Investigating Meteorological and Hydrological Drought in Zarrineh River Basin

Document Type : Original Article

Authors

1 Department of Nature Engineering, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

3 Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.

‎10.22052/deej.2023.248790.1005

Abstract

Introduction: Affecting the hydrologic cycle, climate change may increase the chances of natural hazards occurrence, including drought, which is considered as one of the most destructive types of such hazards. On the other hand, considering the increasing trend of global temperature and its impact on local climates, climate change is predicted to alter the frequency and intensity of extreme events such as drought. Meanwhile, General Circulation Models (GCMs) have been used in recent decades to predict future climate changes under different emission scenarios, and various drought monitoring indicators have been developed to assess drought.
Zarrineh River basin is regarded as one of the main sub-basins supplying the inflow of water to Lake Urmia, which is threatened by numerous long-term droughts. Therefore, as the drying of the lake may bring about a wide range of economic, social, and environmental consequences for the region, monitoring drought and implementing water resources management programs play pivotal roles in preventing the lake to get dried.
 
Material and methods: Located northwest of Iran, Zarrineh River basin covers an area of 12512 km2, being bounded by Iranian West and East Azerbaijan provinces and the Kurdistan province. This study used the meteorological data, including precipitation rate, and minimum and maximum daily temperature collected from four synoptic stations during the 1990-2018 period to simulate runoff. Also, the monthly runoff rate was collected from five hydrometric stations during the 1996-2017 period to calibrate and validate the SWAT model. Moreover, the data obtained from the general circulation model (HADGEM2-ES) and statistical downscaling methods (CCT) were used to simulate precipitation and temperature under RCP2.6 and RCP8.5 emission scenarios for the future period (2025-2049). Finally, the SPI was applied to evaluate the meteorological drought in the base and the future periods, and the SRI obtained from the outputs of the SWAT model was used to evaluate the hydrological drought.
 
Results: The data collected from five hydrometric stations were parameterized and calibrated on discontinuous stream networks. Accordingly, it was found that the R2 values varied from 0.52 and 0.70 for calibration and from 0.44 to 0.73 for validation. However, the NSE values varied from 0.52 to 0.64 for calibration and from 0.42 to 0.64 for the validation stage. Moreover, the model’s outputs were found to be satisfactory for most hydrometric stations, indicating the applicability of the SWAT model to the ZRB.
On the other hand, based on the CCT model under the RCP8.5, the results of temperature and precipitation variations throughout the 2025-2049 period indicated that compared to the observation period, annual precipitation would decrease by 2.9% in the future period, and the annual minimum and maximum temperature rates would increase by 2.4°C and 3.6°C, respectively. Furthermore, the analysis of the annual temperature and precipitation changes under the RCP2.6 revealed that compared to the observation period, the precipitation rate would increase by 3.6%, and the annual minimum and maximum temperature would increase by 1.8°C and 3°C, respectively.
Moreover, the results of the SPI analysis for the future period under the RCP8.5 indicated the occurrence of the extreme drought event. However, while the frequency of severe drought did not change significantly for the future period under both scenarios, the frequency of moderate drought decreased for the future period compared to the base period. On the other hand, the most extreme hydrological drought in terms of the SRI was observed in basin 9 during the base period (equal to -3.02). It was also found that the most hydrological drought occurred in basins 10 and 2 throughout the base period. Furthermore, the most extreme hydrological drought for the future period was found as -4.13 in sub-basin 8 under the RCP8.5, which is greater than that of the base period.
 
Discussion and conclusion: The results suggested the satisfactory applicability of the SWAT model for simulating runoff in Zarrineh River basin, as the model considers almost all the physical conditions of the basin for the simulation process, possessing a wide variety of inputs to do so. The results of the analysis of temperature changes for the future period showed that the average minimum and maximum annual temperature would increase in the basin.
Moreover, the results of the analysis of annual temperature and precipitation changes under the RCP2.6 revealed that compared to the observation period, the precipitation rate would increase by 3.6%, and the annual minimum and maximum temperature rate would increase by 1.8°C and 3°C in future, respectively. On the other hand, according to the results of SPI and SRI analysis for the future period, it was found that the intensity of meteorological and hydrological drought would increase on average in the basin under both scenarios (RCP2.6 and RCP8.5). Also, the results of the RCP8.5 suggested the possibility of a more severe drought compared to the RCP8.5.
Considering an increase in minimum and maximum temperature found for the future period, we can expect an increase in the evaporation rate, probably leading to an increase in the severity of drought and a decrease in water resources of the Zarrineh River basin, which, in turn, will reduce the discharge of the basin’s water flow to the Urmia Lake.
 

Keywords

References

  1. Abbaspour, K.C., Faramarzi, M., Ghasemi, S.S., and Yang, H., 2009. Assessing the impact of climate change on water resources in Iran. Journal of Water resources research, 45(10), W10434.
  2. Alizadeh, E., mousavi, H., Yarahmadi, J., and Faraji, A., 2020. Assessment the Impact of Climate Change on Precipitation in Non-Observed data using the CCT Toolkit Case study: Daryan sub basin. Journal of Geography and Planning, 24 (73), 323-305 (in Farsi).
  3. Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Williams, J.R., 1998. Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resources Association, 34(1), 73-89.
  4. Ashraf Vaghefi, S., Abbaspour, N., Kamali, B., and Mikayilov, F., 2017. A toolkit for climate change analysis and pattern recognition for extreme weather conditions – Case study: California-Baja California Peninsula. Environmental Modelling and Software, 96, 181-198.
  5. Azareh, A., Rafiei Sardooi, E., and Jafari Gadaneh, M., 2021. Investigating the Effect of Climate Change on Future Temperature, Precipitation, and Droughts Using BNU-ESM and HadGEM2 Models. Desert Ecosystem Engineering Journal, 10(31), 95-110 (in Farsi).
  6. Bahri, M., Dastorani, M., and Goodarzi, M., 2013. Drought prediction for 2011-2030 under the effect of climate change (case study: Eskandari watershed, Isfahan province). The 9th Iranian National Watershed Management Science and Engineering Conference, 9th and 9th of November 2013, Yazd University (in Farsi).
  7. Boroughani, M., Hashemi, H., Hosseini, SH., Pourhashemi, S., and Berndtsson, R., 2020. Desiccating Lake Urmia: A New Dust Source of Regional Importance. IEEE Geoscience and Remote Sensing Letters, 17(9), 1483-1487.
  8. Ebrahimi Khusfi, Z., and Mirakbari, M., 2020. Assessment the Impact of Climate Change on the Drought of Jazmourian Wetland Using CanESM2 Model. Desert Management, 7(14), 149-166 (in Farsi).
  9. Emami, F., and Koch, M., 2018. Evaluation of statistical-downscaling/bias-correction methods to predict hydrologic responses to climate change in the Zarrine river basin, Iran. Climate, 6(2): 30-30.
  10. Freitas, A.A., Drumond, A., Carvalho, V.S.B., Reboita, M.S., Silva, B.C., and Uvo, C.B., 2022. Drought Assessment in São Francisco River Basin, Brazil: Characterization through SPI and Associated Anomalous Climate Patterns. Atmosphere,13,
  11. Grillakis, M.G., 2019. Increase in severe and extreme soil moisture droughts for Europe under climate change. Science of The Total Environment, 660, 1245-1255.
  12. Guo, y., Lu, X., Zhang, J., Li, K., Wang, R., Rong, G., Liu, X., and Tong, Z., 2022. Joint analysis of drought and heat events during maize (Zea mays L.) growth periods using copula and cloud models: A case study of Songliao Plain. Journal of Agricultural Water Management, 259, 107238.
  13. Hari, V., Rakovec, O., Markonis, Y., Hanel, M., and Kumar, R., 2020. Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming. Sci. Rep, 10,1 –10.
  14. Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F., 2013. A trend-preserving bias correction – the ISI-MIP approach. Earth Syst. Dynam., 4(2), 219-236.
  15. , 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, Cambridge University Press, Cambridge UK; New York, USA.
  16. Javan, Kh., 2021. Investigation of meteorological drought in Urmia using SPI under climate change scenarios (RCP. Journal of Climate Change Research, 2(5), 81-94.
  17. Jung, I.W., and Chang, H., 2012. Climate change impacts on spatial patterns in drought risk in the Willamette River Basin, Oregon, USA. Theor Appl Climatol 108:355–371.
  18. Leng, G., Tang, Q., and Rayburg, S., 2015. Climate change impacts on meteorological, agricultural and hydrological droughts in China. Glob Planet Change, 126:23–34.
  19. McKee, T.B., Doesken, N.J., and Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the International 8th Conference on Applied Climatology. American Meteorological Society, Anaheim, CA, USA, 17–22 January. pp. 179– 184.
  20. Morid, S., Moghaddam, M., Paymozd, Sh., and Ghaemi, H., 2005. "Design of Tehran province drought monitoring system. Final Report. "Water Resources Management Co. (WRMC-Iran), 196p. (In Farsi)
  21. Neitsch, S., Arnolad, J. G., Kiniry, J., Williams, J., and King, K., 2005. Soil and water assessment tool (SWAT) theoretical documentation. Blackland Reaserch Center, Texas Agriclulture Experiment Station, Temple, Texas (BRC Report 02-02).
  22. Padrón, R.S., Gudmundsson, L., Decharme , B., Ducharne., A.,   Lawrence, D.M., Mao , J., Peano , D.,   Krinner , G., Kim , H., and Seneviratne, S.I., 2020. Observed changes in dry-season water availability attributed to human-induced climate change. Nat. Geosci. 13, 477–481.
  23. Pourkarimi, Z., Moghaddasi, M., Mohseni Movahed, A., and Delavar, M. A. J. I. D., 2018. The Effect of Climate Change on the Hydrological and Agricultural Drought Characteristics in Zarinehrud Basin Using SRI and SSWI Indices and SWAT Model. Iranian Journal of Soil and Water Research, 49(5), 11451157. (In Farsi).
  24. Salehpourjam, A., Mohseni Saravi, M., and Khalighi, Sh., 2015. Investigation of Climate Change Effect on Drought Characteristics in the Future Period using the HadCM3 model (Case Study: Northwest of Iran). Journal of Range and Watershed Management. 67 (4), 537-548 (in Farsi).
  25. Satoh, Y., Yoshimura, K., Pokhrel, Y., Kim, H., Shiogama, H., Yokohata, T., Hanasaki, N., Wada, Y., Burek, P., Byers, E., Schmied, H.M., Gerten, D., Ostberg, S., Gosling, S.N., Boulange, J. E. S., and Oki, T., 2022. The timing of unprecedented hydrological drought under climate change. Nature Communications, 13(1), 3287.
  26. Shokrikochek, S., and Behnia, A., 2013. Drought Monitoring and Prediction of Khuzestan Province, Iran Drought Using SPI drought Index and Markov Chain, Journal of Irrigation Science and Engineering, No. 3, pp. 1-12 (in Farsi).
  27. Shukla, S.H., and Wood A.W., 2008. Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters 35: 1-7.
  28. Vaghefi, S.A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, h., and Abbaspor, K.C., 2019. The future of extreme climate in Iran. Sci Rep 9, 1464.
  29. Valeh, S., Motamedvairi, B., Kiadaliri, H., and Ahmadi, H., 2021. Hydrological simulation of Ammameh basin by artificial neural network and SWAT models. Physics and Chemistry of the Earth, Parts A/B/C, 123, 103014.
  30. Vicente-Serrano, S.M., Beguerı´a, S., and Lo´pez-Moreno, J.I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718.
  31. Vu, M.T., Raghavan, V.S., and Liong, S.Y., 2015. Ensemble climate projection for hydrometeorological drought over a river basin in Central Highland, Vietnam. KSCE Journal of Civil Engineering, 19(2), 427-433.
  32. Wilhite, D.A., and Glantz, M.H., 1985. Understanding the drought phenomenon: the role of definitions. Water Int 10(3):111–120.
  33. Yu, Y., Liu, J., Yang, Z., Cao, Y., Chang, J., and Mei, C., 2018. Effect of climate change on water resources in the Yuanshui River Basin: a SWAT model assessment. Arabian Journal of Geosciences, 11(11): 270-270.
  34. Zhao, L., Lyu, A., Wu, J., Hayes, M., Tang, Z., He, B., Liu, J., and Liu, M., 2014. Impact of meteorological drought on streamflow drought in Jinghe River Basin of China. Chin Geogr Sci 24:694–705.
Desert Ecosystem Engineering
Volume 11, Issue 37
May 2023
Pages 15-26

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