Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province)

Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province) | 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 Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province) Ameneh Roshan, Khalil Ghorbani, Meysam Salarijazi, Ebrahim Asadi Oskouei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2708441/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 In most arid and semiarid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes influencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these fluctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the fluctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indicators in a 6-month time window for a long-term statistical period (1989–2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immeddergiately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coefficient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indicators, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the significance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future. Groundwater drought SPI SPEI SWI M5 decision tree Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Drought is not a natural calamity, but rather a periodic and climatic occurrence that can occur in every region. Due to the harm droughts impose to the agriculture sector and water supplies in arid and semi-arid regions where water scarcity is a problem, they are frequently regarded as a catastrophe. Drought is one of the most significant natural calamities, particularly for farmers. Drought can happen anywhere in the world since it is triggered by an abnormally low amount of precipitation compared to the average or expected climatic value. Lack of water, plant desiccation, and a drop in surface and subterranean water levels are only some of the insidious secondary repercussions of drought that are harmful to both individuals and the ecosystem. More than 85% of worldwide losses are caused by extreme weather events, with drought being a key factor. Due to drought, the world has lost 722 billion USD and 23 million people in the first decade of the 21st century (Ghozat et al., 2022 ; Gopalakrishnan, 2013 ). It is predicted that the global air temperature will rise by 0.78 to 1.5 degrees Celsius (Solomon, S. et al., 2007 ), which will alter the rainfall patterns and subsequently increase the frequency and severity of droughts (Zarch et al., 2011 ). This climate change poses significant risks to water resources, environmental sustainability, and social and economic growth (Malik et al., 2020 ; Rahman et al., 2022 ). Consequently, monitoring drought and its features, and early warning are essential for mitigating its regional and global effects. For the study of drought, monitoring systems are utilized. There are a variety of drought classifications based on distinct features. Meteorological drought occurs when the amount of precipitation during a period is less than its typical value. Thus, type of drought is calculated using precipitation-based indices such as SPI or bivariate indices such as SPEI, which are based on precipitation and evapotranspiration. Meteorological drought indices, which are derived from meteorological data, are utilized more frequently than hydrological and agricultural indices due to better data availability, wider spatial distribution, and the ability of atmospheric general circulation models to predict them for future years under various climate change scenarios. Consequently, numerous researchers attempt to establish a connection between these indicators. (Teimoori et al., 2015 ) compared meteorological and hydrological drought using the SPI and SSI (Standardized Stream flow Index) indices. The SSI index is calculated similarly to the SPI index, with the exception that former uses the river's flow rate instead of precipitation. According to the findings of this study, there is no perfect match between these two indicators; nevertheless, this match can be improved by incorporating previous time steps. Comparative research of SPI and SWI in the Marand plain of Iran by (Zeinali et al., 2017 ) found that there is a 1% association between these two indicators and that the subsurface water resources are affected by the drought with a five-month lag. This delay between meteorological and hydrological drought was confirmed by (Maleki nejad & Soleimani-Motlaq, 2011 ) in Chaghalvandi Basin (located in Lorestan province) and (Salahi et al., 2018 ) in Marand plain located in East Azarbaijan province by comparing SPI and SWI indices, as well as (Abbasi et al., 2016 ) in Qorveh and Dehgolan plains by comparing SPI and GRI indices. In a 21-year study conducted in Jordan (Yarmouk Basin), Hind (Mohammad et al., 2018 ) determined that this basin is especially susceptible to regular droughts and that severe drought occurrences has had a negative effect on the groundwater level. (Kubiak-Wójcicka & Bąk, 2018 ) evaluated meteorological drought and its effect on hydrological drought in the Vistula basin in Poland using the SPI, SWI, and SRI indices over 29 years with three time lags (12-24-48 months), where highest association was found along the Central Vistula and its tributaries, while weakest correlation was found in the foothills. In addition, the results revealed non-climatic elements could have influenced the correlation between coefficients (such as underground reservoirs, urban and industrial consumption). (Aleboali et al., 2016 ) studied the impacts of drought on groundwater resources in the Kashan plain of Iran over a 19-year period using the SPI index and concluded that excessive exploitation of groundwater resources is the cause of water level decline in addition to drought. The contribution of overharvesting to the decline of aquifer levels has been significantly greater than that of drought. As a result, it may be argued that the subsurface water supplies are only partially affected by the climatic drought, and that other causes, such as over harvesting, also have a role. However, excessive harvesting results from the expansion of the cultivated area, the rise in temperature, and the subsequent rise in evapotranspiration and water consumption. The SPI index, which is based on a single precipitation variable, cannot account for this factor. The SPEI bivariate index, based on precipitation and evapotranspiration, was introduced by Winslet (Beguería et al., 2014 ). Other scholars have examined this index and compared it to the SPI index. In a comparative research of two meteorological drought indicators SPI and SPEI in the province of Golestan, (Rezaei Ghaleh & Ghorbani, 2018 ) found a stronger link between these two indices at stations with a more humid climate. (Pei et al., 2020 ) calculated the SPI and SPEI indices at 1, 3, 6, and 12-month intervals for 102 weather stations in Inner Mongolia over 1981–2018 and found that as the time window expanded, the difference between the indices decreased. This difference may become negligible over longer time periods. Compared to drought conditions and plant indices, the SPEI index was deemed more suitable for drought monitoring than the SPI index. (Jipkate et al., 2020 ) compared the SPI and SPEI indices with the SWI index in the Upper Bhima Sub Basin during 2002–2016 and found no direct linear relationship between SWI and SPI and rainfall in the region. (Fung et al., 2020 ) analyzed the SPI and SPEI indices for 1, 3, and 6 months in Peninsular Malaysia to determine the significance of temperature in causing droughts. In the study of temperature-induced fluctuations on the SPEI index, two indices demonstrated distinct performances; the analysis of the two indices was conducted by analyzing the spatial variations in drought frequency, average drought period, average drought intensity, and average maximum drought. Due to the relevance of temperature increase in the establishment of drought, the SPI index is more ideal for a shorter time window of 1 month, but the SPEI index is more suitable for a longer time window of 3 to 6 months. By comparing the SPI and SPEI indices during a 46-year period in Ankara Province, Turkey, (Danandeh Mehr & Vaheddoost, 2020 ) determined that whereas the SPEI index has a declining trend, the SPI index does not exhibit a similar pattern. (Babre et al., 2022 ) assessed groundwater drought periods in the Baltic States using established drought indices and found that meteorological drought indices (SPI, SPEI, and RDI) were substantially linked with groundwater drought conditions in shallow groundwater wells. (Kubicz & Bąk, 2019 ) investigated the response of groundwater to a multi-month meteorological drought in Poland. They discovered that there was no significant linear relationship between the SPI index in time windows of 6, 12, and 24 months and the standardized groundwater level index (SGI), and they concluded that the level of underground water is influenced by factors other than precipitation. (Leelaruban et al., 2017 ) analyzed the association between the SPI index and groundwater level changes in the United States for 6, 9, 12, and 24 months intervals. In addition to these indicators, precipitation and average air temperature data was also utilized. The strongest correlation was found in 17 of the 32 wells with SPI-24, while 12 of the wells had a correlation value of 0.6 or greater and the remaining wells were reasonable correlated. Modeling and determining the link between data has long been a topic of interest, and several research have been undertaken in the field of analyzing and comparing various modeling methods using data mining. (Nourani et al., 2016 ) employed two data mining techniques (associative rules and decision tree) to identify the relationship between the highest monthly precipitation at synoptic stations in Urmia and Tabriz and the surface temperatures of the Black, Mediterranean, and Red seas. Association rules were used to the observational data to reveal hidden trends and patterns, and decision tree-based techniques and algorithms were utilized to identify and choose the most effective groups. The subsurface water level in Ardabil plain was forecasted by (Sattari et al., 2018 ) using support vector regression, the M5 decision tree model, and data from 24 piezometric wells over 17 years (1997–2013). The model's inputs were the underground water level in the previous month, the volume of precipitation input to each cell, and the number of feeding wells, while the model's output was the underground water level in the current month. To measure the effectiveness of the model, the correlation coefficient and the mean square error were calculated. The findings showed that both methods were successful in estimating the groundwater table, but the decision tree method's outputs were more transparent and intuitive to use. (Ghorbani, 2016 ) compared the data mining models M5 decision tree model and k nearest neighbor (KNN) to the IHACRES hydrological model for predicting the monthly river discharge in Arazkouseh station. Due to modeling transparency and the availability of simple regression equations, they confirmed the data mining models' superiority over the hydrological model and determined that the M5 model was the most accurate. In predicting SSI hydrological drought index based on SPI and SPEI meteorological drought indices with machine learning methods, (Shamshirband et al., 2020 ) determined that the M5 tree model provides superior results to SVR and GEP. White box models, like the decision tree model, are useful because they produce accurate outcomes while also allowing the user to easily identify the impact of various factors using straightforward regression analysis. None of the research looking into the cause of the droughts in the study area have used data-driven models, and none of them have looked into splitting the area into a semi-deep aquifer. In this study, we examine the relationship between meteorological drought, using SPI and SPEI indices, and fluctuations in the groundwater level over a 6-month period in semi-deep aquifers in Golestan Province of Iran by comparing the linear multivariate regression method and the decision tree method. The primary purpose is to quantify the impact of air temperature and precipitation on hydrological drought while assessing the efficacy of these two techniques. Materials And Methods Study area The examined area consists of semi-deep wells in Iran's Golestan province, near the southeastern coast of the Caspian Sea (Fig. 1 ). This study utilizes two types of data: the subsurface water level of piezometric wells and meteorological data from the synoptic meteorological station at Hashem-Abad, Gorgan. The length of the common statistical period was determined to be 29 years (1990–2018) based on the available data (Fig. 1 ). Extraction of drought indicators Two indices, SPI (based on precipitation) and SPEI (based on precipitation and evaporation-transpiration), have been used to indicate the meteorological drought condition, while the SWI index (based on the level of the underground water table) has been used to indicate the hydrological drought condition. Standardized Precipitation Index (SPI): (McKee et al., 1993 ) presented the SPI index for the first time. The cumulative probability is computed by forming precipitation time series at various scales and then fitting them with a gamma distribution. The SPI index is then determined by transforming the distribution to standard Z and calculating the value of Z corresponding to each cumulative probability. Based on long-term rainfall data for each meteorological station with a gamma distribution and the maximum likelihood estimation approach, the SPI can be calculated for several time scales, including 1, 3, 6, 9, 12, 24, and 48 months. SPI can be calculated for both short-term and long-term droughts, with its short-term application assessing the impact of drought on moisture and precipitation and its long-term application assessing the impact of drought on agriculture and water resources, i.e. surface water flow and underground water (Rahman et al., 2018 ). Considering that the level of the subsurface water table is tested twice a year, in May and November, a 6-month time window was utilized in this study. This index is classified according to the table (1). Table 1 Classification of SPI and SPEI indices Threshold Severely wet SPI, SPEI > 1.5 Moderately wet 1.5 > SPI, SPEI > 1 Normal 1 > SPI, SPEI > -1 Moderately dry -1.5 > SPI, SPEI > 1.5 Severely dry SPI, SPEI < -1.5 Standardized Meteorological Drought Index of Precipitation, Evapotranspiration and Transpiration (SPEI): In (Beguería et al., 2014 ) introduced the SPEI index. The SPEI index is a multi-quantity index that combines data on precipitation and temperature. Its calculating procedure is identical to that of the SPI index, except the difference between precipitation and potential evaporation and transpiration (PET) is employed to represent the real water balance. In the present study, the SPEI index was calculated using the programming language R. $${\text{P}\text{E}\text{T} = 16\text{K}(10\text{T}/\text{I})}^{\text{m}}$$ 1 $${\text{m} = 6.75 \times 10}^{-7}{\text{I}}^{3}- 7.71 \times {10}^{-5}{\text{I}}^{2}+ 1.79 \times {10}^{-2}$$ 2 $$\text{i} = {\left(\frac{\text{T}}{5}\right)}^{1.514}$$ 3 \(\text{K} =\left( \frac{\text{N}}{12}\right)(\frac{\text{N}\text{D}\text{M}}{30}\) ) (4) Where T is the average monthly temperature in Celsius, m is the dependence factor on I, I is the heat index or the sum of the 12-month index, K is the adjustment factor in terms of month and latitude, NDM is the number of days in a month, and N is the maximum number of hours of radiation. In this way, having the amount of potential evaporation and transpiration, the difference between precipitation (P) and PET for the i th month is obtained based on Eq. 5 . $$\text{D} \text{i} = \text{P} \text{i} – \text{P}\text{E}\text{T}\text{i}$$ 5 Numerous studies have established that the logistic distribution is more effective in determining the probability density function or pdf of the time series D due to its better fit with skewed data and the longer sequence towards the end of the distribution's range. The probability density function of SPEI is therefore computed using the logistic distribution. Based on the logarithmic logistic distribution and Eq. 6 , the density function of the D series is computed. $$\text{f} \left(\text{x}\right) = \frac{{\beta }}{{\alpha }}({\frac{\text{x}-{\gamma }}{{\alpha }})}^{{\beta }-1}{\left[\right(1+\left({\frac{\text{x}-{\gamma }}{{\alpha }})}^{{\beta }}\right)]}^{-2}$$ 6 Where α, β, and γ are scale, shape and main parameters for D in the range of ᵞ < D <∞. For calculating the parameters of the logarithmic logistic distribution, the probability weighted moments approach was utilized as an efficient and straightforward technique. Based on Eq. 7 , the probability distribution function of the D series is also determined. $${\text{F}\left(\text{x}\right) =\left[\right(1+{\left(\frac{\text{a}}{\text{x}-\text{y}}\right)}^{\text{ᵝ}}]}^{-1}$$ 7 The SPEI index can be easily calculated in terms of standardized values (F(x)) $$\text{S}\text{P}\text{E}\text{I} = \text{W} - \frac{{\text{C}}_{0}+{\text{C}}_{1}\text{W}+{\text{C}}_{2}{\text{W}}^{2}}{1+{\text{d}}_{1}\text{W}+{\text{d}}_{2}{\text{W}}^{2}+{\text{d}}_{3}{\text{W}}^{3}}$$ 8 Where \(\text{W} =\sqrt{-2\text{L}\text{N}\left(\text{P}\right)}\) holds for P ≤ 0.5 where P is the probability of exceeding the determined values of D. The values of C0, C1, and C2 along with d1, d2, and d3 are equation constants. The SPEI index is a standard variable, thus it may be compared to other SPEI values in space and time. To compute the SPI and SPEI drought indices, calculation packages in R employing the Penman technique were utilized. SWI Hydrological Drought Index: This measure was developed by (Bhuiyan et al., 2006 ) in order to track changes in subsurface water table levels during hydrological drought experiments. $$\text{S}\text{W}\text{I} = \frac{{\text{W}}_{\text{i}\text{j}}-{\text{W}}_{\text{i}\text{m}}}{{\sigma }}$$ 9 Where, W ij is the seasonal water level of piezometric wells for the i th well and the j th observation; W im is the long-term seasonal average; and, σ is its standard deviation. Positive values indicate drought and negative values indicate normal conditions. The classification of drought based on this index is shown in Table 2 . Table 2 Drought classification based on SWI index Threshold No drought SWI < 0 Mild drought 0 < SWI < 0.99 Moderate drought 1 < SWI < 1.49 Severe drought 1.5 < SWI < 1.99 Extreme drought SWI ≥ 2 Linear multivariate regression model After calculating the meteorological and hydrological drought indicators, the values of these indicators are extracted from the 6-month time window and are introduced as input data to the model along with the average 6-month temperature and the total precipitation values until the months of May and November. This is done because the level of the underground water table in the piezometric stations is measured twice a year, once in May and once in November. The SWI index along with any additional indices or variables that have a lag of six months or longer (up to a maximum of four delays) are specified as independent variables. Considering that in regression models, the best linear relationship passing the data is considered, they avoid separating the data into training and test subsets. However, in the decision tree method, pruning techniques are utilized so that a more comprehensive model can be fitted to the data. Linear multivariate regression model Using multivariate linear regression, it is possible to evaluate the linear relationship between a set of independent variables and a dependent variable while also taking into account the existing relationships between the independent variables. Regression's objective is to explain the variance of the dependent variable, which is partially accomplished by estimating the contribution of variables to this variance. Multivariate regression analysis can be used to examine the impact of multiple independent factors on the dependent variable. $${\text{Y} =\text{b}}_{0}+{\text{b}}_{1}\times {\text{X}}_{1}+\dots +{\text{b}}_{\text{n}}\times {\text{X}}_{\text{n}}+ {\text{u}}_{\text{i}}$$ 10 Y = Dependent variable b 0 = Intercept b 1 , …, b n = Coefficient of Regression X 1 , …, X n = Independent variable u i = disturbance error The general equation of linear multivariate regression is as in Eq. 10 . Decision tree model Decision trees are a mechanism for displaying rules that determine a category or value and are created by sequentially separating data into distinct groups (Fig. 2 ). Decision trees can forecast numerical values as well as a category and class. In this study, one of the most popular decision tree methods, the M5 model, is utilized to predict numerical values. The M5 model introduced by (Quinlan, 1992 ) has the capacity to divide the data set into homogenous subsets based on standard deviation reduction (SDR) and provide a multivariate regression relationship for each subset. $$\text{S}\text{D}\text{R} = \text{s}\text{d} \left(\text{T}\right) - \sum \left|\frac{{\text{T}}_{\text{i}}}{\text{T}}\right|\text{s}\text{d}({\text{T}}_{\text{i})}$$ 11 Where T is the sequence of samples that reach the node. T i is the number of samples with the i th output of the potential series, whereas sd is the standard deviation. In the M5 model, a greedy search is employed to exclude variables with a negligible contribution. Sometimes all variables are eliminated, leaving merely a constant value (Bhattacharya & Solomatine, 2005 ). Modeling error evaluation criteria According to relations 12–14, the coefficient of determination (R2), root mean square error (RMSE), and Nash-Sutcliffe modeling efficiency (NSE) were used to evaluate the accuracy and efficiency of the models in this study. The coefficient of determination demonstrates the relationship between the model's calculated values and the observed values. The effectiveness of Nash-Sutcliffe modeling and the coefficient of determination approaches 1 as the accuracy of the suggested model increases. RMSE also represents the amount of the forecast error; the lower this value, the more accurate the prediction. $${\text{R}}^{2}={\left(\frac{{\sum }_{\text{i}-1}^{\text{N}}\left({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{O}}-\stackrel{-}{{\text{S}\text{W}\text{I}}^{\text{O}}}\right)({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{P}}-\stackrel{-}{{\text{S}\text{W}\text{I}}^{\text{P}}})}{\sqrt{{\sum }_{\text{i}-1}^{\text{N}}{({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{O}}-\stackrel{-}{{\text{S}\text{W}\text{I}}^{\text{O}}})}^{2}{\sum }_{\text{i}-1}^{\text{N}}{({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{P}}-{\text{S}\text{W}\text{I}}^{\text{P}})}^{2}}}\right)}^{2}$$ 12 $$\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{{\sum }_{\text{i}}^{\text{N}}{({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{O}}-\stackrel{-}{{\text{S}\text{W}\text{I}}_{\text{i}}^{\text{P}}})}^{2}}{\text{N}}}$$ 13 $$\text{N}\text{S}\text{E} = 1-\frac{ \sum _{\text{i}=1}^{\text{N}}{({\text{O}}_{\text{i}}-{\text{S}}_{\text{i}})}^{2}}{\sum _{\text{i}=1}^{\text{N}}{({\text{O}}_{\text{i}}-\text{O})}^{2}}$$ 14 In the above relationships, \({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{O}}\) and \({\text{S}\text{W}\text{I}}_{\text{i}}^{\text{P}}\) respectively are the observed and calculated hydrological drought index values by the model in the i th time step, N is the number of time steps, \(\stackrel{-}{{\text{S}\text{W}\text{I}}^{\text{P}}}\) and \(\stackrel{-}{{\text{S}\text{W}\text{I}}^{\text{O}}}\) are also the average values of the observed and calculated hydrological drought index respectively. In addition to the evaluation criteria described above, one-to-one distribution charts around the line were employed for additional comparison and analysis. Results Studying the trend of meteorological and hydrological droughts The Mann-Kendall test was used to investigate drought patterns and variations during the study period. According to Table 3 , all three drought indices, SWI, SPEI, and SPI, have a negative slope and a significant trend, although the SPI index has no trend. These findings indicate that the SWI and SPEI indices accurately reflect drought changes throughout the examined period, which are decreasing due to the negative trend of these indicators. Table 3 The result of Mann-Kendall trend test index first 6 month second 6 month p-value Sen p-value Sen SWI 0.016 -0.072 0.003 -0.099 SPEI 0.0004 -0.08 0.002 -0.076 SPI 0.144 -0.033 0.458 -0.017 Investigating the direct and time-delayed effects of meteorological drought indices on groundwater drought Without taking lag times into account, we first used linear multivariate regression models and the M5 model to examine seasonal differences in the connection between the SPI and SPEI indices and the SWI index (Table 4 ). In the first half of the year, the SPEI index was related to the M5 technique with the highest index correlation (0.3) and the lowest root mean square error (0.82). Table 4 The results of models output based on the direct effect of drought indices Index month first 6 months second 6 months model R 2 RMSE NSE R 2 RMSE NSE SPI regression 0.1 0.96 0.104 0 0.98 0 M5 0.1 0.93 0.104 0 0.98 0 SPEI regression 0.3 0.85 0.3 0.192 0.91 0.19 M5 0.3 0.82 0.3 0.192 0.88 0.19 Following that, the outcomes of the models with one time lag are presented in Table 5 as the following stage. In the first half of the year, the SPI index has the lowest correlation of any of the indices (0.104), while the SPEI index has the strongest correlation of any of the indices (0.56). The correlation of indices has been impacted by the passage of time, which has resulted in an increase of 31% in the correlation of the SPI index and an increase of 34% in the correlation of the SPEI index in the first and second halves of the year, respectively. Table 5 The results of models output based on the one step lag effect of drought indices index month first 6 months second 6 months model R 2 RMSE NSE R 2 RMSE NSE SPI regression 0.104 0.8 0.104 0.2 0.9 0.2 M5 0.104 0.81 0.104 0.2 0.87 0.2 SPEI regression 0.47 0.76 0.46 0.54 0.69 0.54 M5 0.56 0.66 0.55 0.54 0.66 0.54 Additional time lags of SPI and SPEI indicators are taken into consideration in Table 6 . The SPEI index demonstrated the strongest correlation in the latter half of the year, whilst the SPI index demonstrated the weakest correlation in the first part of the year. Table 6 The results of models output based on the more steps delay effect of drought indexes index month first 6 months second 6 months model R 2 RMSE NSE R 2 RMSE NSE SPI regression 0.33 0.84 0.378 0.38 0.83 0.375 M5 0.36 0.8 0.385 0.54 0.67 0.521 SPEI regression 0.64 0.63 0.67 0.75 0.52 0.75 M5 0.7 0.54 0.717 0.75 0.48 0.75 Impact of temperature, precipitation and meteorological drought on hydrological drought The temperature, precipitation, temperature, and precipitation factors have been added to the indicators in Table 7 and their effects have been evaluated. The correlation between SPI-temperature index and SPI-temperature and precipitation index was 0.86, the greatest among all combinations of factors with SPI index. SPEI-temperature and precipitation index have the strongest correlation (0.92), among all combinations of factors and SPEI index. By adding the characteristics of temperature and precipitation to the index, as shown in Table 6 , the temperature parameter has increased the correlation in both indices relative to the precipitation parameter. By adding the temperature and precipitation factors simultaneously to the indices, a higher correlation was detected in the SPI index; nevertheless, the correlation between the SPEI index and the temperature and precipitation parameters in the second half of the year is 92%. Table 7 The result of model’s output based on the effect of temperature and precipitation Index month First 6 months Second 6 months model R 2 RMSE NSE R 2 RMSE NSE SPI and temperature regression 0.69 0.59 0.72 0.73 0.56 0.74 M5 0.83 0.41 0.84 0.86 0.37 0.84 SPI and Precipitation regression 0.33 0.84 0.38 0.54 0.74 0.55 M5 0.36 0.8 0.4 0.54 0.67 0.53 SPI, temperature, Precipitation regression 0.7 0.58 0.65 0.73 0.56 0.74 M5 0.83 0.41 0.77 0.86 0.37 0.86 SPEI and temperature regression 0.66 0.61 0.69 0.75 0.52 0.75 M5 0.79 0.46 0.8 0.87 0.36 0.86 SPEI and Precipitation regression 0.7 0.59 0.72 0.75 0.52 0.75 M5 0.76 0.48 0.73 0.85 0.37 0.85 SPEI, temperature, Precipitation Regression 0.69 0.58 0.72 0.75 0.52 0.75 M5 0.84 0.41 0.79 0.92 0.27 0.92 The correlation around a line for each index and combination with temperature, precipitation, temperature and precipitation, as well as a comparison of the effectiveness of the employed models, are depicted in Figs. 3 – 10 . Discussion Investigating The Direct And Lag Time Effects Of Meteorological Drought On Groundwater According to Table 4 , the results of the direct effect of meteorological drought indices on groundwater drought suggest that the SPEI index has a larger correlation with the SWI index, especially in the first half of the year. The M5 technique produced the same correlation coefficient as the regression method, but with a lower RMSE value, indicating a superior model performance. As indicated in Table 5 of the effect of one-time lag, the time delay was effective and led to an increase in correlation, particularly in the SPEI index. The effect of time delay results is consistent with those of (Abbasi et al., 2016 ), (Maleki nejad & Soleimani-Motlaq, 2011 ) and (Kubiak-Wójcicka & Bąk, 2018 ). The M5 method is superior to the linear multivariate regression method in terms of performance. According to Table 6 , additional lags have led to a bigger increase in the indicators' correlation. This delay between meteorological and hydrological drought has been confirmed by (Maleki nejad & Soleimani-Motlaq, 2011 ) in Chaghalvandi Basin located in Lorestan province; (Salahi et al., 2018 ) in Marand plain located in East Azarbaijan province by comparing SPI and SWI indices; and, also (Abbasi et al., 2016 ) in Iran's Qorve plain and Dehgolan by comparing SPI and GRI indices. Given that the influence of meteorological drought on subsurface water was not especially complex, the linear multivariate regression approach also yielded favorable outcomes, despite the fact that the M5 method had a higher degree of correlation in every case. Investigating The Effect Of Temperature And Precipitation Along With Meteorological Drought On Hydrological Drought According to Table 7 , the correlation between the meteorological drought index and the SWI index is enhanced by the inclusion of temperature and precipitation variables, as compared to the usage of either of these variables alone. The importance of temperature's effect on hydrological drought is demonstrated by the fact that, when added to the SPI index, the correlation is identical to that of SPEI alone. In this region, air temperature has a larger impact on the rate of evaporation and transpiration than precipitation. The underground water tables are slowly replenished due to the surface conditions, but they are swiftly depleted by excessive harvesting. According to (Babre et al., 2022 ), the SPI and SPEI have a good performance for examining the drought impact on semi-deep wells, since they have a different correlation with the SWI index when looking at the effect of delays as well as the effect of precipitation and temperature parameters. In addition, the SPI and SPEI have a good performance for examining the drought impact on semi-deep wells. Throughout the study's phases, the SPEI index showed a stronger correlation with the SWI index than the SPI index, suggesting that it would be preferable for meteorological drought monitoring. Results are consistent with those of (Sung et al., 2020 ), (Moazzam et al., 2022 ), and (Rezaei Ghaleh & Ghorbani, 2018 ). It may be because SPEI takes into account evaporation and transpiration characteristics when determining the occurrence of drought. High temperatures, as determined by (Zhang et al., 2018 ) and (Moazzam et al., 2022 ), enhance the likelihood of drought. The SPI index, which takes precipitation into account, is not able to distinguish these situations. The foregoing findings are consistent with those found by (Pei et al., 2020 ) in Inner Mongolia and by (Danandeh Mehr & Vaheddoost, 2020 ) in Ankara, Turkey. The hydrological drought in the second half of the year is significantly impacted by the increased withdrawals of subsurface water in the first half of the year as a result of the drought conditions. Time delays added to the indicators have also improved the correlation in the second half, highlighting the role that non-climatic factors had in the first half. Excessive harvesting contributed more to the decline of aquifer levels than drought in studies by (Kubiak-Wójcicka & Bąk, 2018 ) and (Aleboali et al., 2016 ). The M5 decision tree models have been found to be more accurate than linear multivariate regression in many instances, and because of the clarity with which basic regression models can be presented, they may be a good choice for modeling in this area. The aforementioned findings are in agreement with those of (Sattari et al., 2018 ) and (Shamshirband et al., 2020 ). Figures (3–10) and tables (4–7) demonstrate that the decision tree method has a higher correlation with the data than the linear multivariate regression method. For both the SPEI index and the second half of the year, this relationship strengthens. Compared to the effects of each variable alone, the combination of precipitation and temperature has a stronger association. Conclusion This study investigated the impact of meteorological drought, precipitation, temperature, and time on hydrological droughts by employing linear multivariate regression models and the M5 decision tree. For this objective, data from semi-deep wells in the province of Golestan were utilized. The most significant findings from the analysis of these models are provided below. Both meteorological and hydrological droughts are typical climatic phenomena of the study area. Based on our results, the SPI index had no trend while the SPEI and SWI exhibited an upward trend which. This can help water resource managers better combat drought. The correlation without time delay and with one lag was strongest in the first half of the year, mostly due to increased water abstraction, greater temperature, and less groundwater recharge. Both utilized approaches had equal correlations, indicating that there are equally effective. Due to the impact of significant harvests in the first half of the year and the surface conditions on the hydrological drought index, the largest correlation for longer lags occurred in the second half of the year. With the influence of time delays, the value of NSE grew, demonstrating the significance of the time lags in the occurrence of hydrological drought. The use of temperature and precipitation variables in conjunction with the meteorological drought index increased the correlation with the SWI index. On the other hand, the effect of air temperature in this region is greater than the effect of precipitation because of the effect on evaporation-transpiration and the slow regeneration of underground water tables. Due to drought and high temperatures, groundwater resources are soon depleted by overharvesting. Both the SPI and SPEI indices are useful for measuring drought conditions in semi-deep aquifers, however the SPEI index is preferable due to the high rate of evaporation and transpiration in the area. The impact of non-climatic factors on hydrological drought, i.e., increased withdrawals from subsurface water due to drought conditions in the first half of the year, has been demonstrated in the second half of the year using the time lag employed in the study. Considering that climate change models in this region do not predict favorable conditions in terms of the state of water resources, it can be concluded that underground water tables are strongly impacted by drought and climate change; therefore, these valuable resources should be more strongly preserved for future generations by proper utilization. Declarations Competing Interests: None. Availability of data and materials: The data that support the findings of this study are available from the corresponding author upon reasonable request. Author Contributions: Ameneh Roshan: Formal analysis, Modeling. Khalil Ghorbani: Supervision; Validation; Writing - original draft, Writing - review & editing. Authorship. Meysam Salarijazi: Investigation; Methodology; Project administration; Resources; Software; Ebrahim Asadi Oskouei: Visualization Ethical approval: We follow the principles of ethics, as it is mentioned, in conducting and publishing this research and Consent to participate: The authors consented to participate in the research. Consent for publication: The authors are content with the publication of the results. Funding: There is no funding for this research. Acknowledgment: This manuscript is extracted from an MSc thesis at the Gorgan University Agricultural Sciences and Natural Resources, Gorgan, Iran. The authors are grateful to the University for providing the conditions for conducting this research. References Abbasi, F., Azarakhshi, M., Chapi, K., & Bashiri, M. (2016). Spatial and Temporal Variations of Groundwater Level in Qorveh-Dehgolan Plain and its Relationship with Drought. 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8","display":"","copyAsset":false,"role":"figure","size":27581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of SPI index and Temperature output for linear multivariable regression and decision tree models: first half of the year (a) and second half of the year (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2708441/v1/e8abd853240afaee10576386.png"},{"id":34731849,"identity":"a26a40aa-2256-44c9-a28c-150e0efc3ff7","added_by":"auto","created_at":"2023-03-23 19:18:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":27600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of SPI index and Rainfall output for linear multivariable regression and decision tree models: first half of the year (a) and second half of the year (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-2708441/v1/cf058985e32db1ab37e96c6d.png"},{"id":34733472,"identity":"3bf2ab86-b262-4c0c-a82d-bd6f1c93ea71","added_by":"auto","created_at":"2023-03-23 19:26:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":28352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of SPI index, Rainfall and Temperature output for linear multivariable regression and decision tree models: first half of the year (a) and second half of the year (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-2708441/v1/1a4f3bbd2d09a40733a388cd.png"},{"id":35890408,"identity":"9726e316-55aa-44ba-82fd-87a6bcc5a9ba","added_by":"auto","created_at":"2023-04-17 18:44:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1238310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2708441/v1/868bfad4-7151-4bdd-b966-907b44ab8562.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrought is not a natural calamity, but rather a periodic and climatic occurrence that can occur in every region. Due to the harm droughts impose to the agriculture sector and water supplies in arid and semi-arid regions where water scarcity is a problem, they are frequently regarded as a catastrophe. Drought is one of the most significant natural calamities, particularly for farmers. Drought can happen anywhere in the world since it is triggered by an abnormally low amount of precipitation compared to the average or expected climatic value. Lack of water, plant desiccation, and a drop in surface and subterranean water levels are only some of the insidious secondary repercussions of drought that are harmful to both individuals and the ecosystem. More than 85% of worldwide losses are caused by extreme weather events, with drought being a key factor. Due to drought, the world has lost 722\u0026nbsp;billion USD and 23\u0026nbsp;million people in the first decade of the 21st century (Ghozat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gopalakrishnan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is predicted that the global air temperature will rise by 0.78 to 1.5 degrees Celsius (Solomon, S. et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which will alter the rainfall patterns and subsequently increase the frequency and severity of droughts (Zarch et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This climate change poses significant risks to water resources, environmental sustainability, and social and economic growth (Malik et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, monitoring drought and its features, and early warning are essential for mitigating its regional and global effects.\u003c/p\u003e \u003cp\u003eFor the study of drought, monitoring systems are utilized. There are a variety of drought classifications based on distinct features. Meteorological drought occurs when the amount of precipitation during a period is less than its typical value. Thus, type of drought is calculated using precipitation-based indices such as SPI or bivariate indices such as SPEI, which are based on precipitation and evapotranspiration. Meteorological drought indices, which are derived from meteorological data, are utilized more frequently than hydrological and agricultural indices due to better data availability, wider spatial distribution, and the ability of atmospheric general circulation models to predict them for future years under various climate change scenarios. Consequently, numerous researchers attempt to establish a connection between these indicators.\u003c/p\u003e \u003cp\u003e(Teimoori et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) compared meteorological and hydrological drought using the SPI and SSI (Standardized Stream flow Index) indices. The SSI index is calculated similarly to the SPI index, with the exception that former uses the river's flow rate instead of precipitation. According to the findings of this study, there is no perfect match between these two indicators; nevertheless, this match can be improved by incorporating previous time steps. Comparative research of SPI and SWI in the Marand plain of Iran by (Zeinali et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that there is a 1% association between these two indicators and that the subsurface water resources are affected by the drought with a five-month lag. This delay between meteorological and hydrological drought was confirmed by (Maleki nejad \u0026amp; Soleimani-Motlaq, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in Chaghalvandi Basin (located in Lorestan province) and (Salahi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in Marand plain located in East Azarbaijan province by comparing SPI and SWI indices, as well as (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in Qorveh and Dehgolan plains by comparing SPI and GRI indices. In a 21-year study conducted in Jordan (Yarmouk Basin), Hind (Mohammad et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) determined that this basin is especially susceptible to regular droughts and that severe drought occurrences has had a negative effect on the groundwater level. (Kubiak-W\u0026oacute;jcicka \u0026amp; Bąk, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) evaluated meteorological drought and its effect on hydrological drought in the Vistula basin in Poland using the SPI, SWI, and SRI indices over 29 years with three time lags (12-24-48 months), where highest association was found along the Central Vistula and its tributaries, while weakest correlation was found in the foothills. In addition, the results revealed non-climatic elements could have influenced the correlation between coefficients (such as underground reservoirs, urban and industrial consumption). (Aleboali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) studied the impacts of drought on groundwater resources in the Kashan plain of Iran over a 19-year period using the SPI index and concluded that excessive exploitation of groundwater resources is the cause of water level decline in addition to drought. The contribution of overharvesting to the decline of aquifer levels has been significantly greater than that of drought.\u003c/p\u003e \u003cp\u003eAs a result, it may be argued that the subsurface water supplies are only partially affected by the climatic drought, and that other causes, such as over harvesting, also have a role. However, excessive harvesting results from the expansion of the cultivated area, the rise in temperature, and the subsequent rise in evapotranspiration and water consumption. The SPI index, which is based on a single precipitation variable, cannot account for this factor. The SPEI bivariate index, based on precipitation and evapotranspiration, was introduced by Winslet (Beguer\u0026iacute;a et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Other scholars have examined this index and compared it to the SPI index. In a comparative research of two meteorological drought indicators SPI and SPEI in the province of Golestan, (Rezaei Ghaleh \u0026amp; Ghorbani, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found a stronger link between these two indices at stations with a more humid climate. (Pei et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) calculated the SPI and SPEI indices at 1, 3, 6, and 12-month intervals for 102 weather stations in Inner Mongolia over 1981\u0026ndash;2018 and found that as the time window expanded, the difference between the indices decreased. This difference may become negligible over longer time periods. Compared to drought conditions and plant indices, the SPEI index was deemed more suitable for drought monitoring than the SPI index. (Jipkate et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) compared the SPI and SPEI indices with the SWI index in the Upper Bhima Sub Basin during 2002\u0026ndash;2016 and found no direct linear relationship between SWI and SPI and rainfall in the region. (Fung et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) analyzed the SPI and SPEI indices for 1, 3, and 6 months in Peninsular Malaysia to determine the significance of temperature in causing droughts. In the study of temperature-induced fluctuations on the SPEI index, two indices demonstrated distinct performances; the analysis of the two indices was conducted by analyzing the spatial variations in drought frequency, average drought period, average drought intensity, and average maximum drought. Due to the relevance of temperature increase in the establishment of drought, the SPI index is more ideal for a shorter time window of 1 month, but the SPEI index is more suitable for a longer time window of 3 to 6 months. By comparing the SPI and SPEI indices during a 46-year period in Ankara Province, Turkey, (Danandeh Mehr \u0026amp; Vaheddoost, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) determined that whereas the SPEI index has a declining trend, the SPI index does not exhibit a similar pattern. (Babre et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) assessed groundwater drought periods in the Baltic States using established drought indices and found that meteorological drought indices (SPI, SPEI, and RDI) were substantially linked with groundwater drought conditions in shallow groundwater wells. (Kubicz \u0026amp; Bąk, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated the response of groundwater to a multi-month meteorological drought in Poland. They discovered that there was no significant linear relationship between the SPI index in time windows of 6, 12, and 24 months and the standardized groundwater level index (SGI), and they concluded that the level of underground water is influenced by factors other than precipitation. (Leelaruban et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) analyzed the association between the SPI index and groundwater level changes in the United States for 6, 9, 12, and 24 months intervals. In addition to these indicators, precipitation and average air temperature data was also utilized. The strongest correlation was found in 17 of the 32 wells with SPI-24, while 12 of the wells had a correlation value of 0.6 or greater and the remaining wells were reasonable correlated.\u003c/p\u003e \u003cp\u003eModeling and determining the link between data has long been a topic of interest, and several research have been undertaken in the field of analyzing and comparing various modeling methods using data mining. (Nourani et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) employed two data mining techniques (associative rules and decision tree) to identify the relationship between the highest monthly precipitation at synoptic stations in Urmia and Tabriz and the surface temperatures of the Black, Mediterranean, and Red seas. Association rules were used to the observational data to reveal hidden trends and patterns, and decision tree-based techniques and algorithms were utilized to identify and choose the most effective groups. The subsurface water level in Ardabil plain was forecasted by (Sattari et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) using support vector regression, the M5 decision tree model, and data from 24 piezometric wells over 17 years (1997\u0026ndash;2013). The model's inputs were the underground water level in the previous month, the volume of precipitation input to each cell, and the number of feeding wells, while the model's output was the underground water level in the current month. To measure the effectiveness of the model, the correlation coefficient and the mean square error were calculated. The findings showed that both methods were successful in estimating the groundwater table, but the decision tree method's outputs were more transparent and intuitive to use. (Ghorbani, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) compared the data mining models M5 decision tree model and k nearest neighbor (KNN) to the IHACRES hydrological model for predicting the monthly river discharge in Arazkouseh station. Due to modeling transparency and the availability of simple regression equations, they confirmed the data mining models' superiority over the hydrological model and determined that the M5 model was the most accurate. In predicting SSI hydrological drought index based on SPI and SPEI meteorological drought indices with machine learning methods, (Shamshirband et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) determined that the M5 tree model provides superior results to SVR and GEP. White box models, like the decision tree model, are useful because they produce accurate outcomes while also allowing the user to easily identify the impact of various factors using straightforward regression analysis.\u003c/p\u003e \u003cp\u003eNone of the research looking into the cause of the droughts in the study area have used data-driven models, and none of them have looked into splitting the area into a semi-deep aquifer. In this study, we examine the relationship between meteorological drought, using SPI and SPEI indices, and fluctuations in the groundwater level over a 6-month period in semi-deep aquifers in Golestan Province of Iran by comparing the linear multivariate regression method and the decision tree method. The primary purpose is to quantify the impact of air temperature and precipitation on hydrological drought while assessing the efficacy of these two techniques.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe examined area consists of semi-deep wells in Iran's Golestan province, near the southeastern coast of the Caspian Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study utilizes two types of data: the subsurface water level of piezometric wells and meteorological data from the synoptic meteorological station at Hashem-Abad, Gorgan. The length of the common statistical period was determined to be 29 years (1990\u0026ndash;2018) based on the available data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExtraction of drought indicators\u003c/h2\u003e \u003cp\u003eTwo indices, SPI (based on precipitation) and SPEI (based on precipitation and evaporation-transpiration), have been used to indicate the meteorological drought condition, while the SWI index (based on the level of the underground water table) has been used to indicate the hydrological drought condition.\u003c/p\u003e \u003cp\u003eStandardized Precipitation Index (SPI): (McKee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) presented the SPI index for the first time. The cumulative probability is computed by forming precipitation time series at various scales and then fitting them with a gamma distribution. The SPI index is then determined by transforming the distribution to standard Z and calculating the value of Z corresponding to each cumulative probability. Based on long-term rainfall data for each meteorological station with a gamma distribution and the maximum likelihood estimation approach, the SPI can be calculated for several time scales, including 1, 3, 6, 9, 12, 24, and 48 months. SPI can be calculated for both short-term and long-term droughts, with its short-term application assessing the impact of drought on moisture and precipitation and its long-term application assessing the impact of drought on agriculture and water resources, i.e. surface water flow and underground water (Rahman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering that the level of the subsurface water table is tested twice a year, in May and November, a 6-month time window was utilized in this study. This index is classified according to the table (1).\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 SPI and SPEI indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverely wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPI, SPEI\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026gt;\u0026thinsp;SPI, SPEI\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;\u0026gt;\u0026thinsp;SPI, SPEI \u0026gt; -1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.5\u0026thinsp;\u0026gt;\u0026thinsp;SPI, SPEI\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverely dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPI, SPEI \u0026lt; -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\u003eStandardized Meteorological Drought Index of Precipitation, Evapotranspiration and Transpiration (SPEI):\u003c/h2\u003e \u003cp\u003eIn (Beguer\u0026iacute;a et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) introduced the SPEI index. The SPEI index is a multi-quantity index that combines data on precipitation and temperature. Its calculating procedure is identical to that of the SPI index, except the difference between precipitation and potential evaporation and transpiration (PET) is employed to represent the real water balance. In the present study, the SPEI index was calculated using the programming language R.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{P}\\text{E}\\text{T} = 16\\text{K}(10\\text{T}/\\text{I})}^{\\text{m}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\text{m} = 6.75 \\times 10}^{-7}{\\text{I}}^{3}- 7.71 \\times {10}^{-5}{\\text{I}}^{2}+ 1.79 \\times {10}^{-2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{i} = {\\left(\\frac{\\text{T}}{5}\\right)}^{1.514}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{K} =\\left( \\frac{\\text{N}}{12}\\right)(\\frac{\\text{N}\\text{D}\\text{M}}{30}\\)\u003c/span\u003e \u003c/span\u003e) (4)\u003c/p\u003e \u003cp\u003eWhere T is the average monthly temperature in Celsius, m is the dependence factor on I, I is the heat index or the sum of the 12-month index, K is the adjustment factor in terms of month and latitude, NDM is the number of days in a month, and N is the maximum number of hours of radiation.\u003c/p\u003e \u003cp\u003eIn this way, having the amount of potential evaporation and transpiration, the difference between precipitation (P) and PET for the i\u003csup\u003eth\u003c/sup\u003e month is obtained based on Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\text{D} \\text{i} = \\text{P} \\text{i} \u0026ndash; \\text{P}\\text{E}\\text{T}\\text{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNumerous studies have established that the logistic distribution is more effective in determining the probability density function or pdf of the time series D due to its better fit with skewed data and the longer sequence towards the end of the distribution's range. The probability density function of SPEI is therefore computed using the logistic distribution. Based on the logarithmic logistic distribution and Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the density function of the D series is computed.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\text{f} \\left(\\text{x}\\right) = \\frac{{\\beta }}{{\\alpha }}({\\frac{\\text{x}-{\\gamma }}{{\\alpha }})}^{{\\beta }-1}{\\left[\\right(1+\\left({\\frac{\\text{x}-{\\gamma }}{{\\alpha }})}^{{\\beta }}\\right)]}^{-2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere α, β, and γ are scale, shape and main parameters for D in the range of \u003cb\u003eᵞ\u003c/b\u003e \u0026lt; D \u0026lt;\u0026infin;. For calculating the parameters of the logarithmic logistic distribution, the probability weighted moments approach was utilized as an efficient and straightforward technique. Based on Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the probability distribution function of the D series is also determined.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${\\text{F}\\left(\\text{x}\\right) =\\left[\\right(1+{\\left(\\frac{\\text{a}}{\\text{x}-\\text{y}}\\right)}^{\\text{ᵝ}}]}^{-1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe SPEI index can be easily calculated in terms of standardized values (F(x))\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{P}\\text{E}\\text{I} = \\text{W} - \\frac{{\\text{C}}_{0}+{\\text{C}}_{1}\\text{W}+{\\text{C}}_{2}{\\text{W}}^{2}}{1+{\\text{d}}_{1}\\text{W}+{\\text{d}}_{2}{\\text{W}}^{2}+{\\text{d}}_{3}{\\text{W}}^{3}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{W} =\\sqrt{-2\\text{L}\\text{N}\\left(\\text{P}\\right)}\\)\u003c/span\u003e\u003c/span\u003e holds for P\u0026thinsp;\u0026le;\u0026thinsp;0.5 where P is the probability of exceeding the determined values of D. The values of C0, C1, and C2 along with d1, d2, and d3 are equation constants. The SPEI index is a standard variable, thus it may be compared to other SPEI values in space and time. To compute the SPI and SPEI drought indices, calculation packages in R employing the Penman technique were utilized.\u003c/p\u003e \u003cp\u003eSWI Hydrological Drought Index: This measure was developed by (Bhuiyan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) in order to track changes in subsurface water table levels during hydrological drought experiments.\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{W}\\text{I} = \\frac{{\\text{W}}_{\\text{i}\\text{j}}-{\\text{W}}_{\\text{i}\\text{m}}}{{\\sigma }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, W\u003csub\u003eij\u003c/sub\u003e is the seasonal water level of piezometric wells for the i\u003csup\u003eth\u003c/sup\u003e well and the j\u003csup\u003eth\u003c/sup\u003e observation; W\u003csub\u003eim\u003c/sub\u003e is the long-term seasonal average; and, σ is its standard deviation. Positive values indicate drought and negative values indicate normal conditions. The classification of drought based on this index is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eDrought classification based on SWI index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWI\u0026thinsp;\u0026lt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;\u0026lt;\u0026thinsp;SWI\u0026thinsp;\u0026lt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;\u0026lt;\u0026thinsp;SWI\u0026thinsp;\u0026lt;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026lt;\u0026thinsp;SWI\u0026thinsp;\u0026lt;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWI\u0026thinsp;\u0026ge;\u0026thinsp;2\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLinear multivariate regression model\u003c/h2\u003e \u003cp\u003eAfter calculating the meteorological and hydrological drought indicators, the values of these indicators are extracted from the 6-month time window and are introduced as input data to the model along with the average 6-month temperature and the total precipitation values until the months of May and November. This is done because the level of the underground water table in the piezometric stations is measured twice a year, once in May and once in November. The SWI index along with any additional indices or variables that have a lag of six months or longer (up to a maximum of four delays) are specified as independent variables. Considering that in regression models, the best linear relationship passing the data is considered, they avoid separating the data into training and test subsets. However, in the decision tree method, pruning techniques are utilized so that a more comprehensive model can be fitted to the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLinear multivariate regression model\u003c/h2\u003e \u003cp\u003eUsing multivariate linear regression, it is possible to evaluate the linear relationship between a set of independent variables and a dependent variable while also taking into account the existing relationships between the independent variables. Regression's objective is to explain the variance of the dependent variable, which is partially accomplished by estimating the contribution of variables to this variance. Multivariate regression analysis can be used to examine the impact of multiple independent factors on the dependent variable.\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${\\text{Y} =\\text{b}}_{0}+{\\text{b}}_{1}\\times {\\text{X}}_{1}+\\dots +{\\text{b}}_{\\text{n}}\\times {\\text{X}}_{\\text{n}}+ {\\text{u}}_{\\text{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;Dependent variable\u003c/p\u003e \u003cp\u003eb\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Intercept\u003c/p\u003e \u003cp\u003eb\u003csub\u003e1\u003c/sub\u003e, \u0026hellip;, b\u003csub\u003en\u003c/sub\u003e = Coefficient of Regression\u003c/p\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e, \u0026hellip;, X\u003csub\u003en\u003c/sub\u003e = Independent variable\u003c/p\u003e \u003cp\u003eu\u003csub\u003ei\u003c/sub\u003e= disturbance error\u003c/p\u003e \u003cp\u003eThe general equation of linear multivariate regression is as in Eq.\u0026nbsp;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDecision tree model\u003c/h2\u003e \u003cp\u003eDecision trees are a mechanism for displaying rules that determine a category or value and are created by sequentially separating data into distinct groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Decision trees can forecast numerical values as well as a category and class. In this study, one of the most popular decision tree methods, the M5 model, is utilized to predict numerical values. The M5 model introduced by (Quinlan, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) has the capacity to divide the data set into homogenous subsets based on standard deviation reduction (SDR) and provide a multivariate regression relationship for each subset.\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{D}\\text{R} = \\text{s}\\text{d} \\left(\\text{T}\\right) - \\sum \\left|\\frac{{\\text{T}}_{\\text{i}}}{\\text{T}}\\right|\\text{s}\\text{d}({\\text{T}}_{\\text{i})}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere T is the sequence of samples that reach the node. T\u003csub\u003ei\u003c/sub\u003e is the number of samples with the i\u003csup\u003eth\u003c/sup\u003e output of the potential series, whereas sd is the standard deviation. In the M5 model, a greedy search is employed to exclude variables with a negligible contribution. Sometimes all variables are eliminated, leaving merely a constant value (Bhattacharya \u0026amp; Solomatine, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eModeling error evaluation criteria\u003c/h2\u003e \u003cp\u003e According to relations 12\u0026ndash;14, the coefficient of determination (R2), root mean square error (RMSE), and Nash-Sutcliffe modeling efficiency (NSE) were used to evaluate the accuracy and efficiency of the models in this study. The coefficient of determination demonstrates the relationship between the model's calculated values and the observed values. The effectiveness of Nash-Sutcliffe modeling and the coefficient of determination approaches 1 as the accuracy of the suggested model increases. RMSE also represents the amount of the forecast error; the lower this value, the more accurate the prediction.\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${\\text{R}}^{2}={\\left(\\frac{{\\sum }_{\\text{i}-1}^{\\text{N}}\\left({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{O}}-\\stackrel{-}{{\\text{S}\\text{W}\\text{I}}^{\\text{O}}}\\right)({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{P}}-\\stackrel{-}{{\\text{S}\\text{W}\\text{I}}^{\\text{P}}})}{\\sqrt{{\\sum }_{\\text{i}-1}^{\\text{N}}{({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{O}}-\\stackrel{-}{{\\text{S}\\text{W}\\text{I}}^{\\text{O}}})}^{2}{\\sum }_{\\text{i}-1}^{\\text{N}}{({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{P}}-{\\text{S}\\text{W}\\text{I}}^{\\text{P}})}^{2}}}\\right)}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$\\text{R}\\text{M}\\text{S}\\text{E}=\\sqrt{\\frac{{\\sum }_{\\text{i}}^{\\text{N}}{({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{O}}-\\stackrel{-}{{\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{P}}})}^{2}}{\\text{N}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$\\text{N}\\text{S}\\text{E} = 1-\\frac{ \\sum _{\\text{i}=1}^{\\text{N}}{({\\text{O}}_{\\text{i}}-{\\text{S}}_{\\text{i}})}^{2}}{\\sum _{\\text{i}=1}^{\\text{N}}{({\\text{O}}_{\\text{i}}-\\text{O})}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the above relationships, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{O}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{S}\\text{W}\\text{I}}_{\\text{i}}^{\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e respectively are the observed and calculated hydrological drought index values by the model in the i\u003csup\u003eth\u003c/sup\u003e time step, N is the number of time steps, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{{\\text{S}\\text{W}\\text{I}}^{\\text{P}}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{{\\text{S}\\text{W}\\text{I}}^{\\text{O}}}\\)\u003c/span\u003e\u003c/span\u003e are also the average values of the observed and calculated hydrological drought index respectively. In addition to the evaluation criteria described above, one-to-one distribution charts around the line were employed for additional comparison and analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudying the trend of meteorological and hydrological droughts\u003c/h2\u003e \u003cp\u003eThe Mann-Kendall test was used to investigate drought patterns and variations during the study period. According to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all three drought indices, SWI, SPEI, and SPI, have a negative slope and a significant trend, although the SPI index has no trend. These findings indicate that the SWI and SPEI indices accurately reflect drought changes throughout the examined period, which are decreasing due to the negative trend of these indicators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eThe result of Mann-Kendall trend test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003efirst 6 month\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003esecond 6 month\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSen\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSen\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPEI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInvestigating the direct and time-delayed effects of meteorological drought indices on groundwater drought\u003c/h2\u003e \u003cp\u003eWithout taking lag times into account, we first used linear multivariate regression models and the M5 model to examine seasonal differences in the connection between the SPI and SPEI indices and the SWI index (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the first half of the year, the SPEI index was related to the M5 technique with the highest index correlation (0.3) and the lowest root mean square error (0.82).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003eThe results of models output based on the direct effect of drought indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003efirst 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003esecond 6 months\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPEI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFollowing that, the outcomes of the models with one time lag are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e as the following stage. In the first half of the year, the SPI index has the lowest correlation of any of the indices (0.104), while the SPEI index has the strongest correlation of any of the indices (0.56). The correlation of indices has been impacted by the passage of time, which has resulted in an increase of 31% in the correlation of the SPI index and an increase of 34% in the correlation of the SPEI index in the first and second halves of the year, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003eThe results of models output based on the one step lag effect of drought indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003efirst 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003esecond 6 months\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPEI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAdditional time lags of SPI and SPEI indicators are taken into consideration in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The SPEI index demonstrated the strongest correlation in the latter half of the year, whilst the SPI index demonstrated the weakest correlation in the first part of the year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of models output based on the more steps delay effect of drought indexes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003efirst 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003esecond 6 months\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPEI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImpact of temperature, precipitation and meteorological drought on hydrological drought\u003c/h2\u003e \u003cp\u003eThe temperature, precipitation, temperature, and precipitation factors have been added to the indicators in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and their effects have been evaluated. The correlation between SPI-temperature index and SPI-temperature and precipitation index was 0.86, the greatest among all combinations of factors with SPI index. SPEI-temperature and precipitation index have the strongest correlation (0.92), among all combinations of factors and SPEI index. By adding the characteristics of temperature and precipitation to the index, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the temperature parameter has increased the correlation in both indices relative to the precipitation parameter. By adding the temperature and precipitation factors simultaneously to the indices, a higher correlation was detected in the SPI index; nevertheless, the correlation between the SPEI index and the temperature and precipitation parameters in the second half of the year is 92%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe result of model’s output based on the effect of temperature and precipitation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eFirst 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSecond 6 months\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPI and temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPI and Precipitation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPI, temperature, Precipitation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPEI and temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPEI and Precipitation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSPEI, temperature, Precipitation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe correlation around a line for each index and combination with temperature, precipitation, temperature and precipitation, as well as a comparison of the effectiveness of the employed models, are depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e–\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e "},{"header":"Discussion","content":"\u003ch3\u003eInvestigating The Direct And Lag Time Effects Of Meteorological Drought On Groundwater\u003c/h3\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the results of the direct effect of meteorological drought indices on groundwater drought suggest that the SPEI index has a larger correlation with the SWI index, especially in the first half of the year. The M5 technique produced the same correlation coefficient as the regression method, but with a lower RMSE value, indicating a superior model performance. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e of the effect of one-time lag, the time delay was effective and led to an increase in correlation, particularly in the SPEI index. The effect of time delay results is consistent with those of (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), (Maleki nejad \u0026amp; Soleimani-Motlaq, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and (Kubiak-Wójcicka \u0026amp; Bąk, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The M5 method is superior to the linear multivariate regression method in terms of performance. According to Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, additional lags have led to a bigger increase in the indicators' correlation. This delay between meteorological and hydrological drought has been confirmed by (Maleki nejad \u0026amp; Soleimani-Motlaq, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in Chaghalvandi Basin located in Lorestan province; (Salahi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in Marand plain located in East Azarbaijan province by comparing SPI and SWI indices; and, also (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in Iran's Qorve plain and Dehgolan by comparing SPI and GRI indices. Given that the influence of meteorological drought on subsurface water was not especially complex, the linear multivariate regression approach also yielded favorable outcomes, despite the fact that the M5 method had a higher degree of correlation in every case.\u003c/p\u003e\u003ch3\u003eInvestigating The Effect Of Temperature And Precipitation Along With Meteorological Drought On Hydrological Drought\u003c/h3\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the correlation between the meteorological drought index and the SWI index is enhanced by the inclusion of temperature and precipitation variables, as compared to the usage of either of these variables alone. The importance of temperature's effect on hydrological drought is demonstrated by the fact that, when added to the SPI index, the correlation is identical to that of SPEI alone. In this region, air temperature has a larger impact on the rate of evaporation and transpiration than precipitation. The underground water tables are slowly replenished due to the surface conditions, but they are swiftly depleted by excessive harvesting. According to (Babre et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the SPI and SPEI have a good performance for examining the drought impact on semi-deep wells, since they have a different correlation with the SWI index when looking at the effect of delays as well as the effect of precipitation and temperature parameters. In addition, the SPI and SPEI have a good performance for examining the drought impact on semi-deep wells. Throughout the study's phases, the SPEI index showed a stronger correlation with the SWI index than the SPI index, suggesting that it would be preferable for meteorological drought monitoring. Results are consistent with those of (Sung et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Moazzam et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and (Rezaei Ghaleh \u0026amp; Ghorbani, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It may be because SPEI takes into account evaporation and transpiration characteristics when determining the occurrence of drought. High temperatures, as determined by (Zhang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and (Moazzam et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), enhance the likelihood of drought. The SPI index, which takes precipitation into account, is not able to distinguish these situations. The foregoing findings are consistent with those found by (Pei et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Inner Mongolia and by (Danandeh Mehr \u0026amp; Vaheddoost, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Ankara, Turkey.\u003c/p\u003e\u003cp\u003eThe hydrological drought in the second half of the year is significantly impacted by the increased withdrawals of subsurface water in the first half of the year as a result of the drought conditions. Time delays added to the indicators have also improved the correlation in the second half, highlighting the role that non-climatic factors had in the first half. Excessive harvesting contributed more to the decline of aquifer levels than drought in studies by (Kubiak-Wójcicka \u0026amp; Bąk, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and (Aleboali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The M5 decision tree models have been found to be more accurate than linear multivariate regression in many instances, and because of the clarity with which basic regression models can be presented, they may be a good choice for modeling in this area. The aforementioned findings are in agreement with those of (Sattari et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and (Shamshirband et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Figures\u0026nbsp;(3–10) and tables (4–7) demonstrate that the decision tree method has a higher correlation with the data than the linear multivariate regression method. For both the SPEI index and the second half of the year, this relationship strengthens. Compared to the effects of each variable alone, the combination of precipitation and temperature has a stronger association.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the impact of meteorological drought, precipitation, temperature, and time on hydrological droughts by employing linear multivariate regression models and the M5 decision tree. For this objective, data from semi-deep wells in the province of Golestan were utilized. The most significant findings from the analysis of these models are provided below. Both meteorological and hydrological droughts are typical climatic phenomena of the study area. Based on our results, the SPI index had no trend while the SPEI and SWI exhibited an upward trend which. This can help water resource managers better combat drought. The correlation without time delay and with one lag was strongest in the first half of the year, mostly due to increased water abstraction, greater temperature, and less groundwater recharge. Both utilized approaches had equal correlations, indicating that there are equally effective. Due to the impact of significant harvests in the first half of the year and the surface conditions on the hydrological drought index, the largest correlation for longer lags occurred in the second half of the year. With the influence of time delays, the value of NSE grew, demonstrating the significance of the time lags in the occurrence of hydrological drought. The use of temperature and precipitation variables in conjunction with the meteorological drought index increased the correlation with the SWI index. On the other hand, the effect of air temperature in this region is greater than the effect of precipitation because of the effect on evaporation-transpiration and the slow regeneration of underground water tables. Due to drought and high temperatures, groundwater resources are soon depleted by overharvesting. Both the SPI and SPEI indices are useful for measuring drought conditions in semi-deep aquifers, however the SPEI index is preferable due to the high rate of evaporation and transpiration in the area. The impact of non-climatic factors on hydrological drought, i.e., increased withdrawals from subsurface water due to drought conditions in the first half of the year, has been demonstrated in the second half of the year using the time lag employed in the study. Considering that climate change models in this region do not predict favorable conditions in terms of the state of water resources, it can be concluded that underground water tables are strongly impacted by drought and climate change; therefore, these valuable resources should be more strongly preserved for future generations by proper utilization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAmeneh Roshan: Formal analysis, Modeling. \u0026nbsp;Khalil Ghorbani: Supervision; Validation; Writing - original draft, Writing - review \u0026amp; editing. Authorship. Meysam Salarijazi: Investigation; Methodology; Project administration; Resources; Software; Ebrahim Asadi Oskouei: Visualization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eWe follow the principles of ethics, as it is mentioned, in conducting and publishing this research and\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eThe authors consented to participate in the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eThe authors are content with the publication of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere is no funding for this research. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eThis manuscript is extracted from an MSc thesis at the\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eGorgan University Agricultural Sciences and Natural Resources, Gorgan, Iran. The authors are grateful to the University for providing the conditions for conducting this research.\u003c/p\u003e\n\u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi, F., Azarakhshi, M., Chapi, K., \u0026amp; Bashiri, M. (2016). Spatial and Temporal Variations of Groundwater Level in Qorveh-Dehgolan Plain and its Relationship with Drought. \u003cem\u003eWater and Soil Science\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3\u0026ndash;2), 143\u0026ndash;155. https://water-soil.tabrizu.ac.ir/article_5845.html\u003c/li\u003e\n\u003cli\u003eAleboali, A., Ghazavi, R., \u0026amp; satatinejad, seyd javad. (2016). 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Cambial phenology and xylogenesis of Juniperus przewalskii over a climatic gradient is influenced by both temperature and drought. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e, \u003cem\u003e260\u003c/em\u003e\u0026ndash;\u003cem\u003e261\u003c/em\u003e. https://doi.org/10.1016/j.agrformet.2018.06.011\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":"Groundwater drought, SPI, SPEI, SWI, M5 decision tree","lastPublishedDoi":"10.21203/rs.3.rs-2708441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2708441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn most arid and semiarid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes influencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these fluctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the fluctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indicators in a 6-month time window for a long-term statistical period (1989\u0026ndash;2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immeddergiately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coefficient of determination of 0.92 and an error value of RMSE\u0026thinsp;=\u0026thinsp;0.27 for the SPEI index. Among the meteorological drought indicators, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the significance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.\u003c/p\u003e","manuscriptTitle":"Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-23 19:18:44","doi":"10.21203/rs.3.rs-2708441/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":"af6c78fd-3456-4f2c-92b9-f321f6b7cf04","owner":[],"postedDate":"March 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-04-17T18:44:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-23 19:18:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2708441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2708441","identity":"rs-2708441","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}