Groundwater drought hazard zoning and relationship between meteorological and hydrological drought indices in the Urmia aquifer

Document Type : Research/Original/Regular Article

Authors

1 Ph.D Student, Department of Rangeland and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran

2 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran

3 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran,

4 ssociate Professor, Department of Geography, Faculty of Literature and Humanities, Urmia University, Urmia, Iran

Abstract

Extended Abstract

Introduction

In recent years, the study of climatic changes has gained significant importance due to the exposure of many regions to climate change conditions. Iran, with its predominantly arid and semi-arid climate, is no exception. Groundwater is a vital resource for Iran’s drinking water, agriculture, and industry, playing a crucial role in its economic development. The horticulture and agriculture sectors in the Urmia aquifer plain heavily depend on groundwater resources. Over the past three decades, reductions in precipitation and excessive extraction of water resources for agricultural and other purposes have led to severe drought, causing irreparable damage to agriculture, industry, and human life. Groundwater drought is characterized by reduced groundwater levels or storage and is influenced by natural and human-induced factors, such as climate change and excessive groundwater extraction. The SPI and SPEI are commonly used to monitor meteorological drought, while the SGI measures hydrological drought. This study investigates sustainable management and protection strategies for the Urmia aquifer, emphasizing the effects of climate change and drought on its groundwater resources. The findings highlight the need for climate change adaptation measures.



Materials and Methods

To analyze the status of the Urmia aquifer, monthly groundwater depth data from piezometers within the aquifer were used to calculate the SGI index. Monthly precipitation data from the Urmia meteorological station in the aquifer plain were utilized to derive the SPI and SPEI indices. The analysis used monthly data from the Urmia aquifer, each providing consistent 20-year datasets. While 30 years is generally recommended as the standard for drought indices, statistical tools such as the correlation coefficient (CC), coefficient of determination (R²), root mean square error (RMSE), Hanna and Heinold index (HH), kappa coefficient (k), Cramer coefficient (V), and class correlation percentage were employed to compare and validate the SPI and SPEI indices for both 20- and 30-year periods. The Pearson correlation coefficient was applied to compare SPI and SPEI at 3, 6, 9, and 12-month scales with the monthly SGI index. ArcMap generated a groundwater drought hazard zoning map, ranking each piezometer based on drought severity, duration, and hazard. The drought severity and duration values were classified into five classes using the Jenks natural interval classification method. The final drought risk values, graded from 1 to 9, were assigned to areas covered by each piezometer using the Thiessen polygon method. Additionally, the correlation for different time lags was estimated to analyze the impact on the correlation between meteorological and hydrological drought indices.



Results and Discussion

The SPI and SPEI indices from 2001 to 2021 exhibited a strong positive correlation with those calculated from 1991 to 2021. Based on the Pearson coefficient, evaluating the correlation of SPI and SPEI indices with the SGI index at 3- to 12-month time scales for each piezometer revealed that correlations were insignificant at 3- and 6-month scales in most piezometers. However, the highest correlations between SGI and SPEI were observed at 9- and 12-month scales. Applying a time lag initially improved correlations across all scales, but correlations diminished beyond a certain point. On average, a time lag of 3 to 5 months increased the correlation. Analysis of the duration and intensity of hydrological drought events indicated that areas with prolonged but low-intensity droughts remained in a drought state for extended periods, struggling to return to equilibrium. Conversely, areas with shorter but more intense droughts experienced intense droughts over short periods but, despite the severity, managed to return to equilibrium. These findings provide practical implications for understanding and predicting drought conditions, offering valuable information for effective groundwater management strategies.



Conclusion

This study investigated the relationship between the hydrological drought SGI derived from groundwater level data and meteorological drought indices over a 20-year statistical period (2001-2021). The shorter analysis period was due to the lack of statistical data and the adequate correspondence between the 20-year monthly data for meteorological drought indices (SPI and SPEI) and the drought index time series obtained from 30-year monthly data. The hydrological drought index (SGI) in the piezometers, which correlated significantly with the drought indices (SPI, SPEI), is primarily influenced by climatic conditions. The low correlation between SPI, SPEI, and the SGI index can be primarily attributed to human factors. Groundwater extraction and social and economic issues are the primary causes of drought in aquifer regions with low SPI-SGI correlation. By assessing drought duration and severity across Thiessen polygons affected by the piezometer, a hydrological drought risk zoning map was developed for the Urmia aquifer. The results indicated that hazard levels 9 and 8 dominated the southern areas of the aquifer, covering 14% and 13% of the surface area, respectively. This map can be a critical tool for selecting appropriate methods to maintain the groundwater level balance. Management plans in high-risk areas should prioritize monitoring human activities such as drilling and water withdrawal, changing crop patterns, and implementing artificial recharge projects. Climatic factors exerted heterogeneous effects on the occurrence of hydrological drought across the entire aquifer. These measures can substantially help the planners and managers in the Urmia Lake Restoration Headquarters and the Regional Water Organization.

Keywords

Main Subjects


منابع
 امیری، وهاب (1399). بررسی پتانسیل نفوذ شورابه به منابع آب زیرزمینی با استفاده از مدل‌سازی عددی مطالعه موردی: آبخوان ساحلی ارومیه. مخاطرات محیط طبیعی، 26، 161-184. doi:10.22111/jneh.2020.32772.1601
بذرافشان، جواد (1381). مطالعه تطبیقی برخی شاخص‌های خشکسالی هواشناسی در چند نمونه اقلیمی ایران، پایان‌نامه کارشناسی ارشد هواشناسی کشاورزی، دانشگاه تهران،کرج.
زاهدی، مجید، و قویدل رحیمی، یوسف (1386). تعیین آستانه خشکسالی و محاسبه میزان بارش قابل اعتماد ایستگاه‌های حوضه آبریز دریاچه ارومیه. پژوهش‌های جغرافیایی، 59، 21-34.
شکوهی، علیرضا و مروتی، رضا (1393). ارزیابی عملکرد شاخص‌های شناسایی خشکسالی و بارش استاندارد از وضعیت ‏خشکسالی حوضه دریاچه ارومیه. مهندسی و مدیریت آبخیز، 6(3)، 232-246. doi:10.22092/ijwmse.2014.101628
طاوسی، تقی، منصوری دانشور، محمدرضا و موقری، علیرضا (1391). پهنه‌بندی شدت خشکی در ایران با استفاده از مدل تبخیر و تعرق هارگریوز-سامانی بر مبنای توپوگرافی رقومی DEM. جغرافیا و پایداری محیط (پژوهش‌نامه جغرافیایی)، 2(3)، 95-110.
عبداللهی اسدآبادی، سجاد، آخوندعلی، علی محمد و میرعباسی نجف آبادی، رسول (1397). مدل‌سازی احتمالاتی و داده مبنای بارش-رواناب با بهره‌گیری از توابع چند متغیره مفصل. هفدهمین کنفرانس هیدرولیک، دانشگاه شهرکرد، ایران.
فتوحی، صمد، مصباح، سیدحمید، و صدری، سعیده (1393). شناسایی و تحلیل ماتریس ریسک خشک شدن تالاب مهارلو و پیامدهای آن بر محیط. اکوبیولوژی تالاب (تالاب)، 6(20)، 54-43.
محسنی ساروی، محسن، صفدری، علی اکبر، ثقفیان، بهرام، و مهدوی، محمد (1383). تحلیل شدت، مدت، فراوانی و گستره خشکسالی‌های حوزه کارون به کمک شاخص بارش استاندارد (SPI). منابع طبیعی ایران، 57(4)، 607-620.
نخعی، محمد، محبی تفرشی، امین و سعدی، توفیق (1402). اررزیابی و پهنه‌بندی مکانی-زمانی خطر خشکسالی آب های زیرزمینی در آبخوان هشتگرد توسط شاخص منبع آب زیرزمینی. زمین شناسی مهندسی، 17(2)، 464-444.
doi:10.22034/JEG.2023.17.4.1019771
 
References
Abdollahi Asadabadi, S., Akhondali, A.M., & Mirabbasi Najafabadi, R. (2017). Probabilistic and data-based modeling of rainfall-runoff using detailed multivariate functions. The 17th Iranian Hydraulic Conference, Shahrekord, Iran. [In Persian]
Abhishek, A., Kinouchi, T., & Sayama, T. (2021). A comprehensive assessment of water storage dynamics and hydroclimatic extremes in the Chao Phraya River Basin during 2002–2020. Journal of Hydrology, 603, 126868. doi:10.1016/j.jhydrol.2021.126868
Abramopoulos, F., Rosenzweig, C., & Choudhury, B. (1988). Improved ground hydrology calculations for global climate models (GCMs): Soil water movement and evapotranspiration. Journal of Climate, 921-941. doi:10.1175/1520-0442(1988)001%3C0921:IGHCFG%3E2.0.CO;2
Abu Arra, A., & Şişman, E. (2024). Innovative Drought Classification Matrix and Acceptable Time Period for Temporal Drought Evaluation. Water Resources Management, 1-23. doi:10.1007/s11269-024-03793-0
Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage, FAO Paper 56, 300(9), D05109.
Amiri, V. (2019). Investigation of the saltwater intrusion potential into groundwater resources using numerical modeling (case study: Urmia coastal aquifer). Journal of Natural Environment Hazards. 26(, 161-184. doi:10.22111/jneh.2020.32772.1601 [In Persian]
Bazrafshan, J. (2015). Comparative study of some meteorological drought indicators in some climatic samples of Iran. Master's thesis in Agricultural Meteorology, University of Tehran, Karaj. [In Persian]
Bazrafshan, O., Parandin, F., & Farokhzadeh, B. (2016). Assessment of hydro-meteorological drought effects on groundwater resources in Hormozgan region-South of Iran. Ecopersia, 4(4), 1569-1584. dor:20.1001.1.23222700.2016.4.4.2.9
Bhuiyan, C. (2004). Various drought indices for monitoring drought condition in Aravalli terrain of India, Proceedings of the XXth ISPRS Congress, Istanbul, Turkey, pp. 12-23
Bloomfield, J., Marchant, B. (2013). Analysis of groundwater drought building on the standardized precipitation index approach. Hydrology and Earth System Sciences. 17(12), 4769-4787. doi:10.5194/hess-17-4769-2013
Burman, R., & Pochop, L. (1994). Evaporation, evapotranspiration, and climatic data. Amsterdam, Elsevier, 278 pages.
Chamanpira, G., Zehtabian, G., Ahmadi, H., & Malekian, A. (2014). Effect of drought on groundwater resources in order to optimize utilization management, case study: Plain Alashtar. Watershed Engineering and Management, 6(1), 10-20. doi:10.22092/ijwmse.2014.101733[In Persian]
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46. doi:10.1177/001316446002000104
Cramér, H. (1999). Mathematical methods of statistics. Princeton University Press, 575 pages.
FID. (2011). Risk Management in DFID. (Online PDF),1-16, Retrieved from https://assets.publishing.service.gov.uk/
Fotouhi, S., Mesbah, S.H., & Sadri, S. (2014). Identifying and analyzing drying risk matrix of Maharlou wetland and its outcomes in the environment. Journal of Wetland Ecobiology. 20, 43-54. [In Persian]
Guo, M., Yue, W., Wang, T., Zheng, N., & Wu, L. (2021). Assessing the use of standardized groundwater index for quantifying groundwater drought over the conterminous US. Journal of Hydrology, 598, 126227. doi:10.1016/j.jhydrol.2021.126227
Hampel, F.R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346), 383-393. doi:10.1080/01621459.1974.10482962
Hanna, S.R., & Heinold, D.W. (1985). Development and application of a simple method for evaluating air quality models. American Petroleum Institute.
Hargreaves, G.H., & Samani, Z.A. (1982). Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108(3), 225-230. doi:10.1061/JRCEA4.0001390
Hellwig, J., De Graaf, I.E.M., Weiler, M., & Stahl, K. (2020). Large‐scale assessment of delayed groundwater responses to drought. Water Resources Research, 56(2), e2019WR025441. doi:10.1029/2019WR025441
Hollinger, S., Isard, S., Welford, M. (1993). A new soil moisture drought index for predicting crop yields. Proceedings of the 8th Conference on Applied Climatology. USA, Anaheim, CA, Pp. 187-190.
Javadzadeh, H., Ataie-Ashtiani, B., Hosseini, S.M., & Simmons, C.T. (2020). Interaction of lake-groundwater levels using cross-correlation analysis: A case study of Lake Urmia Basin, Iran. Science of The Total Environment, 729, 138822. doi:10.1016/j.scitotenv.2020.138822
Leelaruban, N., Padmanabhan, G., & Oduor, P. (2017). Examining the relationship between drought indices and groundwater levels. Water, 9(2), 82. doi:10.3390/w9020082
Li, B., & Rodell, M. (2015). Evaluation of a model-based groundwater drought indicator in the conterminous US. Journal of Hydrology, 526, 78-88. doi:10.1016/j.jhydrol.2014.09.027
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. USA, Anaheim, CA, Pp. 179-183.
Mendicino, G., Senatore, A., & Versace, P. (2008). A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. Journal of Hydrology, 357(3-4), 282-302. doi:10.1016/j.jhydrol.2008.05.005
Mohseni Saravi, M., Safdari, A., Saqhafian, B., & Mahdavi, M. (2005). Severity, Frequency, Duration and Area Analysis of Karoon Basin Droughts Using the Standardized Precipitation Index (SPI). Iranian Journal of Natural Resources, 57)4(, 607-620. [In Persian]
Nakhaei, M., Mohebbi Tafreshi, A., & Saadi, T. (2023). Assessment and spatio-temporal zoning of groundwater drought risk in Hashtgerd aquifer by groundwater resource index. Journal of Engineering Geology, 17)4(, 444-464. doi:10.22034/JEG.2023.17.4.1019771 [In Persian]
Nalbantis, I., & Tsakiris, G. (2009). Assessment of hydrological drought revisited. Water Resources Management, 23(5), 881-897. doi:10.1007/s11269-008-9305-1
Palmer, W.C. (1965). Meteorological drought. Department of Commerce, Weather Bureau. 45, 1-58.
Pearson, K. (1907). Mathematical Contribution to the theory of Evolution. A first study of the statistics of pulmonary tuberculosis. Biometrika, 5(4), 478–478. doi:10.2307/2331696
Sadeghfam, S., Mirahmadi, R., Khatibi, R., Mirabbasi, R., & Nadiri, A.A. (2022). Investigating meteorological groundwater droughts by copula to study anthropogenic impacts. Scientific Reports. 12(1), 8285. doi:10.1038/s41598-022-11768-7
Sharafi, L., Zarafshani, K., Keshavarz, M., Azadi, H., & Van Passel, S. (2020). Drought risk assessment: Towards drought early warning system and sustainable environment in western Iran. Ecological Indicators, 114, 106276. doi:10.1016/j.ecolind.2020.106276
Shokoohi, A., & Morovati, R. (2014). An investigation on the Urmia Lake Basin drought using RDI and SPI indices. Watershed Engineering and Management. 6(3) 232-246. doi:10.22092/ijwmse.2014.101628 [In Persian]
Soleimani Motlagh, M., Ghasemieh, H., Talebi, A., & Abdollahi, K. (2017). Identification and analysis of drought propagation of groundwater during past and future periods. Water Resources Management, 31, 109-125. doi:10.1007/s11269-016-1513-5
Song, G.Y., Hu, H.H., & Yang, M.S. (2023). Practice a reflection on
extreme drought prevention in Chongqing in 2022. Express Water Resources & Hydropower Information, 44, 8–13. doi:10.15974/j.cnki.slsdkb.2023.04.001
Svoboda, M., Hayes, M., & Wood, D. (2012). Standardized Precipitation Index: User guide. Geneva, Switzerland.
Tavoosi, T., Mansuri Daneshvar, M.R., & Movaqqari, A. (2012). The zonation of aridity intensity in Iran using Hargreaves-Samani evapotranspiration model based on digital elevation model (DEM). Geography and Environmental Sustainability, 2(4), 95-110. [In Persian]
Van Lanen, H.A., & Peters, E. (2000). Definition, effects and assessment of groundwater droughts. Pp. 49-61, In: Drought and drought mitigation in Europe. Springer- Netherlands. doi:10.1007/978-94-015-9472-1_4
Vicente-Serrano, S.M., Beguería, S., & López-Moreno, J.I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696-1718. doi:10.1175/2009JCLI2909.1
WMO, (2008). Manual on Low-Flow Estimation and Prediction. World Meteorological Organization. Geneva, Switzerland.
WMO, (2012). Climate and meteorological information requirements for water management. World Meteorological Organization. Geneva, Switzerland.
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557-585.
Yeh, H.F., & Chang, C.F. (2019). Using standardized groundwater index and standardized precipitation index to assess drought characteristics of the Kaoping River Basin, Taiwan. Water Resources, 46(5), 670-678. doi:10.1134/S0097807819050105
Zahedi, M., & Ghavidel Rahimi, Y. (2016). The Determination of drought threshold and computations of dependable rainfall rate for stations of Urmia Lake drainage basin. Geographical Research Quarterly. 39(7):21-34. [In Persian]
Zarei, A.R., Moghimi, M.M. & Bahrami, M. (2019). Comparison of reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI) to evaluate drought severity. Sustainable Water Resources Management, 5, 1345-1356. doi:10.1007/s40899-019-00310-9