Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2021, Vol. 13 Issue (11): 1122-1141    DOI: 10.1007/s40333-021-0092-3     CSTR: 32276.14.s40333-021-0092-3
Research article     
Using statistical models and GIS to delimit the groundwater recharge potential areas and to estimate the infiltration rate: A case study of Nadhour-Sisseb-El Alem Basin, Tunisia
Ali SOUEI1,2,*(), Taher ZOUAGHI3
1Georesources Laboratory, Water Researches and Technologies Center (WRTC) of Borj Cedria, Soliman 8020, Tunisia
2Department of Geology, FST, University of Tunis El Manar, Tunis C P 2092, Tunisia
3Department of Geo-Exploration Techniques, Faculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Download: HTML     PDF(2645KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The water resources of the Nadhour-Sisseb-El Alem Basin in Tunisia exhibit semi-arid and arid climatic conditions. This induces an excessive pumping of groundwater, which creates drops in water level ranging about 1-2 m/a. Indeed, these unfavorable conditions require interventions to rationalize integrated management in decision making. The aim of this study is to determine a water recharge index (WRI), delineate the potential groundwater recharge area and estimate the potential groundwater recharge rate based on the integration of statistical models resulted from remote sensing imagery, GIS digital data (e.g., lithology, soil, runoff), measured artificial recharge data, fuzzy set theory and multi-criteria decision making (MCDM) using the analytical hierarchy process (AHP). Eight factors affecting potential groundwater recharge were determined, namely lithology, soil, slope, topography, land cover/use, runoff, drainage and lineaments. The WRI is between 1.2 and 3.1, which is classified into five classes as poor, weak, moderate, good and very good sites of potential groundwater recharge area. The very good and good classes occupied respectively 27% and 44% of the study area. The potential groundwater recharge rate was 43% of total precipitation. According to the results of the study, river beds are favorable sites for groundwater recharge.

Key words： potential recharge      remote sensing      statistical models      MCDM      Nadhour-Sisseb-El Alem Basin     
Received: 20 January 2021      Published: 10 November 2021
Corresponding Authors: Ali SOUEI (E-mail: soueiali2014@gmail.com)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Ali SOUEI
Taher ZOUAGHI
Cite this article:

Ali SOUEI, Taher ZOUAGHI. Using statistical models and GIS to delimit the groundwater recharge potential areas and to estimate the infiltration rate: A case study of Nadhour-Sisseb-El Alem Basin, Tunisia. Journal of Arid Land, 2021, 13(11): 1122-1141.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0092-3     OR     http://jal.xjegi.com/Y2021/V13/I11/1122

Fig. 1 Location (a) and Landsat image (resolution 30 m) (b) of the study area, Nadhour-Sisseb-El Alem Basin, Tunisia
Fig. 2 Hydrogeologic cross section of the aquifer system in Nadhour-Sisseb-El Alem Basin modified from Souei et al. (2018). S, south; N, north.
Fig. 3 Processing chain of making potential zone map for groundwater by using GIS and remote sensing SRTM (shuttle radar topography mission)
Fig. 4 Major and minor relationship among factors influencing the potential groundwater recharge
Factor Calculation process RW
Lithology (Li) 4×1+1×0.5 4.5
Soil (S) 4×1+1×0.5 4.5
Slope (Sl) 3×1+1×0.5 3.5
Topography (T) 3×1+1×0.5 3.5
Land cover/land use (LU/C) 2×1+3×0.5 3.5
Drainage density (Dd) 2×1+2×0.5 3.0
Lineaments density (Ld) 1×1+3×0.5 2.5
Runoff (Ru) 0×1+3×0.5 1.5
Table 1 Proposed relative weight (RW) obtained from the relationships among factors influencing the potential groundwater recharge
Factor Li S Sl T LU/C Dd Ld Ru W
Li 1.0 0.17
S 4.5 1.0 0.17
Sl 3.5 3.5 1.0 0.13
T 3.5 3.5 3.5 1.0 0.13
LU/C 3.5 3.5 3.5 3.5 1.0 0.13
Dd 3.0 3.0 3.0 3.0 3.0 1.0 0.11
Ld 2.5 2.5 2.5 2.5 2.5 2.5 1.0 0.10
Ru 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.0 0.06
Table 2 Comparison matrix obtained for eight factors for the analytic hierarchy process (AHP)
Factor Classes PR RW AR W Effective rate (ER)
Li Marls and sandy marls 0.15 4.5 0.68 0.17 0.11
Limestone and dolomites 0.43 1.94 0.33
Sand and sandstone 0.63 2.84 0.48
Alluviums and gravelly sand 1.00 4.50 0.76
S Building 0.07 4.5 0.32 0.17 0.05
Hydromorphic and allomorphic soils 0.15 0.68 0.11
Poor soil 0.52 2.34 0.40
Rendzine soil 0.83 3.74 0.63
Mineral soil and wadi beds 1.00 4.50 0.76
Sl 0.0-3.4 1.00 3.5 3.50 0.13 0.46
3.4-9.4 0.78 2.73 0.36
9.4-18.0 0.58 2.03 0.27
18.0-44.0 0.04 0.14 0.02
T 400-500 0.20 3.5 0.70 0,13 0.09
300-400 0.40 1.40 0.18
200-300 0.60 2.10 0.28
100-200 0.80 2.80 0.37
0-100 1.00 3.50 0.46
LU/C Building 0.06 3.5 0.21 0.13 0.03
Forest 0.24 0.84 0.11
Agricultural and waste land 0.65 2.28 0.30
Surface water body 1.00 3.50 0.46
Dd 0.00-0.62 1.00 3.0 3.00 0.11 0.34
0.62-1.25 0.50 1.50 0.17
1.25-1.87 0.20 0.60 0.07
1.87-2.50 0.02 0.06 0.01
Ld 0-10 0.22 2.5 0.60 0.10 0.06
10-30 0.66 1.65 0.16
30-45 1.00 2.50 0.24
Ru Building 0.07 1.5 0.11 0.06 0.01
Marls and clay 0.19 0.29 0.02
Limestone and dolomites with fractures 0.52 0.78 0.04
Sand and sandstone 0.65 0.98 0.06
Alluviums 0.81 1.22 0.07
Wadi beds 1.00 1.50 0.08
Table 3 Classes and ratings for determining potential groundwater recharge zones
Fig. 5 Lithology of the study area
Fig. 6 Soil of the study area
Fig. 7 Slope gradient map of the study area
Fig. 8 Topography of the study area
Fig. 9 Land cover/use of the study area
Fig. 10 Drainage density of the study area
Fig. 11 Lineament density of the study area
Fig. 12 Runoff of the study area
n 5 6 7 8 9 10 11 12
RI 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.54
Table 4 Arbitrary index based on the number of criteria (Saaty, 1980)
Factor Theoretical weight Effective weight
Value Proportion (%) Mean (%) Min (%) Max (%) SD (%)
Li 4.5 17 22 7 45 17
S 4.5 17 20 3 39 16
Sl 3.5 13 14 1 41 17
T 3.5 13 14 7 33 11
LU/C 3.5 13 12 3 51 22
Dd 3.0 11 8 1 56 24
Ld 2.5 9 8 12 53 21
Ru 1.5 6 2 2 31 11
Table 5 Single parameter sensitivity analysis
Fig. 13 Groundwater recharge potential areas. WRI, water recharge index.
Period 4 Feb-17 Aug 2004 11 Feb-4 Aug 2005 25 Feb-15 Aug 2006
Station S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4
NM 51 45 28 - 39 38 38 25 32 31 31 7
VM (×106 m3) 1.615 0.740 0.105 - 2.542 2.023 1.296 0.347 1.675 1.292 0.641 0.081
Reach - R1 R2 R3 - R1 R2 R3 - R1 R2 R3
VL (×106 m3) - 0.875 0.635 - - 0.519 0.727 0.949 - 0.383 0.651 0.560
PVL (%) - 54 86 - - 20 36 73 - 23 50 87
Table 6 Details of the three artificial recharge operations
Period Submersed bed area (×103 m2) Artificial recharge (×106 m3) PE
(mm) (×106 m3) (%)
Feb-Aug 2004 115 1.6 11.11 0.127 8
Feb-Aug 2005 2.5 1.19 0.136 5
Feb-Aug 2006 1.7 1.26 0.145 9
Table 7 Determination of the potential evaporation (PE) in Kairouan meteorological station
Groundwater recharge potential index Infiltration capacity Average precipitation for the period 1997-2013 (mm/a) Percentage of the area (%) Infiltration+PE (×106 m3/a) PE
(×106 m3/a)		 Infiltration (×106 m3/a)
Poor 0.04 364 1 3.64 0.25 3.39
Low 0.17 11 7.86 0.55 7.31
Moderate 0.31 17 20.30 1.42 18.88
Good 0.45 44 76.30 5.34 70.96
Very good 0.69 27 71.79 5.03 66.76
Total 100 179.89 12.59 167.30
Table 8 Determination of the infiltration rate
Fig. 14 Aquifer recharge and groundwater extraction (GWRD, 2013a) (a); temporal variation of the piezometric level (GWRD, 2012) (b); and average annual precipitation (c) (GWRD, 2013b).
[1]   Al Saud M. 2010. Mapping potential areas for groundwater storage in Wadi Aurnah Basin, western Arabian Peninsula, using remote sensing and geographic information system techniques. Hydrogeology Journal, 18: 1481-1495.
doi: 10.1007/s10040-010-0598-9
[2]   Aouragh M H, Essahlaoui A, El Ouali A, et al. 2016. Groundwater potential of Middle Atlas plateaus, Morocco, using fuzzy logic approach, GIS and remote sensing. Geomatics, Natural Hazards and Risk, 8(2): 194-206.
doi: 10.1080/19475705.2016.1181676
[3]   Ayadi M. 2014. Artificial recharge of underground aquifers in Tunisia. In:Ministry of Agriculture and Hydraulic Resources. General Water Resources Direction Report Note TN190. Tunis, Tunisia. (in French).
[4]   Bresciani E. 2011. Modeling of climatic, topographic, geological and anthropogenic controls on underground flows in the basement area. PhD Dissertation. La Chapelle-Thouarault: University Rennes 1. (in French).
[5]   Buckley J J. 1984. The multiple judge, multiple criteria ranking problem: a fuzzy set approach. Fuzzy Sets and Systems, 13(1): 25-37.
doi: 10.1016/0165-0114(84)90024-1
[6]   Chenini I, Ben Mammou A, El May M. 2010. Groundwater recharge zone mapping using GIS-based multi-criteria analysis: a case study in Central Tunisia (Maknassy Basin). Water Resources Management, 24: 921-939.
doi: 10.1007/s11269-009-9479-1
[7]   Elewa H H, Qaddah A A. 2011. Groundwater potentiality mapping in the Sinai Peninsula, Egypt, using remote sensing and GIS-watershed-based modeling. Hydrogeology Journal, 19: 613-628.
doi: 10.1007/s10040-011-0703-8
[8]   Freeze R A, Witherspoon P A. 1967. Theoretical analysis of regional groundwater flow: 2. Effect of water-table configuration and subsurface permeability variation. Water Resources Research, 3(2): 623-634.
doi: 10.1029/WR003i002p00623
[9]   Greenbaum D. 1985. Review of remote sensing applications to groundwater exploration in basement and regolith. British Geological Survey. Nottingham, UK.
[10]   GWRD. 2012. Piezometric level of underground aquifers in Tunisia. In: General Water Resources Direction Report Note TN180. Tunis, Tunisia. (in French)
[11]   GWRD. 2013a. Precipitation in Tunisia. In: General Water Resources Direction Report Note TN179. Tunis, Tunisia. (in French)
[12]   GWRD General Water Resources Direction. 2013b. Aquifer recharge and groundwater extraction. In: General Water Resources Direction Report Note TN380. Tunis, Tunisia. (in French)
[13]   Haitjema H M, Mitchell-Bruker S. 2005. Are water tables a subdued replica of the topography? Ground Water, 43(6): 781-786.
[14]   Harlin J M, Wijeyawickrema C. 1985. Irrigation and groundwater depletion in Caddo County, Oklahoma. Journal of the American Water Resources Association, 21(1): 15-22.
doi: 10.1111/jawr.1985.21.issue-1
[15]   Hillel D. 1982. Introduction to Soil Physcics. San Diego: Academic press. 22.
[16]   Ibn Ali Z, Triki I, Lajili-Ghezal L, et al. 2017. A method to estimate aquifer artificial recharge from a hill dam in Tunisia. Journal of Arid Land, 9(2): 244-255.
doi: 10.1007/s40333-017-0002-x
[17]   Jaiswal R K, Mukherjee S, Krishnamurthy J, et al. 2003. Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development an approach. International Journal of Remote Sensing, 24(5): 993-1008.
doi: 10.1080/01431160210144543
[18]   Jasrotia A S, Bhagat B D, Kumar A, et al. 2013. Remote sensing and GIS approach for delineation of groundwater potential and groundwater quality zones of western Doon Valley, Uttarakhand, India. Journal of the Indian Society of Remote Sensing, 41: 365-377.
doi: 10.1007/s12524-012-0220-9
[19]   Jha M K, Chowdhury A, Chowdary V M, et al. 2007. Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resources Management, 21: 427-467.
doi: 10.1007/s11269-006-9024-4
[20]   Kallali H, Anan M, Jellali S, et al. 2007. GIS-based multi-criteria analysis for potential wastewater aquifer recharge sites. Desalination, 215(1-3): 111-119.
doi: 10.1016/j.desal.2006.11.016
[21]   Kirpich Z P. 1940. Time of concentration of small agricultural watersheds. International Journal of Civil Engineering, 10(6): 362.
[22]   Krishnamurthy J, Mani A, Jayaraman V, et al. 2000. Groundwater resources development in hard rock terrain: An approach using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 2(3-4): 204-215.
doi: 10.1016/S0303-2434(00)85015-1
[23]   Larue J P. 2000. Contribution of concentrated runoff to the erosion of cultivated sandy soils in the west of the Paris Basin: the example of the Dué and Narais basins. Ingenieries-EAT, 21: 51-62. (in French)
[24]   Leberling H. 1981. On finding compromise solutions in multicriteria problems using the fuzzy min-operator. Fuzzy Sets and Systems, 6(2): 105-118.
doi: 10.1016/0165-0114(81)90019-1
[25]   Leduc C, Favreau G, Schroeter P. 2001. Long-term rise in a Sahelian water-table: the Continental Terminal in South-West Niger. Journal of Hydrology, 243(1-2): 43-54.
doi: 10.1016/S0022-1694(00)00403-0
[26]   Lowery B, Hickey W J, Arshad M A, et al. 1997. Soil water parameters and soil quality. In: Doran J W, A J Jones. Methods for Assessing Soil Quality. Madison: Soil Science Society of America, Inc., 143-155.
[27]   Luo W. 1900. Quantifying groundwater-sapping landforms with a hypsometric technique. Journal of Geophysical Research, 105(E1): 1685-1694.
doi: 10.1029/1999JE001096
[28]   Machiwal D, Jha M K, Mal B C. 2011. Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resources Management, 25: 1359-1386.
doi: 10.1007/s11269-010-9749-y
[29]   Madrucci V, Fabio T, de Araújo C C. 2008. Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, São Paulo State, Brazil. Journal of Hydrology, 357(3-4): 153-173.
doi: 10.1016/j.jhydrol.2008.03.026
[30]   Morsli B M, Mazour N, Mededjel A H, et al. 2004. Influence of land use on the risks of runoff and erosion on the semi-arid area of northwestern Algeria. Drought, 15(1): 96-104. (in French)
[31]   Napolitano P, and Fabbri A. 1996. Single-parameter sensitivity analysis for aquifer vulnerability assessment using DRASTIC and SINTACS. In:HydroGIS 96: Application of Geographical Information Systems in Hydrology and Water Resources Management, Proceedings of Vienna Conference. Vienna: IAHS Pub, 235: 559-566.
[32]   Preeja K R, Joseph S, Thomas J, et al. 2011. Identification of groundwater potential zones of a tropical river basin (Kerala, India) using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 39: 83-94
doi: 10.1007/s12524-011-0075-5
[33]   Rashid M, Lone M, Ahmed S. 2012. Integrating geospatial and ground geophysical information as guidelines for groundwater potential zones in hard rock terrains of south India. Environmental Monitoring and Assessment, 184: 4829-4839.
doi: 10.1007/s10661-011-2305-2
[34]   Rahman A, Kumar S, Fazal S, et al. 2012. Assessment of land use/land cover change in the north-west district of Delhi using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 40: 689-697.
doi: 10.1007/s12524-011-0165-4
[35]   Rezaei F, Hamid R, Safavi D, et al. 2013. Groundwater vulnerability assessment using fuzzy logic: A case study in the Zayandehrood Aquifers, Iran. Environmental Management, 51: 267-277.
doi: 10.1007/s00267-012-9960-0
[36]   Riad P H, Billib M H, Hassan A A, et al. 2011a. Water scarcity management in a semi-arid area in Egypt: overlay weighted model and Fuzzy logic to determine the best locations for artificial recharge of groundwater. Nile Basin, Water Science and Engineering Journal, 4(1): 24-35.
[37]   Riad P H, Billib M, Hassan A A, et al. 2011b. Application of the overlay weighted model and Boolean logic to determine the best locations for artificial recharge of groundwater. Journal of Urban and Environmental Engineering, 5(2): 57-66.
doi: 10.4090/juee
[38]   Rognon P. 2000. How to develop artificial groundwater recharge in dry regions. Drought, 11: 289-296. (in French)
[39]   Saaty T L. 1980. The Analytic Hierarchy Process:Planning, Priority Setting, Resource Allocation. New York (NY): McGraw-Hill International Book Company.
[40]   Sauvadet M, Raclot DA, Ben Slimane A, et al. 2012. Determinism of runoff and water erosion from the plot to the slope in a marly Mediterranean environment. Moroccan Journal of Agronomic and Veterinary Sciences, 1: 41-46. (in French)
[41]   Shaban A, Khawlie M, Abdallah C. 2006. Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon. Hydrogeology Journal, 14: 433-443.
doi: 10.1007/s10040-005-0437-6
[42]   Souei A. 2012. Geophysical and hydrogeological characterization of the aquifer systems of the Sisseb-El Alem basin, Central-eastern Tunisia. MSc Thesis. Tunis: University of Tunis El Manar. (in French)
[43]   Souei A, Zouaghi T. 2017. Use of GIS for the integrated management of water resources in the region of Central-Eastern Tunisia: Kairouan Nord. Arid Regions Review, 41(1): 265-268. (in French)
[44]   Souei A, Mhimdi S, Zouaghi T. 2017. Extraction by remote sensing of fracture networks in the region of Sisseb-El Alem; North Kairouan; hydrogeological implication. In: Abstracts of the 1st Atlas Georesources International Congress. Hammamet: LGR, 20-22. (in French)
[45]   Souei A, Atawa M, Zouaghi T. 2018. Hydrogeological framework and geometry modeling via joint gravity and borehole parameters, the Nadhour-Sisseb-El Alem Basin (central-eastern Tunisia). Journal of African Earth Sciences, 139: 133-164.
doi: 10.1016/j.jafrearsci.2017.12.006
[46]   Souei A, Zouaghi T. 2018. 2D seismic reflection contribution to structural and geometric study of Cenozoic aquifer systems in the Sisseb El-Alem basin, central-eastern Tunisia. Arabian Journal of Geosciences 11: 689.
doi: 10.1007/s12517-018-4036-y
[47]   Souei A. 2019. Contribution of geophysics, remote sensing, GIS, and hydrochemistry to the hydrological and hydrogeological study of aquifer systems in the Nadhour-Sisseb-El Alem basin; Central Eastern Tunisia. PhD Dissertation. Tunis: Tunis-El Manar University.
[48]   Souei A, Atawa M, Zouaghi T.2021. 3D seismic velocities modeling and its application in the appreciation of the complex deep framework; the Sisseb-El Alem basin, central-eastern Tunisia. Arabian Journal of Geosciences. 14, 2001 (2021). https://doi.org/10.1007/s12517-021-08300-y.
[49]   Toth J. 1963. A theoretical analysis of groundwater flow in small drainage basins. Journal of Geophysical Research, 68(16): 4795-4812.
doi: 10.1029/JZ068i016p04795
[50]   Tweed S O, Leblanc M, Webb J A, et al. 2007. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments, southeastern Australia. Hydrogeology Journal, 15(1): 75-96.
doi: 10.1007/s10040-006-0129-x
[51]   Wang Y, Liu Y, Jin J. 2018. Contrast effects of vegetation cover change on evapotranspiration during a revegetation period in the Poyang Lake Basin, China. Forests, 9(4): 217.
doi: 10.3390/f9040217
[52]   Winter T C. 2001. The concept of hydrologic landscapes. Journal of the American Water Resources Association, 37(2): 335-349.
doi: 10.1111/jawr.2001.37.issue-2
[53]   Yeh H F, Lee C H, Hsu K C, et al. 2009. GIS for the assessment of the groundwater recharge potential zone. Environmental Geology, 58: 185-195.
doi: 10.1007/s00254-008-1504-9
[54]   Yeh H F, Cheng YS, Lin H I, et al. 2016. Mapping groundwater recharge potential zone using a GIS approach in Hualian River, Taiwan. Sustainable Environment Research, 26(1): 33-43.
doi: 10.1016/j.serj.2015.09.005
[55]   Zadeh L A. 1965. Fuzzy sets. Information and Control, 8(3): 338-353.
doi: 10.1016/S0019-9958(65)90241-X
[56]   Zadeh L A. 1987. A computational theory of dispositions. International Journal of Intelligent Systems, 2: 39-63.
[57]   Zaoui S. 2009. Contribution of GIS in the study of the sub-watershed of Sidi Ayad (Haute Moulouya, Morocco). MSc Thesis. Meknes: Moulay Ismail University. (in French)
[1] Suzan ISMAIL, Hamid MALIKI. Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate: A case study of the Erbil City, Iraq[J]. Journal of Arid Land, 2024, 16(9): 1214-1231.
[2] MAO Zhengjun, WANG Munan, CHU Jiwei, SUN Jiewen, LIANG Wei, YU Haiyong. Feature extraction and analysis of reclaimed vegetation in ecological restoration area of abandoned mines based on hyperspectral remote sensing images[J]. Journal of Arid Land, 2024, 16(10): 1409-1425.
[3] ZHAO Xiaohan, HAN Dianchen, LU Qi, LI Yunpeng, ZHANG Fangmin. Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index[J]. Journal of Arid Land, 2023, 15(8): 920-939.
[4] Orhan DENGİZ, İnci DEMİRAĞ TURAN. Soil quality assessment for desertification based on multi-indicators with the best-worst method in a semi-arid ecosystem[J]. Journal of Arid Land, 2023, 15(7): 779-796.
[5] LONG Yi, JIANG Fugen, DENG Muli, WANG Tianhong, SUN Hua. Spatial-temporal changes and driving factors of eco- environmental quality in the Three-North region of China[J]. Journal of Arid Land, 2023, 15(3): 231-252.
[6] SUN Liquan, GUO Huili, CHEN Ziyu, YIN Ziming, FENG Hao, WU Shufang, Kadambot H M SIDDIQUE. Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China[J]. Journal of Arid Land, 2023, 15(1): 34-51.
[7] HUANG Xiaoran, BAO Anming, GUO Hao, MENG Fanhao, ZHANG Pengfei, ZHENG Guoxiong, YU Tao, QI Peng, Vincent NZABARINDA, DU Weibing. Spatiotemporal changes of typical glaciers and their responses to climate change in Xinjiang, Northwest China[J]. Journal of Arid Land, 2022, 14(5): 502-520.
[8] YAO Kaixuan, Abudureheman HALIKE, CHEN Limei, WEI Qianqian. Spatiotemporal changes of eco-environmental quality based on remote sensing-based ecological index in the Hotan Oasis, Xinjiang[J]. Journal of Arid Land, 2022, 14(3): 262-283.
[9] WANG Jinjie, DING Jianli, GE Xiangyu, QIN Shaofeng, ZHANG Zhe. Assessment of ecological quality in Northwest China (2000-2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality[J]. Journal of Arid Land, 2022, 14(11): 1196-1211.
[10] MA Xiumei, ZHOU Kefa, WANG Jinlin, CUI Shichao, ZHOU Shuguang, WANG Shanshan, ZHANG Guanbin. Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing[J]. Journal of Arid Land, 2022, 14(1): 102-114.
[11] WU Shupu, GAO Xin, LEI Jiaqiang, ZHOU Na, GUO Zengkun, SHANG Baijun. Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index[J]. Journal of Arid Land, 2022, 14(1): 14-33.
[12] Laura B RODRIGUEZ, Silvia S TORRES ROBLES, Marcelo F ARTURI, Juan M ZEBERIO, Andrés C H GRAND, Néstor I GASPARRI. Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina[J]. Journal of Arid Land, 2021, 13(9): 918-933.
[13] WANG Shanshan, ZHOU Kefa, ZUO Qiting, WANG Jinlin, WANG Wei. Land use/land cover change responses to ecological water conveyance in the lower reaches of Tarim River, China[J]. Journal of Arid Land, 2021, 13(12): 1274-1286.
[14] Mahdi BOROUGHANI, Sima POURHASHEMI, Hamid GHOLAMI, Dimitris G KASKAOUTIS. Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia[J]. Journal of Arid Land, 2021, 13(11): 1103-1121.
[15] WANG Jie, LIU Dongwei, MA Jiali, CHENG Yingnan, WANG Lixin. Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin[J]. Journal of Arid Land, 2021, 13(1): 40-55.