mississippi River Basin: A SWAT and Multi-GCM Study , 2010 .

[6]  A. Kurban,et al.  Multi–Model Ensemble Approaches to Assessment of Effects of Local Climate Change on Water Resources of the Hotan River Basin in Xinjiang, China , 2017 .

[7]  F. Anctil,et al.  Assessing the Climatic and Temporal Transposability of the SWAT Model across a Large Contrasted Watershed , 2017 .

[8]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[9]  Qin Li,et al.  Spatio-temporal pattern and changes of evapotranspiration in arid Central Asia and Xinjiang of China , 2012 .

[10]  A. Stein,et al.  A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration , 2017 .

[11]  Javier Senent-Aparicio,et al.  Using SWAT and Fuzzy TOPSIS to Assess the Impact of Climate Change in the Headwaters of the Segura River Basin (SE Spain) , 2017 .

[12]  E. Wood,et al.  Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching , 2010 .

[13]  B. Barnhart,et al.  SWAT hydrologic model parameter uncertainty and its implications for hydroclimatic projections in snowmelt-dependent watersheds , 2014 .

[14]  Jeanine Engelbrecht,et al.  Particular uncertainties encountered in using a pre-packaged SEBS model to derive evapotranspiration in a heterogeneous study area in South Africa , 2011 .

[15]  A. Koutroulis,et al.  Evaluation of precipitation and temperature simulation performance of the CMIP3 and CMIP5 historical experiments , 2016, Climate Dynamics.

[16]  Raghavan Srinivasan,et al.  A GIS-BASED REGIONAL PLANNING TOOL FOR IRRIGATION DEMAND ASSESSMENT AND SAVINGS USING SWAT , 2005 .

[17]  Marinus G. Bos,et al.  Assessment of Water Availability and Consumption in the Karkheh River Basin, Iran—Using Remote Sensing and Geo-statistics , 2010 .

[18]  Zhongbo Su,et al.  Reconnoitering the effect of shallow groundwater on land surface temperature and surface energy balance using MODIS and SEBS , 2011 .

[19]  Zeyong Hu,et al.  An analysis on the influence of spatial scales on sensible heat fluxes in the north Tibetan Plateau based on Eddy covariance and large aperture scintillometer data , 2017, Theoretical and Applied Climatology.

[20]  F. Zwiers,et al.  Changes in temperature and precipitation extremes in the CMIP5 ensemble , 2013, Climatic Change.

[21]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[22]  Junye Wang,et al.  Assessing climate change impacts on fresh water resources of the Athabasca River Basin, Canada. , 2017, The Science of the total environment.

[23]  A. K. Verma,et al.  Assessing Climate Change Impact on Water Balance Components of a River Basin Using SWAT Model , 2015, Water Resources Management.

[24]  Marta M. Jankowska,et al.  Space versus Place in Complex Human-Natural Systems: Spatial and Multi-level Models of Tropical Land Use and Cover Change (LUCC) in Guatemala. , 2012, Ecological modelling.

[25]  Norman B. Wood,et al.  Evaluation of current and projected Antarctic precipitation in CMIP5 models , 2016, Climate Dynamics.

[26]  Albert Olioso,et al.  Evaluation and Aggregation Properties of Thermal Infra-Red-Based Evapotranspiration Algorithms from 100 m to the km Scale over a Semi-Arid Irrigated Agricultural Area , 2017, Remote. Sens..

[27]  M. Rahimzadegan,et al.  Evaluation of SEBS, SEBAL, and METRIC models in estimation of the evaporation from the freshwater lakes (Case study: Amirkabir dam, Iran) , 2018, Journal of Hydrology.

[28]  Jun Wen,et al.  Validation of evapotranspiration and its long-term trends in the Yellow River source region , 2017 .

[29]  Fulong Chen,et al.  Quantifying the Effects of Land Surface Change on Annual Runoff Considering Precipitation Variability by SWAT , 2016, Water Resources Management.

[30]  G. Fu,et al.  Uncertainties in SWAT extreme flow simulation under climate change , 2014 .

[31]  Wei Xia,et al.  Distribution of Actual Evapotranspiration over Qaidam Basin, an Arid Area in China , 2013, Remote. Sens..

[32]  Pan Liu,et al.  Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters , 2015 .

[33]  Z. Su The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .