Drought dynamics in mixed climate regions: insights for water resource management and climate adaptation strategies

Drought dynamics in mixed climate regions: insights for water resource management and climate adaptation strategies | 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 Drought dynamics in mixed climate regions: insights for water resource management and climate adaptation strategies Meral KORKMAZ, Alban KURIQI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4518030/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jul, 2024 Read the published version in Water Conservation Science and Engineering → Version 1 posted 16 You are reading this latest preprint version Abstract Global warming and climate change are causing temperatures to rise, which is having a negative impact on water resources. Climate change is a worldwide problem that affects and will continue to affect the frequency and intensity of natural disasters in many regions of the world. Tunceli region in Turkey, which until ten years ago was known as an environmentally friendly city with abundant water resources and frequent rainfall, is experiencing a decrease in precipitation during the snowy winter season. This situation has made the investigation of climate change impacts an important issue in the region. Therefore, effective climate change adaptation strategies need to be developed. To determine these strategies, in this study, we assessed drought conditions using drought indices such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Normal Precipitation Index (PNI), and Aridity Index (AI). The SPI and RDI analyses were performed in annual reference periods on a time scale of 3, 6, 9, and 12 months using temperature, precipitation, and evaporation data. Consequently, the SPI and RDI results were compared, and both indices show similar behavior in dry, wet, and normal seasons. Nevertheless, RDI shows less variation between different time scales, which is an advantage over SPI and is probably due to the inclusion of potential evapotranspiration in RDI. The variations in PNI between humid and dry sub-humid categorizations throughout the years, combined with the AI results, indicate that the Tunceli region predominantly experiences a climate ranging from dry sub-humid to semi-arid. This study could help decision-makers take effective measures to become more resilient to climate change in temperate climate regions and take important steps toward sustainable water resources management. Climate change Drought RDI SPI Water scarcity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introductıon Climate change poses a significant threat worldwide, leading to severe and erratic weather events such as sudden rainfall, droughts, and forest fires (Allen et al., 2010 ). One of the most notable impacts of climate change is drought, which poses a major threat and has a particular impact on agriculture, water resources, ecosystems, and human life. In drought-prone countries like Turkey, developing effective drought mitigation and adaptation strategies is crucial (Khorrami and Gündüz, 2022 ). Drought is a natural phenomenon that has adverse effects on water resources and human activities, especially agriculture (Pereira et al., 2009 ). It can be defined as a temporary but natural imbalance of water, characterized by less frequent, variable duration and intensity of rainfall below average (Tate and Gustard, 2000 ). It is an event that is difficult to predict or model, leading to reduced availability and carrying capacity of water resources in ecosystems (Pedro-Monzonís et al., 2015 ). Various indices have been proposed and used to define and analyze droughts accurately. The most commonly used drought index is the Standardized Precipitation Index (SPI), a normalized index for calculating deviations from normal precipitation conditions. The SPI requires only monthly precipitation data, is easy to calculate, and is standardized to allow easy comparison between different periods or regions (McKee et al., 1993 ). Merabti et al. ( 2018a ) compared two drought indices, the SPI, and the RDI, at the local level in northeastern Algeria on a time scale of 3, 6, and 12 months. Both indices showed very similar and consistent results when applied to different sites and climatic zones. Only minor differences were found between the indices, with the regression coefficients for the 3-month scale generally larger and closer to 1.0 (Merabti et al., 2018b ). In arid and semi-arid regions, the SPI detected more drought events, while in wetter climates, both the SPI and RDI detected more wet events. This behavior suggests that drought and humidity events in semi-humid climates are primarily related to precipitation and thus play a less significant role in differentiating between RDI and SPI based on potential evapotranspiration (PET). Drought indicies such as SPI and RDI allow an in-depth analysis of drought risk (Jiménez-Donaire et al., 2020 ). Kim and Jehanzaib ( 2020 ) discuss the rapid expansion of drought risk analysis, prediction, and assessment due to climate change and emphasize the importance of accurate monitoring and comprehensive assessment for reliable decision-making on drought risks and water resources. They present new approaches for drought monitoring and risk prediction. Meza et al. ( 2020 ) provide an integrated assessment of drought risk for both irrigated and rain-fed agricultural systems on a global scale and identify regions with high drought risk, such as Southeast Europe and Northern and Southern Africa. Ahmadalipour et al. ( 2019 ) assess drought risk at the national level across Africa, examine the impact of climate change, population growth, and socio-economic vulnerabilities on drought risk, and emphasize the importance of adaptation measures to mitigate drought risk. Liu and Chen ( 2021 ) project future global socio-economic risks for droughts under different climate change scenarios, identify regions with high socio-economic risks and highlight the inequality of future socio-economic risks for droughts between countries. Sahana and Mondal ( 2023 ) assess future trends in drought hazard, vulnerability, and risk in India, highlight the influence of societal developments on drought vulnerability, and point to the need for strong mitigation and adaptation planning. Lehner et al. ( 2017 ) examine changes in drought and risk of successive drought years under 1.5°C and 2°C global warming scenarios and find significant regional differences in future drought risk. Dai et al. ( 2020 ) propose a drought assessment framework to investigate the changes in drought characteristics and risks in China under climate change, providing useful information for effective long-term adaptation strategies. The volume of the world's water resources remains constant due to the water cycle. Still, climate change is altering water availability at different temporal and spatial scales, jeopardizing several ecosystem services (Delpla et al., 2009 ). In drought-prone countries such as Turkey, a handful of studies have recently focused intensively on drought assessment at regional (Gumus and Algin, 2017 ; Mersin et al., 2022 ; Yeşilköy and Şaylan, 2022 ) and country scale (Dabanli, 2018 ; Khorrami and Gündüz, 2022 ; Turkes, 2020 ). Nevertheless, to the best of our knowledge, there is no comprehensive study that analyzes droughts in higher resolution and in the context of supporting the Tunceli region in terms of its status as a protected area, environmentally friendly, and also being a tourist spot for many visitors (Korkmazi and Kilic, 2021 ). Furthermore, since the region is characterized by a temperate climate, effective management of the water resources is crucial to secure sufficient water for water supply, agriculture, and electricity generation through the Uzunçayir dam, which was put into operation in 2009. Thus, this study focuses on the assessment of drought risk in Tunceli, a region in the eastern part of Turkey known for its natural beauty and pristine environment. Tunceli experiences heavy snowfall in winter, but in recent years, precipitation has decreased due to the effects of climate change. This situation has made climate change a major issue in the region. Tunceli is one of the regions most affected by the impact of climate change and is, therefore, at significant risk of drought. Consequently, it is important to develop effective strategies to adapt to climate change. For that purpose, in this study, we employed the SPI and RDI to assess the long-term dynamic of droughts in the Tunceli region. In this context, the study aims to determine the drought risk in Tunceli and propose appropriate climate change adaptation strategies, especially focusing on better management of water resources. 2. Materials and Method 2.1. Study Area The Tunceli region lies in the Upper Euphrates Basin in the Eastern Anatolia region of Turkey and is located between longitudes 38°19' − 40°26' east and latitudes 39°36' − 38°46' north. As shown in Fig. 1 , this region is surrounded by the region s of Bingöl, Erzincan, Elazığ, and Keban Reservoir. It covers an area of 7,774 km² with an altitude that ranges between 800 to over 3000 m above sea level. The region consists of 70% mountains, 25% plateaus, and 5% plains, which indicates the high and rugged terrain of Tunceli. Among the most important geographical features of Tunceli are the high mountains such as the Munzur Mountains, Mercan Mountains, Gobartı Mountains, Zel Mountains, and Sevdin Mountains. These mountains allow for significant rainfall under harsh continental climate conditions and the formation of deep valleys by strong flowing rivers. Important rivers such as Munzur, Mercan, Pülümür, Tağar and Peri flow into the Keban reservoir. There are no significant plains or flatlands in the region, but there are craters and glacial lakes. The natural beauties of Tunceli are mainly concentrated in areas such as the Munzur Valley, Zage, Ali Boğazı, Kutudere, and the Pülümür Valley (Meteoblue, 2023 ). Considering the climate and topography of Tunceli, the rainfall distribution data presented in Fig. 2 could be crucial for planning various aspects such as agriculture, settlements, infrastructure, and natural resource management. Higher elevations tend to receive more precipitation, while valleys and low-lying areas may receive less. Rainfall maps are an important tool in decision-making related to water resource management, droughts, and flood risks. 2.2. Dataset Meteorological data were obtained from the Meteorological Data Bank System (MEVBİS, 2022) (Table 1 ). Missing entries were identified in the evaporation dataset sourced from MEVBIS. Missing data are a common issue in research studies and are typically addressed using various statistical methods (Carpenter and Smuk, 2021 ). Table 1 Selected stations in Tunceli region – temperature and precipitation trends. Station No. Station Name Elevation (m) Latitude (E) Longitude (N) Mean Temperature (°C) Mean Precipitation (mm) 1979 2023 1979 2023 17165 Tunceli 981 39.10 39.54 10.9 13.4 710.6 620.4 753 Pulumur 1550 39.48 39.89 6.8 9.6 979.9 884 17768 Çemişgezek 898 39.06 38.91 12.1 14.9 548.1 476.2 18354 Hozat 1485 39.10 39.22 11 13.4 638.5 564.6 18355 Nazımiye 1602 39.18 39.82 8.2 11.3 954.1 872.6 18179 Ovacık 1280 39.35 39.21 11 13.4 638.5 564.6 5212 Pertek 1000 38.86 39.32 12 14.7 652.4 588.7 17736 Mazgirt 1400 39.0 39.60 10.9 13.4 710.6 620.4 In this study, a linear regression model was employed to estimate the missing evaporation data. Linear regression is an effective tool for predicting missing data in areas such as hydrology and water resources management (Carpenter and Smuk, 2021 ; Machiwal and Jha, 2006 ). The linear regression model defines the relationship between a dependent variable. The components of this equation are: $$Y={\beta }_{0}+{\beta }_{1}{X}_{1}+{\beta }_{2}{X}_{2}+\dots +{\beta }_{p}{X}_{p}+\epsilon$$ 1 \(Y\) : The predicted value, such as monthly evaporation.​ \({\beta }_{0}\) : The intercept term of the model. \({\beta }_{i}\) : The coefficients of the independent variables, each representing the impact of variables in the dataset on evaporation. \({X}_{i}\) : The independent variables of the model may include meteorological factors like temperature and wind speed. \(\epsilon\) : A random error term representing the variation not explained by the model. \(p\) : Represents the p th observations of the variable \({X}_{1},{X}_{2}, {X}_{3}, ,{X}_{p}\) respectively 2.3. Drought analysis In this section, we delve into the analysis of drought dynamics and trends in the Tunceli region using a comprehensive array of indices: the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), the Precipitation Normalization Index (PNI), and the Aridity Index (AI). This approach allows us to quantify the severity and duration of droughts, assess variations in precipitation against long-term averages, and evaluate the degree of aridity based on evaporation and precipitation balances. Through this multifaceted assessment, the study aims to provide a thorough understanding of the impact of drought conditions on the region's hydrological cycle. The RDI quantifies the intensity of drought by incorporating elements of the hydrological cycle, such as precipitation and evaporation. The SPI is calculated by standardizing precipitation data against long-term averages to identify anomalies in precipitation over different periods and assess the onset and duration of droughts. The following "reference periods" and "calculation time intervals" were used in this study, namely, 12, 6, 3, or 1 month, starting in October (Tigkas et al., 2015 ). 12 months: From October to September. 6-month: Options from October to March or April to September. 3-month: Quarterly intervals, starting from October. 1-month: Monthly analysis, beginning in October. User-defined Option: Allows users to select any time scale and starting month for customized analysis. 2.3.1. The Standardized Precipitation Index (SPI) The SPI is calculated exclusively on the basis of precipitation data and, therefore, requires less data and computing time. It can be calculated on different time scales, e.g., 1 month, 3 months, 6 months, 9 months, or 12 months, and can detect approaching droughts earlier than the Palmer Index. According to Eq. 1 , the SPI is derived by dividing the deviation of the precipitation series from its mean value by the standard deviation (Yusof et al., 2014 ). $$SPI=\frac{{P}_{i}-{P}_{0}}{{\sigma }}$$ 1 Where; \({P}_{i},{P}_{1},{P}_{2},\dots ,{P}_{n}\) ; standardized precipitation series, \({P}_{0}\) ; the mean value of the series, σ; the standard deviation. Due to the complexity of calculating precipitation events, the precipitation series are first adjusted to a normal distribution. The SPI method is applied to at least 30 years of continuous monthly precipitation data, and drought classifications are made according to the categories listed in Table 2 . Table 2 Drought classification of SPI, PNI, and RDI (McKee et al., 1993 ; Palmer, 1965 ; Tsakiris et al., 2007 ). Drought Class SPI and RDI PNI Extremely wet ≥ 2 ≥ 110 Very wet 1.5 - 1.99 Moderately wet 1.0 - 1.49 Normal 0.99 - -0.99 80 - 110 Moderately dry −1 to-−1.49 55–80 Severely dry −1.5 t- −1.99 40 − 55 Extremely dry ≤-2 ≤ 40 The meteorological droughts consider the impact of precipitation deficits on water resources and calculate them over periods of 1, 3, 6, 9, and 12 months. These periods are selected on the basis of the duration over which the effects of precipitation deficits on available water resources may be evident. For instance, a decrease in precipitation in a given month may have an immediate impact on soil moisture. At the same time, the effects on groundwater and rivers may be evident over a longer period (Liu and Chen, 2021 ). 2.3.2. Reconnaissance Drought Index (RDI) The RDI is a drought analysis method that uses potential evapotranspiration (PET) values and precipitation data. Data on minimum and maximum temperatures are used to calculate the PET values (Tsakiris et al., 2007 ). The initial α 0 values for estimating the RDI index values in the reference periods are determined from Eq. 2 (i = 1 to N and j = 1 to 12): $${\propto }_{0}^{i}=\frac{{\sum }_{j=1}^{12}{P}_{ij}}{{\sum }_{j=1}^{12}{PET}_{ij}}$$ 2 Here \({P}_{ij}\) and \({PET}_{ij}\) represents the precipitation and evaporation values for the i year and j month, typically starting in October, and N is the total number of years. Potential evapotranspiration is calculated based on Eq. 3 (Tsakiris et al., 2007 ). $$PET=0.0023 \left(\frac{{\text{T}}_{max}+{T}_{min}}{2}+17.8\right)\sqrt{{\text{T}}_{max}-{T}_{min}}.{R}_{a}$$ 3 where \({\text{T}}_{max}\) maximum temperature, \({T}_{min}\) minimum temperature; \({R}_{a}\) is the extraterrestrial solar radiation, which depends on the month of the year and the latitude of the observation area. The Normalized RDI (RDI n ) for each year is calculated using Eq. 4 , where the α 0 parameter is the arithmetic mean of the α 0 values calculated over N years: $${RDI}_{n}^{i}=\frac{{\propto }_{0}^{i}}{\stackrel{-}{{\propto }_{0}}}-1$$ 4 The Standard RDI (RDI st ) is calculated using Eq. 5 : $${RDI}_{st}^{i}=\frac{{y}^{i}-\stackrel{-}{y}}{\stackrel{-}{{\sigma }_{y}}}$$ 5 where \({y}^{i}\) is the value of \(\text{ln}{{\alpha }_{0}}^{i}\) , \(\stackrel{-}{y}\) is the arithmetic mean, and \(\stackrel{-}{{\sigma }_{y}}\) is its standard deviation. The classification of RDI is similar to that of SPI, which is provided in Table 2 . 2.3.3. The Normal Precipitation Index (PNI) The PNI index calculates the deviation of precipitation from its long-term average value. The normal value is 100% and can be calculated for a month, a season, or a year (Elhoussaoui et al., 2021 ). The PNI is one of the basic indicators for drought assessment and is effective for dry and wet time series over a given season. The PNI is calculated using Eq. 6 : $$PNI=\frac{{P}_{i}}{{P}_{0}}*100$$ 6 Where; \({P}_{i}\) , Annual precipitation, \({P}_{0}\) ; the long-term average. The classification of PNI is similar to that of SPI, and RDI is provided in Table 2 . 2.3.4. Aridite Index (AI) The AI can be calculated using the RDI in the DrinC model (Tigkas et al., 2015 ). In contrast to the PNI, the AI indicates the degree of dryness, taking into account factors beyond precipitation. Since high or low temperatures can lead to increased or decreased evaporation, the degree of dryness is often expressed by dividing the annual precipitation amount by the potential evaporation amount, resulting in the AI. The AI is usually expressed as the ratio of annual precipitation (P) to annual potential evaporation (E p ) (Arora, 2002 ). The Aridity Index (AI) is typically calculated using the formula, Eq. 7 : $$AI=\frac{P}{{E}_{\text{p}}}$$ 7 AI is considered an uncertain term, as potential evaporation cannot be measured directly and can only be estimated. AI defines the ratio between annual potential evaporation and average annual precipitation. Depending on the value of AI, typical climate characterizations are as follows: Hyper-arid: AI < 0.05 Arid: 0.05 < AI < 0.20 Semi-arid: 0.20 < AI < 0.50 Dry sub-humid: 0.50 < AI 0.65 3. Results and Discussion 3.1. Precipitation and evaporation trends If precipitation remains constant over a longer period and temperatures rise, it is supposed that drought conditions occur in the region (Easterling et al., 2007 ). Theoretically, the intensity of drought is best explained by calculating drought indices based on potential evaporation and precipitation data. As mentioned above, at least 30 years of data are required to calculate the drought indices (Merabti et al., 2018b ). In this study, 43 years of precipitation and evaporation data were used for the analysis. Figure 3 (left side) shows the total annual rainfall per unit area of approximately 794.27 mm. The upward trend line in the scatter plot on the right shows an increasing trend in rainfall over time, indicating that the climate in the region is becoming increasingly humid. Figure 3 (right) illustrates an overall increasing trend in precipitation across multiple locations in the Tunceli region, as indicated by the majority of data points lying above the y = x line, suggesting enhanced rainfall in the second half of the time series compared to the first. The graph illustrates the relationship between precipitation amounts in the first half of the analyzed time series (on the x-axis) and precipitation amounts in the second half (on the y-axis). The dataset spans 43 years, from 1980 to 2023. The first 20 years (1980–2000) constitute the first half, while the subsequent 23 years (2001–2023) form the second half. This trend analysis, based on the Innovative Trend Analysis method, underscores significant variations in precipitation patterns, critical for regional climate assessments (Şen, 2012 ). The low R² value (0.016) shown in Fig. 3 (left) indicates that the trend line explains only a small portion of the variance in precipitation amounts. This suggests that there may be other factors influencing precipitation amounts that can only be uncovered by a more detailed analysis (Tsakiris et al., 2007 ). To summarize, the upward trend line in the distribution diagram in Fig. 3 (right) indicates a tendency towards increasing precipitation over time, suggesting that the climate in the region may be becoming wetter. 3.2. Precipitation and temperature anomalies The long-term goal of the Paris Agreement is to keep the global temperature rise well below 2°C compared to pre-industrial levels, ideally at 1.5 degrees (Rogelj et al., 2016 ). Achieving this goal will require a gradual reduction in the use of fossil fuels (oil, coal) and a switch to renewable energy sources. The effects of climate change are already present; these include rising air temperatures, melting glaciers, shrinking polar ice caps, rising sea levels, increasing desertification, and more frequent extreme weather events such as heatwaves, droughts, floods, and storms (Ahmadalipour et al., 2019 ). Climate change does not affect all regions equally and has a greater impact on some areas than others. Figure 4 shows an estimate of the average annual temperature for the central region of the region of Tunceli. The data source is taken from ERA5, the fifth generation of atmospheric reanalysis of global climate by ECMWF, which covers the period from 1979 to 2023 with a spatial resolution of 30 km, data taken from Meteoblue ( 2023 ). The data may not accurately reflect conditions at a given location. Therefore, temperatures will often be higher than shown, especially in urban areas, and precipitation may vary locally depending on topography (Manoli et al., 2019 ). The dashed blue line represents the linear trend of climate change. If the trend line rises from left to right, this indicates a positive temperature trend, suggesting that Tunceli is experiencing warming due to climate change. A horizontal trend line would not indicate a clear trend, and if the trend line is decreasing, this indicates that temperatures in Tunceli are cooling over time (Esen, 2022 ). The lower part of the graph shows warming stripes. Each colored stripe represents the average temperature for a year, with blue representing cooler years and red representing warmer years. Figure 4 displays a clear trend of rising temperatures at Tunceli station over the long years. This trend has become increasingly prominent since 2014, with temperatures consistently exceeding previous records. Furthermore, examining the color bands in Fig. 4 reveals that global climate change, which is evident through the gradual increase in temperature, has been on the rise since 1998. Figure 5 presents the average total precipitation forecast for Tunceli; data are taken from Meteoblue ( 2023 ). The dashed blue line in the figure represents the linear trend of climate change. If the trend line slopes upward from left to right, it indicates a positive precipitation trend. If it is horizontal and runs flat, there is no clear trend observed that can be identified. However, a downward slope suggests that conditions are gradually leading to less precipitation, indicating that Tunceli is becoming drier over time. The graph at the bottom of Fig. 5 displays precipitation bars, each colored bar represents the total precipitation for a year, with greener bars indicating wetter years and brown bars indicating drier years. Overall, based on Fig. 5 , it can be observed that as temperatures increase, precipitation decreases in Tunceli. 3.2.1. Drought dynamic according to SPI Understanding how precipitation amounts change over time is vital for water resource management and agricultural planning. Figure 6 shows the SPI calculated over a 3-month period on an annual basis. The analysis reveals that the 3-month SPI values in Tunceli typically range between − 1 and 1, indicating drought conditions within normal limits. Generally, values >-1 signify wet, while values <-1 signify drought conditions. The SPI is a measure that indicates whether precipitation in a particular region is above or below the long-term average. A positive SPI value (+ 1.0 or greater) suggests above-normal precipitation, indicating wet conditions, while a negative value (-1.0 or less) indicates below-normal rainfall, which can lead to dry conditions. The SPI value is calculated for each year and period and is represented by a bar graph. The height of the bars illustrates the magnitude of the SPI value, and the direction indicates whether it is positive (upward) or negative (downward). Figure 7 shows the SPI values for each month from 1980 to 2023, indicating the dry years, drought duration, and also the interval between drought events. From the graph, it appears that there is no long-term extreme drought in the Tunceli region. It is crucial to understand the frequency and recurrence of droughts, as this helps predict future climate patterns and take preventive measures to manage water resources better. Figure 8 shows the SPI values for different periods (3-6-9-12 months) over several years. The 3-month SPI values can predict short-term droughts, which can fluctuate more frequently and suddenly due to seasonal changes. As Fig. 8 indicates, the 3-month SPI values are higher. On the other side, the 6-month SPI analysis smoothens some of the variability observed in the 3-month SPI and provides a better explanation of medium-term moisture trends. The 9-month SPI analysis results align more closely with annual cycles. At the same time, the 12-month SPI reflects long-term trends by smoothing out most short-term fluctuations and highlighting broader drought patterns or periods of high precipitation. As per the results shown in Fig. 9 , the SPI values calculated for 3, 6, 9, and 12 months over the years generally fall in the normal drought class. In Fig. 9 , the SPI was calculated over 9 months. The pie chart shown alongside indicates the percentage distribution of different SPI categories such as Dry, Normal, and Wet. Negative SPI values indicate drier conditions (below-average precipitation), whereas positive values indicate wetter conditions (above-average rainfall). So, there are significant fluctuations between wet and dry periods in the time series. The cyclical nature of SPI values highlights the variability in precipitation over time, suggesting that the region experienced normal drought conditions throughout the period. 3.2.2. Drought dynamic according to RDI The RDI Index is a measure that analyzes drought conditions by using precipitation and potential evapotranspiration data over a reference period. This allows for comparisons to be made between different periods and climates. A positive RDI value indicates normal or humid conditions, while negative values indicate dry conditions. Looking at Fig. 10 , we can see that the 3-month RDI values in Tunceli are generally between − 0.5 and 0.5, indicating that the current drought is within normal limits. From 1980 to 2023, the data has mainly fluctuated between − 0.5 and 0.5, indicating predominantly normal dryness with occasional deviations to mild stress conditions. Figure 11 shows a composite analysis of the RDI over several time scales: 3, 6, 9, and 12 months. This multiscale approach allows the observation of drought conditions with different durations and intensities. During these periods, the RDI values hover around the zero mark, indicating a state of equilibrium in terms of moisture availability. The analysis is in a stable range of normal drought with no significant long-term deviations toward extreme drought or wetness. Figure 12 shows RDI values for 9 months in Tunceli from 1980 to 2021. The RDI values are mostly just below zero, indicating a general tendency towards normal moisture conditions in most years. A spike on the right side of the graph represents a significant drought event within this annual interval. However, the accompanying analysis of the pie chart shows that drought events account for only 5% of the observed period, which yet falls within the Normal conditions during 95% of the analyzed period. This indicates that droughts are less frequent in the Tunceli region, and hydrological conditions are generally stable. 3.2.3. Drought dynamic according to PNI and AI Figure 13 presents the PNI results for Tunceli, which a single station represents over several years. The PNI index uses only precipitation data to analyze its results. The PNI values fluctuate from year to year, surpassing several drought category thresholds. It is important to note that none of the years fall into the 'hyper-arid' category. In contrast, many years fall into the 'humid' and 'dry sub-humid' categories, indicating a fluctuating but mostly non-arid climate. This variability underscores the impact of global warming and necessitates the development of new drought management strategies. Figure 14 shows the AI, categorized into different Aridity Thresholds, calculated over different RDI time scales: 3, 6, 9, and 12 months. The index values indicate a generally dry, sub-humid to semi-arid climate in the region. The 9-month RDI period shows a trend towards drier conditions compared to the other periods, which could indicate seasonal patterns or longer-term shifts in regional aridity. This analysis is crucial to understanding the degree of drought and its distribution over time, which is essential for regional planning and sustainable water resource management. If the AI value is 0.59, the Tunceli region falls into the climate characterization 'dry sub-humid,' which indicates the prevailing moderate aridity conditions. The 3-month, 6-month, 9-month, and 12-month RDI values in the graph are used to measure how arid a region is during specific periods. The values shown range between 0.5 and 0.65, representing the 'Dry sub-humid' category. Higher AI values indicate that the region is more humid and less stressed by water scarcity. The 12-month RDI is typically used to assess climate conditions over a year, reflecting general trends during that time. The other periods (3, 6, and 9 months) are used to observe seasonal changes and short-term climate fluctuations more closely. 4. Discussion This study deals with drought and climate change in Tunceli, a region known for its natural environment and clean water sources that have not yet been affected by urbanization and industrialization. The study examines how a region far from urbanization and industrialization is affected by climate change. It demonstrates that the changes in rainfall and droughts in recent years are closely linked to climate change and global warming. The importance of this study is to respond in a timely manner to the environmental changes caused by global warming and to link the risks to access to water resources in the region. Figure 15 shows the average temperature changes for eight stations between 1980 and 2023. The Paris Agreement aims to limit global warming to 1.5 ℃ above pre-industrial levels (Rogelj et al., 2016 ). Suppose the temperature data from 1980 to 2023 is examined in accordance with this target. In that case, it can be seen that the temperature has risen over time and was above 1.5 ℃ at all stations, especially after 2017. A sudden rise in temperature was observed in 2010 when the temperature changes of the stations were examined over the long term. After 2010, the temperature increase decreased but did not fall below 1.5 ℃ in the last seven years after 2017. Therefore, it can be said that the region is under the influence of global warming, and necessary strategies should be developed to comply with the Paris Agreement. The R² values and equations calculated for the temperature and precipitation for eight stations are included in Table 3 . The R² values indicate how well the model explains the data, while trend equations are used to predict the amount of temperature or precipitation for a given year. In particular, when examining temperature trends, this study shows that R² values explain temperature changes relatively well. An examination of the precipitation data shows that irregularities in the precipitation regime lead to low R² values. Therefore, in studies on drought, climate change, and water resources, more cautious predictions should be made when examining precipitation data. Table 3 Temperature and precipitation equations and R 2 values for eight stations. Station No Station Name Mean Trend Temperature (°C) Mean Trend Precipitation (mm) R 2 Equation R 2 Equation 17165 Tunceli 0,5423 y = 0,0577x + 10,812 0,0482 y = -2,0438x + 712,12 753 Pulumur 0,4284 y = 0,054x + 6,7763 0,0347 y = -2,176x + 982,08 17768 Çemişgezek 0,5902 y = 0,063x + 12,024 0,0505 y = -1,6348x + 549,76 18354 Hozat 0,5465 y = 0,0565x + 10,908 0,0402 y = -1,6791x + 640,19 18355 Nazımiye 0,5601 y = 0,0732x + 8,0691 0,0263 y = -1,8343x + 955,42 18179 Ovacık 0,543 y = 0,563x + 10,91 0,0402 y = -1,6791x + 640,19 5212 Pertek 0,5956 y = 0,0612x + 11,932 0,0286 y = -1,4476x + 653,86 17736 Mazgirt 0,5423 y = 0,5777x + 10,812 0,0483 y = -2,0513x + 712,68 A linear equation was derived from the precipitation data for the years 1979 to 2023, and the future scenario of the precipitation data is shown in Fig. 16 . The data shows that the average annual precipitation has decreased in the period from 1979 to 2050. This decrease in rainfall makes it even more important to investigate the drought situation in the region. The spatial distribution of the analyzed drought frequencies during the seasonal and annual periods varies in each period, but the results from 1980 to 2023 were evaluated. Various indices, such as the SPI, the RDI, the PNI, and the AI, were used to analyze the drought dynamics and climate sensitivity of Tunceli's water resources. Drought calculations were performed over 3-, 6-, 9- and 12-month periods by organizing the precipitation data from October to September for drought analysis using the SPI index. The drought classification of this region seems to be within normal limits due to its climatic characteristics. Nevertheless, a dry period was observed between August 1989 and May 1999. The SPI values for the periods of 3, 6, 9, and 12 months are generally within the normal range. However, the most recent data points (2020) show negative SPI values for all periods, indicating a significant dry period. Regarding the RDI, most of the values are the limits of the normal drought class. The analysis indicates a stable climate regime with no significant long-term deviations towards extreme drought or wetness. Our results show that during the observed period from 1980 to 2021, only 5% of the time was characterized by drought, while normal or wet conditions in Tunceli characterized the remaining 95%. Although the PNI values fluctuate from year to year, none of the years were "extremely dry," while some years were "wet." This indicates a "dry sub-humid" climate that is not prone to drought. The AI values for the 3-month, 6-month, 9-month, and 12-month time scales indicate how dry a region is in certain periods. The values displayed range from 0.5 to 0.65 and represented the category 'dry-semi-humid'. The results of the study show quite similar patterns when comparing different indices, with precipitation being the common parameter, which explains the similarity of the results. This study consisted of a detailed analysis of critical drought indices and long-term drought risks in order to develop effective water resource management and climate adaptation strategies in the region. In summary, a comprehensive analysis of drought and climate trends in Tunceli highlights the significant impact of global climate change on temperatures in this non-industrialized area. Overall, the drought condition for the Tunceli region is classified as a normal drought. Nevertheless, future temperatures and precipitation trends show that drought conditions in the Tunceli region, like in many regions around the globe, will most likely exacerbate if necessary measures are taken globally (Rogelj et al., 2016 ). In light of these observations, it is important that further research comprehensively analyzes the possible causes of this increasing precipitation trend, the variables influencing it, and the long-term climatic impacts. Additional studies may also be necessary to assess the potential environmental, social, and economic impacts of the increasing trend in precipitation data. 5. Conclusions This study presents a comprehensive analysis of drought and climate trends in the Tunceli region, utilizing indices such as SPI, RDI, PNI, and AI. The results indicate that there is currently no extreme drought in the region. However, when examining temperature and precipitation anomalies, it is evident that the region is significantly impacted by global warming. The detailed evaluation of temperature and precipitation trends clearly highlights the significant effects of global climate change in the region. The continuous increase in temperatures and the decrease in precipitation make the risks to water accessibility and quality in Tunceli more pronounced. Both the SPI and the RDI were used to analyze precipitation irregularities and drought trends from 1980 to 2021. Our analysis of the SPI has shown that the values for the 3 months typically oscillate between − 1 and 1, indicating a normal level of drought variability. This suggests that extreme deviations from average rainfall patterns are not frequent in the region in the short term. Conversely, the RDI analysis for the 9 months highlighted years of pronounced drought at times, but the overarching pattern suggests that the region generally experienced normal or wet conditions. These results are particularly instructive as they highlight the lack of a long-term, persistent drought trend in the region while recognizing the presence of short-term droughts and seasonal fluctuations that need to be addressed. The results of the PNI and the AI have enabled a more comprehensive assessment of the regional climate. The fluctuations in PNI values between humid and dry subhumid classifications over the years, together with the AI values, show that the region falls largely within a dry sub-humid to semi-arid climate band. Taking into account all the analyses carried out, no significant long-term trend towards drought could be identified in the region of Tunceli. However, this does not exclude the importance of looking at short-term drought events and seasonal variations. The knowledge gained from this study can guide regional authorities in the management of water resources and the development of drought adaptation strategies. It also highlights the need for further research into how regional drought conditions may evolve in the context of climate change. Declarations Acknowledgment: Alban Kuriqi is grateful for the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS (DOI: 10.54499/UIDB/04625/2020). Ethics approval (Not applicable) Consent to participate (Yes) Consent for publication (Yes) Funding (This study did not receive any funding.) Conflicts of interest/Competing interests (The authors declare that they have no conflict of interest.) Availability of data and material (Upon request) References Ahmadalipour A, Moradkhani H, Castelletti A, Magliocca N (2019) Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of The Total Environment 662:672-686. doi:https://doi.org/10.1016/j.scitotenv.2019.01.278 Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660-684. doi:https://doi.org/10.1016/j.foreco.2009.09.001 Arora VK (2002) The use of the aridity index to assess climate change effect on annual runoff. Journal of Hydrology 265:164-177. doi:https://doi.org/10.1016/S0022-1694(02)00101-4 Carpenter JR, Smuk M (2021) Missing data: A statistical framework for practice. Biometrical Journal 63:915-947. doi:https://doi.org/10.1002/bimj.202000196 Dabanli I (2018) Drought hazard, vulnerability, and risk assessment in Turkey. Arabian Journal of Geosciences 11:538. doi:10.1007/s12517-018-3867-x Dai M, Huang S, Huang Q, Leng G, Guo Y, Wang L, Fang W, Li P, Zheng X (2020) Assessing agricultural drought risk and its dynamic evolution characteristics. Agricultural Water Management 231:106003. doi:https://doi.org/10.1016/j.agwat.2020.106003 Delpla I, Jung AV, Baures E, Clement M, Thomas O (2009) Impacts of climate change on surface water quality in relation to drinking water production. Environment International 35:1225-1233. doi:https://doi.org/10.1016/j.envint.2009.07.001 Easterling DR, Wallis TWR, Lawrimore JH, Heim Jr. RR (2007) Effects of temperature and precipitation trends on U.S. drought. Geophysical Research Letters 34. doi:https://doi.org/10.1029/2007GL031541 Elhoussaoui A, Zaagane M, Benaabidate L (2021) Comparison of various drought indices for assessing drought status of the Northern Mekerra watershed, Northwest of Algeria. Arabian Journal of Geosciences 14:915. doi:10.1007/s12517-021-07269-y Esen F (2022) Tunceli iklim parametrelerine ait zaman serilerinin farklı istatistiksel analiz yöntemleriyle değerlendirilmesi. Türk Coğrafya Dergisi:7-22. doi:10.17211/tcd.1151639 Gumus V, Algin HM (2017) Meteorological and hydrological drought analysis of the Seyhan−Ceyhan River Basins, Turkey. Meteorological Applications 24:62-73. doi:https://doi.org/10.1002/met.1605 Jiménez-Donaire MP, Tarquis A, Giráldez JV (2020) Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain. Nat Hazards Earth Syst Sci 20:21-33. doi:10.5194/nhess-20-21-2020 Khorrami B, Gündüz O (2022) Detection and analysis of drought over Turkey with remote sensing and model-based drought indices. Geocarto International 37:12171-12193. doi:10.1080/10106049.2022.2066197 Kim T-W, Jehanzaib M (2020) Drought Risk Analysis, Forecasting and Assessment under Climate Change. Water, Korkmazi M, Kilic Z (2021) The Effect of Global Climate Change on Drought. 1st International Conference on Sustainable Water Resources Management. CEWRE, Lahore, Pakistan. pp 32-36 Lehner F, Coats S, Stocker TF, Pendergrass AG, Sanderson BM, Raible CC, Smerdon JE (2017) Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters 44:7419-7428. doi:https://doi.org/10.1002/2017GL074117 Liu Y, Chen J (2021) Future global socioeconomic risk to droughts based on estimates of hazard, exposure, and vulnerability in a changing climate. Science of The Total Environment 751:142159. doi:https://doi.org/10.1016/j.scitotenv.2020.142159 Machiwal D, Jha MK (2006) Time series analysis of hydrologic data for water resources planning and management: a review. Journal of Hydrology Hydromechanics 54:237-257. Manoli G, Fatichi S, Schläpfer M, Yu K, Crowther TW, Meili N, Burlando P, Katul GG, Bou-Zeid E (2019) Magnitude of urban heat islands largely explained by climate and population. Nature 573:55-60. doi:10.1038/s41586-019-1512-9 McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology. California. pp 179-183 Merabti A, Martins DS, Meddi M, Pereira LS (2018a) Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales. Water Resources Management 32:1087-1100. doi:10.1007/s11269-017-1856-6 Merabti A, Meddi M, Martins DS, Pereira LS (2018b) Comparing SPI and RDI Applied at Local Scale as Influenced by Climate. Water Resources Management 32:1071-1085. doi:10.1007/s11269-017-1855-7 Mersin D, Gulmez A, Safari MJS, Vaheddoost B, Tayfur G (2022) Drought Assessment in the Aegean Region of Turkey. Pure and Applied Geophysics 179:3035-3053. doi:10.1007/s00024-022-03089-7 Meteoblue (2023) Climate change Tunceli https://www.meteoblue.com/en/climate-change/tunceli_republic-of-türkiye_298846 Meza I, Siebert S, Döll P, Kusche J, Herbert C, Eyshi Rezaei E, Nouri H, Gerdener H, Popat E, Frischen J, Naumann G, Vogt JV, Walz Y, Sebesvari Z, Hagenlocher M (2020) Global-scale drought risk assessment for agricultural systems. Nat Hazards Earth Syst Sci 20:695-712. doi:10.5194/nhess-20-695-2020 Palmer WC (1965) Meteorological drought. US Department of Commerce, Weather Bureau Pedro-Monzonís M, Solera A, Ferrer J, Estrela T, Paredes-Arquiola J (2015) A review of water scarcity and drought indexes in water resources planning and management. Journal of Hydrology 527:482-493. doi:https://doi.org/10.1016/j.jhydrol.2015.05.003 Pereira LS, Cordery I, Iacovides I (2009) Coping with water scarcity: Addressing the challenges. Springer Science & Business Media Rogelj J, den Elzen M, Höhne N, Fransen T, Fekete H, Winkler H, Schaeffer R, Sha F, Riahi K, Meinshausen M (2016) Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534:631-639. doi:10.1038/nature18307 Sahana V, Mondal A (2023) Evolution of multivariate drought hazard, vulnerability and risk in India under climate change. Nat Hazards Earth Syst Sci 23:623-641. doi:10.5194/nhess-23-623-2023 Şen Z (2012) Innovative Trend Analysis Methodology. Journal of Hydrologic Engineering 17:1042-1046. doi:doi:10.1061/(ASCE)HE.1943-5584.0000556 Tate EL, Gustard A (2000) Drought Definition: A Hydrological Perspective. In: Vogt JV, Somma F (eds). Drought and Drought Mitigation in Europe. Springer Netherlands, Dordrecht. pp 23-48 Tigkas D, Vangelis H, Tsakiris G (2015) DrinC: a software for drought analysis based on drought indices. Earth Science Informatics 8:697-709. doi:10.1007/s12145-014-0178-y Tsakiris G, Pangalou D, Vangelis H (2007) Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI). Water Resources Management 21:821-833. doi:10.1007/s11269-006-9105-4 Turkes M (2020) Climate and Drought in Turkey. In: Harmancioglu NB, Altinbilek D (eds). Water Resources of Turkey. Springer International Publishing, Cham. pp 85-125 Yeşilköy S, Şaylan L (2022) Spatial and temporal drought projections of northwestern Turkey. Theoretical and Applied Climatology 149:1-14. doi:10.1007/s00704-022-04029-0 Yusof F, Hui-Mean F, Suhaila J, Yusop Z, Ching-Yee K (2014) Rainfall characterisation by application of standardised precipitation index (SPI) in Peninsular Malaysia. Theoretical and Applied Climatology 115:503-516. doi:10.1007/s00704-013-0918-9 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jul, 2024 Read the published version in Water Conservation Science and Engineering → Version 1 posted Editorial decision: Revision requested 24 Jun, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviews received at journal 17 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers invited by journal 10 Jun, 2024 Editor assigned by journal 06 Jun, 2024 Submission checks completed at journal 06 Jun, 2024 First submitted to journal 02 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4518030","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316250684,"identity":"8ac77d6c-36b1-46eb-8aac-b2c4af63f5c1","order_by":0,"name":"Meral KORKMAZ","email":"","orcid":"","institution":"Munzur University","correspondingAuthor":false,"prefix":"","firstName":"Meral","middleName":"","lastName":"KORKMAZ","suffix":""},{"id":316250686,"identity":"a752dac9-33ef-49d0-8a52-a3efcbb7a70f","order_by":1,"name":"Alban KURIQI","email":"data:image/png;base64,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","orcid":"","institution":"CERIS, Universidade de Lisboa","correspondingAuthor":true,"prefix":"","firstName":"Alban","middleName":"","lastName":"KURIQI","suffix":""}],"badges":[],"createdAt":"2024-06-02 18:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4518030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4518030/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41101-024-00281-9","type":"published","date":"2024-07-23T16:15:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58766668,"identity":"922b53fd-695c-4380-93f1-b063772ac05b","added_by":"auto","created_at":"2024-06-20 22:00:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":200449,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of Tunceli \u0026nbsp;region in Turkey.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/e537d5d3a5cb056e798ef461.png"},{"id":58766420,"identity":"51be96be-6155-4cf5-a104-5792e509a9b8","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142177,"visible":true,"origin":"","legend":"\u003cp\u003ePrecipitation map for Tunceli region in the eastern Anatolia region of Turkey.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/63e92c1598d2ab5c92698ccc.png"},{"id":58766418,"identity":"168d5701-3e93-4c37-b9d0-f81a2c665ff6","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":370520,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of annual precipitation and evaporation (left) for the region of Tunceli and rainfall trend (right) at eight stations in Tunceli.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/a39bc7529352ac5323093e8c.png"},{"id":58766669,"identity":"e2682855-91e3-4403-b308-9c17aa60c1d7","added_by":"auto","created_at":"2024-06-20 22:00:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71311,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual temperature trend and anomalies for Tunceli region.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/6fa840963edf60f6e90f3b7d.png"},{"id":58766422,"identity":"02bfda7f-92c2-45b3-b924-dcff59c29811","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93818,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual precipitation trend and anomalies for Tunceli region.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/c8da74334af88969053cce2f.png"},{"id":58766744,"identity":"447ceede-0b7e-4782-940a-85554b4fabc0","added_by":"auto","created_at":"2024-06-20 22:08:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88099,"visible":true,"origin":"","legend":"\u003cp\u003eResults of SPI (3-month period).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/63fe570b7349f2cadc0c4706.png"},{"id":58766671,"identity":"e6828ba7-bcaa-4a6b-a9c5-a87e7b05782b","added_by":"auto","created_at":"2024-06-20 22:00:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67590,"visible":true,"origin":"","legend":"\u003cp\u003eResults of SPI (3-month period).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/2ed42d3ef8f8b7eb594130e6.png"},{"id":58766427,"identity":"9291afbe-211d-42a7-9168-b7a16b9faf42","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":78968,"visible":true,"origin":"","legend":"\u003cp\u003eResults of SPI (3-6-9-12 month period).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/5bbea3b80506c98e7df308ca.png"},{"id":58766428,"identity":"6321bbb1-e463-4c09-9f33-0d1d99a93a53","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":103351,"visible":true,"origin":"","legend":"\u003cp\u003eResults of SPI (9-month period).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/8e9004ba22fc27c728b4429d.png"},{"id":58766425,"identity":"d8ee755e-c60b-4be4-8209-17267831a588","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":82541,"visible":true,"origin":"","legend":"\u003cp\u003eResults of RDI (3-month period).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/1962f8d2896be69372fe52fc.png"},{"id":58766431,"identity":"6ab12aeb-3e6b-4cee-b79c-ace435f5801c","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":66387,"visible":true,"origin":"","legend":"\u003cp\u003eResults of RDI (3-6-9-12mmonth period).\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/b4a72062edb2fca372a34990.png"},{"id":58766672,"identity":"2715a0ff-726c-4dc1-9c6b-1fec8d568b68","added_by":"auto","created_at":"2024-06-20 22:00:54","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":86735,"visible":true,"origin":"","legend":"\u003cp\u003eResults of RDI (9-month period).\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/2a70185cce043b2a180e1884.png"},{"id":58766424,"identity":"e7bff85e-0adc-4461-b3d7-07658a80ceeb","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":74711,"visible":true,"origin":"","legend":"\u003cp\u003ePrecipitation Normality Index (PNI) results from 1980 to 2021.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/3b3d8f9cc75519a6a35e7bc9.png"},{"id":58766433,"identity":"7500652a-f5bc-4de8-81d8-2e3c648bab7e","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":48889,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Aridity Index.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/9e7af465365822179ce95306.png"},{"id":58766430,"identity":"176cf711-98b5-4ea9-89a6-0c6a7729cd75","added_by":"auto","created_at":"2024-06-20 21:52:54","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":152836,"visible":true,"origin":"","legend":"\u003cp\u003eMean temperature change for eight stations (1979-2023).\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/2c6569420ec92a21c0840aec.png"},{"id":58766673,"identity":"54f0579c-6a08-4f2a-97a8-5f9a3e5cd543","added_by":"auto","created_at":"2024-06-20 22:00:54","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":177931,"visible":true,"origin":"","legend":"\u003cp\u003eFuture forecast of mean precipitation change for eight stations from 1979 to 2050.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/bd3fc2362a5aeceec2fe8d2c.png"},{"id":61597218,"identity":"c1ffac2f-0e47-477b-83c8-e49993ad8e30","added_by":"auto","created_at":"2024-08-01 17:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2565137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4518030/v1/b4a50427-7f8f-4d44-9b21-87f97c5df91b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Drought dynamics in mixed climate regions: insights for water resource management and climate adaptation strategies","fulltext":[{"header":"1. Introductıon","content":"\u003cp\u003eClimate change poses a significant threat worldwide, leading to severe and erratic weather events such as sudden rainfall, droughts, and forest fires (Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). One of the most notable impacts of climate change is drought, which poses a major threat and has a particular impact on agriculture, water resources, ecosystems, and human life. In drought-prone countries like Turkey, developing effective drought mitigation and adaptation strategies is crucial (Khorrami and G\u0026uuml;nd\u0026uuml;z, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrought is a natural phenomenon that has adverse effects on water resources and human activities, especially agriculture (Pereira et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). It can be defined as a temporary but natural imbalance of water, characterized by less frequent, variable duration and intensity of rainfall below average (Tate and Gustard, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). It is an event that is difficult to predict or model, leading to reduced availability and carrying capacity of water resources in ecosystems (Pedro-Monzon\u0026iacute;s et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Various indices have been proposed and used to define and analyze droughts accurately. The most commonly used drought index is the Standardized Precipitation Index (SPI), a normalized index for calculating deviations from normal precipitation conditions. The SPI requires only monthly precipitation data, is easy to calculate, and is standardized to allow easy comparison between different periods or regions (McKee et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMerabti et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e) compared two drought indices, the SPI, and the RDI, at the local level in northeastern Algeria on a time scale of 3, 6, and 12 months. Both indices showed very similar and consistent results when applied to different sites and climatic zones. Only minor differences were found between the indices, with the regression coefficients for the 3-month scale generally larger and closer to 1.0 (Merabti et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). In arid and semi-arid regions, the SPI detected more drought events, while in wetter climates, both the SPI and RDI detected more wet events. This behavior suggests that drought and humidity events in semi-humid climates are primarily related to precipitation and thus play a less significant role in differentiating between RDI and SPI based on potential evapotranspiration (PET). Drought indicies such as SPI and RDI allow an in-depth analysis of drought risk (Jim\u0026eacute;nez-Donaire et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKim and Jehanzaib (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) discuss the rapid expansion of drought risk analysis, prediction, and assessment due to climate change and emphasize the importance of accurate monitoring and comprehensive assessment for reliable decision-making on drought risks and water resources. They present new approaches for drought monitoring and risk prediction. Meza et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provide an integrated assessment of drought risk for both irrigated and rain-fed agricultural systems on a global scale and identify regions with high drought risk, such as Southeast Europe and Northern and Southern Africa. Ahmadalipour et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) assess drought risk at the national level across Africa, examine the impact of climate change, population growth, and socio-economic vulnerabilities on drought risk, and emphasize the importance of adaptation measures to mitigate drought risk.\u003c/p\u003e \u003cp\u003eLiu and Chen (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) project future global socio-economic risks for droughts under different climate change scenarios, identify regions with high socio-economic risks and highlight the inequality of future socio-economic risks for droughts between countries. Sahana and Mondal (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) assess future trends in drought hazard, vulnerability, and risk in India, highlight the influence of societal developments on drought vulnerability, and point to the need for strong mitigation and adaptation planning. Lehner et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examine changes in drought and risk of successive drought years under 1.5\u0026deg;C and 2\u0026deg;C global warming scenarios and find significant regional differences in future drought risk. Dai et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) propose a drought assessment framework to investigate the changes in drought characteristics and risks in China under climate change, providing useful information for effective long-term adaptation strategies. The volume of the world's water resources remains constant due to the water cycle. Still, climate change is altering water availability at different temporal and spatial scales, jeopardizing several ecosystem services (Delpla et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn drought-prone countries such as Turkey, a handful of studies have recently focused intensively on drought assessment at regional (Gumus and Algin, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mersin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yeşilk\u0026ouml;y and Şaylan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and country scale (Dabanli, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khorrami and G\u0026uuml;nd\u0026uuml;z, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Turkes, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, to the best of our knowledge, there is no comprehensive study that analyzes droughts in higher resolution and in the context of supporting the Tunceli region in terms of its status as a protected area, environmentally friendly, and also being a tourist spot for many visitors (Korkmazi and Kilic, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, since the region is characterized by a temperate climate, effective management of the water resources is crucial to secure sufficient water for water supply, agriculture, and electricity generation through the Uzun\u0026ccedil;ayir dam, which was put into operation in 2009.\u003c/p\u003e \u003cp\u003eThus, this study focuses on the assessment of drought risk in Tunceli, a region in the eastern part of Turkey known for its natural beauty and pristine environment. Tunceli experiences heavy snowfall in winter, but in recent years, precipitation has decreased due to the effects of climate change. This situation has made climate change a major issue in the region. Tunceli is one of the regions most affected by the impact of climate change and is, therefore, at significant risk of drought. Consequently, it is important to develop effective strategies to adapt to climate change. For that purpose, in this study, we employed the SPI and RDI to assess the long-term dynamic of droughts in the Tunceli region. In this context, the study aims to determine the drought risk in Tunceli and propose appropriate climate change adaptation strategies, especially focusing on better management of water resources.\u003c/p\u003e"},{"header":"2. Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThe Tunceli region lies in the Upper Euphrates Basin in the Eastern Anatolia region of Turkey and is located between longitudes 38\u0026deg;19' \u0026minus;\u0026thinsp;40\u0026deg;26' east and latitudes 39\u0026deg;36' \u0026minus;\u0026thinsp;38\u0026deg;46' north. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this region is surrounded by the region s of Bing\u0026ouml;l, Erzincan, Elazığ, and Keban Reservoir. It covers an area of 7,774 km\u0026sup2; with an altitude that ranges between 800 to over 3000 m above sea level. The region consists of 70% mountains, 25% plateaus, and 5% plains, which indicates the high and rugged terrain of Tunceli. Among the most important geographical features of Tunceli are the high mountains such as the Munzur Mountains, Mercan Mountains, Gobartı Mountains, Zel Mountains, and Sevdin Mountains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese mountains allow for significant rainfall under harsh continental climate conditions and the formation of deep valleys by strong flowing rivers. Important rivers such as Munzur, Mercan, P\u0026uuml;l\u0026uuml;m\u0026uuml;r, Tağar and Peri flow into the Keban reservoir. There are no significant plains or flatlands in the region, but there are craters and glacial lakes. The natural beauties of Tunceli are mainly concentrated in areas such as the Munzur Valley, Zage, Ali Boğazı, Kutudere, and the P\u0026uuml;l\u0026uuml;m\u0026uuml;r Valley (Meteoblue, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Considering the climate and topography of Tunceli, the rainfall distribution data presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e could be crucial for planning various aspects such as agriculture, settlements, infrastructure, and natural resource management.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHigher elevations tend to receive more precipitation, while valleys and low-lying areas may receive less. Rainfall maps are an important tool in decision-making related to water resource management, droughts, and flood risks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Dataset\u003c/h2\u003e \u003cp\u003eMeteorological data were obtained from the Meteorological Data Bank System (MEVBİS, 2022) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Missing entries were identified in the evaporation dataset sourced from MEVBIS. Missing data are a common issue in research studies and are typically addressed using various statistical methods (Carpenter and Smuk, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected stations in Tunceli region \u0026ndash; temperature and precipitation trends.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003cp\u003e(E)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003cp\u003e(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003cp\u003e(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1979\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTunceli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e710.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e620.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulumur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e979.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026Ccedil;emişgezek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e548.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e476.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHozat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e638.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e564.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNazımiye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e954.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e872.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvacık\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e638.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e564.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePertek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e652.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e588.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMazgirt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e710.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e620.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this study, a linear regression model was employed to estimate the missing evaporation data. Linear regression is an effective tool for predicting missing data in areas such as hydrology and water resources management (Carpenter and Smuk, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Machiwal and Jha, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The linear regression model defines the relationship between a dependent variable.\u003c/p\u003e \u003cp\u003eThe components of this equation are:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Y={\\beta }_{0}+{\\beta }_{1}{X}_{1}+{\\beta }_{2}{X}_{2}+\\dots +{\\beta }_{p}{X}_{p}+\\epsilon$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Y\\)\u003c/span\u003e \u003c/span\u003e: The predicted value, such as monthly evaporation.​\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e \u003c/span\u003e: The intercept term of the model.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\beta }_{i}\\)\u003c/span\u003e \u003c/span\u003e: The coefficients of the independent variables, each representing the impact of variables in the dataset on evaporation.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({X}_{i}\\)\u003c/span\u003e \u003c/span\u003e : The independent variables of the model may include meteorological factors like temperature and wind speed.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\epsilon\\)\u003c/span\u003e \u003c/span\u003e: A random error term representing the variation not explained by the model.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(p\\)\u003c/span\u003e \u003c/span\u003e: Represents the p\u003csub\u003eth\u003c/sub\u003e observations of the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{1},{X}_{2}, {X}_{3}, ,{X}_{p}\\)\u003c/span\u003e\u003c/span\u003e respectively\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Drought analysis\u003c/h2\u003e \u003cp\u003eIn this section, we delve into the analysis of drought dynamics and trends in the Tunceli region using a comprehensive array of indices: the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), the Precipitation Normalization Index (PNI), and the Aridity Index (AI). This approach allows us to quantify the severity and duration of droughts, assess variations in precipitation against long-term averages, and evaluate the degree of aridity based on evaporation and precipitation balances. Through this multifaceted assessment, the study aims to provide a thorough understanding of the impact of drought conditions on the region's hydrological cycle.\u003c/p\u003e \u003cp\u003eThe RDI quantifies the intensity of drought by incorporating elements of the hydrological cycle, such as precipitation and evaporation. The SPI is calculated by standardizing precipitation data against long-term averages to identify anomalies in precipitation over different periods and assess the onset and duration of droughts. The following \"reference periods\" and \"calculation time intervals\" were used in this study, namely, 12, 6, 3, or 1 month, starting in October (Tigkas et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e12 months: From October to September.\u003c/h3\u003e\n\n\u003ch3\u003e6-month: Options from October to March or April to September.\u003c/h3\u003e\n\n\u003ch3\u003e3-month: Quarterly intervals, starting from October.\u003c/h3\u003e\n\n\u003ch3\u003e1-month: Monthly analysis, beginning in October.\u003c/h3\u003e\n\u003cp\u003eUser-defined Option: Allows users to select any time scale and starting month for customized analysis.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3.1. The Standardized Precipitation Index (SPI)\u003c/h2\u003e \u003cp\u003eThe SPI is calculated exclusively on the basis of precipitation data and, therefore, requires less data and computing time. It can be calculated on different time scales, e.g., 1 month, 3 months, 6 months, 9 months, or 12 months, and can detect approaching droughts earlier than the Palmer Index. According to Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the SPI is derived by dividing the deviation of the precipitation series from its mean value by the standard deviation (Yusof et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$SPI=\\frac{{P}_{i}-{P}_{0}}{{\\sigma }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i},{P}_{1},{P}_{2},\\dots ,{P}_{n}\\)\u003c/span\u003e\u003c/span\u003e; standardized precipitation series, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{0}\\)\u003c/span\u003e\u003c/span\u003e; the mean value of the series, σ; the standard deviation. Due to the complexity of calculating precipitation events, the precipitation series are first adjusted to a normal distribution. The SPI method is applied to at least 30 years of continuous monthly precipitation data, and drought classifications are made according to the categories listed 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 of SPI, PNI, and RDI (McKee et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Palmer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Tsakiris et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPI and RDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 - 1.99\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\u003e1.0 - 1.49\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\u003e0.99 - -0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 - 110\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\u0026minus;1 to-\u0026minus;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;80\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\u003e\u0026minus;1.5 t- \u0026minus;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 \u0026minus;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe meteorological droughts consider the impact of precipitation deficits on water resources and calculate them over periods of 1, 3, 6, 9, and 12 months. These periods are selected on the basis of the duration over which the effects of precipitation deficits on available water resources may be evident. For instance, a decrease in precipitation in a given month may have an immediate impact on soil moisture. At the same time, the effects on groundwater and rivers may be evident over a longer period (Liu and Chen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Reconnaissance Drought Index (RDI)\u003c/h2\u003e \u003cp\u003eThe RDI is a drought analysis method that uses potential evapotranspiration (PET) values and precipitation data. Data on minimum and maximum temperatures are used to calculate the PET values (Tsakiris et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The initial α\u003csub\u003e0\u003c/sub\u003e values for estimating the RDI index values in the reference periods are determined from Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e2\u003c/span\u003e (i\u0026thinsp;=\u0026thinsp;1 to N and j\u0026thinsp;=\u0026thinsp;1 to 12):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\propto }_{0}^{i}=\\frac{{\\sum }_{j=1}^{12}{P}_{ij}}{{\\sum }_{j=1}^{12}{PET}_{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PET}_{ij}\\)\u003c/span\u003e\u003c/span\u003erepresents the precipitation and evaporation values for the \u003cem\u003ei\u003c/em\u003e year and \u003cem\u003ej\u003c/em\u003e month, typically starting in October, and N is the total number of years. Potential evapotranspiration is calculated based on Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Tsakiris et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$PET=0.0023 \\left(\\frac{{\\text{T}}_{max}+{T}_{min}}{2}+17.8\\right)\\sqrt{{\\text{T}}_{max}-{T}_{min}}.{R}_{a}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{T}}_{max}\\)\u003c/span\u003e\u003c/span\u003emaximum temperature, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{min}\\)\u003c/span\u003e\u003c/span\u003e minimum temperature; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{a}\\)\u003c/span\u003e\u003c/span\u003eis the extraterrestrial solar radiation, which depends on the month of the year and the latitude of the observation area. The Normalized RDI (RDI\u003csub\u003en\u003c/sub\u003e) for each year is calculated using Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, where the α\u003csub\u003e0\u003c/sub\u003e parameter is the arithmetic mean of the α\u003csub\u003e0\u003c/sub\u003e values calculated over N years:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${RDI}_{n}^{i}=\\frac{{\\propto }_{0}^{i}}{\\stackrel{-}{{\\propto }_{0}}}-1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Standard RDI (RDI\u003csub\u003est\u003c/sub\u003e) is calculated using Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e5\u003c/span\u003e:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${RDI}_{st}^{i}=\\frac{{y}^{i}-\\stackrel{-}{y}}{\\stackrel{-}{{\\sigma }_{y}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}^{i}\\)\u003c/span\u003e\u003c/span\u003e is the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{ln}{{\\alpha }_{0}}^{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{y}\\)\u003c/span\u003e\u003c/span\u003eis the arithmetic mean, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{{\\sigma }_{y}}\\)\u003c/span\u003e\u003c/span\u003eis its standard deviation. The classification of RDI is similar to that of SPI, which is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. The Normal Precipitation Index (PNI)\u003c/h2\u003e \u003cp\u003eThe PNI index calculates the deviation of precipitation from its long-term average value. The normal value is 100% and can be calculated for a month, a season, or a year (Elhoussaoui et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The PNI is one of the basic indicators for drought assessment and is effective for dry and wet time series over a given season. The PNI is calculated using Eq.\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e6\u003c/span\u003e:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$PNI=\\frac{{P}_{i}}{{P}_{0}}*100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}\\)\u003c/span\u003e\u003c/span\u003e, Annual precipitation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{0}\\)\u003c/span\u003e\u003c/span\u003e; the long-term average. The classification of PNI is similar to that of SPI, and RDI is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Aridite Index (AI)\u003c/h2\u003e \u003cp\u003eThe AI can be calculated using the RDI in the DrinC model (Tigkas et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In contrast to the PNI, the AI indicates the degree of dryness, taking into account factors beyond precipitation. Since high or low temperatures can lead to increased or decreased evaporation, the degree of dryness is often expressed by dividing the annual precipitation amount by the potential evaporation amount, resulting in the AI. The AI is usually expressed as the ratio of annual precipitation (P) to annual potential evaporation (E\u003csub\u003ep\u003c/sub\u003e) (Arora, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Aridity Index (AI) is typically calculated using the formula, Eq.\u0026nbsp;\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e7\u003c/span\u003e:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$AI=\\frac{P}{{E}_{\\text{p}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAI is considered an uncertain term, as potential evaporation cannot be measured directly and can only be estimated. AI defines the ratio between annual potential evaporation and average annual precipitation. Depending on the value of AI, typical climate characterizations are as follows:\u003c/p\u003e \u003cp\u003eHyper-arid: AI\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003eArid: 0.05\u0026thinsp;\u0026lt;\u0026thinsp;AI\u0026thinsp;\u0026lt;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003eSemi-arid: 0.20\u0026thinsp;\u0026lt;\u0026thinsp;AI\u0026thinsp;\u0026lt;\u0026thinsp;0.50\u003c/p\u003e \u003cp\u003eDry sub-humid: 0.50\u0026thinsp;\u0026lt;\u0026thinsp;AI\u0026thinsp;\u0026lt;\u0026thinsp;0.65\u003c/p\u003e \u003cp\u003eHumid: AI\u0026thinsp;\u0026gt;\u0026thinsp;0.65\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Precipitation and evaporation trends\u003c/h2\u003e \u003cp\u003eIf precipitation remains constant over a longer period and temperatures rise, it is supposed that drought conditions occur in the region (Easterling et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Theoretically, the intensity of drought is best explained by calculating drought indices based on potential evaporation and precipitation data. As mentioned above, at least 30 years of data are required to calculate the drought indices (Merabti et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). In this study, 43 years of precipitation and evaporation data were used for the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (left side) shows the total annual rainfall per unit area of approximately 794.27 mm. The upward trend line in the scatter plot on the right shows an increasing trend in rainfall over time, indicating that the climate in the region is becoming increasingly humid.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (right) illustrates an overall increasing trend in precipitation across multiple locations in the Tunceli region, as indicated by the majority of data points lying above the y\u0026thinsp;=\u0026thinsp;x line, suggesting enhanced rainfall in the second half of the time series compared to the first. The graph illustrates the relationship between precipitation amounts in the first half of the analyzed time series (on the x-axis) and precipitation amounts in the second half (on the y-axis). The dataset spans 43 years, from 1980 to 2023. The first 20 years (1980\u0026ndash;2000) constitute the first half, while the subsequent 23 years (2001\u0026ndash;2023) form the second half. This trend analysis, based on the Innovative Trend Analysis method, underscores significant variations in precipitation patterns, critical for regional climate assessments (Şen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The low R\u0026sup2; value (0.016) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (left) indicates that the trend line explains only a small portion of the variance in precipitation amounts. This suggests that there may be other factors influencing precipitation amounts that can only be uncovered by a more detailed analysis (Tsakiris et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To summarize, the upward trend line in the distribution diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (right) indicates a tendency towards increasing precipitation over time, suggesting that the climate in the region may be becoming wetter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Precipitation and temperature anomalies\u003c/h2\u003e \u003cp\u003eThe long-term goal of the Paris Agreement is to keep the global temperature rise well below 2\u0026deg;C compared to pre-industrial levels, ideally at 1.5 degrees (Rogelj et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Achieving this goal will require a gradual reduction in the use of fossil fuels (oil, coal) and a switch to renewable energy sources. The effects of climate change are already present; these include rising air temperatures, melting glaciers, shrinking polar ice caps, rising sea levels, increasing desertification, and more frequent extreme weather events such as heatwaves, droughts, floods, and storms (Ahmadalipour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Climate change does not affect all regions equally and has a greater impact on some areas than others.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows an estimate of the average annual temperature for the central region of the region of Tunceli. The data source is taken from ERA5, the fifth generation of atmospheric reanalysis of global climate by ECMWF, which covers the period from 1979 to 2023 with a spatial resolution of 30 km, data taken from Meteoblue (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The data may not accurately reflect conditions at a given location. Therefore, temperatures will often be higher than shown, especially in urban areas, and precipitation may vary locally depending on topography (Manoli et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dashed blue line represents the linear trend of climate change. If the trend line rises from left to right, this indicates a positive temperature trend, suggesting that Tunceli is experiencing warming due to climate change. A horizontal trend line would not indicate a clear trend, and if the trend line is decreasing, this indicates that temperatures in Tunceli are cooling over time (Esen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The lower part of the graph shows warming stripes. Each colored stripe represents the average temperature for a year, with blue representing cooler years and red representing warmer years. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays a clear trend of rising temperatures at Tunceli station over the long years. This trend has become increasingly prominent since 2014, with temperatures consistently exceeding previous records. Furthermore, examining the color bands in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that global climate change, which is evident through the gradual increase in temperature, has been on the rise since 1998. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the average total precipitation forecast for Tunceli; data are taken from Meteoblue (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dashed blue line in the figure represents the linear trend of climate change. If the trend line slopes upward from left to right, it indicates a positive precipitation trend. If it is horizontal and runs flat, there is no clear trend observed that can be identified. However, a downward slope suggests that conditions are gradually leading to less precipitation, indicating that Tunceli is becoming drier over time. The graph at the bottom of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays precipitation bars, each colored bar represents the total precipitation for a year, with greener bars indicating wetter years and brown bars indicating drier years. Overall, based on Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it can be observed that as temperatures increase, precipitation decreases in Tunceli.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Drought dynamic according to SPI\u003c/h2\u003e \u003cp\u003eUnderstanding how precipitation amounts change over time is vital for water resource management and agricultural planning. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the SPI calculated over a 3-month period on an annual basis. The analysis reveals that the 3-month SPI values in Tunceli typically range between \u0026minus;\u0026thinsp;1 and 1, indicating drought conditions within normal limits. Generally, values \u0026gt;-1 signify wet, while values \u0026lt;-1 signify drought conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe SPI is a measure that indicates whether precipitation in a particular region is above or below the long-term average. A positive SPI value (+\u0026thinsp;1.0 or greater) suggests above-normal precipitation, indicating wet conditions, while a negative value (-1.0 or less) indicates below-normal rainfall, which can lead to dry conditions. The SPI value is calculated for each year and period and is represented by a bar graph. The height of the bars illustrates the magnitude of the SPI value, and the direction indicates whether it is positive (upward) or negative (downward).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the SPI values for each month from 1980 to 2023, indicating the dry years, drought duration, and also the interval between drought events. From the graph, it appears that there is no long-term extreme drought in the Tunceli region. It is crucial to understand the frequency and recurrence of droughts, as this helps predict future climate patterns and take preventive measures to manage water resources better.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the SPI values for different periods (3-6-9-12 months) over several years. The 3-month SPI values can predict short-term droughts, which can fluctuate more frequently and suddenly due to seasonal changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e indicates, the 3-month SPI values are higher. On the other side, the 6-month SPI analysis smoothens some of the variability observed in the 3-month SPI and provides a better explanation of medium-term moisture trends. The 9-month SPI analysis results align more closely with annual cycles. At the same time, the 12-month SPI reflects long-term trends by smoothing out most short-term fluctuations and highlighting broader drought patterns or periods of high precipitation. As per the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the SPI values calculated for 3, 6, 9, and 12 months over the years generally fall in the normal drought class. In Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the SPI was calculated over 9 months. The pie chart shown alongside indicates the percentage distribution of different SPI categories such as Dry, Normal, and Wet.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNegative SPI values indicate drier conditions (below-average precipitation), whereas positive values indicate wetter conditions (above-average rainfall). So, there are significant fluctuations between wet and dry periods in the time series. The cyclical nature of SPI values highlights the variability in precipitation over time, suggesting that the region experienced normal drought conditions throughout the period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Drought dynamic according to RDI\u003c/h2\u003e \u003cp\u003eThe RDI Index is a measure that analyzes drought conditions by using precipitation and potential evapotranspiration data over a reference period. This allows for comparisons to be made between different periods and climates. A positive RDI value indicates normal or humid conditions, while negative values indicate dry conditions. Looking at Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, we can see that the 3-month RDI values in Tunceli are generally between \u0026minus;\u0026thinsp;0.5 and 0.5, indicating that the current drought is within normal limits. From 1980 to 2023, the data has mainly fluctuated between \u0026minus;\u0026thinsp;0.5 and 0.5, indicating predominantly normal dryness with occasional deviations to mild stress conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows a composite analysis of the RDI over several time scales: 3, 6, 9, and 12 months. This multiscale approach allows the observation of drought conditions with different durations and intensities. During these periods, the RDI values hover around the zero mark, indicating a state of equilibrium in terms of moisture availability. The analysis is in a stable range of normal drought with no significant long-term deviations toward extreme drought or wetness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows RDI values for 9 months in Tunceli from 1980 to 2021. The RDI values are mostly just below zero, indicating a general tendency towards normal moisture conditions in most years. A spike on the right side of the graph represents a significant drought event within this annual interval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, the accompanying analysis of the pie chart shows that drought events account for only 5% of the observed period, which yet falls within the Normal conditions during 95% of the analyzed period. This indicates that droughts are less frequent in the Tunceli region, and hydrological conditions are generally stable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Drought dynamic according to PNI and AI\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e presents the PNI results for Tunceli, which a single station represents over several years. The PNI index uses only precipitation data to analyze its results. The PNI values fluctuate from year to year, surpassing several drought category thresholds. It is important to note that none of the years fall into the 'hyper-arid' category. In contrast, many years fall into the 'humid' and 'dry sub-humid' categories, indicating a fluctuating but mostly non-arid climate. This variability underscores the impact of global warming and necessitates the development of new drought management strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows the AI, categorized into different Aridity Thresholds, calculated over different RDI time scales: 3, 6, 9, and 12 months. The index values indicate a generally dry, sub-humid to semi-arid climate in the region. The 9-month RDI period shows a trend towards drier conditions compared to the other periods, which could indicate seasonal patterns or longer-term shifts in regional aridity. This analysis is crucial to understanding the degree of drought and its distribution over time, which is essential for regional planning and sustainable water resource management.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIf the AI value is 0.59, the Tunceli region falls into the climate characterization 'dry sub-humid,' which indicates the prevailing moderate aridity conditions. The 3-month, 6-month, 9-month, and 12-month RDI values in the graph are used to measure how arid a region is during specific periods. The values shown range between 0.5 and 0.65, representing the 'Dry sub-humid' category. Higher AI values indicate that the region is more humid and less stressed by water scarcity. The 12-month RDI is typically used to assess climate conditions over a year, reflecting general trends during that time. The other periods (3, 6, and 9 months) are used to observe seasonal changes and short-term climate fluctuations more closely.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study deals with drought and climate change in Tunceli, a region known for its natural environment and clean water sources that have not yet been affected by urbanization and industrialization. The study examines how a region far from urbanization and industrialization is affected by climate change. It demonstrates that the changes in rainfall and droughts in recent years are closely linked to climate change and global warming. The importance of this study is to respond in a timely manner to the environmental changes caused by global warming and to link the risks to access to water resources in the region. Figure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e shows the average temperature changes for eight stations between 1980 and 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Paris Agreement aims to limit global warming to 1.5 ℃ above pre-industrial levels (Rogelj et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Suppose the temperature data from 1980 to 2023 is examined in accordance with this target. In that case, it can be seen that the temperature has risen over time and was above 1.5 ℃ at all stations, especially after 2017. A sudden rise in temperature was observed in 2010 when the temperature changes of the stations were examined over the long term. After 2010, the temperature increase decreased but did not fall below 1.5 ℃ in the last seven years after 2017. Therefore, it can be said that the region is under the influence of global warming, and necessary strategies should be developed to comply with the Paris Agreement. The R\u0026sup2; values and equations calculated for the temperature and precipitation for eight stations are included in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The R\u0026sup2; values indicate how well the model explains the data, while trend equations are used to predict the amount of temperature or precipitation for a given year. In particular, when examining temperature trends, this study shows that R\u0026sup2; values explain temperature changes relatively well. An examination of the precipitation data shows that irregularities in the precipitation regime lead to low R\u0026sup2; values. Therefore, in studies on drought, climate change, and water resources, more cautious predictions should be made when examining precipitation data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemperature and precipitation equations and R\u003csup\u003e2\u003c/sup\u003e values for eight stations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean Trend\u003c/p\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean Trend\u003c/p\u003e \u003cp\u003ePrecipitation (mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTunceli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,0577x\u0026thinsp;+\u0026thinsp;10,812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -2,0438x\u0026thinsp;+\u0026thinsp;712,12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulumur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,4284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,054x\u0026thinsp;+\u0026thinsp;6,7763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -2,176x\u0026thinsp;+\u0026thinsp;982,08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026Ccedil;emişgezek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,063x\u0026thinsp;+\u0026thinsp;12,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -1,6348x\u0026thinsp;+\u0026thinsp;549,76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHozat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,0565x\u0026thinsp;+\u0026thinsp;10,908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -1,6791x\u0026thinsp;+\u0026thinsp;640,19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNazımiye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,0732x\u0026thinsp;+\u0026thinsp;8,0691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -1,8343x\u0026thinsp;+\u0026thinsp;955,42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvacık\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,563x\u0026thinsp;+\u0026thinsp;10,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -1,6791x\u0026thinsp;+\u0026thinsp;640,19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePertek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,0612x\u0026thinsp;+\u0026thinsp;11,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -1,4476x\u0026thinsp;+\u0026thinsp;653,86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMazgirt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0,5777x\u0026thinsp;+\u0026thinsp;10,812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey = -2,0513x\u0026thinsp;+\u0026thinsp;712,68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA linear equation was derived from the precipitation data for the years 1979 to 2023, and the future scenario of the precipitation data is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e. The data shows that the average annual precipitation has decreased in the period from 1979 to 2050. This decrease in rainfall makes it even more important to investigate the drought situation in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe spatial distribution of the analyzed drought frequencies during the seasonal and annual periods varies in each period, but the results from 1980 to 2023 were evaluated. Various indices, such as the SPI, the RDI, the PNI, and the AI, were used to analyze the drought dynamics and climate sensitivity of Tunceli's water resources. Drought calculations were performed over 3-, 6-, 9- and 12-month periods by organizing the precipitation data from October to September for drought analysis using the SPI index. The drought classification of this region seems to be within normal limits due to its climatic characteristics. Nevertheless, a dry period was observed between August 1989 and May 1999. The SPI values for the periods of 3, 6, 9, and 12 months are generally within the normal range. However, the most recent data points (2020) show negative SPI values for all periods, indicating a significant dry period.\u003c/p\u003e \u003cp\u003eRegarding the RDI, most of the values are the limits of the normal drought class. The analysis indicates a stable climate regime with no significant long-term deviations towards extreme drought or wetness. Our results show that during the observed period from 1980 to 2021, only 5% of the time was characterized by drought, while normal or wet conditions in Tunceli characterized the remaining 95%.\u003c/p\u003e \u003cp\u003eAlthough the PNI values fluctuate from year to year, none of the years were \"extremely dry,\" while some years were \"wet.\" This indicates a \"dry sub-humid\" climate that is not prone to drought. The AI values for the 3-month, 6-month, 9-month, and 12-month time scales indicate how dry a region is in certain periods. The values displayed range from 0.5 to 0.65 and represented the category 'dry-semi-humid'. The results of the study show quite similar patterns when comparing different indices, with precipitation being the common parameter, which explains the similarity of the results. This study consisted of a detailed analysis of critical drought indices and long-term drought risks in order to develop effective water resource management and climate adaptation strategies in the region.\u003c/p\u003e \u003cp\u003eIn summary, a comprehensive analysis of drought and climate trends in Tunceli highlights the significant impact of global climate change on temperatures in this non-industrialized area. Overall, the drought condition for the Tunceli region is classified as a normal drought. Nevertheless, future temperatures and precipitation trends show that drought conditions in the Tunceli region, like in many regions around the globe, will most likely exacerbate if necessary measures are taken globally (Rogelj et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In light of these observations, it is important that further research comprehensively analyzes the possible causes of this increasing precipitation trend, the variables influencing it, and the long-term climatic impacts. Additional studies may also be necessary to assess the potential environmental, social, and economic impacts of the increasing trend in precipitation data.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study presents a comprehensive analysis of drought and climate trends in the Tunceli region, utilizing indices such as SPI, RDI, PNI, and AI. The results indicate that there is currently no extreme drought in the region. However, when examining temperature and precipitation anomalies, it is evident that the region is significantly impacted by global warming. The detailed evaluation of temperature and precipitation trends clearly highlights the significant effects of global climate change in the region. The continuous increase in temperatures and the decrease in precipitation make the risks to water accessibility and quality in Tunceli more pronounced. Both the SPI and the RDI were used to analyze precipitation irregularities and drought trends from 1980 to 2021. Our analysis of the SPI has shown that the values for the 3 months typically oscillate between \u0026minus;\u0026thinsp;1 and 1, indicating a normal level of drought variability. This suggests that extreme deviations from average rainfall patterns are not frequent in the region in the short term.\u003c/p\u003e \u003cp\u003eConversely, the RDI analysis for the 9 months highlighted years of pronounced drought at times, but the overarching pattern suggests that the region generally experienced normal or wet conditions. These results are particularly instructive as they highlight the lack of a long-term, persistent drought trend in the region while recognizing the presence of short-term droughts and seasonal fluctuations that need to be addressed.\u003c/p\u003e \u003cp\u003eThe results of the PNI and the AI have enabled a more comprehensive assessment of the regional climate. The fluctuations in PNI values between humid and dry subhumid classifications over the years, together with the AI values, show that the region falls largely within a dry sub-humid to semi-arid climate band.\u003c/p\u003e \u003cp\u003eTaking into account all the analyses carried out, no significant long-term trend towards drought could be identified in the region of Tunceli. However, this does not exclude the importance of looking at short-term drought events and seasonal variations. The knowledge gained from this study can guide regional authorities in the management of water resources and the development of drought adaptation strategies. It also highlights the need for further research into how regional drought conditions may evolve in the context of climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e Alban Kuriqi is grateful for the Foundation for Science and Technology\u0026apos;s support through funding UIDB/04625/2020 from the research unit CERIS (DOI: 10.54499/UIDB/04625/2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Not applicable)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Yes)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(Yes)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(This study did not receive any funding.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(The authors declare that they have no conflict of interest.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Upon request)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmadalipour A, Moradkhani H, Castelletti A, Magliocca N (2019) Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of The Total Environment 662:672-686. doi:https://doi.org/10.1016/j.scitotenv.2019.01.278\u003c/li\u003e\n\u003cli\u003eAllen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660-684. doi:https://doi.org/10.1016/j.foreco.2009.09.001\u003c/li\u003e\n\u003cli\u003eArora VK (2002) The use of the aridity index to assess climate change effect on annual runoff. Journal of Hydrology 265:164-177. doi:https://doi.org/10.1016/S0022-1694(02)00101-4\u003c/li\u003e\n\u003cli\u003eCarpenter JR, Smuk M (2021) Missing data: A statistical framework for practice. Biometrical Journal 63:915-947. doi:https://doi.org/10.1002/bimj.202000196\u003c/li\u003e\n\u003cli\u003eDabanli I (2018) Drought hazard, vulnerability, and risk assessment in Turkey. Arabian Journal of Geosciences 11:538. doi:10.1007/s12517-018-3867-x\u003c/li\u003e\n\u003cli\u003eDai M, Huang S, Huang Q, Leng G, Guo Y, Wang L, Fang W, Li P, Zheng X (2020) Assessing agricultural drought risk and its dynamic evolution characteristics. Agricultural Water Management 231:106003. doi:https://doi.org/10.1016/j.agwat.2020.106003\u003c/li\u003e\n\u003cli\u003eDelpla I, Jung AV, Baures E, Clement M, Thomas O (2009) Impacts of climate change on surface water quality in relation to drinking water production. Environment International 35:1225-1233. doi:https://doi.org/10.1016/j.envint.2009.07.001\u003c/li\u003e\n\u003cli\u003eEasterling DR, Wallis TWR, Lawrimore JH, Heim Jr. RR (2007) Effects of temperature and precipitation trends on U.S. drought. Geophysical Research Letters 34. doi:https://doi.org/10.1029/2007GL031541\u003c/li\u003e\n\u003cli\u003eElhoussaoui A, Zaagane M, Benaabidate L (2021) Comparison of various drought indices for assessing drought status of the Northern Mekerra watershed, Northwest of Algeria. Arabian Journal of Geosciences 14:915. doi:10.1007/s12517-021-07269-y\u003c/li\u003e\n\u003cli\u003eEsen F (2022) Tunceli iklim parametrelerine ait zaman serilerinin farklı istatistiksel analiz y\u0026ouml;ntemleriyle değerlendirilmesi. T\u0026uuml;rk Coğrafya Dergisi:7-22. doi:10.17211/tcd.1151639\u003c/li\u003e\n\u003cli\u003eGumus V, Algin HM (2017) Meteorological and hydrological drought analysis of the Seyhan\u0026minus;Ceyhan River Basins, Turkey. Meteorological Applications 24:62-73. doi:https://doi.org/10.1002/met.1605\u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Donaire MP, Tarquis A, Gir\u0026aacute;ldez JV (2020) Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain. Nat Hazards Earth Syst Sci 20:21-33. doi:10.5194/nhess-20-21-2020\u003c/li\u003e\n\u003cli\u003eKhorrami B, G\u0026uuml;nd\u0026uuml;z O (2022) Detection and analysis of drought over Turkey with remote sensing and model-based drought indices. Geocarto International 37:12171-12193. doi:10.1080/10106049.2022.2066197\u003c/li\u003e\n\u003cli\u003eKim T-W, Jehanzaib M (2020) Drought Risk Analysis, Forecasting and Assessment under Climate Change. Water, \u003c/li\u003e\n\u003cli\u003eKorkmazi M, Kilic Z (2021) The Effect of Global Climate Change on Drought. 1st International Conference on Sustainable Water Resources Management. CEWRE, Lahore, Pakistan. pp 32-36\u003c/li\u003e\n\u003cli\u003eLehner F, Coats S, Stocker TF, Pendergrass AG, Sanderson BM, Raible CC, Smerdon JE (2017) Projected drought risk in 1.5\u0026deg;C and 2\u0026deg;C warmer climates. Geophysical Research Letters 44:7419-7428. doi:https://doi.org/10.1002/2017GL074117\u003c/li\u003e\n\u003cli\u003eLiu Y, Chen J (2021) Future global socioeconomic risk to droughts based on estimates of hazard, exposure, and vulnerability in a changing climate. Science of The Total Environment 751:142159. doi:https://doi.org/10.1016/j.scitotenv.2020.142159\u003c/li\u003e\n\u003cli\u003eMachiwal D, Jha MK (2006) Time series analysis of hydrologic data for water resources planning and management: a review. Journal of Hydrology Hydromechanics 54:237-257. \u003c/li\u003e\n\u003cli\u003eManoli G, Fatichi S, Schl\u0026auml;pfer M, Yu K, Crowther TW, Meili N, Burlando P, Katul GG, Bou-Zeid E (2019) Magnitude of urban heat islands largely explained by climate and population. Nature 573:55-60. doi:10.1038/s41586-019-1512-9\u003c/li\u003e\n\u003cli\u003eMcKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology. California. pp 179-183\u003c/li\u003e\n\u003cli\u003eMerabti A, Martins DS, Meddi M, Pereira LS (2018a) Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales. Water Resources Management 32:1087-1100. doi:10.1007/s11269-017-1856-6\u003c/li\u003e\n\u003cli\u003eMerabti A, Meddi M, Martins DS, Pereira LS (2018b) Comparing SPI and RDI Applied at Local Scale as Influenced by Climate. Water Resources Management 32:1071-1085. doi:10.1007/s11269-017-1855-7\u003c/li\u003e\n\u003cli\u003eMersin D, Gulmez A, Safari MJS, Vaheddoost B, Tayfur G (2022) Drought Assessment in the Aegean Region of Turkey. Pure and Applied Geophysics 179:3035-3053. doi:10.1007/s00024-022-03089-7\u003c/li\u003e\n\u003cli\u003eMeteoblue (2023) Climate change Tunceli https://www.meteoblue.com/en/climate-change/tunceli_republic-of-t\u0026uuml;rkiye_298846 \u003c/li\u003e\n\u003cli\u003eMeza I, Siebert S, D\u0026ouml;ll P, Kusche J, Herbert C, Eyshi Rezaei E, Nouri H, Gerdener H, Popat E, Frischen J, Naumann G, Vogt JV, Walz Y, Sebesvari Z, Hagenlocher M (2020) Global-scale drought risk assessment for agricultural systems. Nat Hazards Earth Syst Sci 20:695-712. doi:10.5194/nhess-20-695-2020\u003c/li\u003e\n\u003cli\u003ePalmer WC (1965) Meteorological drought. US Department of Commerce, Weather Bureau\u003c/li\u003e\n\u003cli\u003ePedro-Monzon\u0026iacute;s M, Solera A, Ferrer J, Estrela T, Paredes-Arquiola J (2015) A review of water scarcity and drought indexes in water resources planning and management. Journal of Hydrology 527:482-493. doi:https://doi.org/10.1016/j.jhydrol.2015.05.003\u003c/li\u003e\n\u003cli\u003ePereira LS, Cordery I, Iacovides I (2009) Coping with water scarcity: Addressing the challenges. Springer Science \u0026amp; Business Media\u003c/li\u003e\n\u003cli\u003eRogelj J, den Elzen M, H\u0026ouml;hne N, Fransen T, Fekete H, Winkler H, Schaeffer R, Sha F, Riahi K, Meinshausen M (2016) Paris Agreement climate proposals need a boost to keep warming well below 2\u0026thinsp;\u0026deg;C. Nature 534:631-639. doi:10.1038/nature18307\u003c/li\u003e\n\u003cli\u003eSahana V, Mondal A (2023) Evolution of multivariate drought hazard, vulnerability and risk in India under climate change. Nat Hazards Earth Syst Sci 23:623-641. doi:10.5194/nhess-23-623-2023\u003c/li\u003e\n\u003cli\u003eŞen Z (2012) Innovative Trend Analysis Methodology. Journal of Hydrologic Engineering 17:1042-1046. doi:doi:10.1061/(ASCE)HE.1943-5584.0000556\u003c/li\u003e\n\u003cli\u003eTate EL, Gustard A (2000) Drought Definition: A Hydrological Perspective. In: Vogt JV, Somma F (eds). Drought and Drought Mitigation in Europe. Springer Netherlands, Dordrecht. pp 23-48\u003c/li\u003e\n\u003cli\u003eTigkas D, Vangelis H, Tsakiris G (2015) DrinC: a software for drought analysis based on drought indices. Earth Science Informatics 8:697-709. doi:10.1007/s12145-014-0178-y\u003c/li\u003e\n\u003cli\u003eTsakiris G, Pangalou D, Vangelis H (2007) Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI). Water Resources Management 21:821-833. doi:10.1007/s11269-006-9105-4\u003c/li\u003e\n\u003cli\u003eTurkes M (2020) Climate and Drought in Turkey. In: Harmancioglu NB, Altinbilek D (eds). Water Resources of Turkey. Springer International Publishing, Cham. pp 85-125\u003c/li\u003e\n\u003cli\u003eYeşilk\u0026ouml;y S, Şaylan L (2022) Spatial and temporal drought projections of northwestern Turkey. Theoretical and Applied Climatology 149:1-14. doi:10.1007/s00704-022-04029-0\u003c/li\u003e\n\u003cli\u003eYusof F, Hui-Mean F, Suhaila J, Yusop Z, Ching-Yee K (2014) Rainfall characterisation by application of standardised precipitation index (SPI) in Peninsular Malaysia. Theoretical and Applied Climatology 115:503-516. doi:10.1007/s00704-013-0918-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"water-conservation-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wcse","sideBox":"Learn more about [Water Conservation Science and Engineering](http://link.springer.com/journal/41101)","snPcode":"41101","submissionUrl":"https://submission.nature.com/new-submission/41101/3","title":"Water Conservation Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate change, Drought, RDI, SPI, Water scarcity","lastPublishedDoi":"10.21203/rs.3.rs-4518030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4518030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal warming and climate change are causing temperatures to rise, which is having a negative impact on water resources. Climate change is a worldwide problem that affects and will continue to affect the frequency and intensity of natural disasters in many regions of the world. Tunceli region in Turkey, which until ten years ago was known as an environmentally friendly city with abundant water resources and frequent rainfall, is experiencing a decrease in precipitation during the snowy winter season. This situation has made the investigation of climate change impacts an important issue in the region. Therefore, effective climate change adaptation strategies need to be developed. To determine these strategies, in this study, we assessed drought conditions using drought indices such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Normal Precipitation Index (PNI), and Aridity Index (AI). The SPI and RDI analyses were performed in annual reference periods on a time scale of 3, 6, 9, and 12 months using temperature, precipitation, and evaporation data. Consequently, the SPI and RDI results were compared, and both indices show similar behavior in dry, wet, and normal seasons. Nevertheless, RDI shows less variation between different time scales, which is an advantage over SPI and is probably due to the inclusion of potential evapotranspiration in RDI. The variations in PNI between humid and dry sub-humid categorizations throughout the years, combined with the AI results, indicate that the Tunceli region predominantly experiences a climate ranging from dry sub-humid to semi-arid. This study could help decision-makers take effective measures to become more resilient to climate change in temperate climate regions and take important steps toward sustainable water resources management.\u003c/p\u003e","manuscriptTitle":"Drought dynamics in mixed climate regions: insights for water resource management and climate adaptation strategies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 21:52:49","doi":"10.21203/rs.3.rs-4518030/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-24T16:27:55+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"240317385026739617992633666296371968745","date":"2024-06-23T08:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314400016294861618368587532822278353536","date":"2024-06-22T23:01:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T08:24:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T06:15:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T07:10:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T13:15:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228415054966763644066085316069128435793","date":"2024-06-12T16:16:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26003761510583096475284348391229031426","date":"2024-06-12T04:14:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77447240823533713420175996288640887715","date":"2024-06-11T01:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42657658973795428115405140744688160777","date":"2024-06-10T16:05:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229846336470166116606911983860930405226","date":"2024-06-10T10:08:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-10T09:08:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-06T10:13:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-06T10:12:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Conservation Science and Engineering","date":"2024-06-02T18:10:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"water-conservation-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wcse","sideBox":"Learn more about [Water Conservation Science and Engineering](http://link.springer.com/journal/41101)","snPcode":"41101","submissionUrl":"https://submission.nature.com/new-submission/41101/3","title":"Water Conservation Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"91e8d4d0-7184-437f-965e-6044b31af778","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T17:18:38+00:00","versionOfRecord":{"articleIdentity":"rs-4518030","link":"https://doi.org/10.1007/s41101-024-00281-9","journal":{"identity":"water-conservation-science-and-engineering","isVorOnly":false,"title":"Water Conservation Science and Engineering"},"publishedOn":"2024-07-23 16:15:45","publishedOnDateReadable":"July 23rd, 2024"},"versionCreatedAt":"2024-06-20 21:52:49","video":"","vorDoi":"10.1007/s41101-024-00281-9","vorDoiUrl":"https://doi.org/10.1007/s41101-024-00281-9","workflowStages":[]},"version":"v1","identity":"rs-4518030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4518030","identity":"rs-4518030","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}