Abstract
Climate change has been the main environmental challenge in recent years. In this research, the impact of climate change on the Arjan Wetland, Iran, has been investigated. The global climate models of canESM2 and hadGEM2 were used to predict the air temperature and precipitation for 2025–2066 in scenarios of RCP 2.6, 4.5, and 8.5. Temperature and precipitation data were downscaled using SDSM and LARS-WG software, respectively. Then, the wetland area was measured by processing the Landsat satellite images using the MNDWI algorithm. Forecast data were applied to estimate the wetland area for 2025–2066 and the scPDSI drought index. The results indicate that the estimated areas of the future period will slightly decrease; as a result, 90% of the areas in the years 2045–2066 are less than 800 hectares, and more than1100 hectares have not been dried in these years. The reduction of the area compared to the observation period is due to climate change and it shows the Arjan wetland is going towards drought. In 2036–2039, 2042, 2052, 2056–2058, and 2062, severe droughts will occur in wetlands under three scenarios with an area of less than 200 ha. According to the climate projections, the wetland will experience severe wet years in 2025, 2044, and 2045. So, the wetlands are probably less threatened by climate change than anticipated prior to this study.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abbasian MS, Najafi MR, Abrishamchi A (2021) Increasing risk of meteorological drought in the Lake Urmia basin under climate change: introducing the precipitation–temperature deciles index. J Hydrol 592:125586. https://doi.org/10.1016/j.jhydrol.2020.125586
Abdulla F, Al-Shurafat AW (2020) Assessment of the impact of potential climate change on the surface water of a trans-boundary basin: case study Yarmouk River. Procedia Manuf 44:172–179. https://doi.org/10.1016/j.promfg.2020.02.219
Al-Shurafat AWS, Abdullah AF (2016) The spatial and temporal simulation of the hydrological water budget for Yarmouk river basin under current and projected future climate. Master thesis, Jordan University of Science and Technology, Jordan
Almagro A, Oliveira PTS, Rosolem R, Hagemann S, Nobre CA (2020) Performance evaluation of Eta/HadGEM2-ES and Eta/MIROC5 precipitation simulations over Brazil. Atmos Res 244:105053. https://doi.org/10.1016/j.atmosres.2020.105053
Chim K, Tunnicliffe J, Shamseldin A, Chan K (2021) Identifying future climate change and drought detection using CanESM2 in the upper Siem Reap River, Cambodia. Dyn Atmos Ocean 94:101182. https://doi.org/10.1016/j.dynatmoce.2020.101182
Cordeiro MCR, Martinez J-M, Peña-Luque S (2021) Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with level 2A processors. Remote Sens Environ 253:112209. https://doi.org/10.1016/j.rse.2020.112209
Dehghan Z, Fathian F, Eslamian S (2015) Comparative assessment of SDSM, IDW and LARS-WG models for simulation and downscaling of temperature and precipitation. J Water Soil 29:1376–1390 (In Persian)
Fang-fang Z, Bing Z, Jun-sheng L, Qian S, Yuanfeng W, Yang S (2011) Comparative analysis of automatic water identification method based on multispectral remote sensing. Procedia Environ Sci 11:1482–1487. https://doi.org/10.1016/j.proenv.2011.12.223
Fiseha BM, Am Melesse, Romano E, Volpi E, Fiori A (2012) Statistical downscaling of precipitation and temperature for the Upper Tiber Basin in Central Italy. Int J Water Sci 1
Gautam VK, Gaurav PK, Murugan P, Annadurai M (2015) Assessment of surface water Dynamicsin Bangalore using WRI, NDWI, MNDWI, supervised classification and KT transformation. Aquat Procedia 4:739–746. https://doi.org/10.1016/j.aqpro.2015.02.095
Gebrechorkos SH, Bernhofer C, Hülsmann S (2020) Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach. Sci Total Environ 742:140504. https://doi.org/10.1016/j.scitotenv.2020.140504
Ghebrezgabher MG, Yang T, Yang X, Wang X, Khan M (2016) Extracting and analyzing forest and woodland cover change in Eritrea based on landsat data using supervised classification. Egypt J Remote Sens Sp Sci 19:37–47. https://doi.org/10.1016/j.ejrs.2015.09.002
Haibo Y, Zongmin W, Hongling Z, Yu G (2011) Water body extraction methods study based on RS and GIS. Procedia Environ Sci 10:2619–2624. https://doi.org/10.1016/j.proenv.2011.09.407
Huang S, Dahal D, Young C, Chander G, Liu S (2011) Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota. Remote Sens Environ 115:3377–3389. https://doi.org/10.1016/j.rse.2011.08.002
Iranian National Commission for UNESCO (2018). https://fa.irunesco.org/
Javaherian M, Ebrahimi H, Aminnejad B (2021) Prediction of changes in climatic parameters using CanESM2 model based on RCP scenarios (case study): Lar dam basin. Ain Shams Eng J 12:445–454. https://doi.org/10.1016/j.asej.2020.04.012
Karimi V, Habibnejadrooshan M, Abkar A (2011) Investigation of meteorological drought Indixes in Mazandaran synoptic stations. Irrig Water Eng 2:15–25
Khan MS, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382. https://doi.org/10.1016/j.jhydrol.2005.06.035
Khazaei MR, Tahsinzadeh N, Sharafati A (2019) Uncertainty investigation of precipitation and temperature scenarios for the Sira Basin under Climate Change Impact. Iran-Watershed Manage Sci Eng Vol. 13, 46, Fall 2019. (In Persian)
King L, Solaiman T, Simonovic SP (2009) Assessment of climatic vulnerability in the Upper Thames River Basin. Department of Civil and Environmental Engineering, The University of Western Ontario
Kumar KS, Rathnam EV, Sridhar V (2021) Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India. Sci Total Environ 763:142994. https://doi.org/10.1016/j.scitotenv.2020.142994
Lashkari A, Irannezhad M, Zare H, Labzovskii L (2021) Assessing long-term spatio-temporal variability in humidity and drought in Iran using Pedj Drought Index (PDI). J Arid Environ 185:104336. https://doi.org/10.1016/j.jaridenv.2020.104336
Li L, Su H, Du Q, Wu T (2021) A novel surface water index using local background information for long term and large-scale landsat images. ISPRS J Photogramm Remote Sens 172:59–78. https://doi.org/10.1016/j.isprsjprs.2020.12.003
Lopes P (2009) Assessment of statistical downscaling methods–application and comparison of two statistical methods to a single site in Lisbon. Ser Earth Environ Sci 6 2. IOP Confhttps://doi.org/10.1088/1755-1307/6/2/022015
Mahmoudi P, Rigi A, Kamak MM (2019) Evaluating the sensitivity of precipitation-based drought indices to different lengths of record. J Hydrol 579:124181. https://doi.org/10.1016/j.jhydrol.2019.124181
Meenu R, Rehana S, Mujumdar PP (2013) Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC-HMS and SDSM. Hydrol Process 27:1572–1589. https://doi.org/10.1002/hyp.9220
Mehta D, Yadav S, Ladavia C, Caloiero T (2023) Drought projection using GCM & statistical downscaling technique: a case study of Sirohi District. Results Eng 20:101605. https://doi.org/10.1016/j.rineng.2023.101605
Miryaghoubzadeh M, Khosravi SA, Zabihi M (2019) A review of drought indices and their performance. J Water Sustainable Dev 6:103–112 (In Persian)
Mohamadian A, Kouhi M, Adineh Baigi A, Rasouli SJ, Bazrafshan B (2010) Comparison of monitoring of drought using SPI, DI and PNI and Zoning them (Case study: Northern Khorasan Province). J Water Soil Conserv 17(1). (In Persian)
Mohseni O, Stefan HG, Erickson TR (1998) A nonlinear regression model for weekly stream temperatures. Water Resour Res 34:2685–2692. https://doi.org/10.1029/98WR01877
Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111. https://doi.org/10.1002/joc.1498
Morid R, Shimatani Y, Sato T (2020) An integrated framework for prediction of climate change impact on habitat suitability of a river in terms of water temperature, hydrological and hydraulic parameters. J Hydrol 587:124936. https://doi.org/10.1016/j.jhydrol.2020.124936
Ogunrinde AT, Oguntunde PG, Olasehinde DA, Fasinmirin JT, Akinwumiju AS (2020) Drought spatiotemporal characterization using self-calibrating Palmer Drought Severity Index in the northern region of Nigeria. Results Eng 5:100088. https://doi.org/10.1016/j.rineng.2019.100088
Rajabi A, Shabanlou S (2012) Climate index changes in future by using SDSM in Kermanshah, Iran. J Environ Res Dev. 7
Rehana S, Naidu GS (2021) Development of hydro-meteorological drought index under climate change–semi-arid river basin of Peninsular India. J Hydrol 594:125973. https://doi.org/10.1016/j.jhydrol.2021.125973
Rezvanfar I, Heidarzadeh N (2017) Investigating the effects of climate change on the area of Iran’s wetland using remote sensing (Arjan wetlands). Master thesis, Kharazmi University, Tehran, Iran
Sadeghi M, Raisi Ardakani E (2018) Investigation of factors affecting the drying of Arjan wetland. 15th conference of Geological Society of Iran, Tehran. https://civilica.com/doc/135217 (In Persian)
Salajegheh A, Rafiei Sardoii E, Moghaddamnia A, Malekian A, Araghinejad S, Khalighi Sigarodi S, Saleh Pourjam A (2017) Performance assessment of LARS-WG and SDSM downscaling models in simulation of precipitation and temperature. Iran Water soil Res 48:253–262 (In persian)
Sarp G, Ozcelik M (2017) Water body extraction and change detection using time series: a case study of Lake Burdur, Turkey. J Taibah Univ Sci 11:381–391. https://doi.org/10.1016/j.jtusci.2016.04.005
Schnorbus MA, Cannon AJ (2014) Statistical emulation of streamflow projections from a distributed hydrological model: application to CMIP3 and CMIP5 climate projections for British Columbia, Canada. Water Resour Res 50:8907–8926. https://doi.org/10.1002/2014WR015279
Shagega FP, Munishi SE, Kongo VM (2019) Prediction of future climate in Ngerengere river catchment, Tanzania. Phys Chem Earth 112:200–209. https://doi.org/10.1016/j.pce.2018.12.002
Sobhani B, Eslahi M, Babaeian I (2014) The efficiency of SDSM and LARS-WG statistical exponential microscale models in simulating meteorological variables in the catchment area of Lake Urmia. Natural Geography Res. Volume 47, Number 4, winter 2014, 499–516 pp. (In Persian)
Tan ML, Yusop Z, Chua VP, Chan NW (2017) Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia. Atmos Res 189:1–10. https://doi.org/10.1016/j.atmosres.2017.01.008
Tukimat NNA, Syukri NA, Malek MA (2019) Projection the long-term ungauged rainfall using integrated statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. https://doi.org/10.1016/j.heliyon.2019.e02456. Heliyon 5
Vrochidou A-E, Tsanis IK, Grillakis MG, Koutroulis AG (2013) The impact of climate change on hydrometeorological droughts at a basin scale. J Hydrol 476:290–301. https://doi.org/10.1016/j.jhydrol.2012.10.046
Wells N, Goddard S, Hayes MJ (2004) A self-calibrating Palmer drought severity index. J Clim 17:2335–2351. https://doi.org/10.1175/1520-0442(2004)017%3C2335:ASPDSI%3E2.0.CO;2
Wen Z, Zhang C, Shao G, Wu S, Atkinson PM (2021) Ensembles of multiple spectral water indices for improving surface water classification. Int J Appl Earth Obs Geoinf 96:102278. https://doi.org/10.1016/j.jag.2020.102278
Zhao C, Brissette F, Chen J, Martel J-L (2020) Frequency change of future extreme summer meteorological and hydrological droughts over North America. J Hydrol 584:124316. https://doi.org/10.1016/j.jhydrol.2019.124316
Funding
The authors declare that no funds, grants, or other supports were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
“All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Mahdiyeh Eghbal, and Nima Heidarzadeh. The first draft of the manuscript was written by Negar Esmaeili and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”
Corresponding author
Ethics declarations
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Heidarzadeh, N., Eghbal, M., Esmaeili, N. et al. Predicting the impact of climate change on the area of wetlands using remote sensing. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04969-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00704-024-04969-9