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Volume 39, Issue 4 (2024)                   GeoRes 2024, 39(4): 459-470 | Back to browse issues page
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Ejtemaei B, Moghtaderi G. Spatial Analysis of the Impacts of Land Use, Climate and Water Resources on the Sustainability of Villages in the Mond Watershed. GeoRes 2024; 39 (4) :459-470
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1- Department of Geography, Faculty of Social Sciences, Payame Noor University, Tehran, Iran
* Corresponding Author Address: Department of Geography, Faculty of Social Sciences, Payame Noor University, Artesh Boulevard, First of Shahrak-e Naft, Nakhl Street, Tehran, Iran. (ejtemaei@pnu.ac.ir)
Abstract   (653 Views)
Aims: This study aims to investigate the effects of land use, climate, and water resources on the sustainability of villages in the Mond watershed.
Methodology: This study integrates analytical methods using satellite data, statistical analyses, and literature reviews to comprehensively analyze and assess the status of water resources, land use changes, and climatic conditions in the villages of the Mond watershed. Data were collected using the Google Earth Engine platform. These data include remote sensing and satellite images, such as GRACE data for assessing groundwater changes and MODIS and Landsat data for evaluating land use changes from 2015 to 2023. For analyzing land use changes, satellite images and GIS-based spatial statistics were utilized to analyze spatial distribution maps of the villages.
Findings: The villages in the Mond watershed faced multiple challenges, including declining water resources, climatic fluctuations and increased pressure on natural resources. A gradual reduction in groundwater reserves, as well as a decrease in water coverage area from 2015 to 2023, was observed. Excessive pressure on groundwater led to an annual decline of nearly one meter, resulting in the most significant land use changes occurring in agricultural lands (37%) and residential areas (13.6%).
Conclusion: Optimal water resource management and adaptation of cropping patterns to climatic conditions are essential for the sustainability of the villages in the Mond watershed.
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References
1. Ahmadzadeh H, Davarpanah M (2023). Spatial analysis of flood risk with a land use planning and management approach in Urmia City. Geography and Environmental Hazards. 12(2):63-80. [Persian] [Link]
2. Asghari Saraskanroud S, Mohammadnejad V, Emami H (2019). Analysis of land use changes using pixel-based and object-oriented methods and the effects of these changes on soil erosion (case study: Maragheh county). Quantitative Geomorphological Research. 8(1):160-178. [Persian] [Link]
3. Cui J, Zhu M, Liang Y, Qin G, Li J, Liu Y (2022). Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on google earth engine from 2000 to 2020. International Journal of Geo-Information. 11(3):163. [Link] [DOI:10.3390/ijgi11030163]
4. De Sousa JHS, Moreira AR, Nascimento AAD, Ribeiro GDN, Oliveira Neto JND, Prado Júnior LSD (2022). Assessment of land use and cover in the Sucuru Watershed using Google Earth Engine. Revista Verde De Agroecologia e Desenvolvimento Sustentável. 17(4):235-241. [Link] [DOI:10.18378/rvads.v17i4.9621]
5. Denaee Daryan M, Riyahi V (2021). Investigation of land use changes (case study: Azarshahr city). Geography and Human Relationships. 3(4):183-200. [Persian] [Link]
6. Famiglietti JS (2014). The global groundwater crisis. Nature Climate Change. 4(11):945-948. [Link] [DOI:10.1038/nclimate2425]
7. Farokhnia A, Morid S, Dalaver M (2018). Study of land use change in the Urmia lake water shed based on landsat-tm images and pixel-based and object-based classification techniques. Iranian Journal of Irrigation and Drainage. 12(4):823-839. [Persian] [Link]
8. Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, et al (2005). Global consequences of land use. Science. 309(5734):570-574. [Link] [DOI:10.1126/science.1111772]
9. Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, et al (2011). Solutions for a cultivated planet. Nature. 478(7369):337-342. [Link] [DOI:10.1038/nature10452]
10. Gautam L, Rai R (2022). Land use and land cover change analysis using google earth engine in Manamati watershed of Kathmandu district, Nepal. The Third Pole Journal of Geography Education. 22:49-60. [Link] [DOI:10.3126/ttp.v22i01.52560]
11. Gleeson T, Wada Y, Bierkens MF, Van Beek LP (2012). Water balance of global aquifers revealed by groundwater footprint. Nature. 488(7410):197-200. [Link] [DOI:10.1038/nature11295]
12. Kazemi Garajeh M, Haji F, Tohidfar M, Sadeqi A, Ahmadi R, Kariminejad N (2024). Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine. Scientific Reports. 14(1):5469. [Link] [DOI:10.1038/s41598-024-56160-9]
13. Lal R (2015). Restoring soil quality to mitigate soil degradation. Sustainability. 7(5):5875-5895. [Link] [DOI:10.3390/su7055875]
14. Lambin EF, Meyfroidt P (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences. 108(9):3465-3472. [Link] [DOI:10.1073/pnas.1100480108]
15. Osman MA, Abdel-Rahman EM, Onono JO, Olaka LA, Elhag MM, Adan M, et al (2023). Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA-artificial neural network model. PLoS One. 18(7):e0288694. [Link] [DOI:10.1371/journal.pone.0288694]
16. Pande CB (2022). Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach. Geocarto International. 37(26):13860-13880. [Link] [DOI:10.1080/10106049.2022.2086622]
17. Rockstrom J, Steffen W, Noone K, Persson Å, Chapin FS, Lambin EF, et al (2009). A safe operating space for humanity. Nature. 461(7263):472-475. [Link] [DOI:10.1038/461472a]
18. Seto KC, Güneralp B, Hutyra LR (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences. 109(40):16083-16088. [Link] [DOI:10.1073/pnas.1211658109]
19. Seto KC, Reenberg A, Boone CG, Fragkias M, Haase D, Langanke T, et al (2012). Urban land teleconnections and sustainability. Proceedings of the National Academy of Sciences. 109(20):7687-7692. [Link] [DOI:10.1073/pnas.1117622109]
20. Siddique AB, Rayhan E, Sobhan F, Das N, Fazal MA, Riya SC, et al (2024). Spatio-temporal analysis of land use and land cover changes in a wetland ecosystem of Bangladesh using a machine-learning approach. Frontiers in Water. 6:1394863. [Link] [DOI:10.3389/frwa.2024.1394863]
21. Tesfaye W, Elias E, Warkineh B, Tekalign M, Abebe G (2024). Modeling of land use and land cover changes using google earth engine and machine learning approach: Implications for landscape management. Environmental Systems Research. 13(1):31. [Link] [DOI:10.1186/s40068-024-00366-3]
22. Thornton PK, Herrero M (2015). Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nature Climate Change. 5(9):830-836. [Link] [DOI:10.1038/nclimate2754]
23. Xiong H, Ma C, Li M, Tan J, Wang Y (2023). Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China. Science of the Total Environment. 866:161430. [Link] [DOI:10.1016/j.scitotenv.2023.161430]
24. Zhang M, Kafy AA, Xiao P, Han S, Zou S, Saha M, et al (2023). Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China. Urban Climate. 47:101347. [Link] [DOI:10.1016/j.uclim.2022.101347]