Skip to main content
Log in

Assessment of Climate Change Impacts on Drought and Wet Spells in Lake Urmia Basin

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

Drought is recognized as a natural hazard and environmental disaster, and has caused extensive impact worldwide. The increasing frequency and severity of droughts associated with global climate change is an important issue in agriculture and water resources. Given the critical situation of water resources in the Lake Urmia Basin, predicting drought characteristics in future periods is very important in this basin. In this study, to evaluate the future drought and wet spells in Lake Urmia Basin, the daily outputs of the second-generation Canadian Earth System Model (CanESM2) model under RCP2.6, RCP4.5 and RCP8.5 emission scenarios were projected and downscaled using the Statistical Downscaling Model (SDSM) model for two periods, 2031–2050 and 2051–2070. Subsequently, the drought status and its trends in the baseline period (1986–2005) and future periods were investigated using precipitation data and the Standardized Precipitation Index (SPI). Then, the drought and wet spell characteristics including occurrence, persistence and the stationary probability of each class were calculated using the Markov chain model. The results showed that the probability of droughts in the stations of Lake Urmia Basin increased in the future. Also, by increasing SPI timescales, drought persistence increased under all three scenarios. On the other hand, by increasing the SPI timescales, the intensity of droughts and wet spells decreased, while their persistence increased.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The United Nations Office for Disaster Reduction.

  2. Urmia Lake and Restoration Program.

References

  • AghaKouchak, A., Cheng, L., Mazdiyasni, O., & Farahmand, A. (2014). Global warming and changes in risk of concurrent climate extremes: insights from the 2014 California drought. Geophysical Research Letters, 41(24), 8847–8852.

    Article  Google Scholar 

  • AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., & Hasanzadeh, E. (2015). Aral Sea syndrome desiccates Lake Urmia: call for action. Journal of Great Lakes Research, 41(1), 307–311.

    Article  Google Scholar 

  • Ahmadebrahimpour, E., Aminnejad, B., & Khalili, K. (2019). Assessing future drought conditions under a changing climate: a case study of the Lake Urmia basin in Iran. Water Supply, 19(6), 1851–1861.

    Article  Google Scholar 

  • Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., & Tagipour, A. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5), 1.

    Article  Google Scholar 

  • Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L., Flato, G. M., & Merryfield, W. J. (2011). Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophysical Research Letters, 38(5), 1.

    Article  Google Scholar 

  • Azizzadeh, M., & Javan, K. (2015). Analyzing trends in reference evapotranspiration in the northwest part of Iran. Journal of Ecological Engineering, 16(2), 1.

    Article  Google Scholar 

  • Azizzadeh, M., & Javan, K. (2018). Temporal and spatial distribution of extreme precipitation indices over the lake Urmia Basin Iran. Environmental Resources Research, 6(1), 25–40.

    Google Scholar 

  • Bhat, U. N., & Miller, G. K. (2002). Elements of applied stochastic processes (Vol. 3). Hoboken: Wiley.

    Google Scholar 

  • Breinl, K., Di Baldassarre, G., Mazzoleni, M., Lun, D., & Vico, G. (2020). Extreme dry and wet spells face changes in their duration and timing. Environmental Research Letters, 15(7), 074040.

    Article  Google Scholar 

  • Chen, H., Xu, C. Y., & Guo, S. (2012). Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology, 434, 36–45.

    Article  Google Scholar 

  • Choi, Y. W., Ahn, J. B., Suh, M. S., Cha, D. H., Lee, D. K., Hong, S. Y., & Kang, H. S. (2016). Future changes in drought characteristics over South Korea using multi-regional climate models with the standardized precipitation index. Asia-Pacific Journal of Atmospheric Sciences, 52(2), 209–222.

    Article  Google Scholar 

  • Chong-Hai, X. U., & Ying, X. (2012). The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble. Atmospheric and Oceanic Science Letters, 5(6), 527–533.

    Article  Google Scholar 

  • Dehghan, S., Salehnia, N., Sayari, N., & Bakhtiari, B. (2020). Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province Iran. Journal of Arid Land, 12, 318–330.

    Article  Google Scholar 

  • Delju, A. H., Ceylan, A., Piguet, E., & Rebetez, M. (2013). Observed climate variability and change in Urmia Lake Basin Iran. Theoretical and applied climatology, 111(1–2), 285–296.

    Article  Google Scholar 

  • Dibike, Y. B., Gachon, P., St-Hilaire, A., Ouarda, T. B. M. J., & Nguyen, V. T. V. (2008). Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada. Theoretical and Applied Climatology, 91(1–4), 149–170.

    Article  Google Scholar 

  • Ebrahimi Khusfi, Z., & Mirakbari, M. (2020). Assessment the Impact of Climate Change on the Drought of Jazmourian Wetland Using CanESM2 Model. Desert Management, 7(14), 149–166.

    Google Scholar 

  • Filho, L. W., Musa, H., Cavan, G., O’Hare, P., & Seixas, J. (Eds.). (2016). Climate change adaptation. Resilience and Hazards: Springer International Publishing.

    Google Scholar 

  • Ford, T. W., McRoberts, D. B., Quiring, S. M., & Hall, R. E. (2015). On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma, USA. Geophysical Research Letters, 42(22), 9790–9798.

    Article  Google Scholar 

  • Ghamghami, M., & Irannejad, P. (2019). An analysis of droughts in Iran during 1988–2017. SN Applied Sciences, 1(10), 1217.

    Article  Google Scholar 

  • Golian, S., Mazdiyasni, O., & AghaKouchak, A. (2015). Trends in meteorological and agricultural droughts in Iran. Theoretical and applied climatology, 119(3–4), 679–688.

    Article  Google Scholar 

  • Gutiérrez, J. M., San-Martín, D., Brands, S., Manzanas, R., & Herrera, S. (2013). Reassessing statistical downscaling techniques for their robust application under climate change conditions. Journal of Climate, 26(1), 171–188.

    Article  Google Scholar 

  • Hao, Z., & AghaKouchak, A. (2013). Multivariate standardized drought index: a parametric multi-index model. Advances in Water Resources, 57, 12–18.

    Article  Google Scholar 

  • Hao, Z., AghaKouchak, A., & Phillips, T. J. (2013). Changes in concurrent monthly precipitation and temperature extremes. Environmental Research Letters, 8(3), 034014.

    Article  Google Scholar 

  • Hassanzadeh, E., Zarghami, M., & Hassanzadeh, Y. (2012). Determining the main factors in declining the Urmia Lake level by using system dynamics modelling. Water Resources Management, 26(1), 129–145.

    Article  Google Scholar 

  • Hayes, M., Svoboda, M., Wall, N., & Widhalm, M. (2011). The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bulletin of the American Meteorological Society, 92(4), 485–488.

    Article  Google Scholar 

  • Heinrich, G., & Gobiet, A. (2012). The future of dry and wet spells in Europe: a comprehensive study based on the ENSEMBLES regional climate models. International Journal of Climatology, 32(13), 1951–1970.

    Article  Google Scholar 

  • Hirsch, R. M., & Slack, J. R. (1984). A nonparametric trend test for seasonal data with serial dependence. Water Resources Research, 20(6), 727–732.

    Article  Google Scholar 

  • Baghanam, AH., Eslahi, M., Sheikhbabaei, A., & Seifi, A. J. (2020). Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods. Theoretical and Applied Climatology, 141, 1135–1150.

    Article  Google Scholar 

  • Hosseinizadeh, A., SeyedKaboli, H., Zareie, H., Akhondali, A., & Farjad, B. (2015). Impact of climate change on the severity, duration, and frequency of drought in a semi-arid agricultural basin. Geoenvironmental Disasters, 2(1), 23.

    Article  Google Scholar 

  • Huang, J., Zhang, J., Zhang, Z., Xu, C., Wang, B., & Yao, J. (2011). Estimation of future precipitation change in the Yangtze River basin by using the statistical downscaling method. Stochastic Environmental Research and Risk Assessment, 25(6), 781–792.

    Article  Google Scholar 

  • Chen, H.-P., Sun, J.-Q., Chen, X.-L. (2013). Future changes of drought and flood events in China under a global warming scenario. Atmospheric and Oceanic Science Letters, 6(1), 8–13.

    Article  Google Scholar 

  • IPCC. (2007). Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University.

    Google Scholar 

  • IPCC. (2013). Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Stocker TF et al. (Eds.), (pp 1552) Cambridge: Cambridge University Press

  • IPCC. (2014). Climate change 2014: synthesis report, contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. Core Writing Team, Pachauri RK, Meyer LA (Eds.). (p. 151) Geneva: IPCC

  • Jana, B. K., & Majumder, M. (Eds.). (2010). Impact of climate change on natural resource management. Berlin: Springer.

    Google Scholar 

  • Javan, K., & Teimouri, M. (2019). Spatial analysis of occurrence probability of dusty days in west and southwest of Iran. Arabian Journal of Geosciences, 12(15), 477.

    Article  Google Scholar 

  • Jolliffe, I. T., & Stephenson, D. B. (Eds.). (2012). Forecast verification: a practitioner’s guide in atmospheric science. Hoboken: Wiley.

    Google Scholar 

  • Kang, H., & Sridhar, V. (2018). Assessment of future drought conditions in the Chesapeake Bay watershed. JAWRA Journal of the American Water Resources Association, 54(1), 160–183.

    Article  Google Scholar 

  • Kiem, A. S., & Austin, E. K. (2013). Drought and the future of rural communities: opportunities and challenges for climate change adaptation in regional Victoria Australia. Global Environmental Change, 23(5), 1307–1316.

    Article  Google Scholar 

  • Lee, J. H., Kwon, H. H., Jang, H. W., & Kim, T. W. (2016). Future changes in drought characteristics under extreme climate change over South Korea. Advances in Meteorology, 1, 1.

    Google Scholar 

  • Lee, S. H., Yoo, S. H., Choi, J. Y., & Bae, S. (2017). Assessment of the impact of climate change on drought characteristics in the Hwanghae Plain, North Korea using time series SPI and SPEI: 1981–2100. Water, 9(8), 579.

    Article  Google Scholar 

  • Leng, G., Tang, Q., & Rayburg, S. (2015). Climate change impacts on meteorological, agricultural and hydrological droughts in China. Global and Planetary Change, 126, 23–34.

    Article  Google Scholar 

  • Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., & Stafford-Smith, M. (2014). A compound event framework for understanding extreme impacts. Wiley Interdisciplinary Reviews: Climate Change, 5(1), 113–128.

    Google Scholar 

  • Li, Z., Zheng, F. L., Liu, W. Z., & Flanagan, D. C. (2010). Spatial distribution and temporal trends of extreme temperature and precipitation events on the Loess Plateau of China during 1961–2007. Quaternary International, 226(1–2), 92–100.

    Article  Google Scholar 

  • Lin, H., Wang, J., Li, F., Xie, Y., Jiang, C., & Sun, L. (2020). Drought trends and the extreme drought frequency and characteristics under climate change based on SPI and HI in the upper and middle reaches of the Huai River Basin China. Water, 12(4), 1100.

    Article  Google Scholar 

  • Lohani, V. K., Loganathan, G. V., & Mostaghimi, S. (1998). Long-term analysis and short-term forecasting of dry spells by the Palmer Drought Severity Index. Hydrology Research, 29(1), 21–40.

    Article  Google Scholar 

  • Loukas, A., Vasiliades, L., & Tzabiras, J. (2008). Climate change effects on drought severity. Advances in Geosciences, 17, 23–29.

    Article  Google Scholar 

  • Ma, D., Deng, H., Yin, Y., Wu, S., & Zheng, D. (2019). Sensitivity of arid/humid patterns in China to future climate change under a high-emissions scenario. Journal of Geographical Sciences, 29(1), 29–48.

    Article  Google Scholar 

  • Mahmoudi, P., Rigi, A., & Kamak, M. M. (2019). A comparative study of precipitation-based drought indices with the aim of selecting the best index for drought monitoring in Iran. Theoretical and Applied Climatology, 137(3–4), 3123–3138.

    Article  Google Scholar 

  • Manatsa, D., Mukwada, G., Siziba, E., & Chinyanganya, T. (2010). Analysis of multidimensional aspects of agricultural droughts in Zimbabwe using the Standardized Precipitation Index (SPI). Theoretical and Applied Climatology, 102(3–4), 287–305.

    Article  Google Scholar 

  • McKee, T.B., Doesken, N.J., Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology (Vol. 17, No. 22, pp. 179–183). Boston: American Meteorological Society

  • Meresa, H., Osuch, M., & Romanowicz, R. (2016). Hydro-meteorological drought projections into the 21-st century for selected Polish catchments. Water, 8(5), 206.

    Article  Google Scholar 

  • Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of hydrology, 391(1–2), 202–216.

    Article  Google Scholar 

  • Modarres, R., & da Silva, V. D. P. R. (2007). Rainfall trends in arid and semi-arid regions of Iran. Journal of arid environments, 70(2), 344–355.

    Article  Google Scholar 

  • Moon, S. E., Ryoo, S. B., & Kwon, J. G. (1994). A Markov chain model for daily precipitation occurrence in South Korea. International Journal of Climatology, 14(9), 1009–1016.

    Article  Google Scholar 

  • Moreira, E. E., Paulo, A. A., Pereira, L. S., & Mexia, J. T. (2006). Analysis of SPI drought class transitions using log-linear models. Journal of Hydrology, 331(1–2), 349–359.

    Article  Google Scholar 

  • Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P., & Meehl, G. A. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747.

    Article  Google Scholar 

  • Muller, J. C. Y. (2014). Adapting to climate change and addressing drought–learning from the red cross red crescent experiences in the Horn of Africa. Weather and Climate Extremes, 3, 31–36.

    Article  Google Scholar 

  • Murphy, J. (1999). An evaluation of statistical and dynamical techniques for downscaling local climate. Journal of Climate, 12(8), 2256–2284.

    Article  Google Scholar 

  • Nazeri Tahroudi, M., Ahmadi, F., & Khalili, K. (2018). Impact of 30 years changing of River Flow on Urmia Lake Basin. AUT Journal of Civil Engineering, 2(1), 115–122.

    Google Scholar 

  • Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., & Molajou, A. (2019). ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method. Theoretical and Applied Climatology, 137(3–4), 1729–1746.

    Article  Google Scholar 

  • Oguntunde, P. G., Abiodun, B. J., & Lischeid, G. (2017). Impacts of climate change on hydro-meteorological drought over the Volta Basin, West Africa. Global and Planetary Change, 155, 121–132.

    Article  Google Scholar 

  • Patel, N. R., & Yadav, K. (2015). Monitoring the Spatio-temporal pattern of drought stress using an integrated drought index over Bundelkhand region India. Natural Hazards, 77(2), 663–677.

    Article  Google Scholar 

  • Paulo, A. A., & Pereira, L. S. (2007). Prediction of SPI drought class transitions using Markov chains. Water Resources Management, 21(10), 1813.

    Article  Google Scholar 

  • Ruiz-Ramos, M., & Mínguez, M. I. (2010). Evaluating uncertainty in climate change impacts on crop productivity in the Iberian Peninsula. Climate Research, 44(1), 69–82.

    Article  Google Scholar 

  • Saada, N., & Abu-Romman, A. (2017). Multi-site modelling and simulation of the standardized precipitation index (SPI) in Jordan. Journal of Hydrology: Regional Studies, 14, 83–91.

    Google Scholar 

  • Salehpour Jam, A., Mohseni Saravi, M., Bazrafshan, J., & Khalighi Sigaroodi, S. H. (2015). Investigation of climate change effect on drought characteristics in the future period using the HadCM3 Model (Case Study: NorthWest of Iran). Iranian Journal of Natural Resources, 67(4), 1.

    Google Scholar 

  • Sayari, N., Bannayan, M., Alizadeh, A., & Farid, A. (2013). Using drought indices to assess climate change impacts on drought conditions in the northeast of Iran (case study: Kashafrood basin). Meteorological Applications, 20(1), 115–127.

    Article  Google Scholar 

  • Sehgal, V., Sridhar, V., & Tyagi, A. (2017). Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations. Journal of Hydrology, 545, 226–250.

    Article  Google Scholar 

  • Shahbazi, A. N. (2017). Climate change impacts under CMIP5 RCP scenarios on agricultural drought and crop virtual water content. Fresenius Environmental Bulletin, 26(11), 6701–6711.

    Google Scholar 

  • Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., & Stahl, K. (2015). Candidate distributions for climatological drought indices (SPI and SPEI). International Journal of Climatology, 35(13), 4027–4040.

    Article  Google Scholar 

  • Svoboda, M., Hayes, M., & Wood, D. (2012). Standardized precipitation index user guide. Switzerland: World Meteorological Organization Geneva.

    Google Scholar 

  • Tabari, H., & Talaee, P. H. (2011). Temporal variability of precipitation over Iran: 1966–2005. Journal of Hydrology, 396(3–4), 313–320.

    Article  Google Scholar 

  • Taheri, M., Emadzadeh, M., Gholizadeh, M., Tajrishi, M., Ahmadi, M., & Moradi, M. (2019). Investigating the temporal and spatial variations of water consumption in Urmia Lake River Basin considering the climate and anthropogenic effects on the agriculture in the basin. Agricultural Water Management, 213, 782–791.

    Article  Google Scholar 

  • Taie Semiromi, M., & Koch, M. (2017). Downscaling of daily precipitation using a hybrid model of Artificial Neural Network, Wavelet, and Quantile Mapping in Gharehsoo River Basin Iran. AGUFM, 1, H11Q – H18.

    Google Scholar 

  • Thilakarathne, M., & Sridhar, V. (2017). Characterization of future drought conditions in the Lower Mekong River Basin. Weather and Climate Extremes, 17, 47–58.

    Article  Google Scholar 

  • Tourian, M. J., Elmi, O., Chen, Q., Devaraju, B., Roohi, S., & Sneeuw, N. (2015). A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran. Remote Sensing of Environment, 156, 349–360.

    Article  Google Scholar 

  • ULRP, Urmia lake and restoration program (2020). https://www.ulrp.ir/

  • UNISDR, the United Nations office for disaster reduction, “Impacts of disasters since the 1992 Rio de Janeiro earth summit. (2012). Available online: http://www.unisdr.org/files/27162_infographic.pdf.

  • Vasiliades, L., Loukas, A., & Patsonas, G. (2009). Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts. Natural Hazards and Earth System Sciences, 9(3), 879.

    Article  Google Scholar 

  • Vogt, J., Barbosa, P., Hofer, B., Magni, D., Jager, A. D., Singleton, A., Calcagni, L. (2011). Developing a European drought observatory for monitoring, assessing and forecasting droughts across the European continent. AGUFM. NH24A-07.

  • Vrochidou, A. E., Tsanis, I. K., Grillakis, M. G., & Koutroulis, A. G. (2013). The impact of climate change on hydrometeorological droughts at a basin scale. Journal of Hydrology, 476, 290–301.

    Article  Google Scholar 

  • Wang, J., Lin, H., Huang, J., Jiang, C., Xie, Y., & Zhou, M. (2019). Variations of drought tendency, frequency, and characteristics and their responses to climate change under CMIP5 RCP Scenarios in Huai River Basin China. Water, 11(10), 2174.

    Article  Google Scholar 

  • Wang, X., Zhuo, L., Li, C., Engel, B. A., Sun, S., & Wang, Y. (2020). Temporal and spatial evolution trends of drought in northern Shaanxi of China: 1960–2100. Theoretical and Applied Climatology, 139(3), 965–979.

    Article  Google Scholar 

  • Wilby, R. L., & Dawson, C. W. (2007). SDSM 4.2-A decision support tool for the assessment of regional climate change impacts. User Manual, 94, 1.

    Google Scholar 

  • Wilby, R. L., Dawson, C. W., & Barrow, E. M. (2002). SDSM—a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2), 145–157.

    Article  Google Scholar 

  • Wilby, R. L., Dawson, C. W., Murphy, C., Connor, P. O., & Hawkins, E. (2014). The statistical DownScaling model-decision centric (SDSM-DC): conceptual basis and applications. Climate Research, 61(3), 259–276.

    Article  Google Scholar 

  • Wilby, R. L., & Harris, I. (2006). A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK. Water Resources Research, 42(2).

  • Wilks, D. S. (2011). Statistical methods in the atmospheric sciences (Vol. 100). Cambridge: Academic Press.

    Google Scholar 

  • WMO (World Meteorological Organization) (2012) Standardized precipitation index user guide. WMO No: 1090: Geneva, Switzerland

  • Wu, H., Hayes, M. J., Wilhite, D. A., & Svoboda, M. D. (2005). The effect of the length of record on the standardized precipitation index calculation. International Journal of Climatology, 25(4), 505–520.

    Article  Google Scholar 

  • Wu, S., & Yan, X. (2019). Variations in droughts and wet spells and their influences in China: 1924–2013. Theoretical and Applied Climatology, 135(1–2), 623–631.

    Article  Google Scholar 

  • Yeh, C. F., Wang, J., Yeh, H. F., & Lee, C. H. (2015). SDI and Markov chains for regional drought characteristics. Sustainability, 7(8), 10789–10808.

    Article  Google Scholar 

  • Yue, S., & Wang, C. (2004). The Mann–Kendall test modified by effective sample size to detect a trend in serially correlated hydrological series. Water Resources Management, 18(3), 201–218.

    Article  Google Scholar 

  • Zamani, R., & Berndtsson, R. (2019). Evaluation of CMIP5 models for west and southwest Iran using TOPSIS-based method. Theoretical and Applied Climatology, 137(1–2), 533–543.

    Article  Google Scholar 

  • Zarghami, M. (2011). Effective watershed management; a case study of Urmia Lake Iran. Lake and Reservoir Management, 27(1), 87–94.

    Article  Google Scholar 

  • Zoljoodi, M., & Didevarasl, A. (2014). Water-level fluctuations of Urmia Lake: relationship with the long-term changes of meteorological variables (solutions for water-crisis management in Urmia Lake Basin). Atmospheric and Climate Sciences, 4(03), 358.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kh. Javan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Davarpanah, S., Erfanian, M. & Javan, K. Assessment of Climate Change Impacts on Drought and Wet Spells in Lake Urmia Basin. Pure Appl. Geophys. 178, 545–563 (2021). https://doi.org/10.1007/s00024-021-02656-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-021-02656-8

Keywords

Navigation