Assessment and monitoring of the Dead Sea surface area and water level using remote sensing and GIS techniques

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The primary drivers include the diversion of water from the Jordan River and its tributaries, as well as mineral extraction activities on both sides of the lake. The aim of this study is to analyze the thematic map of 1971 and satellite images of 1984, 1994, 2004, 2014 and 2022 of the Dead Sea to determine the surface area and water level of the Dead Sea and its declining rate. CA-Markov model were employed to generate projected surface area of Dead Sea for periods 2034 and 2050. Time series of observed and future using RPC’s 4.5 and 8.5 of climate data especially temperature were analysis has been implemented to track the climate behavior. Statistical analyses of Kendall correlation matrix were performed on observed and predicted of surface area, water level and temperature. The study shows that the Dead Sea has shrunk by 41.8% during the period from 1971 to 2022, while the water sea level is expected to decrease 12.63 m and 33 m for period 2034 and 2050 respectively. In addition, there were a significant inverse relationship between surface area, water level and temperature with correlation (r=-0.79; p = 0.001) and (r=-0.82; p = 0.001), respectively. It is worth highlighting that from 2022 to 2050, the mean annual temperature is expected to rise by at least 1 ˚C. The long-term strategic vision for stabilizing Dead Sea water levels envisions a two-fold approach: ( 1 ) augmenting natural inflow through the introduction of 300–400 million MCM from manufactured sources channeled into the Jordan River, and ( 2 ) implementing a reduction in water extraction by Dead Sea industries up to a maximum of 330 million MCM. Dead Sea Climate change Remote sensing CA-Markov surface area water level 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 1. Introduction Comprehending alterations in land use, vegetation, and coastal dynamics stands as a pivotal pursuit for myriad planning, geographic, and engineering researches (Shalaby and Tateishi, 2007; Shoman et al., 2019; Alharthi et al., 2020; Khawaldah et al., 2020). Understanding of the intricate processes governing sensor properties is paramount for interpreting and analyzing remotely sensed images, which serve as indispensable tools for quantifying and studying surface properties (Kishcha et al., 2019; Aldogom et al., 2020; Oroud, 2020; Al-Mashagbah et al., 2021). Leveraging the power of satellite imagery, we gain unprecedented insights into the Earth's surface, unraveling the complex tapestry of physical and biological phenomena that shape the global environment (Nehorai et al., 2009; Huang, et al., 2019). In an era of continuous monitoring, satellite images are a formidable asset, providing a continuous stream of information concerning the Earth's surface (Ghatasheh et al., 2016; El-Kafrawy et al., 2017; Zhang et al., 2017; Kishcha et al., 2018; USGS, 2023; NASA, 2023). This invaluable resource enables us to decipher the mechanisms shaping life's conditions on our planet, encompassing diverse phenomena such as global weather patterns, tectonic activities, surface vegetation dynamics, ocean currents, polar ice fluctuations, and pollution patterns (Farhan and Al-Bakri, 2019; Aymen et al., 2020; Aldogom et al., 2020 ). With an extensive database of remote sensing imagery spanning historical and contemporary periods, we gain the ability to dissect the spatiotemporal patterns of environmental elements and the profound impacts of human activities over past decades, enabling the quantification of critical parameters such as water levels, surface area, and rates of decline (Adwan, 2018; Jiang et al., 2017; Liping et al., 2018; Chen et al., 2006). The water surface variation has become one of the most deliberate environmental challenge globally (Kale et al., 2021). Even so, water surface dynamic assessment and monitoring is a vitally important for terrestrial ecosystem and civilization (Che et al., 2017; Oroud, 2020). An extensive and comprehensive studies insisted that the DS is exposing to various changes recently. Leading to extraordinary drop of Lake surface water level. Where, the negative water balance play a crucial role in DS water surface level (Al-Halbouni et al., 2018; Ronen et al., 2019; Morin et al., 2019; Polom et al., 2018; Salameh et al., 2019; Dor et al., 2019; Belmaker et al., 2019; Lu et al., 2020; Oroud, 2020; Hamadneh et al., 2022; Ezersky et al., 2020; Tierney et al., 2022). The current study is driven by the necessity to evaluate and monitor the evolving dynamics of the Dead Sea, an endorheic lake of profound geographical and ecological significance. Positioned within the Jordan Rift Valley, the Dead Sea stands as a unique geographical entity, stretching from the southeastern Anatolian plateau to the northern Red Sea (Abdel-Fattah and Pingitore, 2009; Miebach et al., 2019; Ghazleh et al., 2011; Dor et al., 2019; Salem, 2020; Tierney et al., 2022). This ancient body of water, originating from the separation between the Asian and African continents during the Miocene era, occupies the lowest point on continental land (Ghazleh and Kempe, 2021). Mounting evidence from a spectrum of studies underscores a stark reality: the Dead Sea is experiencing an alarming rate of shrinkage (Fig. 1 ). Annual reports reveal an unprecedented decline, with estimations ranging from 90 cm to 1.5 meters (Nof et al., 2012; Ghatasheh et al., 2013; Al-husban and Almanasyeh, 2017; El-Hallaq and Habboub, 2014; Tierney et al., 2022; Lu et al., 2020). The implications of this decline are far-reaching, as the Dead Sea level has plummeted by a staggering 39 meters (Tierney et al., 2022; Al-husban and Almanasyeh, 2017). The Dead Sea was characterized by two distinct basins, the shallow southern basin, and the deep northern basin, until a pivotal moment in 1976, when the southern basin desiccated due to a drop in sea level, reaching a depth of -400 (Oroud, 2020). Moreover, until 1953, the water level of the Dead Sea oscillated around a historical high of approximately 392 meters below sea level, encompassing an area of 1,050 square kilometers (USGS, 1998; Salameh and El-Naser, 2008; Morin et al. 2009; Abu Ghazleh et al. 2010; Lu et al., 2020; Hamadneh, 2022). Within this research, our primary objective is a comprehensive examination and continuous monitoring of the intricate variations in the water levels of the Dead Sea. This analysis encompasses various temporal perspectives, ranging from historical records to contemporary observations and predictive projections. The undeniable urgency arises from the significant reduction in water levels, emphasizing the critical importance of comprehending the repercussions of this phenomenon. As the nations bordering the Dead Sea confront the socio-economic implications of this ecological transformation, our research functions as a guiding light. It provides direction for the development of well-informed remedial strategies and the promotion of sustainable management practices. To this end, the specific objectives of this study encompass: 1. Conducting a comprehensive assessment of the recent Dead Sea surface area and water level. 2. Employing advanced modeling techniques to generate predictive simulations of the Dead Sea's surface area for the forthcoming years. Through this multifaceted investigation, we strive to shed light on the profound significance of monitoring the Dead Sea's water levels, thereby equipping stakeholders and decision-makers with actionable insights to address the imminent challenges posed by this environmental transformation. 2. Methodology 2.1 Study area The Dead Sea holds the distinction of being Earth's lowest point on the surface, situated at an elevation approximately 434 meters below mean sea level (bmsl) and renowned for its status as the saltiest lake, boasting a salinity level of around 434 grams per liter (gpl) (Tierney et al., 2022; MWI, 2022). Geographically, it is positioned at 31˚20'N, 35˚30'E, as indicated in Fig. 2 . The eastern and western shores of the Dead Sea are bordered by formidable fault escarpments, which form integral parts of the African-Syrian rift system. The valley gently slopes upwards in a northern direction along the Jordan River and southwards along the Wadi Araba (Salameh et al., 2019). Significantly, the elevation of the lake's surface stands at 434 meters below mean sea level (bmsl) (ICL, 2022), establishing its shores as the lowest terrestrial points on Earth. Consequently, it bears the distinction of being the world's deepest hypersaline lake (Ghatasheh et al., 2013; Lu et al., 2020; Oroud, 2020). The primary, northern basin of the Dead Sea spans 50 kilometers in length and attains a width of 15 kilometers at its widest point (El-Hallaq and Habboub, 2014; Morin et al., 2019). This region is characterized as arid, with average rainfall ranging from 50 to 100 millimeters (mm) (Lu et al., 2020). In terms of freshwater inflow, the Jordan River contributes approximately 60%, while groundwater accounts for nearly 25% (Tierney et al., 2022). Moreover, it's crucial to note that the rate of the Dead Sea's retreat has been consistently diminishing, reaching up to 1 meter per year (Hamadneh et al., 2022). Additionally, the water surface level of the Dead Sea has experienced a continuous decline over the last three decades, recently exceeding 1 meter (Salameh et al., 2019; Ezersky et al., 2020). 2.2 Data collection and Image processing This research unfolded in three distinct phases, which can be delineated as follows: data collection, image processing, and accuracy assessment, as visually represented in Fig. 3 . For this study, thematic maps and satellite images from Landsat 5TM, Landsat 8 OLI, and Landsat 9 OLI2 were procured and downloaded from the Royal Jordanian Geographic Center (RJGC) and the official website of the United States Geological Survey (USGS), accessible via the link ( https://glovis.usgs.gov/app ). Concurrently, weather data sources encompassed data obtained from the Ministry of Water and Irrigation (MWI), the official website of the National Center of Atmospheric Research (NCAR), and the IPCC Website. The schematic diagram below outlines the principal steps involved in data collection and assessment. A hard copy of the thematic map, scaled at 1:250,000 for the year 1971, was procured from RJGC. Subsequently, the map underwent scanning, and geometric correction was performed using the intersecting lines within the map's coordinate system. Simultaneously, during the data processing phase, satellite imagery from Landsat 5 TM, Landsat 8 OLI, and Landsat 9 OLI2 was acquired from the USGS website. These images were then amalgamated and clipped to match the study area's boundaries. Notably, both TM and OLI images encompassed multiple bands, each characterized by distinct wavelengths, as detailed in Table 1 . Table 1 Images specification for bands ground resolution, spectral range and images date. Image specification Image Type Landsat-5 TM Landsat-8 OLI Landsat-9 OLI2 Swath width (km) 185 185 185 Wavelength range (µm) B-band (0.45–0.52) B-band (0.452–0.512) B-band (0.450–0.51) G-band (0.52–0.60) G-band (0.533–0.590) G-band (0.53–0.59) R-band (0.63–0.69) R-band (0.636–0.673) R-band (0.64–0.67) NIR- band (0.76–0.90) NIR-band (0.851–0.879) NIR-band (0.842–0.957) Ground resolution (m) Visible/ NIR 30 Visible /NIR 30 Visible /NIR 30 Image date (Year/Month/day) Date of images:- 1984-08-05 1994-08-17 2004-07-27 2014-08-24 2022-08-22 Revisit time (day) 16 16 16 Lunch 01-March, 1984 11-February, 2013 27-September, 2021 * Where, B: Blue, G: Green and R: Red (USGS, 2023). 2.3 Change Detection Analysis To create a comprehensive assessment of recent changes in the Dead Sea, the study employed an on-screen digitizing method, utilizing the thematic map and medium-resolution satellite images spanning the years 1984, 1994, 2004, 2014, and 2022. Subsequently, the manually drawn maps served a dual purpose: validating the model and generating anticipated maps for the years 2034 and 2050. Furthermore, we transformed the digital maps into raster data format using ArcMap 10.8.1 software. The change detection analysis entailed the estimation of shape, area, water level, and volume. In accordance with Akin and Cooley, 2013, the approach for calculating and estimating the watershed's volume involved conceptualizing it as a bowl. This volume can be determined by establishing a plane along its rim and its curved inner surface. To achieve this, a capping surface was constructed by connecting a set of points situated along the divide, while the inner surface was represented by the modern topography derived from the Digital Elevation Model (DEM). In essence, the volume calculation hinges on the disparity between the cap elevation and the topography. 2.4 Climate data Climatic changes have ushered in a plethora of hazards impacting Jordan across various dimensions and directions. These hazards encompass alterations in precipitation distribution and patterns, the advent of extreme temperatures, episodes of drought, instances of flooding, the occurrence of storms, and even the emergence of landslides. The amplification of these hazards' impact is discernible when scrutinizing their frequency and severity over time. The Dead Sea, notably, lacks any outlet, relying on the rapid process of evaporation, which is particularly pronounced in the scorching desert climate. It is unequivocal that climate change will cast its influence across multiple sectors, including agriculture, coastal regions, biodiversity, urban landscapes, society, water resources, and public health. Consequently, the imperative lies in strategic adaptation planning, entailing the formulation of well-defined options and measures to counteract the effects of climate change and foster the development of resilient communities and ecosystems In the context of this study, data on average temperatures spanning the period from 1975 to 2021 were sourced from the Ministry of Water and Irrigation and the National Agricultural Research Center (NARC). Simultaneously, climate projections covering the period from 2022 to 2050 were retrieved from the IPCC website, leveraging the GSM model with the organization of this model presented in a microscale format. To analyze the average temperature, we employed both the Mann-Kendall rank trend test and linear regression trends, comparing observed data with future scenarios under RCP 4.5 and RCP 8.5. 2.5 CA-Markov Model The Markov model, a theoretical framework, encapsulates the integration of stochastic processes predicated on transition probability matrices, facilitating prediction and optimal control (Kuperberg, 2008; Surabuddin et al., 2019; Agbinya, 2020). Within this context, the digital map of the study area serves as an illustrative tool for visualizing recent changes in spatial data over time. Meanwhile, the Markov model assumes the role of governing spatial dynamics through the utilization of transition probabilities. The following equation was used to calculate changes in the study area: TP ( P (kn + 1) ≤ pn + 1│P (kn )) = pn, P (kn − 1 ) = pn − 1,.., P (k1 ) = TP ( P (kn + 1 ) ≤ pn + 1│P(kn )) = pn ……. ( 1 ) In the Markov chain process, denoted as P (k), "k" signifies a specific point in time, with "kn" representing the present moment and "kn + 1" denoting future time instances where changes occur. Similarly, "kn-1" is used to reference preceding changes. In mathematical terms, this definition can be formulated based on the stochastic processes, P (k), for any time instance in the sequence k1 < k2 < ... < kn < kn + 1. Consequently, the random process adheres to Eq. 1. These equations are instrumental in computing the probabilities of both past and present states, observed as transitions from one state to another in the future or as a return to the same state as previously occupied. Therefore, the stochastic model chain comprises a discrete sequence of variables drawn from a discrete feature space. In simpler terms, the future stochastic process remains independent of both its current state and its past state. If we designate P[f] as the Markov chain and pn as a set encompassing N states {p1, p2, p3, ..., pn}, then the transition probability matrix governing the shift from state "j" to state "i" at a given time instant is expressed by equations 2 and 3 (Memarian et al., 2012 ; Song et al., 2020). T j, i Tr(X [f + 1] = i │X [f] = j) …………….. ( 2 ). \(\left[\begin{array}{cc}\begin{array}{ccc}{T}_{\text{1,1}}& {T}_{\text{1,2}}& {T}_{\text{1,3}}\\ {T}_{\text{2,1}}& {T}_{\text{2,2}}& {T}_{\text{2,3}}\\ {T}_{\text{3,1}}& {T}_{\text{3,2}}& {T}_{\text{3,3}}\end{array}& \begin{array}{ccc}--& --& {T}_{1,n}\\ --& --& {T}_{2,n}\\ --& --& {T}_{3,n}\end{array}\\ \begin{array}{ccc}--& --& --\\ --& --& --\\ {T}_{n,1}& {T}_{n,2}& {T}_{n,3}\end{array}& \begin{array}{ccc}--& --& --\\ --& --& --\\ --& --& {T}_{n,n}\end{array}\end{array}\right]\) …………….. ( 3 ). 2.6 CA-Model Validation Model validation and assessment represent essential phases in the modeling process, particularly when the objective is to compare future predictions with the current state. In this regard, one of the most robust methods for evaluating future predictive changes is the utilization of Kappa statistics (Farhan et al., 2023). Consequently, the Markov model is employed to forecast forthcoming alterations, provided the model demonstrates satisfactory performance as indicated by indices such as Kappa (Kno), Kappa for location (Klocation), and Kappa for quantity (Kquantity) (Pontius and Schneider, 2001). Kno serves as a metric for assessing the overall accuracy of the simulation, reflecting the degree of agreement relative to the standard kappa index. Meanwhile, Klocation evaluates the model's aptitude for predicting spatial locations, while Kquantity assesses its ability to predict quantities. The interpretation of these indices hinges on their values. When these indices approach or equal 1, the simulation is deemed exemplary. Conversely, if they approach 0, the simulation is considered ineffective, signifying imperfect consistency between the observed and simulated data. Typically, the Kappa value falls within the range of 0 to 1 (Table 2 ). Therefore, a Kappa value below 0.4 indicates a low likelihood of fair agreement, while values within the range of 0.4 ≤ Kappa ≤ 0.6 denote moderate accuracy. A Kappa value exceeding 0.6 signifies minimal disparities between observed and simulated locations, indicative of substantial agreement (Wu et al., 2008; Qiu and Lu, 2018). Table 2 Interpretation of Kappa values. Kappa-value Interpretation of agreement < 0 Less chance 0–0.2 Slight 0.2–0.4 Fair 0.4–0.6 Moderate 0.6–0.8 Substantial 0.8–1.0 Perfect According to Omar et al., 2014, Kappa statistics method was adopted and calculated computed as the following equations 4, 5 and 6. K no = (P (x) N (f)) / (T (i) – N (f)) ---------------------- ( 4 ). K location = (P (x) N (x)) / (T (x) – N (x)) ---------------------- ( 5 ). K quantity = (P (x) H (x)) / (K (x) – H (x)) ---------------------- ( 6 ). In this context, P (x) signifies the level of information at the medium grid cell, while N (f) is indicative of the absence of information. High consistency is denoted by (T (i)), and H (x) represents the information at the medium layer level. Furthermore, the ideal grid cell-level information, considering heterogeneity or minimal consistency in layer-level information, is represented by K (x) mean. 3. Results and Discussions 3.1 Changing in the Surface area Since the late 1970s, the Dead Sea has been effectively divided into two distinct basins: the northern basin, which continues to function as the Dead Sea itself, and the southern basin, now consisting of the evaporation pools utilized by Israeli and Jordanian mineral industries (Al-Zoubi and Brink 2001; Al-Zoubi et al., 2002; Ronen et al., 2019; Belmaker et al., 2019; Tierney et al., 2022). The construction of these evaporation pools commenced in the late 1960s, occurring on both sides of the border. Subsequently, these facilities have been actively extracting water from the northern basin to facilitate mineral extraction through an evaporation process (Bender, 1968; Salameh et al., 2019; Ezersky et al., 2020). This industrial operation constitutes a significant contributing factor to the negative water balance experienced by the northern basin, as research has indicated a deficit ranging from 250 to 330 million cubic meters per year (RSDSC, 2011). Consequently, starting from this period, it has become preferable to distinguish between the two segments, with a particular focus on the northern region, given the transformation of the southern part into artificial basins (Salameh et al., 2019; Oroud, 2023). The overall trend observed in the northern part area, as depicted in Figure 4, demonstrates a consistent decrease over time. Specifically, it is evident that from 1984 to 2022, the area of the northern part has diminished by approximately 14.2%. Importantly, the trend in this reduction is characterized as nonlinear, with an associated R² value of 0.988 (Table 3). The primary driver behind this decline can be attributed to the construction of dams at the outlets of reefs and valleys that historically replenished the Dead Sea, exacerbated by the region's limited water resources (Salameh et al., 2019; Oroud, 2023). Over the past three decades, decision-makers in the water sector have implemented significant measures aimed at bolstering Jordan's water security and addressing the persistent water deficit. Furthermore, Jordan has had to contend with the Syrian refugee crisis, which has further strained its water resources (MWI, 2022). Some previous studies showed that there is a slight increase in the area of the Dead Sea for the years 1992 and 2010, and this increase is due to the total amount of rainfall are higher than total average in year 1992. Also in 2010 the amount used in the industry and the amount of inflow released from Jordan River were higher (El-Hallaq and Habboub, 2014; Nof et al., 2012). Table 3. Dead Sea surface area during the period 1971 to 2022. *Numbers colored with red are extracted from other studies (Abu Ghazleh et al. 2010; RSDSC, 2011; Ghatasheh et al., 2013; El-Hallaq and Habboub, 2014). In pursuit of a well-structured vision for the future, Jordan has adopted a National Water Strategy, a comprehensive framework guided by a dual-pronged approach encompassing water demand management and water supply management (Alqatarneh and Al-Zboon, 2022). This strategy places significant emphasis on the imperative of enhanced water resource management, with a strong focus on ensuring the sustainability of both current and future water utilization practices. As depicted in Figure 5, Jordan has undertaken the construction of thirteen dams over the past six decades, boasting a cumulative capacity of approximately 335.3 million cubic meters (MCM). Among these dams, the prominent King Talal Dam, situated on the Zarqa River and detailed in Table 4, stands out with a total capacity of 75 MCM. Additionally, the Unity Dam (Al Wihdeh), situated on the Yarmouk River and shared between Jordan and Syria, boasts a total reservoir capacity of 110 MCM. These dams, excluding the Karamah Dam on Wadi Mallaha, are strategically positioned alongside wadis, with their outlets directed toward the Jordan River Valley (JRV). They serve as reservoirs for flood and base flows, playing a crucial role in water regulation and distribution for irrigation purposes (Directorate of Planning and Water Resource, 2005; MWI, 2017). Table 4. Establish date and Capacity MCM of Jordan’s main dams in 2017. Source: MWI, 2023. Dam Establish date Design capacity MCM Wehdeh 2006 110 Wadi Arab 1986 16.8 Zeqlab 1967 4 Kufranjeh 2011 7.8 King Talal 1977 and raised in 1987 75 Karameh 1997 55 WadiShueib 1969 1.4 Kafrain 1967 Raised in Year 1997 8.5 Wala 2003 8.2 Mujeb 2003 29.8 Tanour 2001 16.8 Karak 2017 2 Total 335.3 A discernible transformation is observed in the eastern shores of the southern portion of the Dead Sea, delineated within the red circle in Figure 6. This alteration in bathymetry in the southern expanse of the sea has become more pronounced, attributable to the shrinkage of the area. The changes in the surface area of the Dead Sea over various time phases are also depicted in Figure 5. The examination of surface area alterations spans five distinct time series periods: (1971–1984), (1984–1994), (1994–2004), (2004–2014), and (2014–2022), as detailed in Table 5. In broad terms, the rate of change in the Dead Sea's area fluctuates, ranging from -3.94%. It's noteworthy that the period from 1971 to 1984 has been excluded from analysis due to the separation of the northern part from the southern part, as discussed earlier. Table 5. Rate of changes of Dead Sea; surface area, temperature and rainfall. Rate of changes (%) Intervals Years Surface area Temperature Rainfall 1971-1984 -31.74 -1.74 3.01 1984-1994 -4.75 2.29 5.54 1994-2004 -3.77 1.63 -9.92 2004-2014 -4.22 0.79 -5.31 2014-2022 -3.03 0.39 -1.81 3.2 Climate change effect on the Dead Sea In the pursuit of identifying the factors contributing to the decline in the surface area of the Dead Sea, our investigation delved into meteorological data, specifically examining the annual averages of rainfall and temperature spanning all months and years from 1971 to 2022. This data was sourced from the Ghor Al-Safi station. Notably, our analysis revealed a pronounced and statistically significant increase in annual temperatures from 1971 to 2022, as evidenced by an R² value of 0.541 and a p-value less than 0.0001, as illustrated in Figure 7. Furthermore, our findings indicate a temperature rise of 0.67 degrees Celsius over the past six decades, amplifying the observed heightened rates of evaporation in the Dead Sea, as detailed in Table 5. Some studies suggest that annual evaporation has escalated from 7% in the 1960s to 12% in the 2000s (Shafir and Alpert, 2011). Conversely, the analysis of rainfall data yielded no discernible trend, as indicated by an R² value of 0.013. Nevertheless, Figure 8 visually demonstrates a decline in rainfall data, with Sen's slope measuring -0.81 during the 1971 to 2022 period. Hence, our study underscores the substantial influence of climatic conditions on the behavior of the water body. This behavior emerges as a result of the balance between inflowing water from the tributary area and direct precipitation, with water evaporation acting as a subtractive factor. The process of identifying and evaluating the principal climate change-related hazards involved a comprehensive analysis of historical extreme events and trends. Additionally, we employed climate modeling techniques derived from dynamic downscaling, conducted within the Africa region under the Coordinated Regional Climate Downscaling Experiment (CORDEX) Domain framework. Temperature projections spanning from 2022 to 2050 are graphically represented in Figure 9. These projections were generated using Representative Concentration Pathways (RCPs) 4.5 and 8.5, allowing us to assess future scenarios in comparison to reference historical data spanning from 1971 to 2022. It is noteworthy that within the plethora of available models, only two, namely CSIRO MK3 and HADGEM1 (Harrison, 2009), are consistent with those utilized in the Jordan Second Assessment. Consequently, the Jordanian Second National Report incorporates these two models alongside ECHAM50M. This selective approach was adopted due to the unique relevance of these three General Circulation Models (GCM) outputs to geographic data points within Jordan (moenv, 2020). As per Figure 10, the analysis employing Linear Regression and Mann-Kendall trends reveals a significant upward trend in annual temperatures over the next three decades. The rate of increase is estimated at 1.1 ºC for RCP 4.5 and 1.2 ºC for RCP 8.5, with corresponding coefficients of determination (R²) of 0.67 (p-value < 0.0001) and 0.88 (p-value < 0.0001), respectively. In the context of investigating the impact of climatic changes on the Dead Sea, the temperature variable emerges as the central focus, as depicted in Table 6. Following the application of Mann-Kendall analysis, an inverse correlation between temperature and the surface area of the Dead Sea is established, with an R² value of 0.5, a correlation coefficient (r) of -0.71, and a p-value of less than 0.003. It is worth noting that the catchment areas of the Yarmouk Basin, Zarqa Amman Basin, and Mujib Basin play a vital role in replenishing the Dead Sea, serving as its primary tributaries. However, the presence of dams at the outlets of these tributaries has resulted in a reduction in recharge, as previously mentioned. Therefore, when examining the climatic component, specifically rainfall, it is imperative to consider these three watersheds, which collectively account for nearly half of the Hashemite Kingdom of Jordan's land area. Table 6. Kendall statistical analysis for surface area, temperature and rainfall. p-values Variables Surface area Temperature Rainfall Surface area 0 0.003 0.062 Temperature 0.003 0 0.213 Rainfall 0.062 0.213 0 Correlation matrix (Kendall) Variables Surface area Temperature Rainfall Surface area 1 -0.709 0.455 Temperature -0.709 1 -0.309 Rainfall 0.455 -0.309 1 Coefficients of determination (Kendall) Variables Surface area Temperature Rainfall Surface area 1 0.503 0.207 Temperature 0.503 1 0.096 Rainfall 0.207 0.096 1 *Values in bold are different from 0 with a significance level alpha=0.05 Intensive human water consumption, exemplified by Israel's transfer of 420 MCM/year from the Upper Jordan River to the Negev via the National Water Carrier, constitutes the primary factor behind the Dead Sea's dramatic recession. Regression analysis, as depicted in Figure 4 with an R² value of 0.988, supports this assertion by demonstrating the high degree of fit between a third-degree polynomial and the observed decline in water levels. (Salameh et al., 2019). 3.3 Future projections of the Dead Sea surface area Dead Sea surface area projections for the years 2034 and 2050 have been established using the CA-Markov chain model (Figure 11). The analysis of transition class probabilities for these years reveals a potential decline in the Dead Sea's surface area, ranging from 5% to 9.5%, respectively. Moreover, previous research has indicated a significant decrease in annual rainfall, at a rate of 1.2 mm per year until 2100 (moenv, 2020; Enzel et al., 2022; Oroud, 2023). Additionally, rising annual temperatures are expected to elevate evaporation rates, consequently impacting the Dead Sea's salinity percentage, which has already reached 34%, one of the highest recorded in water bodies. Regarding the Dead Sea's water level, numerous studies have corroborated its continuous decline, exceeding one meter annually (Lensky and Dante, 2015; Al-husban and Almanasyeh, 2017; moenv, 2020; Tierney et al., 2022). This decline has triggered an ongoing ecological crisis, primarily attributable to human activities. The principal factors contributing to the Dead Sea's retreat encompass the diversion of waters from the Jordan River and its tributaries, alongside mineral extraction industries operating on both sides of the Sea. These activities have inflicted irreversible harm on the natural environment, infrastructure, and tourism. The prediction of the future Dead Sea level is a complex task requiring rigorous analysis. Sea level data spanning from 1926 to 2022, obtained from (MWI, 2022), was meticulously examined. Through regression analysis, a second-degree polynomial relationship was established, as illustrated in Figure 12, resulting in an R² value of 0.99, indicating the representativeness of the derived equation. Subsequently, this equation was applied to project Dead Sea surface levels for the years 2035 and 2050. To establish the relationship between the Dead Sea's surface area, water level, and temperature, the study conducted Kendall statistical and regression analyses, the results of which are summarized in Table 7. Notably, it becomes evident that there exists an inverse correlation between surface area and water levels over time. Conversely, there is a direct relationship between increasing temperature and time, as illustrated in Figure 13. For the time span from 2022 to 2034 and 2050, the study anticipate a reduction in the Sea level and surface area, amounting to approximately 12.63 meters, 33 meters, 562.8 square kilometers, and 536.3 square kilometers, respectively. These findings underscore significant inverse relationships between surface area, water level, and temperature, as evidenced by R² values of 0.63 and 0.67, respectively. Notably, the relationship between water level and temperature exhibits nonlinearity, with an R² value of 0.71. It is worth highlighting that from 2022 to 2050, the mean annual temperature is expected to rise by at least 1 ºC. Table 7. Kendall statistical analysis for surface area, Surface area and temperature. p-values Variables Surface area Surface level Temperature Surface area 0 < 0.0001 0.000 Surface level < 0.0001 0 0.000 Temperature 0.000 0.000 0 Correlation matrix (Kendall) Variables Surface area Surface level Temperature Surface area 1 0.974 -0.795 Surface level 0.974 1 -0.821 Temperature -0.795 -0.821 1 Coefficients of determination (Kendall) Variables Surface area Surface level Temperature Surface area 1 0.949 0.632 Surface level 0.949 1 0.673 Temperature 0.632 0.673 1 *Values in bold are different from 0 with a significance level alpha=0.05. The Orthophotography from above of a series of coastal cliffs of the Dead Sea shorelines and its clearly shows the degree of decline in the level of the Dead Sea. The distance between individual cliffs were digitized and mapped and on the Google earth pro (see Figure 14). Although we see the shape of the gradient occurring at the edges of the Dead Sea as a potential natural experiment for cliff formation, this makes us realize that there are many differences in the actual processes, rates, and magnitudes between these cliffs and other cliffs that occur in seas and oceans. The use of GIS is considered an effective tool in determining coastal areas and the limits of low water levels through the surface forms that later appear on the coast’s borders, which are considered a multi-dimensional natural hazard, especially in what are called sinkhole, as they have a spatial dimension (Enzel et al., 2022). In addition, it is important in supporting spatial decision-making by building multi-criteria models to identify areas that could experience a future decline in surface water levels. Coastal boundaries have been created to show the extent of the decline in the surface water level of the Dead Sea since 1926 until this moment, in addition to a possible decrease in the years 2034 and 2050. 3.4 Model validation Based on the data presented in Table 8, the Kappa statistics demonstrate a high degree of agreement and consistency between the simulated and observed values of LULC (Land Use and Land Cover) classes, with only minor discrepancies. Specifically, Location and K-overall surpass 0.98 and 0.96, respectively. Furthermore, Kappa indices of agreement were applied to validate various alterations that might occur in the maps depicting the Dead Sea's surface area for the years 2034 and 2050. The results affirm the effectiveness of the CA-Markov model as a valuable tool for simulating and analyzing diverse changes in these upcoming years. Consequently, the model can be deemed reliable and dependable for predicting future alterations in the surface area of various features. Table 8. Results of model validation. Validation based on the maps produced Observed- LULC Predicted-LULC Compatibility degrees-(%) Kappa-indices (0-1) K- location K- overall 1994 - 2004 2014 2014 98 0.98 0.96 2004 -2014 2022 2022 99 0.98 0.98 4. Conclusions The Dead Sea as experienced several fluctuations during the Holocene caused by climatic changes. However, the most recent lowering of the lake since 1970 is mainly due to impact of major projects through the construction of dams to store fresh water that directly feeds the Dead Sea together with mineral extraction industries in the southern basin of the Dead Sea. The imbalance between the amount of water entering the Dead Sea and the water evaporating from its surface has caused the water level to drop at an alarming rate. The study estimated that the Dead Sea's surface lowered by more than 30 meters since the early 20th century. The model functions can be used to predict the near future changes in the surface area and water level. Therefore, the Dead Sea level is expected to drop to -450.6 m and − 471 m in 2034 and 2050 respectively, and the surface area will decline 30 km 2 in 2034 and 56 km 2 in 2050 of its current area. The rapid decline of the sea level had several negative consequences. The shrinking surface area of the sea has resulted in the formation of sinkholes along its shores posing a threat to human infrastructure agriculture and tourism. Furthermore the high salt content of the Dead Sea which has made it a unique natural phenomenon and a popular tourist attraction is becoming more concentrated as the water level decreases. This increased salinity can have various environmental implications affecting the ecosystem and potentially altering the balance of the marine life in the sea. In summary while the specific impacts of climate change on the Dead Sea are not fully understood it is clear that the changing climate and human activities have contributed to the sea's declining water level. Addressing these challenges requires a comprehensive approach that considers both local and global factors emphasizing sustainable water management and climate change mitigation efforts. In conclusion the declining water level of the Dead Sea is a significant concern that requires attention and sustainable solutions. The preservation of this unique natural wonder is essential not only for its cultural touristic and ecological value but also for the well-being and livelihoods of the communities in the region. Declarations Acknowledgements The authors are grateful to the Head, Department of Geography-School of Arts for providing the required resources and unconditional support during to complete the research Author contributions Ibrahim Farhan and Lina Salameh conceptualized the theme, wrote the main manuscript text, prepared tables and Figures. Mohmmad Mahafdah and Edlic Sathiamurthy reviewed and finalized the manuscript. Funding There is no funding for this research Data availability All the data used in the present research has been acquired from National Agricultural Research Center (NARC), Intergovernmental Panel on Climate Change (IPCC), Google earth Pro and United States Geological Survey (USGS). The data can be obtained from the link attached. https://portal.jordan.gov.jo/wps/portal/Home/OpenDataMain/OpenDataUser/#/manageDataSets, https://www.ipcc-data.org/ and https://earthexplorer.usgs.gov/. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3830128","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272728033,"identity":"abd16908-171c-4b71-9ed0-5b248a63a51e","order_by":0,"name":"Ibrahim 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study area\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/738b3d3edefb70656e90b533.png"},{"id":51191113,"identity":"413e5d3c-4f36-43a2-a0a5-8e509c86ac50","added_by":"auto","created_at":"2024-02-15 17:01:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87137,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram shows different steps and procedures.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/f48055c5ff2060e73ec870f3.png"},{"id":51190129,"identity":"2fa009a6-9622-4adb-974e-c579ffabedb8","added_by":"auto","created_at":"2024-02-15 16:53:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21754,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear regression for the northern part of the Dead Sea.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/043a526fb5d40749b6e6b3e9.png"},{"id":51190138,"identity":"461bc292-b2e9-4a4e-810a-abfbd7b71efe","added_by":"auto","created_at":"2024-02-15 16:53:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":706357,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of existing dams along the northwestern and southwestern region of Jordan.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/b8ffa21e983a87756c906be9.png"},{"id":51190141,"identity":"fb9b5367-91a3-40ee-83bf-acb90331f0a3","added_by":"auto","created_at":"2024-02-15 16:53:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":342960,"visible":true,"origin":"","legend":"\u003cp\u003eRepresents the change in shape of the Dead Sea Surface area from 1971 to 2022.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/d6a86ed2394d9972a6523c2e.png"},{"id":51191115,"identity":"0288b651-0ef6-4187-91ba-3a699a946beb","added_by":"auto","created_at":"2024-02-15 17:01:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":39988,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis for the mean annual of temperature (°C) for the Dead Sea.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/a9dc701aab8dc151a73a1585.png"},{"id":51191114,"identity":"9a1f0349-2305-480a-ba34-7185a4a53c6a","added_by":"auto","created_at":"2024-02-15 17:01:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":35097,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis for the mean annual of rainfall (mm) for the Dead Sea.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/0a5b5523ab5c760f6b465d57.png"},{"id":51190136,"identity":"896b3159-5242-4468-b3d7-859f45178d2c","added_by":"auto","created_at":"2024-02-15 16:53:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":69273,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of temperature during the period from 2022 to 2050.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/74ff1d4776b89b115eae0c20.png"},{"id":51190134,"identity":"06a8ec54-6cbd-443a-909f-ec31525f9401","added_by":"auto","created_at":"2024-02-15 16:53:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":84432,"visible":true,"origin":"","legend":"\u003cp\u003eTrend RCPs 4.5 and 8.5 for the mean annual temperature during the period 2022 to 2050.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/2d3b36a06abe0ee27ef8bfef.png"},{"id":51190137,"identity":"c06e6a10-feba-48ae-969f-d5c299fad44f","added_by":"auto","created_at":"2024-02-15 16:53:01","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":484010,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation map of the Dead Sea surface area for years 2034 and 2050.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/b1c150eb0efbd54ff5a99f48.png"},{"id":51190135,"identity":"a4939984-e116-42af-896c-0f2ee06c40b7","added_by":"auto","created_at":"2024-02-15 16:53:00","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":34738,"visible":true,"origin":"","legend":"\u003cp\u003eSecond polynomial equation for Sea levels during the period 1926-2022.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/c2ac999a2a12d2bea96416b8.png"},{"id":51190140,"identity":"48b45ae1-47c1-4ddd-8c4d-5dcc0836848c","added_by":"auto","created_at":"2024-02-15 16:53:01","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":76141,"visible":true,"origin":"","legend":"\u003cp\u003eRegression and trend analysis between temperature, surface area and surface level.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/4305c60a6dcc9993d2af4c36.png"},{"id":51190142,"identity":"c5bf3f0d-4f20-4ba9-9b38-d168b4abae4f","added_by":"auto","created_at":"2024-02-15 16:53:01","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":518757,"visible":true,"origin":"","legend":"\u003cp\u003ePhotos and map of the borders of the Dead Sea, ancient, current and projected, which show the extent of the decline of the Dead Sea.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/5b3394eb6d6a9cc1debe2ae3.png"},{"id":57138463,"identity":"d49132d1-99f0-4dd2-9478-9b60d3b05ae8","added_by":"auto","created_at":"2024-05-25 13:46:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3258742,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3830128/v1/979a534e-6a43-4519-b8df-a35908c0aff6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment and monitoring of the Dead Sea surface area and water level using remote sensing and GIS techniques","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eComprehending alterations in land use, vegetation, and coastal dynamics stands as a pivotal pursuit for myriad planning, geographic, and engineering researches (Shalaby and Tateishi, 2007; Shoman et al., 2019; Alharthi et al., 2020; Khawaldah et al., 2020). Understanding of the intricate processes governing sensor properties is paramount for interpreting and analyzing remotely sensed images, which serve as indispensable tools for quantifying and studying surface properties (Kishcha et al., 2019; Aldogom et al., 2020; Oroud, 2020; Al-Mashagbah et al., 2021). Leveraging the power of satellite imagery, we gain unprecedented insights into the Earth\u0026apos;s surface, unraveling the complex tapestry of physical and biological phenomena that shape the global environment (Nehorai et al., 2009; Huang, et al., 2019). In an era of continuous monitoring, satellite images are a formidable asset, providing a continuous stream of information concerning the Earth\u0026apos;s surface (Ghatasheh et al., 2016; El-Kafrawy et al., 2017; Zhang et al., 2017; Kishcha et al., 2018; USGS, 2023; NASA, 2023). This invaluable resource enables us to decipher the mechanisms shaping life\u0026apos;s conditions on our planet, encompassing diverse phenomena such as global weather patterns, tectonic activities, surface vegetation dynamics, ocean currents, polar ice fluctuations, and pollution patterns (Farhan and Al-Bakri, 2019; Aymen et al., 2020; Aldogom et al., 2020 ). With an extensive database of remote sensing imagery spanning historical and contemporary periods, we gain the ability to dissect the spatiotemporal patterns of environmental elements and the profound impacts of human activities over past decades, enabling the quantification of critical parameters such as water levels, surface area, and rates of decline (Adwan, 2018; Jiang et al., 2017; Liping et al., 2018; Chen et al., 2006).\u003c/p\u003e\n\u003cp\u003eThe water surface variation has become one of the most deliberate environmental challenge globally (Kale et al., 2021). Even so, water surface dynamic assessment and monitoring is a vitally important for terrestrial ecosystem and civilization (Che et al., 2017; Oroud, 2020). An extensive and comprehensive studies insisted that the DS is exposing to various changes recently. Leading to extraordinary drop of Lake surface water level. Where, the negative water balance play a crucial role in DS water surface level (Al-Halbouni et al., 2018; Ronen et al., 2019; Morin et al., 2019; Polom et al., 2018; Salameh et al., 2019; Dor et al., 2019; Belmaker et al., 2019; Lu et al., 2020; Oroud, 2020; Hamadneh et al., 2022; Ezersky et al., 2020; Tierney et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe current study is driven by the necessity to evaluate and monitor the evolving dynamics of the Dead Sea, an endorheic lake of profound geographical and ecological significance. Positioned within the Jordan Rift Valley, the Dead Sea stands as a unique geographical entity, stretching from the southeastern Anatolian plateau to the northern Red Sea (Abdel-Fattah and Pingitore, 2009; Miebach et al., 2019; Ghazleh et al., 2011; Dor et al., 2019; Salem, 2020; Tierney et al., 2022). This ancient body of water, originating from the separation between the Asian and African continents during the Miocene era, occupies the lowest point on continental land (Ghazleh and Kempe, 2021). Mounting evidence from a spectrum of studies underscores a stark reality: the Dead Sea is experiencing an alarming rate of shrinkage (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Annual reports reveal an unprecedented decline, with estimations ranging from 90 cm to 1.5 meters (Nof et al., 2012; Ghatasheh et al., 2013; Al-husban and Almanasyeh, 2017; El-Hallaq and Habboub, 2014; Tierney et al., 2022; Lu et al., 2020). The implications of this decline are far-reaching, as the Dead Sea level has plummeted by a staggering 39 meters (Tierney et al., 2022; Al-husban and Almanasyeh, 2017). The Dead Sea was characterized by two distinct basins, the shallow southern basin, and the deep northern basin, until a pivotal moment in 1976, when the southern basin desiccated due to a drop in sea level, reaching a depth of -400 (Oroud, 2020). Moreover, until 1953, the water level of the Dead Sea oscillated around a historical high of approximately 392 meters below sea level, encompassing an area of 1,050 square kilometers (USGS, 1998; Salameh and El-Naser, 2008; Morin et al. 2009; Abu Ghazleh et al. 2010; Lu et al., 2020; Hamadneh, 2022).\u003c/p\u003e\n\u003cp\u003eWithin this research, our primary objective is a comprehensive examination and continuous monitoring of the intricate variations in the water levels of the Dead Sea. This analysis encompasses various temporal perspectives, ranging from historical records to contemporary observations and predictive projections. The undeniable urgency arises from the significant reduction in water levels, emphasizing the critical importance of comprehending the repercussions of this phenomenon. As the nations bordering the Dead Sea confront the socio-economic implications of this ecological transformation, our research functions as a guiding light. It provides direction for the development of well-informed remedial strategies and the promotion of sustainable management practices.\u003c/p\u003e\n\u003cp\u003eTo this end, the specific objectives of this study encompass:\u003c/p\u003e\n\u003cp\u003e1. Conducting a comprehensive assessment of the recent Dead Sea surface area and water level.\u003c/p\u003e\n\u003cp\u003e2. Employing advanced modeling techniques to generate predictive simulations of the Dead Sea\u0026apos;s surface area for the forthcoming years.\u003c/p\u003e\n\u003cp\u003eThrough this multifaceted investigation, we strive to shed light on the profound significance of monitoring the Dead Sea\u0026apos;s water levels, thereby equipping stakeholders and decision-makers with actionable insights to address the imminent challenges posed by this environmental transformation.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Dead Sea holds the distinction of being Earth's lowest point on the surface, situated at an elevation approximately 434 meters below mean sea level (bmsl) and renowned for its status as the saltiest lake, boasting a salinity level of around 434 grams per liter (gpl) (Tierney et al., 2022; MWI, 2022). Geographically, it is positioned at 31˚20'N, 35˚30'E, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe eastern and western shores of the Dead Sea are bordered by formidable fault escarpments, which form integral parts of the African-Syrian rift system. The valley gently slopes upwards in a northern direction along the Jordan River and southwards along the Wadi Araba (Salameh et al., 2019). Significantly, the elevation of the lake's surface stands at 434 meters below mean sea level (bmsl) (ICL, 2022), establishing its shores as the lowest terrestrial points on Earth. Consequently, it bears the distinction of being the world's deepest hypersaline lake (Ghatasheh et al., 2013; Lu et al., 2020; Oroud, 2020). The primary, northern basin of the Dead Sea spans 50 kilometers in length and attains a width of 15 kilometers at its widest point (El-Hallaq and Habboub, 2014; Morin et al., 2019). This region is characterized as arid, with average rainfall ranging from 50 to 100 millimeters (mm) (Lu et al., 2020). In terms of freshwater inflow, the Jordan River contributes approximately 60%, while groundwater accounts for nearly 25% (Tierney et al., 2022). Moreover, it's crucial to note that the rate of the Dead Sea's retreat has been consistently diminishing, reaching up to 1 meter per year (Hamadneh et al., 2022). Additionally, the water surface level of the Dead Sea has experienced a continuous decline over the last three decades, recently exceeding 1 meter (Salameh et al., 2019; Ezersky et al., 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and Image processing\u003c/h2\u003e \u003cp\u003eThis research unfolded in three distinct phases, which can be delineated as follows: data collection, image processing, and accuracy assessment, as visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For this study, thematic maps and satellite images from Landsat 5TM, Landsat 8 OLI, and Landsat 9 OLI2 were procured and downloaded from the Royal Jordanian Geographic Center (RJGC) and the official website of the United States Geological Survey (USGS), accessible via the link (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://glovis.usgs.gov/app\u003c/span\u003e\u003cspan address=\"https://glovis.usgs.gov/app\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Concurrently, weather data sources encompassed data obtained from the Ministry of Water and Irrigation (MWI), the official website of the National Center of Atmospheric Research (NCAR), and the IPCC Website. The schematic diagram below outlines the principal steps involved in data collection and assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA hard copy of the thematic map, scaled at 1:250,000 for the year 1971, was procured from RJGC. Subsequently, the map underwent scanning, and geometric correction was performed using the intersecting lines within the map's coordinate system. Simultaneously, during the data processing phase, satellite imagery from Landsat 5 TM, Landsat 8 OLI, and Landsat 9 OLI2 was acquired from the USGS website. These images were then amalgamated and clipped to match the study area's boundaries. Notably, both TM and OLI images encompassed multiple bands, each characterized by distinct wavelengths, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImages specification for bands ground resolution, spectral range and images date.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eImage specification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImage Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLandsat-5 TM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eLandsat-8 OLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLandsat-9 OLI2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwath width (km)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eWavelength range (\u0026micro;m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eB-band (0.45\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eB-band (0.452\u0026ndash;0.512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eB-band (0.450\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eG-band (0.52\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eG-band (0.533\u0026ndash;0.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eG-band (0.53\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eR-band (0.63\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eR-band (0.636\u0026ndash;0.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eR-band (0.64\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNIR- band (0.76\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eNIR-band (0.851\u0026ndash;0.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNIR-band (0.842\u0026ndash;0.957)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGround resolution (m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisible/ NIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eVisible /NIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVisible /NIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImage date\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Year/Month/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDate of images:-\u003c/p\u003e \u003cp\u003e1984-08-05 1994-08-17\u003c/p\u003e \u003cp\u003e2004-07-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e2014-08-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2022-08-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRevisit time (day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLunch\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e01-March, 1984\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e11-February, 2013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e27-September, 2021\u003c/b\u003e\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\u003e \u003cb\u003e*\u003c/b\u003e Where, B: Blue, G: Green and R: Red (USGS, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Change Detection Analysis\u003c/h2\u003e \u003cp\u003eTo create a comprehensive assessment of recent changes in the Dead Sea, the study employed an on-screen digitizing method, utilizing the thematic map and medium-resolution satellite images spanning the years 1984, 1994, 2004, 2014, and 2022. Subsequently, the manually drawn maps served a dual purpose: validating the model and generating anticipated maps for the years 2034 and 2050. Furthermore, we transformed the digital maps into raster data format using ArcMap 10.8.1 software.\u003c/p\u003e \u003cp\u003eThe change detection analysis entailed the estimation of shape, area, water level, and volume. In accordance with Akin and Cooley, 2013, the approach for calculating and estimating the watershed's volume involved conceptualizing it as a bowl. This volume can be determined by establishing a plane along its rim and its curved inner surface. To achieve this, a capping surface was constructed by connecting a set of points situated along the divide, while the inner surface was represented by the modern topography derived from the Digital Elevation Model (DEM). In essence, the volume calculation hinges on the disparity between the cap elevation and the topography.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Climate data\u003c/h2\u003e \u003cp\u003eClimatic changes have ushered in a plethora of hazards impacting Jordan across various dimensions and directions. These hazards encompass alterations in precipitation distribution and patterns, the advent of extreme temperatures, episodes of drought, instances of flooding, the occurrence of storms, and even the emergence of landslides. The amplification of these hazards' impact is discernible when scrutinizing their frequency and severity over time. The Dead Sea, notably, lacks any outlet, relying on the rapid process of evaporation, which is particularly pronounced in the scorching desert climate. It is unequivocal that climate change will cast its influence across multiple sectors, including agriculture, coastal regions, biodiversity, urban landscapes, society, water resources, and public health. Consequently, the imperative lies in strategic adaptation planning, entailing the formulation of well-defined options and measures to counteract the effects of climate change and foster the development of resilient communities and ecosystems In the context of this study, data on average temperatures spanning the period from 1975 to 2021 were sourced from the Ministry of Water and Irrigation and the National Agricultural Research Center (NARC). Simultaneously, climate projections covering the period from 2022 to 2050 were retrieved from the IPCC website, leveraging the GSM model with the organization of this model presented in a microscale format. To analyze the average temperature, we employed both the Mann-Kendall rank trend test and linear regression trends, comparing observed data with future scenarios under RCP 4.5 and RCP 8.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 CA-Markov Model\u003c/h2\u003e \u003cp\u003eThe Markov model, a theoretical framework, encapsulates the integration of stochastic processes predicated on transition probability matrices, facilitating prediction and optimal control (Kuperberg, 2008; Surabuddin et al., 2019; Agbinya, 2020). Within this context, the digital map of the study area serves as an illustrative tool for visualizing recent changes in spatial data over time. Meanwhile, the Markov model assumes the role of governing spatial dynamics through the utilization of transition probabilities. The following equation was used to calculate changes in the study area:\u003c/p\u003e \u003cp\u003eTP ( P (kn\u0026thinsp;+\u0026thinsp;1)\u0026thinsp;\u0026le;\u0026thinsp;pn\u0026thinsp;+\u0026thinsp;1│P (kn ))\u0026thinsp;=\u0026thinsp;pn, P (kn\u0026thinsp;\u0026minus;\u0026thinsp;1 )\u0026thinsp;=\u0026thinsp;pn\u0026thinsp;\u0026minus;\u0026thinsp;1,.., P (k1 )\u0026thinsp;=\u0026thinsp;TP ( P (kn\u0026thinsp;+\u0026thinsp;1 )\u0026thinsp;\u0026le;\u0026thinsp;pn\u0026thinsp;+\u0026thinsp;1│P(kn ))\u0026thinsp;=\u0026thinsp;pn \u0026hellip;\u0026hellip;. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn the Markov chain process, denoted as P (k), \"k\" signifies a specific point in time, with \"kn\" representing the present moment and \"kn\u0026thinsp;+\u0026thinsp;1\" denoting future time instances where changes occur. Similarly, \"kn-1\" is used to reference preceding changes. In mathematical terms, this definition can be formulated based on the stochastic processes, P (k), for any time instance in the sequence k1\u0026thinsp;\u0026lt;\u0026thinsp;k2 \u0026lt; ... \u0026lt; kn\u0026thinsp;\u0026lt;\u0026thinsp;kn\u0026thinsp;+\u0026thinsp;1. Consequently, the random process adheres to Eq.\u0026nbsp;1. These equations are instrumental in computing the probabilities of both past and present states, observed as transitions from one state to another in the future or as a return to the same state as previously occupied. Therefore, the stochastic model chain comprises a discrete sequence of variables drawn from a discrete feature space. In simpler terms, the future stochastic process remains independent of both its current state and its past state. If we designate P[f] as the Markov chain and pn as a set encompassing N states {p1, p2, p3, ..., pn}, then the transition probability matrix governing the shift from state \"j\" to state \"i\" at a given time instant is expressed by equations 2 and 3 (Memarian et al., 2012 ; Song et al., 2020).\u003c/p\u003e \u003cp\u003eT j, i Tr(X [f\u0026thinsp;+\u0026thinsp;1]\u0026thinsp;=\u0026thinsp;i │X [f]\u0026thinsp;=\u0026thinsp;j) \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\left[\\begin{array}{cc}\\begin{array}{ccc}{T}_{\\text{1,1}}\u0026amp; {T}_{\\text{1,2}}\u0026amp; {T}_{\\text{1,3}}\\\\ {T}_{\\text{2,1}}\u0026amp; {T}_{\\text{2,2}}\u0026amp; {T}_{\\text{2,3}}\\\\ {T}_{\\text{3,1}}\u0026amp; {T}_{\\text{3,2}}\u0026amp; {T}_{\\text{3,3}}\\end{array}\u0026amp; \\begin{array}{ccc}--\u0026amp; --\u0026amp; {T}_{1,n}\\\\ --\u0026amp; --\u0026amp; {T}_{2,n}\\\\ --\u0026amp; --\u0026amp; {T}_{3,n}\\end{array}\\\\ \\begin{array}{ccc}--\u0026amp; --\u0026amp; --\\\\ --\u0026amp; --\u0026amp; --\\\\ {T}_{n,1}\u0026amp; {T}_{n,2}\u0026amp; {T}_{n,3}\\end{array}\u0026amp; \\begin{array}{ccc}--\u0026amp; --\u0026amp; --\\\\ --\u0026amp; --\u0026amp; --\\\\ --\u0026amp; --\u0026amp; {T}_{n,n}\\end{array}\\end{array}\\right]\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 CA-Model Validation\u003c/h2\u003e \u003cp\u003eModel validation and assessment represent essential phases in the modeling process, particularly when the objective is to compare future predictions with the current state. In this regard, one of the most robust methods for evaluating future predictive changes is the utilization of Kappa statistics (Farhan et al., 2023). Consequently, the Markov model is employed to forecast forthcoming alterations, provided the model demonstrates satisfactory performance as indicated by indices such as Kappa (Kno), Kappa for location (Klocation), and Kappa for quantity (Kquantity) (Pontius and Schneider, 2001). Kno serves as a metric for assessing the overall accuracy of the simulation, reflecting the degree of agreement relative to the standard kappa index. Meanwhile, Klocation evaluates the model's aptitude for predicting spatial locations, while Kquantity assesses its ability to predict quantities. The interpretation of these indices hinges on their values. When these indices approach or equal 1, the simulation is deemed exemplary. Conversely, if they approach 0, the simulation is considered ineffective, signifying imperfect consistency between the observed and simulated data. Typically, the Kappa value falls within the range of 0 to 1 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, a Kappa value below 0.4 indicates a low likelihood of fair agreement, while values within the range of 0.4\u0026thinsp;\u0026le;\u0026thinsp;Kappa\u0026thinsp;\u0026le;\u0026thinsp;0.6 denote moderate accuracy. A Kappa value exceeding 0.6 signifies minimal disparities between observed and simulated locations, indicative of substantial agreement (Wu et al., 2008; Qiu and Lu, 2018).\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\u003eInterpretation of Kappa values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterpretation of agreement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess chance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.2\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubstantial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.8\u0026ndash;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerfect\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\u003eAccording to Omar et al., 2014, Kappa statistics method was adopted and calculated computed as the following equations 4, 5 and 6.\u003c/p\u003e \u003cp\u003eK\u003csub\u003eno\u003c/sub\u003e= (P (x) N (f)) / (T (i) \u0026ndash; N (f)) ---------------------- (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eK\u003csub\u003elocation\u003c/sub\u003e= (P (x) N (x)) / (T (x) \u0026ndash; N (x)) ---------------------- (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eK\u003csub\u003equantity\u003c/sub\u003e= (P (x) H (x)) / (K (x) \u0026ndash; H (x)) ---------------------- (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, P (x) signifies the level of information at the medium grid cell, while N (f) is indicative of the absence of information. High consistency is denoted by (T (i)), and H (x) represents the information at the medium layer level. Furthermore, the ideal grid cell-level information, considering heterogeneity or minimal consistency in layer-level information, is represented by K (x) mean.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cp\u003e\u003cstrong\u003e3.1 Changing in the Surface area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the late 1970s, the Dead Sea has been effectively divided into two distinct basins: the northern basin, which continues to function as the Dead Sea itself, and the southern basin, now consisting of the evaporation pools utilized by Israeli and Jordanian mineral industries (Al-Zoubi and Brink 2001; Al-Zoubi et al., 2002;\u0026nbsp;Ronen et al., 2019;\u0026nbsp;Belmaker et al., 2019;\u0026nbsp;Tierney et al., 2022). The construction of these evaporation pools commenced in the late 1960s, occurring on both sides of the border. Subsequently, these facilities have been actively extracting water from the northern basin to facilitate mineral extraction through an evaporation process (Bender, 1968;\u0026nbsp;Salameh et al., 2019;\u0026nbsp;Ezersky et al., 2020). This industrial operation constitutes a significant contributing factor to the negative water balance experienced by the northern basin, as research has indicated a deficit ranging from 250 to 330 million cubic meters per year (RSDSC, 2011). Consequently, starting from this period, it has become preferable to distinguish between the two segments, with a particular focus on the northern region, given the transformation of the southern part into artificial basins (Salameh et al., 2019;\u0026nbsp;Oroud, 2023). The overall trend observed in the northern part area, as depicted in Figure 4, demonstrates a consistent decrease over time. Specifically, it is evident that from 1984 to 2022, the area of the northern part has diminished by approximately 14.2%. Importantly, the trend in this reduction is characterized as nonlinear, with an associated R\u0026sup2; value of 0.988 (Table 3).\u003c/p\u003e\n\u003cp\u003eThe primary driver behind this decline can be attributed to the construction of dams at the outlets of reefs and valleys that historically replenished the Dead Sea, exacerbated by the region\u0026apos;s limited water resources (Salameh et al., 2019; Oroud, 2023). Over the past three decades, decision-makers in the water sector have implemented significant measures aimed at bolstering Jordan\u0026apos;s water security and addressing the persistent water deficit. Furthermore, Jordan has had to contend with the Syrian refugee crisis, which has further strained its water resources (MWI, 2022). Some previous studies showed that there is a slight increase in the area of the Dead Sea for the years 1992 and 2010, and this increase is due to the total amount of rainfall are higher than total average in year 1992. Also in 2010 the amount used in the industry and the amount of inflow released from Jordan River were higher (El-Hallaq and Habboub, 2014; Nof et al., 2012).\u003c/p\u003e\n\u003cp\u003eTable 3. Dead Sea surface area during the period 1971 to 2022.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"889\" height=\"380\"\u003e\u003c/p\u003e\n\u003cp\u003e*Numbers colored with red are extracted from other studies (Abu Ghazleh et al. 2010; RSDSC, 2011; Ghatasheh et al., 2013; El-Hallaq and Habboub, 2014).\u003c/p\u003e\n\u003cp\u003eIn pursuit of a well-structured vision for the future, Jordan has adopted a National Water Strategy, a comprehensive framework guided by a dual-pronged approach encompassing water demand management and water supply management (Alqatarneh and Al-Zboon, 2022). This strategy places significant emphasis on the imperative of enhanced water resource management, with a strong focus on ensuring the sustainability of both current and future water utilization practices. As depicted in Figure 5, Jordan has undertaken the construction of thirteen dams over the past six decades, boasting a cumulative capacity of approximately 335.3 million cubic meters (MCM). Among these dams, the prominent King Talal Dam, situated on the Zarqa River and detailed in Table 4, stands out with a total capacity of 75 MCM. Additionally, the Unity Dam (Al Wihdeh), situated on the Yarmouk River and shared between Jordan and Syria, boasts a total reservoir capacity of 110 MCM. These dams, excluding the Karamah Dam on Wadi Mallaha, are strategically positioned alongside wadis, with their outlets directed toward the Jordan River Valley (JRV). They serve as reservoirs for flood and base flows, playing a crucial role in water regulation and distribution for irrigation purposes (Directorate of Planning and Water Resource, 2005; MWI, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Establish date and Capacity MCM of Jordan\u0026rsquo;s main dams in 2017. Source: MWI, 2023.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDam\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstablish date\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign capacity MCM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eWehdeh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eWadi Arab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e1986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eZeqlab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e1967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eKufranjeh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eKing Talal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e1977 and raised in 1987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eKarameh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eWadiShueib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e1969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eKafrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e1967 Raised in Year 1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eWala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eMujeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eTanour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eKarak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16062176165803%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40587219343696%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.43350604490501%\"\u003e\n \u003cp\u003e\u003cstrong\u003e335.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA discernible transformation is observed in the eastern shores of the southern portion of the Dead Sea, delineated within the red circle in Figure 6. This alteration in bathymetry in the southern expanse of the sea has become more pronounced, attributable to the shrinkage of the area. The changes in the surface area of the Dead Sea over various time phases are also depicted in Figure 5. The examination of surface area alterations spans five distinct time series periods: (1971\u0026ndash;1984), (1984\u0026ndash;1994), (1994\u0026ndash;2004), (2004\u0026ndash;2014), and (2014\u0026ndash;2022), as detailed in Table 5. In broad terms, the rate of change in the Dead Sea\u0026apos;s area fluctuates, ranging from -3.94%. It\u0026apos;s noteworthy that the period from 1971 to 1984 has been excluded from analysis due to the separation of the northern part from the southern part, as discussed earlier.\u003c/p\u003e\n\u003cp\u003eTable 5. Rate of changes of Dead Sea; surface area, temperature and rainfall.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"492\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003eRate of changes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.71399594320487%\"\u003e\n \u003cp\u003eIntervals Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.71399594320487%\"\u003e\n \u003cp\u003e1971-1984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e-31.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.71399594320487%\"\u003e\n \u003cp\u003e1984-1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e-4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.71399594320487%\"\u003e\n \u003cp\u003e1994-2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e-3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e-9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.71399594320487%\"\u003e\n \u003cp\u003e2004-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e-4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e-5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.71399594320487%\"\u003e\n \u003cp\u003e2014-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e-3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29817444219067%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e-1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Climate change effect on the Dead Sea\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the pursuit of identifying the factors contributing to the decline in the surface area of the Dead Sea, our investigation delved into meteorological data, specifically examining the annual averages of rainfall and temperature spanning all months and years from 1971 to 2022. This data was sourced from the Ghor Al-Safi station. Notably, our analysis revealed a pronounced and statistically significant increase in annual temperatures from 1971 to 2022, as evidenced by an R\u0026sup2; value of 0.541 and a p-value less than 0.0001, as illustrated in Figure 7. Furthermore, our findings indicate a temperature rise of 0.67 degrees Celsius over the past six decades, amplifying the observed heightened rates of evaporation in the Dead Sea, as detailed in Table 5. Some studies suggest that annual evaporation has escalated from 7% in the 1960s to 12% in the 2000s (Shafir and Alpert, 2011). Conversely, the analysis of rainfall data yielded no discernible trend, as indicated by an R\u0026sup2; value of 0.013. Nevertheless, Figure 8 visually demonstrates a decline in rainfall data, with Sen\u0026apos;s slope measuring -0.81 during the 1971 to 2022 period. Hence, our study underscores the substantial influence of climatic conditions on the behavior of the water body. This behavior emerges as a result of the balance between inflowing water from the tributary area and direct precipitation, with water evaporation acting as a subtractive factor.\u003c/p\u003e\n\u003cp\u003eThe process of identifying and evaluating the principal climate change-related hazards involved a comprehensive analysis of historical extreme events and trends. Additionally, we employed climate modeling techniques derived from dynamic downscaling, conducted within the Africa region under the Coordinated Regional Climate Downscaling Experiment (CORDEX) Domain framework. Temperature projections spanning from 2022 to 2050 are graphically represented in Figure 9. These projections were generated using Representative Concentration Pathways (RCPs) 4.5 and 8.5, allowing us to assess future scenarios in comparison to reference historical data spanning from 1971 to 2022. It is noteworthy that within the plethora of available models, only two, namely CSIRO MK3 and HADGEM1 (Harrison, 2009), are consistent with those utilized in the Jordan Second Assessment. Consequently, the Jordanian Second National Report incorporates these two models alongside ECHAM50M. This selective approach was adopted due to the unique relevance of these three General Circulation Models (GCM) outputs to geographic data points within Jordan (moenv, 2020).\u003c/p\u003e\n\u003cp\u003eAs per Figure 10, the analysis employing Linear Regression and Mann-Kendall trends reveals a significant upward trend in annual temperatures over the next three decades. The rate of increase is estimated at 1.1 \u0026ordm;C for RCP 4.5 and 1.2 \u0026ordm;C for RCP 8.5, with corresponding coefficients of determination (R\u0026sup2;) of 0.67 (p-value \u0026lt; 0.0001) and 0.88 (p-value \u0026lt; 0.0001), respectively. In the context of investigating the impact of climatic changes on the Dead Sea, the temperature variable emerges as the central focus, as depicted in Table 6. Following the application of Mann-Kendall analysis, an inverse correlation between temperature and the surface area of the Dead Sea is established, with an R\u0026sup2; value of 0.5, a correlation coefficient (r) of -0.71, and a p-value of less than 0.003. It is worth noting that the catchment areas of the Yarmouk Basin, Zarqa Amman Basin, and Mujib Basin play a vital role in replenishing the Dead Sea, serving as its primary tributaries. However, the presence of dams at the outlets of these tributaries has resulted in a reduction in recharge, as previously mentioned. Therefore, when examining the climatic component, specifically rainfall, it is imperative to consider these three watersheds, which collectively account for nearly half of the Hashemite Kingdom of Jordan\u0026apos;s land area.\u003c/p\u003e\n\u003cp\u003eTable 6.\u0026nbsp;Kendall\u0026nbsp;statistical analysis for surface area, temperature and rainfall.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.864951768488744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.29581993569132%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.864951768488744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.29581993569132%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.864951768488744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.29581993569132%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.864951768488744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.919614147909968%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.29581993569132%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"98.87640449438203%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation matrix (Kendall)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1235955056179776%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.709\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.709\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"98.87640449438203%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients of determination (Kendall)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1235955056179776%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.314102564102566%\" colspan=\"2\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.83974358974359%\" colspan=\"2\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.724358974358974%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2820512820512822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;*Values in bold are different from 0 with a significance level alpha=0.05\u003c/p\u003e\n\u003cp\u003eIntensive human water consumption, exemplified by Israel\u0026apos;s transfer of 420 MCM/year from the Upper Jordan River to the Negev via the National Water Carrier, constitutes the primary factor behind the Dead Sea\u0026apos;s dramatic recession. Regression analysis, as depicted in Figure 4 with an R\u0026sup2; value of 0.988, supports this assertion by demonstrating the high degree of fit between a third-degree polynomial and the observed decline in water levels. (Salameh et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Future projections of the Dead Sea surface area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDead Sea surface area projections for the years 2034 and 2050 have been established using the CA-Markov chain model (Figure 11). The analysis of transition class probabilities for these years reveals a potential decline in the Dead Sea\u0026apos;s surface area, ranging from 5% to 9.5%, respectively. Moreover, previous research has indicated a significant decrease in annual rainfall, at a rate of 1.2 mm per year until 2100 (moenv, 2020; Enzel et al., 2022; Oroud, 2023). Additionally, rising annual temperatures are expected to elevate evaporation rates, consequently impacting the Dead Sea\u0026apos;s salinity percentage, which has already reached 34%, one of the highest recorded in water bodies. Regarding the Dead Sea\u0026apos;s water level, numerous studies have corroborated its continuous decline, exceeding one meter annually (Lensky and Dante, 2015; Al-husban and Almanasyeh, 2017; moenv, 2020; Tierney et al., 2022). This decline has triggered an ongoing ecological crisis, primarily attributable to human activities. The principal factors contributing to the Dead Sea\u0026apos;s retreat encompass the diversion of waters from the Jordan River and its tributaries, alongside mineral extraction industries operating on both sides of the Sea. These activities have inflicted irreversible harm on the natural environment, infrastructure, and tourism.\u003c/p\u003e\n\u003cp\u003eThe prediction of the future Dead Sea level is a complex task requiring rigorous analysis. Sea level data spanning from 1926 to 2022, obtained from (MWI, 2022), was meticulously examined. Through regression analysis, a second-degree polynomial relationship was established, as illustrated in Figure 12, resulting in an R\u0026sup2; value of 0.99, indicating the representativeness of the derived equation. Subsequently, this equation was applied to project Dead Sea surface levels for the years 2035 and 2050.\u003c/p\u003e\n\u003cp\u003eTo establish the relationship between the Dead Sea\u0026apos;s surface area, water level, and temperature, the study conducted Kendall statistical and regression analyses, the results of which are summarized in Table 7. Notably, it becomes evident that there exists an inverse correlation between surface area and water levels over time. Conversely, there is a direct relationship between increasing temperature and time, as illustrated in Figure 13. For the time span from 2022 to 2034 and 2050, the study anticipate a reduction in the Sea level and surface area, amounting to approximately 12.63 meters, 33 meters, 562.8 square kilometers, and 536.3 square kilometers, respectively. These findings underscore significant inverse relationships between surface area, water level, and temperature, as evidenced by R\u0026sup2; values of 0.63 and 0.67, respectively. Notably, the relationship between water level and temperature exhibits nonlinearity, with an R\u0026sup2; value of 0.71. It is worth highlighting that from 2022 to 2050, the mean annual temperature is expected to rise by at least 1 \u0026ordm;C.\u003c/p\u003e\n\u003cp\u003eTable 7.\u0026nbsp;Kendall\u0026nbsp;statistical analysis for surface area,\u0026nbsp;Surface area\u0026nbsp;and temperature.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.80327868852459%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.360655737704917%\" colspan=\"2\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.80327868852459%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.360655737704917%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSurface level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.80327868852459%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.360655737704917%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.80327868852459%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.918032786885245%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.360655737704917%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"98.85245901639344%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation matrix (Kendall)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.974\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.795\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eSurface level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.974\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.795\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"98.85245901639344%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients of determination (Kendall)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\"\u003e\n \u003cp\u003eSurface level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eSurface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.949\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.632\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eSurface level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.949\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.673\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.75409836065574%\" valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.632\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.75409836065574%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.673\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.1475409836065573%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Values in bold are different from 0 with a significance level alpha=0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The Orthophotography from above of a series of coastal cliffs of the Dead Sea shorelines and its clearly shows the degree of decline in the level of the Dead Sea. The distance between individual cliffs were digitized and mapped and on the Google earth pro (see Figure 14). Although we see the shape of the gradient occurring at the edges of the Dead Sea as a potential natural experiment for cliff formation, this makes us realize that there are many differences in the actual processes, rates, and magnitudes between these cliffs and other cliffs that occur in seas and oceans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of GIS is considered an effective tool in determining coastal areas and the limits of low water levels through the surface forms that later appear on the coast\u0026rsquo;s borders, which are considered a multi-dimensional natural hazard, especially in what are called sinkhole, as they have a spatial dimension (Enzel et al., 2022). In addition, it is important in supporting spatial decision-making by building multi-criteria models to identify areas that could experience a future decline in surface water levels. Coastal boundaries have been created to show the extent of the decline in the surface water level of the Dead Sea since 1926 until this moment, in addition to a possible decrease in the years 2034 and 2050.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Model validation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the data presented in Table 8, the Kappa statistics demonstrate a high degree of agreement and consistency between the simulated and observed values of LULC (Land Use and Land Cover) classes, with only minor discrepancies. Specifically, Location and K-overall surpass 0.98 and 0.96, respectively. Furthermore, Kappa indices of agreement were applied to validate various alterations that might occur in the maps depicting the Dead Sea\u0026apos;s surface area for the years 2034 and 2050. The results affirm the effectiveness of the CA-Markov model as a valuable tool for simulating and analyzing diverse changes in these upcoming years. Consequently, the model can be deemed reliable and dependable for predicting future alterations in the surface area of various features.\u003c/p\u003e\n\u003cp\u003eTable 8. Results of model validation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.394736842105264%\" rowspan=\"2\"\u003e\n \u003cp\u003eValidation based on the maps produced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.980263157894736%\" rowspan=\"2\"\u003e\n \u003cp\u003eObserved- LULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.269736842105264%\" rowspan=\"2\"\u003e\n \u003cp\u003ePredicted-LULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.42105263157895%\" rowspan=\"2\"\u003e\n \u003cp\u003eCompatibility degrees-(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.93421052631579%\" colspan=\"2\"\u003e\n \u003cp\u003eKappa-indices (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eK-\u003csub\u003elocation\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eK-\u003csub\u003eoverall\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e1994 - 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.980263157894736%\" valign=\"top\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.269736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.42105263157895%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.967105263157896%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.967105263157896%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e2004 -2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.980263157894736%\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.269736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.42105263157895%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.967105263157896%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.967105263157896%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe Dead Sea as experienced several fluctuations during the Holocene caused by climatic changes. However, the most recent lowering of the lake since 1970 is mainly due to impact of major projects through the construction of dams to store fresh water that directly feeds the Dead Sea together with mineral extraction industries in the southern basin of the Dead Sea.\u003c/p\u003e \u003cp\u003eThe imbalance between the amount of water entering the Dead Sea and the water evaporating from its surface has caused the water level to drop at an alarming rate. The study estimated that the Dead Sea's surface lowered by more than 30 meters since the early 20th century. The model functions can be used to predict the near future changes in the surface area and water level. Therefore, the Dead Sea level is expected to drop to -450.6 m and \u0026minus;\u0026thinsp;471 m in 2034 and 2050 respectively, and the surface area will decline 30 km\u003csup\u003e2\u003c/sup\u003e in 2034 and 56 km\u003csup\u003e2\u003c/sup\u003e in 2050 of its current area. The rapid decline of the sea level had several negative consequences. The shrinking surface area of the sea has resulted in the formation of sinkholes along its shores posing a threat to human infrastructure agriculture and tourism. Furthermore the high salt content of the Dead Sea which has made it a unique natural phenomenon and a popular tourist attraction is becoming more concentrated as the water level decreases. This increased salinity can have various environmental implications affecting the ecosystem and potentially altering the balance of the marine life in the sea. In summary while the specific impacts of climate change on the Dead Sea are not fully understood it is clear that the changing climate and human activities have contributed to the sea's declining water level. Addressing these challenges requires a comprehensive approach that considers both local and global factors emphasizing sustainable water management and climate change mitigation efforts.\u003c/p\u003e \u003cp\u003eIn conclusion the declining water level of the Dead Sea is a significant concern that requires attention and sustainable solutions. The preservation of this unique natural wonder is essential not only for its cultural touristic and ecological value but also for the well-being and livelihoods of the communities in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Head, Department of Geography-School of Arts for providing the required resources and unconditional support during to complete the research\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIbrahim Farhan and Lina Salameh conceptualized the theme, wrote the main manuscript text, prepared tables and Figures. Mohmmad Mahafdah and Edlic Sathiamurthy reviewed and finalized the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding for this research \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the data used in the present research has been acquired from National Agricultural Research Center (NARC), Intergovernmental Panel on Climate Change (IPCC), Google earth Pro and United States Geological Survey (USGS). The data can be obtained from the link attached. https://portal.jordan.gov.jo/wps/portal/Home/OpenDataMain/OpenDataUser/#/manageDataSets, https://www.ipcc-data.org/ and https://earthexplorer.usgs.gov/. The information about local of climate data has been acquired from Ministry of Water and Irrigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eAbdel-Fattah, A., \u0026amp; Pingitore, N. E. (2009). Low levels of toxic elements in Dead Sea black mud and mud-derived cosmetic products.\u0026nbsp;Environmental Geochemistry and Health,\u0026nbsp;31, 487-492.\u003c/li\u003e\n \u003cli\u003eAkin, E., \u0026amp; Cooley, S. (2013). Lake Basin Volume. \u003cem\u003eGIS 4 Geomorphology\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAbu Ghazleh, S., et al. 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Spatial and temporal surface temperature patterns across the Dead Sea as investigated from thermal images and thermodynamic concepts. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e(1-2), 569-579.\u003c/li\u003e\n \u003cli\u003eOroud, I. M. (2023). The future fate of the Dead Sea: total disappearance or a dwarfed hypersaline hot lake?. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, 129816.\u003c/li\u003e\n \u003cli\u003ePolom, U., Alrshdan, H., Al-Halbouni, D., Holohan, E. P., Dahm, T., Sawarieh, A. \u0026amp; Krawczyk, C. M. (2018). Shear wave reflection seismic yields subsurface dissolution and subrosion patterns: application to the Ghor Al-Haditha sinkhole site, Dead Sea, Jordan. \u003cem\u003eSolid Earth\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 1079-1098.\u003c/li\u003e\n \u003cli\u003ePontius, R. G., \u0026amp; Schneider L. C. (2001). Modeling land-use change in the Ipswich watershed, Massachusetts, USA, Agriculture, Ecosystems \u0026amp; Environment, Volume 85, Issues 1\u0026ndash;3, 83-94.\u003c/li\u003e\n \u003cli\u003eQiu, Y., \u0026amp; Lu, J. (2018). Dynamic simulation of Spartina alterniflora based on CA-markov model-a case study of Xiangshan bay of Ningbo city, China. Aquatic Invasions, 13(2), 299\u0026ndash;309.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRonen, A., Ezersky, M., Beck, A., Gatenio, B., \u0026amp; Simhayov, R. B. (2019). Use of GPR method for prediction of sinkholes formation along the Dead Sea Shores, Israel. \u003cem\u003eGeomorphology\u003c/em\u003e, \u003cem\u003e328\u003c/em\u003e, 28-43.\u003c/li\u003e\n \u003cli\u003eRSDSC (Red Sea to Dead Sea Water Conveyance), (2011), Red Sea - Dead Sea Water Conveyance Study Program-Final report.\u003c/li\u003e\n \u003cli\u003eSalameh, E., Alraggad, M., \u0026amp; Amaireh, M. (2019). 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New York, 41, United State.\u003c/li\u003e\n \u003cli\u003eWu, X.Q., Hu, Y.M., He, H.S. \u0026amp; Bu, R.C., (2008). Accuracy evaluation and its application of SLEUTH urban growth model. Geomatics.\u003c/li\u003e\n \u003cli\u003eZhang, X., Church, J. A., Monselesan, D., \u0026amp; McInnes, K. L. (2017). Sea level projections for the Australian region in the 21st century. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(16), 8481-8491.\u003c/li\u003e\n \u003cli\u003eOmar, N. \u0026nbsp;Q., Ahamad, M. \u0026nbsp;S. \u0026nbsp;S., Wan Hussin, W. \u0026nbsp;M. A., Samat, N., \u0026amp; Binti Ahmad, S. \u0026nbsp;Z. (2014). \u0026nbsp; Markov \u0026nbsp; \u0026nbsp;CA, \u0026nbsp; Multi \u0026nbsp; Regression, \u0026nbsp; \u0026nbsp;and \u0026nbsp; Multiple \u0026nbsp; Decision \u0026nbsp; \u0026nbsp;Making \u0026nbsp; for \u0026nbsp; Modeling Historical Changes in Kirkuk City, Iraq. \u0026nbsp;Journal of the Indian Society of Remote Sensing, 42(1), 165\u0026ndash;178.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dead Sea, Climate change, Remote sensing, CA-Markov, surface area, water level","lastPublishedDoi":"10.21203/rs.3.rs-3830128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3830128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSituated at Earth's lowest continental point, the Dead Sea experiences a worrying decline in water levels. The primary drivers include the diversion of water from the Jordan River and its tributaries, as well as mineral extraction activities on both sides of the lake. The aim of this study is to analyze the thematic map of 1971 and satellite images of 1984, 1994, 2004, 2014 and 2022 of the Dead Sea to determine the surface area and water level of the Dead Sea and its declining rate. CA-Markov model were employed to generate projected surface area of Dead Sea for periods 2034 and 2050. Time series of observed and future using RPC\u0026rsquo;s 4.5 and 8.5 of climate data especially temperature were analysis has been implemented to track the climate behavior. Statistical analyses of Kendall correlation matrix were performed on observed and predicted of surface area, water level and temperature. The study shows that the Dead Sea has shrunk by 41.8% during the period from 1971 to 2022, while the water sea level is expected to decrease 12.63 m and 33 m for period 2034 and 2050 respectively. In addition, there were a significant inverse relationship between surface area, water level and temperature with correlation (r=-0.79; p\u0026thinsp;=\u0026thinsp;0.001) and (r=-0.82; p\u0026thinsp;=\u0026thinsp;0.001), respectively. It is worth highlighting that from 2022 to 2050, the mean annual temperature is expected to rise by at least 1 ˚C. The long-term strategic vision for stabilizing Dead Sea water levels envisions a two-fold approach: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) augmenting natural inflow through the introduction of 300\u0026ndash;400\u0026nbsp;million MCM from manufactured sources channeled into the Jordan River, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) implementing a reduction in water extraction by Dead Sea industries up to a maximum of 330\u0026nbsp;million MCM.\u003c/p\u003e","manuscriptTitle":"Assessment and monitoring of the Dead Sea surface area and water level using remote sensing and GIS techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-15 16:52:55","doi":"10.21203/rs.3.rs-3830128/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38f9cdcb-36e7-439e-8c03-11c86bf44f03","owner":[],"postedDate":"February 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-25T13:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-15 16:52:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3830128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3830128","identity":"rs-3830128","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}