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Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal

Email: aml@fe.up.pt

Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email: jzzhang@mail.xjtu.edu.cn


Changes in Water Quality and Quantity of the Northern Iran's Wetlands under Land Use Change

Journal of Environmental Accounting and Management 12(3) (2024) 285--297 | DOI:10.5890/JEAM.2024.09.005

Azita Mehrani$^{1}$, Khosro Shahidi Hamedani$^{2}$, Dara Shahidi Hamedani$^{3}$

$^{1}$ Faculty of Natural Resources and Environment. Science and Research Branch, Islamic Azad University, Tehran, Iran

$^{2}$ Islamic Azad University, Gorgan Branch, Gorgan, Iran

$^{3}$ Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

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Abstract

Wetland modeling under land use change (LUC) plays an important role in the maintenance of these valuable ecosystems. Here, the LUC effect on hydrological changes in Ajigol, Almagol and Alago international wetlands in northern Iran, was investigated using Long-term hydrological impact assessment (L-THIA) model and satellite images (Landsat 5, 7 and 8) during 1987-2018. Correlation test was also performed between LUC parameter and water quality changes during 2003-2018. The results showed that LUCs have directly affected the wetland hydrology in which a correlation coefficient (R-squared) of 0.70, 0.64 and 0.60 was observed between modeled and actual water volume in Almago, Ajigol and Alagol wetlands, respectively. In terms of water quality changes, agricultural land, distance from agricultural lands and urban land changes were closely related to the phosphate, nitrate, acidity, pH and turbidity changes of wetland water over time ($P<0.05$).

Acknowledgments

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References

  1. [1]  Kaplan, G. and Avdan, U. (2017), Mapping and monitoring wetlands using sentinel-2 satellite imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017. 4th International GeoAdvances Workshop, 14--15 October 2017, Safranbolu, Karabuk, Turkey.
  2. [2]  Pal, S. and Talukdar, S. (2018), Application of frequency ratio and logistic regression models for assessing physical wetland vulnerability in Punarbhaba river basin of Indo-Bangladesh, Human and Ecological Risk Assessment: An International Journal, 24(5), 1291-1311.
  3. [3]  Singh, M. and Sinha, R. (2022), A basin-scale inventory and hydrodynamics of floodplain wetlands based on time-series of remote sensing data, Remote Sensing Letters, 13(1), 1-13.
  4. [4]  Rapinel, S., Fabre, E., Dufour, S., Arvor, D., Mony, C., and Hubert-Moy, L. (2019), Mapping potential, existing and efficient wetlands using free remote sensing data, Journal of Environmental Management, 247, 829-839.
  5. [5]  Perennou, C., Guelmami, A., Paganini, M., Philipson, P., Poulin, B., Strauch, A., and Geijzendorffer, I.R. (2018), Mapping Mediterranean wetlands with remote sensing: a good-looking map is not always a good map, In Advances in Ecological Research, 58, 243-277.
  6. [6]  Baker, C., Lawrence, R., Montagne, C., and Patten, D. (2006), Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models, Wetlands, 26(2), 465-474.
  7. [7]  Nong, X., Shao, D., Zhong, H., and Liang, J. (2020), Evaluation of water quality in the South-to-North Water Diversion Project of China using the water quality index (WQI) method, Water Research, 178, 115781.
  8. [8]  Jamal, S. and Ahmad, W.S. (2020), Assessing land use land cover dynamics of wetland ecosystems using Landsat satellite data, SN Applied Sciences, 2, 1-24.
  9. [9]  Heintzman, L.J. and McIntyre, N.E. (2019), Quantifying the effects of projected urban growth on connectivity among wetlands in the Great Plains (USA), Landscape and Urban Planning, 186, 1-12.
  10. [10]  Maltby, E. and Acreman, M.C. (2011), Ecosystem services of wetlands: pathfinder for a new paradigm, Hydrological Sciences Journal, 56(8), 1341-1359.
  11. [11]  Haidary, A., Amiri, B.J., Adamowski, J., Fohrer, N., and Nakane, K. (2013), Assessing the impacts of four land use types on the water quality of wetlands in Japan, Water Resources Management, 27, 2217-2229.
  12. [12]  Li, T., Bai, F., Han, P., and Zhang, Y. (2016), Non-point source pollutant load variation in rapid urbanization areas by remote sensing, Gis and the L-THIA model: A case in Bao'an District, Shenzhen, China, Environmental management, 58, 873-888.
  13. [13]  Li, J., Jiang, H., Bai, Y., Alatalo, J.M., Li, X., Jiang, H., and Xu, J. (2016), Indicators for spatial--temporal comparisons of ecosystem service status between regions: A case study of the Taihu River Basin, China, Ecological Indicators, 60, 1008-1016.
  14. [14]  Geijzendorffer, I.R., Beltrame, C., Chazee, L., Gaget, E., Galewski, T., Guelmami, A., and Grillas, P. (2019), A more effective Ramsar Convention for the conservation of Mediterranean wetlands, Frontiers in Ecology and Evolution, 7, 21.
  15. [15]  Ding, J., Jiang, Y., Fu, L., Liu, Q., Peng, Q., and Kang, M. (2015), Impacts of land use on surface water quality in a subtropical River Basin: a case study of the Dongjiang River Basin, Southeastern China, Water, 7(8), 4427-4445.
  16. [16]  Wang, B., Waters, C., Orgill, S., Gray, J., Cowie, A., Clark, A., and Li Liu, D. (2018), High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia, Science of the Total Environment, 630, 367-378.
  17. [17]  Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019), Remote sensing techniques for soil organic carbon estimation: A review, Remote Sensing, 11(6), 676.
  18. [18]  Taylor, N.G., Grillas, P., Al Hreisha, H., Balk\i z, \"{O}., Borie, M., Boutron, O., and Sutherland, W.J. (2021), The future for Mediterranean wetlands: 50 key issues and 50 important conservation research questions, Regional Environmental Change, 21, 1-17.
  19. [19]  Naboureh, A., Rezaei Moghaddam, M.H., Feizizadeh, B., and Blaschke, T. (2017), An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain, Arabian Journal of Geosciences, 10, 1-16.
  20. [20]  Bhaduri, B., Harbor, J.O.N., Engel, B., and Grove, M. (2000), Assessing watershed-scale, long-term hydrologic impacts of land-use change using a GIS-NPS model, Environmental Management, 26, 643-658.
  21. [21]  Tang, Z., Engel, B.A., Pijanowski, B.C., and Lim, K.J. (2005), Forecasting land use change and its environmental impact at a watershed scale, Journal of Environmental Management, 76(1), 35-45.
  22. [22]  Lim, K.J., Engel, B.A., Tang, Z., Muthukrishnan, S., Choi, J., and Kim, K. (2006), Effects of calibration on L-THIA GIS runoff and pollutant estimation, Journal of Environmental Management, 78(1), 35-43.
  23. [23]  Mirzaei, M., Solgi, E., and Salmanmahiny, A. (2016), Assessment of impacts of land use changes on surface water using L-THIA model (case study: Zayandehrud river basin), Environmental Monitoring and Assessment, 188, 1-19.
  24. [24]  Li, N., Xu, Y.P., and Guo, H.C. (2007), Analysis of long-term impact of urbanization on surface run-off in Xitiaoxi river basin, Environmental Informatics Archives, 5, 346-353.
  25. [25]  Engel, B.A., Choi, J.Y., Harbor, J., and Pandey, S. (2003), Web-based DSS for hydrologic impact evaluation of small watershed land use changes. Computers and Electronics in Agriculture, 39(3), 241-249.
  26. [26]  Leaver, J.D. and Unsworth, C.P. (2007), System dynamics modelling of spring behaviour in the Orakeikorako geothermal field, New Zealand, Geothermics, 36(2), 101-114.
  27. [27]  Abdullahi, S. and Pradhan, B. (2016), Sustainable brownfields land use change modeling using GIS-Based weights-of-evidence approach, Applied spatial analysis and policy, 9, 21-38.
  28. [28]  Karimi Sangchini, E., Ownegh, M., Sadoddin, A., and Yousefi Mobarhan, E. (2020), Predicting the impacts of land cover management scenarios on the run-off volume and river pollutants using the L-THIA model for the Hablehrud basin, Watershed Management Research Journal, 33(3), 36-52.
  29. [29]  Zhang, J. and Yu, X. (2021), Analysis of land use change and its influence on runoff in the Puhe River Basin, Environmental Science and Pollution Research, 28, 40116-40125.
  30. [30]  Ebrahimi, K., Karimirad, I., and Araghinejad, S. (2018), Assessing the impact of land-use changes on recharging of a multilayer aquifer, Iranian Journal of Watershed Management Science and Engineering, 12(43), 50-60.
  31. [31]  Zanganeh Asadi, M. A., Amir Ahmadi, A., and Naemi Tabar, M. (2021), Efficiency evaluation of the VIKOR, L-THIA, and Artificial Neural Network (ANT) models in flood zone analysis (case study: Khorasan Razavi province), Iranian Journal of Ecohydrology, 8(1), 89-108.
  32. [32]  Hong, Z., Zhao, Q., Chang, J., Peng, L., Wang, S., Hong, Y., and Ding, S. (2020), Evaluation of water quality and heavy metals in wetlands along the Yellow River in Henan Province, Sustainability, 12(4), 1300.
  33. [33]  Park, S.R. and Lee, S.W. (2020), Spatially varying and scale-dependent relationships of land use types with stream water quality, International Journal of Environmental Research and Public Health, 17(5), 1673.
  34. [34]  Cai, Z., Zhu, R., Ruggiero, E., Newman, G., and Horney, J.A. (2023), Calculating the environmental impacts of low-impact development using long-term hydrologic impact assessment: a review of model applications, Land, 12(3), 612.