Examining the evolving patterns of recent droughts and climate categorization's impact on groundwater reserves through the utilization of GRI and SPI indices in the southern plain of the Sefidroud Basin, Iran

Examining the evolving patterns of recent droughts and climate categorization's impact on groundwater reserves through the utilization of GRI and SPI indices in the southern plain of the Sefidroud Basin, Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Examining the evolving patterns of recent droughts and climate categorization's impact on groundwater reserves through the utilization of GRI and SPI indices in the southern plain of the Sefidroud Basin, Iran Ebrahim Yousefi Mobarhan, Ali Khaleghi, Samira Zandifar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4447426/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Groundwater reservoirs are acknowledged as a vital and cost-effective water source, and comprehending their significance and efficient utilization can foster sustainable development of social and economic activities in a region, particularly in arid and semi-arid regions. This study aimed to explore the correlation between the GRI index and standard rainfall, followed by the classification of the region's climate using the De Martonne method. The research focused on analyzing the temporal variations in recent droughts and climate classification concerning underground water resources in the Qorveh-Dehgolan plain. The GRI calculation method was employed to assess groundwater depletion over periods ranging from 1 to 48 months in the Qorveh-Dehgolan plain. Additionally, ArcGIS software was utilized to zone the groundwater levels and drought severity. The study conducted a correlation analysis between GRI and SPI indicators through linear regression in Excel, revealing a lack of significant correlation between the two indicators (P-value = 0.053 and R 2 value = 0.003), despite their distinctiveness. Furthermore, the drought classification based on SPI indicated that the region fell into the normal drought category. The climatic classification derived from three nearby stations was established as semi-arid using the De Martonne method. De Martonne GRI climate classification correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Drought is a climatic event that has the potential to manifest in any location, causing significant economic, social, and environmental harm. This natural phenomenon impacts diverse environmental sectors, including underground water resources, but its influence on this critical resource has been relatively understudied [ 42 ]. Drought is a regular and ongoing aspect of the climate, although many mistakenly perceive it as a rare occurrence. This natural phenomenon is prevalent across almost all climate regions, albeit with varying characteristics from one region to another. Drought is a transient condition, distinct from aridity, which is confined to regions with minimal rainfall and represents a permanent climate state [ 2 ]. The issue of climate change has been a topic of discussion since the early 1980s, with factors such as shifts in the Earth's axis, increased greenhouse gas emissions from human activities, geographical positioning, proximity to large bodies of water, and prevailing winds being cited, with greenhouse gas increases playing a more significant role [ 6 ]. The impact of meteorological drought on the underground water system typically unfolds over monthly and yearly intervals [ 37 ], [ 7 ]. Given that underground aquifers primarily rely on precipitation or interplay with surface water for sustenance, any alterations in precipitation patterns and surface water dynamics due to climate change will inevitably impact the underground water system. Groundwater drought involves a blend of physical hazards and human susceptibilities associated with the decline in groundwater availability and accessibility during drought episodes [ 38 ]. Numerous research studies have been undertaken in this field: [ 25 ] examined the influence of meteorological drought on both surface and underground water resources using SDI, SPEI, SPI, and GRI indices. In this study, to explore the interplay of droughts, the correlation between these indices was analyzed across different time frames. The findings of the study revealed that the SPEI index exhibits a stronger correlation with the GRI index over 24 and 48-month periods compared to the last 3 months, suggesting the impact of meteorological drought on underground water resources after two or more years. [ 21 ]. examined the groundwater droughts in the Shahrekord Plain by utilizing the GRI index. They analyzed data from 32 piezometric wells in the Shahrekord Plain spanning 31 years (1985–2015). [ 17 ] assessed meteorological indicators of underground and hydrological water resources to forecast and monitor drought in a semi-arid climate. Their study in central Iran in 1970 focused on the Standard Precipitation Index (SPI), Flow Drought Index (SDI), and Groundwater Resources Index (GRI), revealing a higher prevalence of meteorological drought compared to the other types of droughts in the study area. Furthermore, the findings indicated a trend toward increased aridity in the study region over the past three decades. [ 35 ] conducted a study analyzing the spatial and temporal variations in aridity indices such as Lang, De Martonne, United Nations Environment Program (UNEP), and Erinc over a span of 31 years to assess arid regions and changing drought conditions in Iraq. The findings revealed spatial alterations, indicating that approximately 27% of the country is characterized by arid and semi-arid regions. Regarding temporal changes, a decline in drought indices was observed across all monitoring stations. This trend suggests that the anticipated reduction in rainfall and rise in temperatures in this region may exacerbate the situation in the future. [ 18 ], they investigated surface meteorological and hydrological drought using the standardized precipitation index (SPI) and standardized runoff index (SRI), while also examining the impact of hydro-aerial droughts on groundwater through the use of the Groundwater Resources Index (GRI). The findings indicated the occurrence of both dry and wet conditions in the region based on three indices during the initial and subsequent decades. A notable correlation was observed between GRI and SPI on a 12-month time scale. Given the significance of the subject matter and the availability of relevant data in the study area such as rainfall, groundwater levels, and temperature, an effort is being made to establish a connection between the GRI index and SPI, with a focus on evaluating their mutual influence. The decline in groundwater levels and its associated ramifications are recognized as present challenges in the country. Factors contributing to this include diminishing precipitation levels, recent droughts, and increased agricultural, livestock, and industrial activities, alongside a rise in population, particularly over the past decade, leading to heightened water demand and consequently diminished underground water reserves and a negative water balance. The aquifers in the Qorveh-Dehglan plains have experienced depletion [ 1 ]. In essence, this study aims to explore the temporal variations of recent droughts affecting the underground water resources of the Qorveh-Dehgolan plain, utilizing GRI and SPI indicators within the study area. 2 Data and methods 2.1 Study area The Qorveh-Dehgolan Plain is part of the Sefidroud large watershed, one of the 11 areas or plains within it. It has an average annual rainfall of 352 mm and experiences a semi-arid, cold climate. Situated to the east of Sanandaj city and northwest of Hamedan, its geographical coordinates range from 47°42'14" to 48°06'04" east longitude and 35°06'21" to 35°09' north latitude. Geographically, the Qorveh-Dehgolan Plain falls within the Sanandaj-Sirjan construction zone, recognized as one of the most active construction areas in Iran [ 32 ] and [ 41 ]. The southern part of the Qorveh plain comprises metamorphic rocks like schist, marble, amphibolite, and gneiss, interspersed with various igneous masses. Additionally, the tectonic forces are more prominent in the southern regions compared to the northern areas. Within the northern section of the plain, magmatic activities dating from the Miocene to the early Quaternary period led to the formation of basalt and dendrite volcanic formations. The boundaries of the plain are delineated by red sandstone heights and dolomitic limestones to the west, dolomitic limestones to the east, Plio-Pleistocene formations to the north and northeast, and internal igneous and metamorphic formations to the south. The Bi-Khir Heights are adjacent to the Qorveh Plain [ 1 ]. The temperatures in the region range from a minimum of 23 degrees Celsius to a maximum of 41 degrees Celsius. The average annual relative humidity stands at 45%, with evaporation peaking over 350 mL in July. The soil quality in these lands is highly suitable for irrigation and agriculture, exhibiting good performance in cultivating various agricultural and indigenous plants at a low cost. The surface soils are deep, with moderate to heavy textures and excellent water retention capacity. The study area's location is illustrated in Fig. 1 . 2.2 Drought Index Survey Drought is recognized as one of the most destructive natural calamities. Among the array of natural disasters posing threats to both humans and the environment, drought ranks highest in terms of occurrence frequency and damage intensity [ 19 ]. Over the long run, this phenomenon depletes water resources as it leads to the desiccation of surface and underground waterways. To quantify this phenomenon, drought indices are utilized. Typically, these indicators are computed in a localized manner, necessitating spatial processing and the generation of pertinent maps. This research utilizes the Groundwater Resource Index (GRI) as a dependable and practical model. The GRI index, formulated by [ 24 ], serves as a reliable metric for monitoring groundwater drought conditions. The GRI index value is determined using Eq. 1 : $$GRI= \frac{{D}_{y,m}-{\mu }_{D,m}}{{\sigma }_{D.m}}$$ 1 Here, \(D_{(y,m)}\) represents the groundwater level values in year \(y\) and month \(m\), while \(\mu_{(D,m)}\) and \(\sigma_{(D,m)}\) denotes the monthly mean and standard deviation of the groundwater level values over the statistical period, accordingly[ 24 ]. The categorization of the GRI index values is detailed in Table 1 . Table 1 Classification of drought severity according to GRI index values Row Drought classes GRI 1 Very intense fear ≥ 2 2 severe fear 2–1.5 3 Moderate fear 1.5–1 4 Mild fear 1–0.5 5 normal 0.5–0.5- 6 Mild drought 1- − 0.5- 7 Moderate drought 1/5- – 1- 8 severe drought 2- − 1.5- 2.3 Investigating long-term changes and fluctuations in underground water levels To examine the enduring shifts and fluctuations in the underground water level, and to identify intervals of rise and decline in the water level, a typical water level profile was constructed for the Qorveh aquifer over the specified statistical timeframe. The unit hydrograph, or representative water graph, acts as a composite hydrograph illustrating the aquifers in the region, facilitating the observation of water level variations over multiple years and the ascents and descents of the aquifer water level. By analyzing the unit's hydrograph, the peak and trough periods of the water level in the aquifer can be determined. This unit hydrograph is generated by plotting a graph showcasing the average depth of underground water over the months within the chosen timeframe. 2.4 Determination of standard precipitation index (SPI) The deployment of monitoring systems plays a vital role in formulating strategies for managing drought. To provide a quantitative assessment of this phenomenon, drought indices come into play. Among these indices is the Standard Precipitation Index (SPI), developed for gauging rainfall levels over periods ranging from one to 48 months [ 23 ]. The SPI solely focuses on precipitation measurement and operates under two key assumptions. Firstly, it assumes that precipitation variability far surpasses that of other variables like potential evaporation, transpiration, and temperature. Secondly, it presumes that other variables exhibit negligible temporal trends. This index stands out as one of the most prevalent tools for drought monitoring, characterized by its inclusion of a time dimension. The choice of SPI duration is determined by the specific impact of drought on various agricultural and hydrological resources, varying from a single month to several years. Although precipitation data is pivotal for SPI calculations, it is limited to a three-month timeframe in the calculation process. Eq. 2 is utilized to derive the SPI index, subsequently categorized based on predetermined values as outlined in Table 2 . $$SPI=\frac{{X}_{ik}-\stackrel{-}{{X}_{t}}}{\delta i}$$ 2 where SPI stands for the standard precipitation index; represents the standard deviation of the data for the i-th station; denotes the average rainfall across stations (in millimeters); signifies the rainfall values for the i-th station and k observations (in millimeters). Table 2 Classification of SPI index Row Severity of drought SPI 1 Acute fear 2.00> 2 severe fear 1.99–1.55 3 Moderate fear 1.49-1.00 4 normal 0.99-0.00 5 Moderate drought 0.00- -0.99 6 severe drought -1.00- -1.49 7 Acute drought -1.50- -1.99 2.5 Climatic classification with the De Martonne method The De Martonne index is a commonly utilized tool in climate research. Introduced by [ 11 ] and further discussed by [ 12 ] and [ 22 ], this index relies on the aridity index, incorporating temperature and rainfall data to classify the climate of a given region. De Martonne formulated an empirical equation (Eq. 3 ) to determine the climatic classification of an area. $$I= \frac{P}{t+10}$$ 3 In this context: I represent the drought index, P denotes the average annual precipitation in millimeters, T indicates the annual mean temperature in degrees Celsius. Within this framework, De Martonne delineated six distinct climate categories, as outlined in Table 3 . Table 3 Climate classification by the De Martonne method Row Index value Climate type 1 < 10 Arid 2 10 < X < 20 semi-arid 3 20 < X < 24 Mediterranean 4 24 < X < 28 Sub-humid 5 28 < X 35 very wet 3 Results 3.1 Drought time monitoring using SPI The extent of drought in the researched region is evaluated using the Standardized Precipitation Index (SPI) across varying timeframes of 1, 3, 6, 9, 12, 24, and 48 months. The assessment of drought distribution in the area based on SPI alterations indicates moderate drought for the 1 month, normal conditions at the 3-month mark according to SPI categorization, and normal status for the 6 months studied. The classification of the SPI index places the area in the normal category for the 9 months. Over the 12 months, the area falls into the moderate category, with the 24-month and 48-month durations classified as normal and medium, respectively. The drought classes for the specified periods are detailed in Table 4 (refer to Figs. 2 to 5 ). In the examination by [ 15 ] on groundwater and drought indices in the Sharif Abad Basin, Qom, and the research by [ 17 ], the findings align with those of [ 39 ] and [ 24 ] regarding spatial and qualitative changes in underground water in Dehgolan's Qarove plain. Table 4 Classification of droughts based on periods Row time (months) Classification of drought 1 1 Moderate 2 3 Normal 3 6 Normal 4 9 Normal 5 12 Moderate 6 24 Normal 7 48 Moderate 3.2 Drought time monitoring of groundwater resources of the plain In recent times, there has been a noticeable rise in the utilization of underground water in this plain due to the declining availability of surface water resources, leading to a significant drop in the underground water levels across most of the region. While various drought indices have been introduced to forecast and assess drought severity, no specific index has been devised to gauge the extent of groundwater drought in these plains. Figure 6 displays the GRI index values throughout a 20-year statistical period (2000–2020) for the Qorveh aquifer. As depicted in the graph, the GRI index displays a decreasing trend towards drought over the 20 years, with the drought index turning negative post-2010. The negative trend in index values persisted until the end of the statistical period, signifying a decline in underground water levels in the area. The most severe underground water drought occurred in 2017, registering an index value of -1.52. 3.3 Changes in the groundwater level Areas with negative values indicate a decline in the underground water level by the end of the period compared to the initial level. Conversely, areas with positive values signify an increase in the underground water level by the end of the period relative to the beginning, suggesting a decrease in the water level supply in those regions. As evident from the illustration, there was a higher concentration of wells in the initial period (2001–2008) within the areas exhibiting a negative decline, with a substantial number of wells present in such locations. The most significant and least reductions in the underground water level were 2.3 meters and 4.7 meters, respectively, in the region. Moving to the subsequent period (2008–2013), only a restricted number of wells persisted in areas with a positive decline, indicating that the wells previously in positive decline regions transitioned to negative decline areas, witnessing a more pronounced decline compared to the first period. Consequently, a reduction in the underground water level was observable across all parts, with over 90% of the plain experiencing a decline ranging from 5.7 to 1 meter, suggesting unsatisfactory conditions concerning the underground water level possibly attributed to increased well exploitation and aquifer discharge leading to lowered water levels in this aquifer section. In the third period (2013–2018), there was a small region in the south of the plain with a decline exceeding 7 meters, representing the most substantial decrease in the underground water level across the three-time frames. While some areas remained unchanged, parts of the western region experienced an increase in the water level. Additionally, certain sections of the area saw level increases of up to 9 meters. Data from various sections of the plain reveal an escalation in water level decline in each subsequent period compared to the preceding one. These findings align closely with the research conducted by [ 4 ] in the Dehgolan Plain, along with the studies by [ 43 ]. and [ 4 ], which indicated an average drop in groundwater levels ranging from 3.2 to 15.5 meters over two ten-year periods (2008–2011) and a reduction of 1.2 to 5.7 meters in the Qorveh aquifer over two five-year periods (2004–2014). Figure 7 illustrates the zoning of the underground water level. 3.4 Determining the type of climate in the region Based on the results, the climate classification for this region relied on the available rainfall and temperature data from the synoptic stations located near or within the study area. The stations examined included Qorveh station, Bijar, and Garmab, each categorized as semi-arid or arid based on their operational methodologies, as detailed in Table 5 . [ 21 ] utilized the De Martonne method to determine the climate type of Abu Musa Island, identifying a De Martonne dryness coefficient of 0.31, indicative of a dry climate. Meanwhile, employed an ambrothermic curve, De Martonne drought coefficient, and Amberge climate to ascertain the climate of Kerman province in their study. Table 5 Climate classification of De Martonne Row Station Name Index value Climatic class 1 Qorveh 17.33 Semi-arid 2 Bijar 18.19 Semi-arid 3 Garmab 4.96 Arid 3.5 Investigating the relationship between the GRI index and the SPI A more in-depth analysis of the correlation chart comparing standard rainfall indices and GRI revealed that, during the examined period, the GRI index exhibited an increase as drought severity intensified. Furthermore, the correlation between GRI and SPI over a span of 20 years showed insignificance (with a P-value of 0.053 and an R 2 value of 0.003), although the trend pattern of the GRI index aligned with the standard rainfall index. The diagram depicted in Fig. 8 illustrates a weak correlation between GRI and SPI indicators in this context. Broadly speaking, during rainy periods, the GRI value tends to rise, while in times of drought, the groundwater levels tend to decrease, in line with findings from previous studies by Khan et al. in North Bangladesh, [ 13 ] in the Bahabad Plain of Yazd, [ 33 ] in the Fasa plain, [ 9 ] in the Al-Shatter plain, and [ 6 ] in the Marvdasht Khorameh plain of Fars province, examining drought and its impacts on groundwater levels. As indicated in Fig. 8 , during the year 2019, characterized by abundant rainfall and water across Iran, an increase in the SPI index corresponded with a rise in the dust storm index (GRI) in that year, which notably surged compared to previous years. Additionally, the research results regarding the relationship between GRI and SPI indicators align with studies conducted by [ 30 ] in the Lordegan Plain, [ 5 ] in the watershed of Azam Herat River in Yazd province, and [ 2 ] in the Mehran plain of Ilam province. 4 Discussion Groundwater serves as the primary water source for agricultural requirements, particularly in arid and semi-arid regions. To ensure sustainable agriculture, meticulous management and planning for the utilization of these resources are essential, necessitating adequate understanding of the spatial variations in underground water levels over specific durations. Recent decades have seen a slight decline in groundwater levels due to excessive extraction. This study delved into the relationship between groundwater levels and the standard rainfall index from the years 2000 to 2020, yielding the following outcomes. Based on the results and the analysis of the SPI index, the surveyed area was categorized as experiencing normal drought conditions. These findings align with previous studies by [ 30 ], [ 39 ], and [ 24 ]. Climate classification results from three synoptic stations in the region indicated that the area falls within the semi-arid climatic category. However, with the ongoing decrease in precipitation, this region can transition into the dry climate classification. These findings correspond with studies by [ 21 ] and [ 28 ]. The underground water level in the Qorveh-Dehgolan plain has shown a decreasing trend across all piezometers, with a substantial decline in the overall underground water level. The most significant drop in the underground water level between 2012 and 2017 was recorded by the piezometer in Vihej village, while the smallest drop was observed in the piezometer of De-Rashi. The average underground water level of the Qorveh-Dehgolan plain over the 20-year statistical period highlighted the largest decline during the water year 2009–2010, with a 2.85-meter decrease compared to the preceding year. 5 Conclusion Groundwater serves as the primary water source for agricultural requirements, particularly in arid and semi-arid regions. To ensure sustainable agriculture, meticulous management and planning for the utilization of these resources are essential, necessitating adequate understanding of the spatial variations in underground water levels over specific durations. Recent decades have seen a slight decline in groundwater levels due to excessive extraction. This study delved into the relationship between groundwater levels and the standard rainfall index from the years 2000 to 2020, yielding the following outcomes. Based on the results and the analysis of the SPI index, the surveyed area was categorized as experiencing normal drought conditions. These findings align with previous studies by [ 30 ], [ 39 ], and [ 24 ]. Climate classification results from three synoptic stations in the region indicated that the area falls within the semi-arid climatic category. However, with the ongoing decrease in precipitation, this region can transition into the dry climate classification. These findings correspond with studies by [ 21 ] and [ 28 ] Over the 20-year statistical period, the GRI index displayed a declining trend towards drought, with the drought index turning negative post-2010 and continuing in this negative direction until the end of the statistical period. Upon studying the relationship between the two indicators, it was observed that they do not exhibit a significant correlation. In periods of drought, the underground water level decreases, whereas, during heavy rainfall periods, the level of underground water in the region sees an increase. The underground water level in the Qorveh-Dehgolan plain has shown a decreasing trend across all piezometers, with a substantial decline in the overall underground water level. The most significant drop in the underground water level between 2012 and 2017 was recorded by the piezometer in Vihej village, while the smallest drop was observed in the piezometer of De-Rashi. The average underground water level of the Qorveh-Dehgolan plain over the 20-year statistical period highlighted the largest decline during the water year 2009–2010, with a 2.85-meter decrease compared to the preceding year. Declarations Funding The study did not receive any financial backing from pertinent institutions. Acknowledgments The authors express deep gratitude for the assistance and insights provided by the specialists at the Research and Education Center for Agriculture and Natural Resources in Semnan province. Data Accessibility To provide the judges with the necessary data, these data will be made accessible as required. Conflict of Interest This research is free from any conflicting interests. Author Contributions Dr. Ebrahim Yousefi Mobarhan, the first author of this paper, created the first draft and analyzed the sections related to GRI and SPI. Mr. Ali Khaleghi, the second author, contributed to the illustration of the figures and revised the text of the article. Dr. Samira Zandifard contributed to the final review of the article and the final review of the article References Abbasi F, Farzadmehr J, Chapi K, Bashiri M, Azarakhshi M. Spatial and Temporal Variations of Groundwater Quality Parameters in Qorveh- Dehgolan Plain and Its Relationship with Drought. Hydrogeology, 1(2):11-23. (2016). https://doi.org/10.22034/hydro.2016.5002. Abbasinia A, morshedi J, Zohoriyan M, Ghorbaniyan J. Analysis and Comparison of SPI and GRI Indices in Assessing Meteorological Drought and Groundwater, Case Study: Mehran Plain, Ilam Province. Physical Geography Quarterly, 14(Physical Geography Quarterly):95-114. (2021). Ahmadi, Fardin and Ranjbar, Hojat Elah, investigation of the drop of underground water level in Dehgolan plain using GIS, 31st Earth Sciences Conference, Tehran, https://civilica.com/doc/187255. (2013). Azizi HR, ebrahimi H, mohamad vali samani H, khaki V. Effect of Meteorological Drought on Groundwater Resources of Varamin Plain Using SPI, NISTOR and GRI index. Iranian Journal of Irrigation & Drainage, 14(6);2125-2135. (2021). https://doi.org/20.1001.1.20087942.2021.14.6.7.8. Bakhtiari B, Mahdavi N, Sayari N. Variations and sensitivity analysis on Aridity Index (AI) in some climate samples in Iran. Iran-Water Resources Research, 17(1):1-15. (2021) https://doi.org/20.1001.1.17352347.1400.17.1.1.2. Bhuiyan, C., Singh, R. P., & Kogan, F. N. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289-302. (2006). https://doi.org/10.1016/j.jag.2006.03.002. Bhunia P, Das P, Maiti R. Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Systems and Environment, 4(1):43-55. (2020). https://doi.org/10.1007/s41748-019-00137-6. Cammalleri C, Spinoni J, Barbosa P, Toreti A, Vogt JV. The effects of non‐stationarity on SPI for operational drought monitoring in Europe. International Journal of Climatology, 42(6):3418-3430. (2022). https://doi.org/10.1002/joc.7424. Chamanpira G, Zehtabian G, Ahmadi H, Malekian A. Effect of drought on groundwater resources in order to optimize utilization management, case study: Plain Alashtar. Watershed Engineering and Management, 6(1):10-20. (2014). https://doi.org/10.22092/ijwmse.2014.101733. Danandeh Mehr A, Sorman AU, Kahya E, Hesami Afshar M. Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, 65(2):254-268. (2020). https://doi.org/10.1080/02626667.2019.1691218. De Martonne, E. A new climatological function: The Aridity Index. Gauthier-Villars, Paris, France, 485. (1926). Deniz A, Toros H, Incecik S. Spatial variations of climate indices in Turkey. International Journal of climatology, 31(3):394-403. (2011). https://doi.org/10.1002/joc.2081. Eimani M, Talebi Esfandarani A. 2011. Effects of drought on aquifer level changes in Yazd Bahabad plain, using GRI indicators and SPI. In Fourth Iranian Water Resources Management Conference, Amirkabir University of Technology, Tehran (Vol. 8). Ekrami M, Malekinejadand H, Ekhtesasi MR. Investigation of the effect of meteorological and hydrological drought in ground water resources. Journal of Iran Watershed Management Science and Engineering, 20: 47-54. (2014). https://doi.org/10.22092/ijwmse.2019.123305.1566. Forootan E, Golpayegani F. The Investigation of the relationship between groundwater level variation and drought index (Case study: Sharifabad Watershed in Qom Province). Journal of Environmental Science and Technology, 22(5):255-265. (2020). https://doi.org/10.22034/jest.2018.29165.3774. Jahanshahi A, Shahedi K. Evaluation of meteorological, hydrological and groundwater resources indicators for drought monitoring and forecasting in a semi-arid climate. Desert, 23(1):29-43. (2018). https://doi.org/10.22059/JDESERT.2018.66346. Jahanshahi A, Moghaddam Nia A, Nohtani M. Assessment of Drought Impact on Groundwater Resources Using SPI Index (Case study: Shahr-e-Babak Plain, Kerman Province). Desert Ecosystem Engineering, 5(10):85-98. (2022). Javadinejad S, Dara R, Jafary F. Evaluation of hydro-meteorological drought indices for characterizing historical and future droughts and their impact on groundwater. Resources Environment and Information Engineering, 2(1):71-83. (2020). https://doi.org/10.25082/REIE.2020.01.003. Khan S, Gabriel HF, Rana T. Standard precipitation index to track drought and assess impact of rainfall on watertables in irrigation areas. Irrigation and Drainage Systems, 22:159-177. (2008). https://doi.org/10.1007/s10795-008-9049-3. Khosravi Dehkordi A, MIRABBASI, RASOUL, SAMADI BOROUJENI, HOSSEIN, & Ghasemi Dastgerdi, Ahmad Reza. Monitoring and forecasting of groundwater drought in Shahrekord plain using Groundwater Resource Index (GRI) and Markov chain model. HYDROGEOLOGY, 4(1), 111-125. (2019). https://doi.org/10.22034/HYDRO.2019.6985. Mazidi A, enayatpour M, Hosseini SS. Climate determination of Kerman province using ambrothermic curve methods, Domarten drought coefficient, Amberjeh climate view. Geography and Human Relationships, 4(2):35-43. (2021). https://doi.org/10.22034/gahr.2021.287987.1565. McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 17(22):179-183. (1993). Mendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. Journal of Hydrology, 357(3-4):282-302. (2008). https://doi.org/10.1016/j.jhydrol.2008.05.005. Torabi, M., & Shokoohi, A. Research Paper Investigating Changes in the Intensity of Short-Term Rainfall in the Last Half Century in the Context of Data Scarcity. (2023). https://doi.org/10.30495/wsrcj.2023.70259.11331. Mendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. Journal of Hydrology, 357(3-4):282-302. (2008). https://doi.org/10.1016/j.jhydrol.2008.05.005. MIRAKBARI, M., Mortezaie Farizhendi, GH., & MOHSENI SARAVI, M. Investigation of the Effect of Meteorological Drought On Surface and Ground Water Resources by Indices SPI, SPEI, SDI and GRI. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 12(42 ), 70-80. (2018). SID. https://sid.ir/paper/134747/en. Mortezaii Frizhandi G, mirakbari M. Hydro logical drought monitoring using SDI and GRI indicators In the watershed of Azam Herat. Journal of Range and Watershed Management, 71(3):775-785. (2018). https://doi.org/10.22059/jrwm.2018.255693.1254. Mortezaii G, Lotfi J, Khalighi Sigarodi S, Saravi M, Nazari Samini A. Analysis and evaluation of hydrological drought indicators in Kurdistan Province. Watershed Engineering and Management, 12(2):441-453. (2020). https://doi.org/10.22092/ijwmse.2019.123305.1566. Mousavi SA, Solaimani K, Shokrian F, Roshun SH. Investigation of the Relationship between Groundwater Variations and Drought Using SPI and GRI Indices in Lordegan Plain. Journal of Watershed Management Research, 12(23):65-74. (2021). https://doi.org/10.52547/jwmr.12.23.65. Nayak PC, Vijaya Kumar SV, Rao PRS, Vijay T. Recharge source identification using isotope analysis and groundwater flow modeling for Puri city in India. Applied Water Science, 7:3583-3598. (2017). https://doi.org/10.1007/s13201-016-0497-x. Noori Z, Malekian A. Zoning of Hydrogeological Drought Severity in Arid Regions and It’s Relaion with Meteorological Drought (case study: Garmsar Plain). Journal of Range and Watershed Management, 76(2):103-114. (2023). https://doi.org/10.22059/jrwm.2021.120126.847. Pei Z, Fang S, Wang L, Yang W. Comparative analysis of drought indicated by the SPI and SPEI at various timescales in inner Mongolia, China. Water, 12(7):1925. (2020). https://doi.org/10.3390/w12071925. Rahmati O, Nazari Samani A, Mahdavi M. Assessing the effectiveness of the analytic hierarchy process as a groundwater predicting tool (Case study: Ghorve-Dehgolan plain). Journal of Range and Watershed Managment, 70(4):869-879. (2017). https://doi.org/10.22059/jrwm.2018.31578.569. Saif M, 2012. Evaluation of Drought Effects on Groundwater Resources of Fasa Plain in Fars Province. Master's dissertation on Hydrogeology, Mashhad: Ferdowsi University of Mashhad. (In Persian). Sarker MH, Ahmed S, Alam MS, Begum D, et al. Development and forecasting drought indices using SPI (standardized precipitation index) for local level agricultural water management. Atmospheric and Climate Sciences, 11(01):32. (2020). https://doi.org/10.4236/acs.2021.111003. Şarlak N, Mahmood Agha OM. Spatial and temporal variations of aridity indices in Iraq. Theoretical and Applied Climatology, 133:89-99. (2018). https://doi.org/10.1007/s00704-017-2163-0. Shahid S, Hazarika MK. Groundwater drought in the northwestern districts of Bangladesh. Water resources management, 24:1989-2006. (2010). https://doi.org/10.1007/s11269-009-9534-y. Van Lanen HA, Peters E. Definition, effects and assessment of groundwater droughts. In Drought and drought mitigation in Europe (pp. 49-61). Dordrecht: Springer Netherlands. (2000). https://doi.org/10.1007/978-94-015-9472-1_4. Villholth KG, Tøttrup C, Stendel M, Maherry A. Integrated mapping of groundwater drought risk in the Southern African Development Community (SADC) region. Hydrogeology Journal, 21(4):863-885. (2013). https://doi.org/10.1007/s10040-013-0968-1. Yasamani Y, Mosaedi A, Mohamadzadeh H. 2011. Evaluation influence groundwater drought on the Torbat jam-Fariman plain using GRI and SPI index. In 16th Symposium of Geological Society (pp. 7-14). Yousefi Mobarhan E, Karimi Sangchini E. Continuous Rainfall-Runoff Modeling Using HMS-SMA with Emphasis on the Different Calibration Scale. Journal of Chinese Soil and Water Conservation, 52 (2): 112-119. (2021). https://doi.org/10.29417/JCSWC.202106_52(2).0005. Yousefi Mobarhan E, zandifar S. Investigating and Temporal Monitoring of GRI Index on the Fluctuations of Groundwater Table (Case Study: Zanjan Plain). Journal of Water and Soil Resources Conservation, 12(4):87-99. (2023). https://doi.org/10.30495/wsrcj.2023.72140.11361. Yousefi Mobarhan, Ebrahim, & PEYROWAN, HAMIDREZA. Investigating the Sustainability and Interactive Effects of Physical-chemical Properties of Erosion-sensitive Marl and Rangeland Vegetation in Arid and Semiarid Areas (Case Study: Shahrood Town). GEOGRAPHY AND ENVIRONMENTAL SUSTAINABILITY, 12(42), 57-74. (2022). https://doi.org/10.22126/GES.2022.7322.2499. Zareei, A., Nekouei Esfahani, A., Norouzi, E., Kakapour, V., & Zareei, S. Identification of feeding and drainage areas of Qorveh plain aquifer using geographic information system (GIS). (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4447426","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313810264,"identity":"9b04cda4-bcdb-411e-982c-d090fc313d0a","order_by":0,"name":"Ebrahim Yousefi Mobarhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3PsQqCQBzH8b8c/A36m+uJRa+gCE1hDyM0tTlFUEJgU4/QQ0hwc+DQKwQtlmtLQ1AglEo0eo1B98XhN/g5TwCV6oczEUDLykGdb4kVAzCnIvg1iQCQV0tKTCA3p2LIO9AeT28Tv4vATudD4+nkeUY85giGOPZEUF4MPW/SQJwdDWwjSuc1sQQrCaHdREYVoeJZfyW0xEJOHKgI7mqiXUUqJzzF0N3EAUdmbG1N7AmZ5F/M1TLJLoXP+6t1cn2I2cjUl6e8iQBrOe9RPvQekvTsM7W79G2VSqX6x15LRzhHTOR6IwAAAABJRU5ErkJggg==","orcid":"","institution":"Semnan Agriculture and Natural Resources Research \u0026 Education Center, AREEO","correspondingAuthor":true,"prefix":"","firstName":"Ebrahim","middleName":"Yousefi","lastName":"Mobarhan","suffix":""},{"id":313810266,"identity":"d1531902-f478-4b9c-b803-72204a000052","order_by":1,"name":"Ali Khaleghi","email":"","orcid":"","institution":"Semnan University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Khaleghi","suffix":""},{"id":313810267,"identity":"3eba35fc-f764-4af8-9a08-29be27abd897","order_by":2,"name":"Samira Zandifar","email":"","orcid":"","institution":"Semnan Agriculture and Natural Resources Research \u0026 Education Center, AREEO","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Zandifar","suffix":""}],"badges":[],"createdAt":"2024-05-20 07:42:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4447426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4447426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58485747,"identity":"415449a3-20fa-4366-adc7-41da0b436df6","added_by":"auto","created_at":"2024-06-17 09:08:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":881924,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the studied area in the Iran country\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/6af01981fb0d4f933b2e8cbf.png"},{"id":58485743,"identity":"924cd344-a453-4cc4-a464-bcf66923be97","added_by":"auto","created_at":"2024-06-17 09:08:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29607,"visible":true,"origin":"","legend":"\u003cp\u003eChart of changes in standard rainfall index in 1- and 3-month period\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/c296a01c9d16f76c741f6bcc.png"},{"id":58486225,"identity":"8a5ff916-0178-48e8-ba1f-5bf585468620","added_by":"auto","created_at":"2024-06-17 09:16:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19086,"visible":true,"origin":"","legend":"\u003cp\u003eChart of changes in standard rainfall index in 6- and 9-month period\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/a4f688286b74e583ac08a44d.png"},{"id":58485745,"identity":"f5cd02ca-0ff0-4a89-82e8-8be5d339a9e4","added_by":"auto","created_at":"2024-06-17 09:08:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16429,"visible":true,"origin":"","legend":"\u003cp\u003eChart of changes in standard rainfall index in 12- and 24-month period\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/d7350e0239c13fedc0fad18c.png"},{"id":58486224,"identity":"8d50417f-5241-4bff-972a-111f13017db0","added_by":"auto","created_at":"2024-06-17 09:16:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9734,"visible":true,"origin":"","legend":"\u003cp\u003eChart of changes in the standard rainfall index in the 48 months\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/1a50c809aaa85ef226b20b6d.png"},{"id":58485744,"identity":"447631d0-509c-45c5-bd65-c3cfaa35e7ff","added_by":"auto","created_at":"2024-06-17 09:08:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":196962,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the GRI index of the aquifer in the plain for twenty years (2000-20).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/2a8c2e4698fa25b53ccb76de.png"},{"id":58485751,"identity":"66546567-10c0-425c-85bc-1f7907498e94","added_by":"auto","created_at":"2024-06-17 09:08:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":166893,"visible":true,"origin":"","legend":"\u003cp\u003eZoning changes of groundwater drop (in cm) in three time periods\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/7ca194d2c5debdb0b87b2506.png"},{"id":58485752,"identity":"a443bfbd-8f44-420f-8e2a-8d6e8932da4d","added_by":"auto","created_at":"2024-06-17 09:08:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":217295,"visible":true,"origin":"","legend":"\u003cp\u003eChart of the relationship of changes between the GRI index and the SPI\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/235457b55cf4ba6a1279662a.png"},{"id":62786491,"identity":"81c625dc-32d1-4240-abea-36d58344c387","added_by":"auto","created_at":"2024-08-19 13:21:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1931990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4447426/v1/20a3668d-9f64-4642-85c0-f5b0510c6390.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining the evolving patterns of recent droughts and climate categorization's impact on groundwater reserves through the utilization of GRI and SPI indices in the southern plain of the Sefidroud Basin, Iran","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDrought is a climatic event that has the potential to manifest in any location, causing significant economic, social, and environmental harm. This natural phenomenon impacts diverse environmental sectors, including underground water resources, but its influence on this critical resource has been relatively understudied [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Drought is a regular and ongoing aspect of the climate, although many mistakenly perceive it as a rare occurrence. This natural phenomenon is prevalent across almost all climate regions, albeit with varying characteristics from one region to another. Drought is a transient condition, distinct from aridity, which is confined to regions with minimal rainfall and represents a permanent climate state [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The issue of climate change has been a topic of discussion since the early 1980s, with factors such as shifts in the Earth's axis, increased greenhouse gas emissions from human activities, geographical positioning, proximity to large bodies of water, and prevailing winds being cited, with greenhouse gas increases playing a more significant role [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The impact of meteorological drought on the underground water system typically unfolds over monthly and yearly intervals [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given that underground aquifers primarily rely on precipitation or interplay with surface water for sustenance, any alterations in precipitation patterns and surface water dynamics due to climate change will inevitably impact the underground water system. Groundwater drought involves a blend of physical hazards and human susceptibilities associated with the decline in groundwater availability and accessibility during drought episodes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Numerous research studies have been undertaken in this field: [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] examined the influence of meteorological drought on both surface and underground water resources using SDI, SPEI, SPI, and GRI indices. In this study, to explore the interplay of droughts, the correlation between these indices was analyzed across different time frames. The findings of the study revealed that the SPEI index exhibits a stronger correlation with the GRI index over 24 and 48-month periods compared to the last 3 months, suggesting the impact of meteorological drought on underground water resources after two or more years. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. examined the groundwater droughts in the Shahrekord Plain by utilizing the GRI index. They analyzed data from 32 piezometric wells in the Shahrekord Plain spanning 31 years (1985\u0026ndash;2015). [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] assessed meteorological indicators of underground and hydrological water resources to forecast and monitor drought in a semi-arid climate. Their study in central Iran in 1970 focused on the Standard Precipitation Index (SPI), Flow Drought Index (SDI), and Groundwater Resources Index (GRI), revealing a higher prevalence of meteorological drought compared to the other types of droughts in the study area. Furthermore, the findings indicated a trend toward increased aridity in the study region over the past three decades. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] conducted a study analyzing the spatial and temporal variations in aridity indices such as Lang, De Martonne, United Nations Environment Program (UNEP), and Erinc over a span of 31 years to assess arid regions and changing drought conditions in Iraq. The findings revealed spatial alterations, indicating that approximately 27% of the country is characterized by arid and semi-arid regions. Regarding temporal changes, a decline in drought indices was observed across all monitoring stations. This trend suggests that the anticipated reduction in rainfall and rise in temperatures in this region may exacerbate the situation in the future. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], they investigated surface meteorological and hydrological drought using the standardized precipitation index (SPI) and standardized runoff index (SRI), while also examining the impact of hydro-aerial droughts on groundwater through the use of the Groundwater Resources Index (GRI). The findings indicated the occurrence of both dry and wet conditions in the region based on three indices during the initial and subsequent decades. A notable correlation was observed between GRI and SPI on a 12-month time scale. Given the significance of the subject matter and the availability of relevant data in the study area such as rainfall, groundwater levels, and temperature, an effort is being made to establish a connection between the GRI index and SPI, with a focus on evaluating their mutual influence. The decline in groundwater levels and its associated ramifications are recognized as present challenges in the country. Factors contributing to this include diminishing precipitation levels, recent droughts, and increased agricultural, livestock, and industrial activities, alongside a rise in population, particularly over the past decade, leading to heightened water demand and consequently diminished underground water reserves and a negative water balance. The aquifers in the Qorveh-Dehglan plains have experienced depletion [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In essence, this study aims to explore the temporal variations of recent droughts affecting the underground water resources of the Qorveh-Dehgolan plain, utilizing GRI and SPI indicators within the study area.\u003c/p\u003e"},{"header":"2 Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Qorveh-Dehgolan Plain is part of the Sefidroud large watershed, one of the 11 areas or plains within \u003cb\u003eit. It\u003c/b\u003e has an average annual rainfall of 352 mm and experiences a semi-arid, cold climate. Situated to the east of Sanandaj city and northwest of Hamedan, its geographical coordinates range from 47\u0026deg;42'14\" to 48\u0026deg;06'04\" east longitude and 35\u0026deg;06'21\" to 35\u0026deg;09' north latitude. Geographically, the Qorveh-Dehgolan Plain falls within the Sanandaj-Sirjan construction zone, recognized as one of the most active construction areas in Iran [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The southern part of the Qorveh plain comprises metamorphic rocks like schist, marble, amphibolite, and gneiss, interspersed with various igneous masses. Additionally, the tectonic forces are more prominent in the southern regions compared to the northern areas. Within the northern section of the plain, magmatic activities dating from the Miocene to the early Quaternary period led to the formation of basalt and dendrite volcanic formations. The boundaries of the plain are delineated by red sandstone heights and dolomitic limestones to the west, dolomitic limestones to the east, Plio-Pleistocene formations to the north and northeast, and internal igneous and metamorphic formations to the south. The Bi-Khir Heights are adjacent to the Qorveh Plain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The temperatures in the region range from a minimum of 23 degrees Celsius to a maximum of 41 degrees Celsius. The average annual relative humidity stands at 45%, with evaporation peaking over 350 mL in July. The soil quality in these lands is highly suitable for irrigation and agriculture, exhibiting good performance in cultivating various agricultural and indigenous plants at a low cost. The surface soils are deep, with moderate to heavy textures and excellent water retention capacity. The study area's location is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Drought Index Survey\u003c/h2\u003e \u003cp\u003eDrought is recognized as one of the most destructive natural calamities. Among the array of natural disasters posing threats to both humans and the environment, drought ranks highest in terms of occurrence frequency and damage intensity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Over the long run, this phenomenon depletes water resources as it leads to the desiccation of surface and underground waterways. To quantify this phenomenon, drought indices are utilized. Typically, these indicators are computed in a localized manner, necessitating spatial processing and the generation of pertinent maps. This research utilizes the Groundwater Resource Index (GRI) as a dependable and practical model. The GRI index, formulated by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], serves as a reliable metric for monitoring groundwater drought conditions. The GRI index value is determined using Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$GRI= \\frac{{D}_{y,m}-{\\mu }_{D,m}}{{\\sigma }_{D.m}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \\(D_{(y,m)}\\) represents the groundwater level values in year \\(y\\) and month \\(m\\), while \\(\\mu_{(D,m)}\\) and \\(\\sigma_{(D,m)}\\) denotes the monthly mean and standard deviation of the groundwater level values over the statistical period, accordingly[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The categorization of the GRI index values is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of drought severity according to GRI index values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGRI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery intense fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esevere fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;0.5-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1- \u0026minus;\u0026thinsp;0.5-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/5- \u0026ndash; 1-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esevere drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2- \u0026minus;\u0026thinsp;1.5-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Investigating long-term changes and fluctuations in underground water levels\u003c/h2\u003e \u003cp\u003eTo examine the enduring shifts and fluctuations in the underground water level, and to identify intervals of rise and decline in the water level, a typical water level profile was constructed for the Qorveh aquifer over the specified statistical timeframe. The unit hydrograph, or representative water graph, acts as a composite hydrograph illustrating the aquifers in the region, facilitating the observation of water level variations over multiple years and the ascents and descents of the aquifer water level. By analyzing the unit's hydrograph, the peak and trough periods of the water level in the aquifer can be determined. This unit hydrograph is generated by plotting a graph showcasing the average depth of underground water over the months within the chosen timeframe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Determination of standard precipitation index (SPI)\u003c/h2\u003e \u003cp\u003eThe deployment of monitoring systems plays a vital role in formulating strategies for managing drought. To provide a quantitative assessment of this phenomenon, drought indices come into play. Among these indices is the Standard Precipitation Index (SPI), developed for gauging rainfall levels over periods ranging from one to 48 months [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The SPI solely focuses on precipitation measurement and operates under two key assumptions. Firstly, it assumes that precipitation variability far surpasses that of other variables like potential evaporation, transpiration, and temperature. Secondly, it presumes that other variables exhibit negligible temporal trends. This index stands out as one of the most prevalent tools for drought monitoring, characterized by its inclusion of a time dimension. The choice of SPI duration is determined by the specific impact of drought on various agricultural and hydrological resources, varying from a single month to several years. Although precipitation data is pivotal for SPI calculations, it is limited to a three-month timeframe in the calculation process. Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is utilized to derive the SPI index, subsequently categorized based on predetermined values as outlined in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$SPI=\\frac{{X}_{ik}-\\stackrel{-}{{X}_{t}}}{\\delta i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere SPI stands for the standard precipitation index;\u003c/p\u003e \u003cp\u003erepresents the standard deviation of the data for the i-th station;\u003c/p\u003e \u003cp\u003edenotes the average rainfall across stations (in millimeters);\u003c/p\u003e \u003cp\u003esignifies the rainfall values for the i-th station and k observations (in millimeters).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of SPI index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverity of drought\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esevere fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate fear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49-1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99-0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00- -0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esevere drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.00- -1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.50- -1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Climatic classification with the De Martonne method\u003c/h2\u003e \u003cp\u003eThe De Martonne index is a commonly utilized tool in climate research. Introduced by [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and further discussed by [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], this index relies on the aridity index, incorporating temperature and rainfall data to classify the climate of a given region. De Martonne formulated an empirical equation (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to determine the climatic classification of an area.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$I= \\frac{P}{t+10}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this context:\u003c/p\u003e \u003cp\u003eI represent the drought index,\u003c/p\u003e \u003cp\u003eP denotes the average annual precipitation in millimeters,\u003c/p\u003e \u003cp\u003eT indicates the annual mean temperature in degrees Celsius.\u003c/p\u003e \u003cp\u003eWithin this framework, De Martonne delineated six distinct climate categories, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClimate classification by the De Martonne method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClimate type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026lt;\u0026thinsp;X\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esemi-arid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;\u0026lt;\u0026thinsp;X\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMediterranean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026thinsp;\u0026lt;\u0026thinsp;X\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSub-humid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026thinsp;\u0026lt;\u0026thinsp;X\u0026thinsp;\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003every wet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Drought time monitoring using SPI\u003c/h2\u003e \u003cp\u003eThe extent of drought in the researched region is evaluated using the Standardized Precipitation Index (SPI) across varying timeframes of 1, 3, 6, 9, 12, 24, and 48 months. The assessment of drought distribution in the area based on SPI alterations indicates moderate drought for the 1 month, normal conditions at the 3-month mark according to SPI categorization, and normal status for the 6 months studied. The classification of the SPI index places the area in the normal category for the 9 months. Over the 12 months, the area falls into the moderate category, with the 24-month and 48-month durations classified as normal and medium, respectively. The drought classes for the specified periods are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (refer to Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the examination by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] on groundwater and drought indices in the Sharif Abad Basin, Qom, and the research by [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the findings align with those of [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] regarding spatial and qualitative changes in underground water in Dehgolan's Qarove plain.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of droughts based on periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification of drought\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Drought time monitoring of groundwater resources of the plain\u003c/h2\u003e \u003cp\u003eIn recent times, there has been a noticeable rise in the utilization of underground water in this plain due to the declining availability of surface water resources, leading to a significant drop in the underground water levels across most of the region. While various drought indices have been introduced to forecast and assess drought severity, no specific index has been devised to gauge the extent of groundwater drought in these plains. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the GRI index values throughout a 20-year statistical period (2000\u0026ndash;2020) for the Qorveh aquifer. As depicted in the graph, the GRI index displays a decreasing trend towards drought over the 20 years, with the drought index turning negative post-2010. The negative trend in index values persisted until the end of the statistical period, signifying a decline in underground water levels in the area. The most severe underground water drought occurred in 2017, registering an index value of -1.52.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Changes in the groundwater level\u003c/h2\u003e \u003cp\u003eAreas with negative values indicate a decline in the underground water level by the end of the period compared to the initial level. Conversely, areas with positive values signify an increase in the underground water level by the end of the period relative to the beginning, suggesting a decrease in the water level supply in those regions. As evident from the illustration, there was a higher concentration of wells in the initial period (2001\u0026ndash;2008) within the areas exhibiting a negative decline, with a substantial number of wells present in such locations. The most significant and least reductions in the underground water level were 2.3 meters and 4.7 meters, respectively, in the region. Moving to the subsequent period (2008\u0026ndash;2013), only a restricted number of wells persisted in areas with a positive decline, indicating that the wells previously in positive decline regions transitioned to negative decline areas, witnessing a more pronounced decline compared to the first period. Consequently, a reduction in the underground water level was observable across all parts, with over 90% of the plain experiencing a decline ranging from 5.7 to 1 meter, suggesting unsatisfactory conditions concerning the underground water level possibly attributed to increased well exploitation and aquifer discharge leading to lowered water levels in this aquifer section. In the third period (2013\u0026ndash;2018), there was a small region in the south of the plain with a decline exceeding 7 meters, representing the most substantial decrease in the underground water level across the three-time frames. While some areas remained unchanged, parts of the western region experienced an increase in the water level. Additionally, certain sections of the area saw level increases of up to 9 meters. Data from various sections of the plain reveal an escalation in water level decline in each subsequent period compared to the preceding one. These findings align closely with the research conducted by [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] in the Dehgolan Plain, along with the studies by [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. and [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which indicated an average drop in groundwater levels ranging from 3.2 to 15.5 meters over two ten-year periods (2008\u0026ndash;2011) and a reduction of 1.2 to 5.7 meters in the Qorveh aquifer over two five-year periods (2004\u0026ndash;2014). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the zoning of the underground water level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Determining the type of climate in the region\u003c/h2\u003e \u003cp\u003eBased on the results, the climate classification for this region relied on the available rainfall and temperature data from the synoptic stations located near or within the study area. The stations examined included Qorveh station, Bijar, and Garmab, each categorized as semi-arid or arid based on their operational methodologies, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] utilized the De Martonne method to determine the climate type of Abu Musa Island, identifying a De Martonne dryness coefficient of 0.31, indicative of a dry climate. Meanwhile, employed an ambrothermic curve, De Martonne drought coefficient, and Amberge climate to ascertain the climate of Kerman province in their study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClimate classification of De Martonne\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimatic class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQorveh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-arid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBijar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-arid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGarmab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Investigating the relationship between the GRI index and the SPI\u003c/h2\u003e \u003cp\u003eA more in-depth analysis of the correlation chart comparing standard rainfall indices and GRI revealed that, during the examined period, the GRI index exhibited an increase as drought severity intensified. Furthermore, the correlation between GRI and SPI over a span of 20 years showed insignificance (with a P-value of 0.053 and an R\u003csup\u003e2\u003c/sup\u003e value of 0.003), although the trend pattern of the GRI index aligned with the standard rainfall index. The diagram depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates a weak correlation between GRI and SPI indicators in this context. Broadly speaking, during rainy periods, the GRI value tends to rise, while in times of drought, the groundwater levels tend to decrease, in line with findings from previous studies by Khan et al. in North Bangladesh, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] in the Bahabad Plain of Yazd, [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] in the Fasa plain, [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] in the Al-Shatter plain, and [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] in the Marvdasht Khorameh plain of Fars province, examining drought and its impacts on groundwater levels. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, during the year 2019, characterized by abundant rainfall and water across Iran, an increase in the SPI index corresponded with a rise in the dust storm index (GRI) in that year, which notably surged compared to previous years. Additionally, the research results regarding the relationship between GRI and SPI indicators align with studies conducted by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in the Lordegan Plain, [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] in the watershed of Azam Herat River in Yazd province, and [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] in the Mehran plain of Ilam province.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eGroundwater serves as the primary water source for agricultural requirements, particularly in arid and semi-arid regions. To ensure sustainable agriculture, meticulous management and planning for the utilization of these resources are essential, necessitating adequate understanding of the spatial variations in underground water levels over specific durations. Recent decades have seen a slight decline in groundwater levels due to excessive extraction. This study delved into the relationship between groundwater levels and the standard rainfall index from the years 2000 to 2020, yielding the following outcomes. Based on the results and the analysis of the SPI index, the surveyed area was categorized as experiencing normal drought conditions. These findings align with previous studies by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Climate classification results from three synoptic stations in the region indicated that the area falls within the semi-arid climatic category. However, with the ongoing decrease in precipitation, this region can transition into the dry climate classification. These findings correspond with studies by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The underground water level in the Qorveh-Dehgolan plain has shown a decreasing trend across all piezometers, with a substantial decline in the overall underground water level. The most significant drop in the underground water level between 2012 and 2017 was recorded by the piezometer in Vihej village, while the smallest drop was observed in the piezometer of De-Rashi. The average underground water level of the Qorveh-Dehgolan plain over the 20-year statistical period highlighted the largest decline during the water year 2009\u0026ndash;2010, with a 2.85-meter decrease compared to the preceding year.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eGroundwater serves as the primary water source for agricultural requirements, particularly in arid and semi-arid regions. To ensure sustainable agriculture, meticulous management and planning for the utilization of these resources are essential, necessitating adequate understanding of the spatial variations in underground water levels over specific durations. Recent decades have seen a slight decline in groundwater levels due to excessive extraction. This study delved into the relationship between groundwater levels and the standard rainfall index from the years 2000 to 2020, yielding the following outcomes. Based on the results and the analysis of the SPI index, the surveyed area was categorized as experiencing normal drought conditions. These findings align with previous studies by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Climate classification results from three synoptic stations in the region indicated that the area falls within the semi-arid climatic category. However, with the ongoing decrease in precipitation, this region can transition into the dry climate classification. These findings correspond with studies by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Over the 20-year statistical period, the GRI index displayed a declining trend towards drought, with the drought index turning negative post-2010 and continuing in this negative direction until the end of the statistical period. Upon studying the relationship between the two indicators, it was observed that they do not exhibit a significant correlation. In periods of drought, the underground water level decreases, whereas, during heavy rainfall periods, the level of underground water in the region sees an increase. The underground water level in the Qorveh-Dehgolan plain has shown a decreasing trend across all piezometers, with a substantial decline in the overall underground water level. The most significant drop in the underground water level between 2012 and 2017 was recorded by the piezometer in Vihej village, while the smallest drop was observed in the piezometer of De-Rashi. The average underground water level of the Qorveh-Dehgolan plain over the 20-year statistical period highlighted the largest decline during the water year 2009\u0026ndash;2010, with a 2.85-meter decrease compared to the preceding year.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study did not receive any financial backing from pertinent institutions.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors express deep gratitude for the assistance and insights provided by the specialists at the Research and Education Center for Agriculture and Natural Resources in Semnan province.\u003c/p\u003e\n\u003cp\u003eData Accessibility\u003c/p\u003e\n\u003cp\u003eTo provide the judges with the necessary data, these data will be made accessible as required.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThis research is free from any conflicting interests.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eDr. Ebrahim Yousefi Mobarhan, the first author of this paper, created the first draft and analyzed the sections related to GRI and SPI. Mr. Ali Khaleghi, the second author, contributed to the illustration of the figures and revised the text of the article. Dr. Samira Zandifard contributed to the final review of the article and the final review of the article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi F, Farzadmehr J, Chapi K, Bashiri M, Azarakhshi M. Spatial and Temporal Variations of Groundwater Quality Parameters in Qorveh- Dehgolan Plain and Its Relationship with Drought. Hydrogeology, 1(2):11-23. (2016). https://doi.org/10.22034/hydro.2016.5002.\u003c/li\u003e\n\u003cli\u003eAbbasinia A, morshedi J, Zohoriyan M, Ghorbaniyan J. Analysis and Comparison of SPI and GRI Indices in Assessing Meteorological Drought and Groundwater, Case Study: Mehran Plain, Ilam Province. Physical Geography Quarterly, 14(Physical Geography Quarterly):95-114. (2021).\u003c/li\u003e\n\u003cli\u003eAhmadi, Fardin and Ranjbar, Hojat Elah, investigation of the drop of underground water level in Dehgolan plain using GIS, 31st Earth Sciences Conference, Tehran, https://civilica.com/doc/187255. (2013).\u003c/li\u003e\n\u003cli\u003eAzizi HR, ebrahimi H, mohamad vali samani H, khaki V. Effect of Meteorological Drought on Groundwater Resources of Varamin Plain Using SPI, NISTOR and GRI index. Iranian Journal of Irrigation \u0026amp; Drainage, 14(6);2125-2135. (2021). https://doi.org/20.1001.1.20087942.2021.14.6.7.8. \u003c/li\u003e\n\u003cli\u003eBakhtiari B, Mahdavi N, Sayari N. Variations and sensitivity analysis on Aridity Index (AI) in some climate samples in Iran. Iran-Water Resources Research, 17(1):1-15. (2021) https://doi.org/20.1001.1.17352347.1400.17.1.1.2.\u003c/li\u003e\n\u003cli\u003eBhuiyan, C., Singh, R. P., \u0026amp; Kogan, F. N. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289-302. (2006). https://doi.org/10.1016/j.jag.2006.03.002. \u003c/li\u003e\n\u003cli\u003eBhunia P, Das P, Maiti R. Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Systems and Environment, 4(1):43-55. (2020). https://doi.org/10.1007/s41748-019-00137-6. \u003c/li\u003e\n\u003cli\u003eCammalleri C, Spinoni J, Barbosa P, Toreti A, Vogt JV. The effects of non‐stationarity on SPI for operational drought monitoring in Europe. International Journal of Climatology, 42(6):3418-3430. (2022). https://doi.org/10.1002/joc.7424. \u003c/li\u003e\n\u003cli\u003eChamanpira G, Zehtabian G, Ahmadi H, Malekian A. Effect of drought on groundwater resources in order to optimize utilization management, case study: Plain Alashtar. Watershed Engineering and Management, 6(1):10-20. (2014). https://doi.org/10.22092/ijwmse.2014.101733. \u003c/li\u003e\n\u003cli\u003eDanandeh Mehr A, Sorman AU, Kahya E, Hesami Afshar M. Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, 65(2):254-268. (2020). https://doi.org/10.1080/02626667.2019.1691218. \u003c/li\u003e\n\u003cli\u003eDe Martonne, E. A new climatological function: The Aridity Index. Gauthier-Villars, Paris, France, 485. (1926).\u003c/li\u003e\n\u003cli\u003eDeniz A, Toros H, Incecik S. Spatial variations of climate indices in Turkey. International Journal of climatology, 31(3):394-403. (2011). https://doi.org/10.1002/joc.2081. \u003c/li\u003e\n\u003cli\u003eEimani M, Talebi Esfandarani A. 2011. Effects of drought on aquifer level changes in Yazd Bahabad plain, using GRI indicators and SPI. In Fourth Iranian Water Resources Management Conference, Amirkabir University of Technology, Tehran (Vol. 8).\u003c/li\u003e\n\u003cli\u003eEkrami M, Malekinejadand H, Ekhtesasi MR. Investigation of the effect of meteorological and\u003cspan dir=\"RTL\"\u003e \u003c/span\u003ehydrological drought in ground water resources. Journal of Iran Watershed Management Science and\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eEngineering, 20: 47-54. (2014). https://doi.org/10.22092/ijwmse.2019.123305.1566. \u003c/li\u003e\n\u003cli\u003eForootan E, Golpayegani F. The Investigation of the relationship between groundwater level variation and drought index (Case study: Sharifabad Watershed in Qom Province). Journal of Environmental Science and Technology, 22(5):255-265. (2020). https://doi.org/10.22034/jest.2018.29165.3774. \u003c/li\u003e\n\u003cli\u003eJahanshahi A, Shahedi K. Evaluation of meteorological, hydrological and groundwater resources indicators for drought monitoring and forecasting in a semi-arid climate. Desert, 23(1):29-43. (2018). https://doi.org/10.22059/JDESERT.2018.66346.\u003c/li\u003e\n\u003cli\u003eJahanshahi A, Moghaddam Nia A, Nohtani M. Assessment of Drought Impact on Groundwater Resources Using SPI Index (Case study: Shahr-e-Babak Plain, Kerman Province). Desert Ecosystem Engineering, 5(10):85-98. (2022).\u003c/li\u003e\n\u003cli\u003eJavadinejad S, Dara R, Jafary F. Evaluation of hydro-meteorological drought indices for characterizing historical and future droughts and their impact on groundwater. Resources Environment and Information Engineering, 2(1):71-83. (2020). https://doi.org/10.25082/REIE.2020.01.003.\u003c/li\u003e\n\u003cli\u003eKhan S, Gabriel HF, Rana T. Standard precipitation index to track drought and assess impact of rainfall on watertables in irrigation areas. Irrigation and Drainage Systems, 22:159-177. (2008). https://doi.org/10.1007/s10795-008-9049-3.\u003c/li\u003e\n\u003cli\u003eKhosravi Dehkordi A, MIRABBASI, RASOUL, SAMADI BOROUJENI, HOSSEIN, \u0026amp; Ghasemi Dastgerdi, Ahmad Reza. Monitoring and forecasting of groundwater drought in Shahrekord plain using Groundwater Resource Index (GRI) and Markov chain model. HYDROGEOLOGY, 4(1), 111-125. (2019). https://doi.org/10.22034/HYDRO.2019.6985.\u003c/li\u003e\n\u003cli\u003eMazidi A, enayatpour M, Hosseini SS. Climate determination of Kerman province using ambrothermic curve methods, Domarten drought coefficient, Amberjeh climate view. Geography and Human Relationships, 4(2):35-43. (2021). https://doi.org/10.22034/gahr.2021.287987.1565.\u003c/li\u003e\n\u003cli\u003eMcKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 17(22):179-183. (1993).\u003c/li\u003e\n\u003cli\u003eMendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. Journal of Hydrology, 357(3-4):282-302. (2008). https://doi.org/10.1016/j.jhydrol.2008.05.005.\u003c/li\u003e\n\u003cli\u003eTorabi, M., \u0026amp; Shokoohi, A. Research Paper Investigating Changes in the Intensity of Short-Term Rainfall in the Last Half Century in the Context of Data Scarcity. (2023). https://doi.org/10.30495/wsrcj.2023.70259.11331.\u003c/li\u003e\n\u003cli\u003eMendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. Journal of Hydrology, 357(3-4):282-302. (2008). https://doi.org/10.1016/j.jhydrol.2008.05.005.\u003c/li\u003e\n\u003cli\u003eMIRAKBARI, M., Mortezaie Farizhendi, GH., \u0026amp; MOHSENI SARAVI, M. Investigation of the Effect of Meteorological Drought On Surface and Ground Water Resources by Indices SPI, SPEI, SDI and GRI. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 12(42 ), 70-80. (2018). SID. https://sid.ir/paper/134747/en.\u003c/li\u003e\n\u003cli\u003eMortezaii Frizhandi G, mirakbari M. Hydro logical drought monitoring using SDI and GRI indicators In the watershed of Azam Herat. Journal of Range and Watershed Management, 71(3):775-785. (2018). https://doi.org/10.22059/jrwm.2018.255693.1254.\u003c/li\u003e\n\u003cli\u003eMortezaii G, Lotfi J, Khalighi Sigarodi S, Saravi M, Nazari Samini A. Analysis and evaluation of hydrological drought indicators in Kurdistan Province. Watershed Engineering and Management, 12(2):441-453. (2020). https://doi.org/10.22092/ijwmse.2019.123305.1566.\u003c/li\u003e\n\u003cli\u003eMousavi SA, Solaimani K, Shokrian F, Roshun SH. Investigation of the Relationship between Groundwater Variations and Drought Using SPI and GRI Indices in Lordegan Plain. Journal of Watershed Management Research, 12(23):65-74. (2021). https://doi.org/10.52547/jwmr.12.23.65.\u003c/li\u003e\n\u003cli\u003eNayak PC, Vijaya Kumar SV, Rao PRS, Vijay T. Recharge source identification using isotope analysis and groundwater flow modeling for Puri city in India. Applied Water Science, 7:3583-3598. (2017). https://doi.org/10.1007/s13201-016-0497-x.\u003c/li\u003e\n\u003cli\u003eNoori Z, Malekian A. Zoning of Hydrogeological Drought Severity in Arid Regions and It\u0026rsquo;s Relaion with Meteorological Drought (case study: Garmsar Plain). Journal of Range and Watershed Management, 76(2):103-114. (2023). https://doi.org/10.22059/jrwm.2021.120126.847.\u003c/li\u003e\n\u003cli\u003ePei Z, Fang S, Wang L, Yang W. Comparative analysis of drought indicated by the SPI and SPEI at various timescales in inner Mongolia, China. Water, 12(7):1925. (2020). https://doi.org/10.3390/w12071925.\u003c/li\u003e\n\u003cli\u003eRahmati O, Nazari Samani A, Mahdavi M. Assessing the effectiveness of the analytic hierarchy process as a groundwater predicting tool (Case study: Ghorve-Dehgolan plain). Journal of Range and Watershed Managment, 70(4):869-879. (2017). https://doi.org/10.22059/jrwm.2018.31578.569.\u003c/li\u003e\n\u003cli\u003eSaif M, 2012. Evaluation of Drought Effects on Groundwater Resources of Fasa Plain in Fars Province. Master\u0026apos;s dissertation on Hydrogeology, Mashhad: Ferdowsi University of Mashhad. (In Persian).\u003c/li\u003e\n\u003cli\u003eSarker MH, Ahmed S, Alam MS, Begum D, et al. Development and forecasting drought indices using SPI (standardized precipitation index) for local level agricultural water management. Atmospheric and Climate Sciences, 11(01):32. (2020). https://doi.org/10.4236/acs.2021.111003.\u003c/li\u003e\n\u003cli\u003eŞarlak N, Mahmood Agha OM. Spatial and temporal variations of aridity indices in Iraq. Theoretical and Applied Climatology, 133:89-99. (2018). https://doi.org/10.1007/s00704-017-2163-0.\u003c/li\u003e\n\u003cli\u003eShahid S, Hazarika MK. Groundwater drought in the northwestern districts of Bangladesh. Water resources management, 24:1989-2006. (2010). https://doi.org/10.1007/s11269-009-9534-y.\u003c/li\u003e\n\u003cli\u003eVan Lanen HA, Peters E. Definition, effects and assessment of groundwater droughts. In Drought and drought mitigation in Europe (pp. 49-61). Dordrecht: Springer Netherlands. (2000). https://doi.org/10.1007/978-94-015-9472-1_4.\u003c/li\u003e\n\u003cli\u003eVillholth KG, T\u0026oslash;ttrup C, Stendel M, Maherry A. Integrated mapping of groundwater drought risk in the Southern African Development Community (SADC) region. Hydrogeology Journal, 21(4):863-885. (2013). https://doi.org/10.1007/s10040-013-0968-1.\u003c/li\u003e\n\u003cli\u003eYasamani Y, Mosaedi A, Mohamadzadeh H. 2011. Evaluation influence groundwater drought on the Torbat jam-Fariman plain using GRI and SPI index. In 16th Symposium of Geological Society (pp. 7-14).\u003c/li\u003e\n\u003cli\u003eYousefi Mobarhan E, Karimi Sangchini E. Continuous Rainfall-Runoff Modeling Using HMS-SMA with Emphasis on the Different Calibration Scale. Journal of Chinese Soil and Water Conservation, 52 (2): 112-119. (2021). https://doi.org/10.29417/JCSWC.202106_52(2).0005.\u003c/li\u003e\n\u003cli\u003eYousefi Mobarhan E, zandifar S. Investigating and Temporal Monitoring of GRI Index on the Fluctuations of Groundwater Table (Case Study: Zanjan Plain). Journal of Water and Soil Resources Conservation, 12(4):87-99. (2023). https://doi.org/10.30495/wsrcj.2023.72140.11361.\u003c/li\u003e\n\u003cli\u003eYousefi Mobarhan, Ebrahim, \u0026amp; PEYROWAN, HAMIDREZA. Investigating the Sustainability and Interactive Effects of Physical-chemical Properties of Erosion-sensitive Marl and Rangeland Vegetation in Arid and Semiarid Areas (Case Study: Shahrood Town). GEOGRAPHY AND ENVIRONMENTAL SUSTAINABILITY, 12(42), 57-74. (2022). https://doi.org/10.22126/GES.2022.7322.2499.\u003c/li\u003e\n\u003cli\u003eZareei, A., Nekouei Esfahani, A., Norouzi, E., Kakapour, V., \u0026amp; Zareei, S. Identification of feeding and drainage areas of Qorveh plain aquifer using geographic information system (GIS). (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"De Martonne, GRI, climate classification, correlation","lastPublishedDoi":"10.21203/rs.3.rs-4447426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4447426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater reservoirs are acknowledged as a vital and cost-effective water source, and comprehending their significance and efficient utilization can foster sustainable development of social and economic activities in a region, particularly in arid and semi-arid regions. This study aimed to explore the correlation between the GRI index and standard rainfall, followed by the classification of the region's climate using the De Martonne method. The research focused on analyzing the temporal variations in recent droughts and climate classification concerning underground water resources in the Qorveh-Dehgolan plain. The GRI calculation method was employed to assess groundwater depletion over periods ranging from 1 to 48 months in the Qorveh-Dehgolan plain. Additionally, ArcGIS software was utilized to zone the groundwater levels and drought severity. The study conducted a correlation analysis between GRI and SPI indicators through linear regression in Excel, revealing a lack of significant correlation between the two indicators (P-value\u0026thinsp;=\u0026thinsp;0.053 and R\u003csup\u003e2\u003c/sup\u003e value\u0026thinsp;=\u0026thinsp;0.003), despite their distinctiveness. Furthermore, the drought classification based on SPI indicated that the region fell into the normal drought category. The climatic classification derived from three nearby stations was established as semi-arid using the De Martonne method.\u003c/p\u003e","manuscriptTitle":"Examining the evolving patterns of recent droughts and climate categorization's impact on groundwater reserves through the utilization of GRI and SPI indices in the southern plain of the Sefidroud Basin, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 09:08:46","doi":"10.21203/rs.3.rs-4447426/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29f8c710-ed97-44f8-b901-c862579fd1ca","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-19T13:13:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 09:08:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4447426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4447426","identity":"rs-4447426","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}