Anthropogenic drought dominates groundwater depletion in Iran

Evaluation of fluoride exposure using disability-adjusted life years and health risk assessment in south-western Iran: A novel Monte Carlo simulation

Consumption of fluoride-contaminated water is a worldwide concern, especially in developing countries, including Iran. However, there are restricted studies of non-single-value health risk assessment and the disease burden regarding fluoride intake nationwide. Prolonged exposure to excessive fluoride has been linked to adverse health effects such as dental and skeletal fluorosis. This can lead to under-mineralization of hard tissues, causing aesthetic concerns for teeth and changes in bone structure, increasing the risk of fractures. As such, we aimed to implement probability-based frameworks using Monte Carlo methods to explore the potential adverse effects of fluoride via the ingestion route. This platform consists of two sectors: 1) health risk assessment of various age categories coupled with a variance decomposition technique to measure the contributions of predictor variables in the outcome of the health risk model, and 2) implementing Monte Carlo methods in dose-response curves to explore the fluoride-induced burden of diseases of dental fluorosis and skeletal fractures in terms of disability-adjusted life years (DALYs). For this purpose, total water samples of 8053 (N=8053) from 57 sites were analyzed in Fars and Bushehr Provinces. The mean fluoride concentrations were 0.75 mg/L and 1.09 mg/L, with maximum fluoride contents of 6.5 mg/L and 3.22 mg/L for the Fars and Bushehr provinces, respectively. The hazard quotient of the 95th percentile (HQ>1) revealed that all infants and children in the study area were potentially vulnerable to over-receiving fluoride. Sobol' sensitivity analysis indices, including first-order, second-order, and total order, disclosed that fluoride concentration (Cw), ingestion rate (IRw), and their mutual interactions were the most influential factors in the health risk model. DALYs rate of dental fluorosis was as high as 981.45 (uncertainty interval: UI 95 % 353.23-1618.40) in Lamerd, and maximum DALYs of skeletal fractures occurred in Mohr 71.61(49.75-92.71), in Fars Province, indicated severe dental fluorosis but mild hazard regarding fractures. Residents of the Tang-e Eram in Bushehr Province with a DALYs rate of 3609.40 (1296.68-5993.73) for dental fluorosis and a DALYs rate of 284.67 (199.11-367.99) for skeletal fractures were the most potentially endangered population. By evaluating the outputs of the DALYs model, the gap in scenarios of central tendency exposure and reasonable maximum exposure highlights the role of food source intake in over-receiving fluoride. This research insists on implementing defluoridation programs in fluoride-endemic zones to combat the undesirable effects of fluoride. The global measures presented in this research aim to address the root causes of contamination and help policymakers and authorities mitigate fluoride's harmful impacts on the environment and public health.

A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

Uncovering the impacts of depleting aquifers: A remote sensing analysis of land subsidence in Iran

Intensive groundwater pumping, previously unrecognized in its full extent, is blamed for aquifer degradation and widespread land subsidence in Iran. We use a 100-meter resolution satellite survey from 2014 to 2020 to assess the recent implications of groundwater usage across the country. Results indicate that approximately 56,000 km2 (3.5%) of the country's area is subject to land subsidence, primarily linked to irrigation; 3000 km2 of this area experiences subsidence rates greater than 10 cm/year. The central plateau catchment hosts two-thirds of the country's depleting aquifers, with locations sinking at rates higher than 35 cm/year. The results suggest an annual groundwater depletion of 1.7 billion cubic meters (BCM) from confined and semiconfined aquifers, with the long-term inelastic compaction for most aquifers being approximately one order of magnitude larger than their seasonal elastic response. This underscores the permanent loss of aquifers that jeopardizes the sustainability of water resources across Iran.

High-throughput DNA sequence analysis elucidates novel insight into the genetic basis of adaptation in local sheep

Understanding how evolutionary factors related to climate adaptation and human selection have influenced the genetic architecture of domesticated animals is of great interest in biology. In the current study, by using 304 whole genomes from different geographical regions (including Europe, north Africa, Southwest Asia, east Asia, west Africa, south Asia, east Africa, Australia and Turkey), We evaluate global sheep population dynamics in terms of genetic variation and population structure. We further conducted comparative population analysis to study the genetic underpinnings of climate adaption to local environments and also morphological traits. In order to identify genomic signals under selection, we applied fixation index (FST) and also nucleotide diversity (θπ) statistical measurements. Our results revealed several candidate genes on different chromosomes under selection for local climate adaptation (e.g. HOXC12, HOXC13, IRF1, FGD2 and GNAQ), body size (PDGFA, HMGA2, PDE3A) and also morphological related traits (RXFP2). The discovered candidate genes may offer newel insights into genetic underpinning of regional adaptation and commercially significant features in local sheep.

The catastrophic effects of groundwater intensive exploitation and Megadrought on aquifers in Central Chile: Global change impact projections in water resources based on groundwater balance modeling

Central Chile is undergoing its most severe drought since 2010, affecting ecosystems, water supply, agriculture, and industrial uses. The government's short-term measures, such as increasing groundwater extraction (by 383 % from 1997 to 2022), are exacerbating the situation, leading to long-term hydrological deterioration. The objective of this research is to establish the main processes driving the water table depth evolution within Central Chile over the period 1979-2023. This is done by conducting groundwater balances on five major hydrological basins of Central Chile. For the Megadrought (MD) period (2010-2022), the groundwater level depths reflect not only the recharge variability but, especially, the forcing trend of groundwater withdrawals: they represent 35 % and 65 %, respectively, of the total phreatic level drawdown. This result underlines the dominant role played by groundwater withdrawals in the current delicate state of Central Chile's groundwater resources, while revealing that drought is a new complex phenomenon to deal with, in the midterm, to revert the current water resource trend in Central Chile. Our study moreover presents the impact of climate change in the basin in the framework of six different groundwater withdrawal scenarios. In the worst case (i.e., RCP8.5), the aquifer recharge decreases 18 % with respect to 1979-1997, which is the period assumed to be unaffected by the impact of MD and withdrawals. Such a reduction may be irrelevant in the dynamics of the aquifer system if the current extraction rate does not change. The estimated recovery time needed to reach aquifer conditions equal to those of the unaffected period is approximately 50 years.

Letter to the Editor

A paper recently published entitled "Water crisis in Iran: A system dynamics approach on water, energy, food, land, and climate (WEFLC) nexus" (Barati et al., 2023). In the mentioned study, a WEFLC model is developed to analyze the water scarcity in Iran. Water crisis, as a complex and challenging issue, has different interdependencies in the context of socio-ecological systems (SES), making it an incorrigible issue. The original paper attempted to assess the water resource dynamics through a systemic lens and explore the impact of various driving forces of water resource planning and management on the water crisis. Iran is a well-studied country, especially around water-related problems. Many interesting facts and findings through the water scarcity analysis in the context of WEFLC are mentioned in the original paper. For instance, it is highlighted that "Mitigation and adaptation policies must be system-oriented and coherent at sectors." However, the original paper did not benefit enough from the previous studies and the full potential of available data. Moreover, some arguments contradict previous findings and, in some cases, are logically flawed. The original paper barely alludes to the nonlinear functional relationships among the components of WEFLC, the core expected component in complex system analysis. Incorrect problem statement formation, flawed methodology, insufficient information on the applied method, ambiguity in models' coupling or cohesion, lack of rational explanation, and inappropriate interpretations of abnormal findings may even mislead many readers. This paper aims to point out some concerns related to the problems mentioned above in the published study, with suggestions to improve the current study and methodological notes for future research.

A detailed study on quantification and modeling of drought characteristics using different copula families

This study delves into analyzing drought patterns in Baluchistan by applying copula-based bivariate probabilistic models complemented by Severity Duration Frequency (SDF) curves. The calculation of the Standardized Precipitation Index (SPI) hinges on monthly aggregate precipitation data from ten distinct sites compiled over six-month periods. After evaluating various parametric distributions, the Log-Normal distribution emerges as suitable for modeling drought severity and duration. A range of bivariate copulas is employed to simulate the characteristics of drought severity and duration, which are then compared against observed data. Remarkably, the Gumbel copula classified as an extreme value copula-outperforms its counterparts according to diverse statistical benchmarks. By utilizing the dependence function, we derive the conditional distribution of drought variables: severity and duration. These conditional distributions subsequently inform the calculation of return periods, forming the basis for constructing SDF diagrams at fixed recurrence levels across the study region. The study's finding indicates that a severe drought could occur over the region with higher return periods for a specific duration. The implications of this research are significant, showcasing the potential of copula-based joint modeling techniques to generate frequency curves for drought severity and duration. This development holds promise for effective water resource management and the formulation of strategies to mitigate the impact of drought in vulnerable regions.

Rapid groundwater decline and some cases of recovery in aquifers globally

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

The dialectics of disaster: Considerations on hazards and vulnerability in the age of climate breakdown, with a brief case study of Khuzestan

In a historical moment inundated by disasters, understanding and conceptualising the phenomenon is a matter of some importance. No framework for doing so has been more productive than that developed by Wisner and his colleagues. But their so-called 'Progression of Vulnerability' (pressure and release [PAR] model) framework was conceived before the onset of the climate crisis. And that crisis, as the saying goes, changes everything.

Contribution

What follows is an immanent critique of the framework, with an eye towards shifting some of its parameters in order to account for the process of climate breakdown now multiplying disasters across the globe.

Decline in Iran's groundwater recharge

Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran-a global hotspot for groundwater depletion. Using an extended database comprising abstractions from over one million groundwater wells, springs, and qanats, from 2002 to 2017, here we show a significant decline of around -3.8 mm/yr in the nationwide groundwater recharge. This decline is primarily attributed to unsustainable water and environmental resources management, exacerbated by decadal changes in climatic conditions. However, it is important to note that the former's contribution outweighs the latter. Our results show the average annual amount of nationwide groundwater recharge (i.e., ~40 mm/yr) is more than the reported average annual runoff in Iran (i.e., ~32 mm/yr), suggesting the surface water is the main contributor to groundwater recharge. Such a decline in groundwater recharge could further exacerbate the already dire aquifer depletion situation in Iran, with devastating consequences for the country's natural environment and socio-economic development.

Spatiotemporal variability and controlling factors of groundwater depletion in endorheic basins of Northwest China

Recent global groundwater overpumping is threatening ecosystem stability and food security, particularly in arid basins. A solid investigation regarding the drivers of groundwater depletion is vital for groundwater restoration, hitherto, yet it remains largely unquantified. Here, a framework to quantify the contribution of natural forcing (NF) and anthropogenic perturbations (AP) to groundwater storage anomalies (GWSA) variability by separating the GWSA estimated by the Gravity Recovery and Climate Experiment (GRACE) satellite into natural- and human-induced GWSA was proposed in the northwest endorheic basin (NWEB) of China. Further, a multiple linear regression model was established for GWSA change prediction. Our results showed that, during the period 2003-2020, the GWSA depleted at a rate of 0.25 cm yr-1 in the entire NWEB. In addition, GWSA was found to decrease significantly (exceeding 1 cm yr-1) in the west of NWEB where there are heavily irrigated areas, and has become one of the regions with the most serious groundwater depletion in China. Whereas a significantly increasing trend (greater than 0.5 cm yr-1) was observed in the Qaidam basin and south part of the Tarim River basin, becoming a groundwater enrichment reservoir in NWEB. The negative contribution of AP to groundwater depletion has increased from 3% to 95% in the last decade, as determined by separating the effects of NF and AP on GWSA. The rapid expansion of the cropland area and the increase in water use due to population growth are investigated to be the main reasons for GWSA depletion, particularly in the North Tianshan Rivers, Turpan-Hami, and Tarim River basins. Therefore, we conclude that AP are dominating and accelerating groundwater depletion in the NWEB. The increase of GWSA in the Qaidam basin has been attributed to the increase in solid water melt and regional precipitation. The western route project of China's south-north water diversion and water-saving irrigation are important ways to solve the problem of groundwater depletion in NWEB. Our results emphasize that a more feasible framework capable of reliably identifying the driving factors of groundwater storage change is a necessary tool for promoting the sustainable management of groundwater resources under both NF and AP in arid endorheic basins.

Spatio-temporal analysis of climate and irrigated vegetation cover changes and their role in lake water level depletion using a pixel-based approach and canonical correlation analysis

Lake Urmia, located in northwest Iran, was among the world's largest hypersaline lakes but has now experienced a 7 m decrease in water level, from 1278 m to 1271 over 1996 to 2019. There is doubt as to whether the pixel-based analysis (PBA) approach's answer to the lake's drying is a natural process or a result of human intervention. Here, a non-parametric Mann-Kendall trend test was applied to a 21-year record (2000-2020) of satellite data products, i.e., temperature, precipitation, snow cover, and irrigated vegetation cover (IVC). The Google Earth Engine (GEE) cloud-computing platform utilized over 10 sub-basins in three provinces surrounding Lake Urmia to obtain and calculate pixel-based monthly and seasonal scales for the products. Canonical correlation analysis was employed in order to understand the correlation between variables and lake water level (LWL). The trend analysis results show significant increases in temperature (from 1 to 2 °C during 2000-2020) over May-September, i.e., in 87 %-25 % of the basin. However, precipitation has seen an insignificant decrease (from 3 to 9 mm during 2000-2019) in the rainy months (April and May). Snow cover has also decreased and, when compared with precipitation, shows a change in precipitation patterns from snow to rain. IVC has increased significantly in all sub-basins, especially the southern parts of the lake, with the West province making the largest contribution to the development of IVC. According to the PBA, this analysis underpins the very high contribution of IVC to the drying of the lake in more detail, although the contribution of climate change in this matter is also apparent. The development of IVC leads to increased water consumption through evapotranspiration and excess evaporation caused by the storage of water for irrigation. Due to the decreased runoff caused by consumption exceeding the basin's capacity, the lake cannot be fed sufficiently.

Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform

Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth's surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologies capturing high-resolution and multi-temporal views of landscapes are incredibly beneficial in wetland mapping compared to traditional methods. Taking advantage of the Google Earth Engine's computational power and multi-source earth observation data from Sentinel-1 multi-spectral sensor and Sentinel-2 radar, we generated a 10 m nationwide wetlands inventory map for Iran. The whole country is mapped using an object-based image processing framework, containing SNIC superpixel segmentation and a Random Forest classifier that was performed for four different ecological zones of Iran separately. Reference data was provided by different sources and through both field and office-based methods. Almost 70% of this data was used for the training stage and the other 30% for evaluation. The whole map overall accuracy was 96.39% and the producer's accuracy for wetland classes ranged from nearly 65 to 99%. It is estimated that 22,384 km² of Iran are covered with water bodies and wetland classes, and emergent and shrub-dominated are the most common wetland classes in Iran. Considering the water crisis that has been started in Iran, the resulting ever-demanding map of Iranian wetland sites offers remarkable information about wetland boundaries and spatial distribution of wetland species, and therefore it is helpful for both governmental and commercial sectors.

A mathematical meta-model for assessing the self-sufficient water resources carrying capacity across different spatial scales in Iran

Hydrological modeling, water accounting assessments, and land evaluations are well-known techniques to carry out water resources carrying capacity (WRCC) assessments at multiple spatial levels. Using the results of an existing process-based model for assessing WRCC from very fine to national spatial scales, we propose a mathematical meta-model, i.e., a set of easily applicable simplified equations to assess WRCC as a function of high-quality agricultural lands for optimistic to realistic scenarios. These equations are based on multi-scale spatial results. Scales include national scale (L0), watersheds (L1), sub-watersheds (L2), and water management hydrological units (L3). Applying the meta-model for different scales could support spatial planning and water management. This method can quantify the effects of individual and collective behavior on self-sufficient WRCC and the level of dependency on external food resources in each area. Carrying capacity can be seen as the inverse of the ecological footprint. Hence, using publicly available data on the ecological footprint in Iran, the results of the proposed method are validated and give an estimation of lower and upper bounds for all biocapacity of the lands. Moreover, the results confirm the law of diminishing returns in the economy for the carrying capacity assessment across spatial scales. The proposed meta-model could be considered a complex manifest of land, water, plants, and human interaction for food production, and it could be used as a powerful tool in spatial planning studies.

Combining urban metabolism and reinforcement learning concepts for sustainable water resources management: A nexus approach

Modeling Water-Energy-Food (WEF) nexus is necessary for integrated water resources management (IWRM), especially in urban areas. This paper presents a new urban water metabolism-based methodology for WEF nexus modeling and management. A behavioral simulation model is used to incorporate the characteristics of stakeholders in an urban area. Modified versions of the Borda count, Copeland rule, and fallback bargaining procedures are implemented to choose the socially acceptable management scenarios. Finally, the selected scenarios' effectiveness is evaluated using the fairness and total utility indices. The applicability of the proposed methodology is evaluated by applying it to the Kan River basin, Tehran, Iran, which is suffering from some water and environmental issues. The considered management scenarios include adding new water sources, leakage control plans, using rubber dams for enhancing groundwater recharge, revising water allocation priorities, and developing semi-centralized or decentralized reuse strategies for reclaimed wastewater. Results illustrate that considering different fluxes (i.e., water quantity, pollutants, energy, greenhouse gases (GHG), and materials) is as important as incorporating the social characteristics of stakeholders. Simulating the socially acceptable scenario shows that the aquifer's average water level improves by 3 (m), and its average nitrate concentration reduces by 16 (mg/L) in comparison with the business as usual (BAU) scenario. In addition, by implementing different water reuse strategies, which are energy-intensive, total energy consumption is reduced by 5% due to less groundwater pumping. Also, the selected scenario decreases GHG emissions by 18% and increases the sequestrated carbon dioxide by 20%. In conclusion, the proposed decision support tool can provide policies for sustainable water resources management considering water quality and quantity issues, energy usage, and GHG emission.

Weather or not? The role of international sanctions and climate on food prices in Iran

Introduction

The scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.

Method

Data were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.

Results and discussion

The price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security.

Genomic analysis of indigenous goats in Southwest Asia reveals evidence of ancient adaptive introgression related to desert climate

Understanding how evolutionary pressures related to climate change have shaped the current genetic background of domestic animals is a fundamental pursuit of biology. Here, we generated whole-genome sequencing data from native goat populations in Iraq and Pakistan. Combined with previously published data on modern, ancient (Late Neolithic to Medieval periods), and wild Capra species worldwide, we explored the genetic population structure, ancestry components, and signatures of natural positive selection in native goat populations in Southwest Asia (SWA). Results revealed that the genetic structure of SWA goats was deeply influenced by gene flow from the eastern Mediterranean during the Chalcolithic period, which may reflect adaptation to gradual warming and aridity in the region. Furthermore, comparative genomic analysis revealed adaptive introgression of the KITLG locus from the Nubian ibex ( C. nubiana) into African and SWA goats. The frequency of the selected allele at this locus was significantly higher among goat populations located near northeastern Africa. These results provide new insights into the genetic composition and history of goat populations in the SWA region.

Role of urban boundary layer dynamics and ventilation efficiency in a severe air pollution episode in Tehran, Iran

Tehran faces a significant health challenge due to severe air pollution episodes during wintertime, which are associated with high concentrations of fine particulate matter with a diameter less than 2.5 µm (PM2.5). In this study, we investigated meteorology conditions of one of the severe air pollution episodes, occurred from 27th December 2020 to 15th January 2021, using the Weather Research and Forecasting (WRF) model. To gain insights into this episode, we also modeled a clean episode for comparison. Model validation of land surface temperature using remote sensing showed acceptable performance as well as ground observations for other parameters. We then calculated the ventilation coefficient (VC) from the WRF outputs and analyzed the results statistically. Results indicate the severe reduction in both VC and planetary boundary layer height (PBLH) during the polluted episode. We further linked the decrease in PBLH and VC of the polluted episode to a high-pressure system above 1020 hPa. In contrast, the results for the clean episode indicate that the low-pressure system as low as 1010 hPa led to higher PBLH and VC than during the polluted episode. This low-pressure system favors the reduction of PM2.5 levels to lower than 21 μgm−3.

Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series

Iran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran's hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km[Formula: see text]) and a significant cultivation intensification (48,000 km[Formula: see text]). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km[Formula: see text]), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran's natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km[Formula: see text], mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran's anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.

Tree-ring-based seasonal temperature reconstructions and ecological implications of recent warming on oak forest health in the Zagros Mountains, Iran

Abrupt changes in temperature have especially strong impacts on fragile ecosystems located in semi-arid regions. In this study, we analyzed tree-ring widths (TRW) of Mediterranean cypress (Cupressus sempervirens var. horizontalis) in the Zagros Mountains, Iran. Furthermore, we separately measured earlywood width (EWW) and latewood width (LWW) of Persian oak (Quercus brantii Lindl.) to examine if intra-annual resolution of tree-ring parameters of Q. brantii tree rings can be used as high-resolution paleoclimate proxies. Climate-growth relationships revealed that mean monthly maximum temperatures (Tmax) are a dominant factor determining radial tree growth and negatively affect both oak and cypress in the Zagros Mountains. Accordingly, we reconstructed two different seasonal windows of past Tmax variability, namely, January-March and June-August over the periods 1860-2015 and 1560-2015, respectively. Regime shift detection identified twelve warm and nine cold significant regime shifts in our summer Tmax reconstructions. The longest hot summer period occurred in the twentieth century, and two warm regime shifts occurred in 1999 and 2008. The highest values of the warm summer regime shift index occurred in 2008, which coincided with fungal pathogen attacks and insect outbreak of the oak leaf roller moth (Tortrix viridana L.) in the Zagros oak woodlands. Interestingly, we found common warm and cold periods in historic climate variability between the summer and winter Tmax reconstructions. Warm and cold regime shifts occurred simultaneously from 1955 to 2015, and significant regional warm summer and winter regime shifts have occurred between 2008 and 2015. The winter and summer Tmax reconstructions show high spatial correlations with large areas in West Asia, North Africa, and the eastern Mediterranean region. Our results strengthen initial studies on past climate variability in Iran and contribute to an enhanced understanding of past temperature variability in West Asia.

Climate Change and Food Security Prioritizing Indices: Applying Analytical Hierarchy Process (AHP) and Social Network Analysis (SNA)

Food security and climate change are multidimensional issues. Therefore, a lack of knowledge about the most essential variables made these concepts more complex for decision-making and highlighted the need for credible decision support methods. Here, we aim to develop an accurate tool by using the analytic hierarchy process (AHP) method to explore the priority indicator of food security under climate change in Iran and social network analysis (SNA) to support decisions. The following steps were conducted for the AHP approach: a literature review, a Likert questionnaire and experts’ interviews for variable selection and the variables’ weight determination and prioritization by pairwise comparison questionnaire, designed based on the hierarchy matrix of the criteria and sub-criteria of food security and climate change. The SNA was employed to understand the robustness of the informants’ points of view for indicator selection. After the analysis, 61 criteria were extracted. Sustainability was the important criterion, weighted 0.248. The most important sub-criteria (indicators): groundwater sources, household income, underweight adolescent ratio, food wastage and an annual average of precipitation, weighted 0.095, 0.091, 0.125, 0.227 and 0.236, respectively. The SNA showed that professionals with academic origins focused on the sustainability component. The AHP tool is a credible technique to distinguish the most important criteria. The results might be employed to estimate or predict food security under climate change and simplify decision making in Iran.

The edge of the petri dish for a nation: Water resources carrying capacity assessment for Iran

Different methods have been proposed in population dynamics to estimate carrying capacity (K). This study estimates K for Iran, using three novel methods by integrating land and water limits into assessments based on Human Appropriated Net Primary Production (HANPP). The first method uses land suitability as the limiting resource. It gives theoretical estimates for K. The second method which is based on the first method, uses land suitability and water resources availability as limiting resources assuming highly efficient agriculture, also resulting in theoretical estimates for K. The third method is based on the second method assuming a lower, more realistic agricultural efficiency. The third therefore results in more realistic estimates. Four spatial hydrological scale levels were considered to estimate food production. Also, nine scenarios were defined: a reference one reflecting the current situation, five others for the first method, two for the second method, and finally, one scenario for the third method. Results show severe limitations on food production by the availability of suitable land, water availability, and crop productivity for agriculture. We estimated theoretical values for K using land and water limiting resources separately. Two realistic scenarios considering realistic agricultural productivity and water use at national and local levels were assessed, resulting in 35.5 and 20 million people, respectively. These are alarming values compared to the current population of Iran (84 million). Moreover, our conservative estimations are still higher than any assessment when considering social, economic, or political barriers. This research provides a systematic analysis of carrying capacity in Iran, showing the importance of food import on Iranians' lives, relevant to land, water, and food policies.

Climate Change and Urban Citizens: The Role of Media in Publicising the Conservation of Green Spaces and Mitigation of Air Pollution

Urbanisation has become a challenge as the urban population grows while cities’ land areas, amenities, and green spaces have remained relatively unchanged or even declined. While urban areas are growing, the link between humans and nature is fading. Increasingly, cities are being affected by climate change impacts and so, the role of media in providing updated and correct knowledge to the public is becoming more valuable. Based on this theoretical ground, the research evaluated two printed Iranian newspapers’ functionality in informing the public on Tehran climate based on two main themes of air pollution and greenery spaces, spanning seven years (2007–2014). The paper evaluated the tone, style, and outline of messages publicised by the press media to explore the following questions: Which types of news are dominantly conceptualised as the significant debates and concerns on Tehran’s climatic issues? Who is mainly writing about Tehran’s climatic issues? Is the public being informed effectively on the surrounding arguments and issues by reading newspapers? As such, five self-descriptive indicators were developed: ‘Layout’ (Title, Subject, Content), ‘Message’ (Public Awareness, Educating, Alarming), ‘Contributor’ (Columnist, Researcher, Authority), ‘Spatiality’ (Local, Provincial, National, International), and ‘Allocated space’ (10% to 100%). A text analysis of Persian newspapers using a Structured Query Language (SQL) was employed to extract data. It was found that the news articles mostly covered public awareness, followed by alarming messages on climate. The findings highlighted the critical role of researchers in generating scientific news while encouraging media for disseminating more educating messages on climate change in urban areas.

Estimation of Groundwater Depletion in Iran’s Catchments Using Well Data

Iran is experiencing significant water challenges that have now turned water security into a national priority. By estimating secular trend groundwater storage in Iran between 2002 and 2017, we see that there is an intensive negative trend, even −4400 Mm3 in some areas. These estimations show shifting in the climate and extra extraction from aquifers for agricultural use in some areas in Iran. The secular trend of groundwater storage changes across the whole of Iran inferred from observation well data is −20.08 GT/yr. The secular trends of GWS changes based on observation well data are: −11.55 GT/yr for the Central Plateau basin, −3.60 GT/yr for the Caspian Sea basin, −3.0 GT/yr for the Persian Gulf and Oman Sea basin, −0.53 GT/yr for the Urmieh Lake basin, −0.57 GT/yr for the Eastern Boundary basin, and −0.83 GT/yr for the Gharaghom basin. The most depleted sub-basin (Kavir Markazi) has secular trends of GWS changes of −4.503 GT/yr. This study suggests that groundwater depletion is the largest single contributor to the observed negative trend of groundwater storage changes in Iran, the majority of which occurred after the drought in 2007. The groundwater loss that has been accrued during the study period is particularly alarming for Iran, which is already facing severe water scarcity.

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001-2015 via a combination of three indices for vegetation (NPP-net primary production, NDVI-normalized difference vegetation index, LAI-leaf area index) and two climate indices (LST-land surface temperature, P-precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann-Kendall non-parametric test, and the Theil-Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest-southeast belt spanning central Iran, which has experienced significant vegetation decline (2001-2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran's major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.