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Item type:Publication, Access status: with files , Groundwater variability is strongly linked to soil temperature across global aquifers(Wiley, 2026-08-05); ; ; ; Groundwater plays a critical role in land-atmosphere interactions by regulating soil moisture and surface energy fluxes, yet its contribution to land-surface processes and heat extremes remains poorly quantified globally. Here, we combine observation-based aquifer records with gridded groundwater table depth (GWD) model outputs (2000–2015) to assess how GWD annual fluctuations covary with soil moisture, evaporation, soil temperature, and heatwave frequency across 1,658 aquifers worldwide. Soil temperature shows the most frequently dominant correlation with GWD among statistically significant, robust relationships (37% of aquifers), followed by evaporation (28%), heatwave frequency (22%), and soil moisture (13%). Aquifers with declining GWD suggest different coupling patterns compared to stable aquifers, with more negative relationships between GWD and evaporation and positive associations between GWD and soil temperature. Our results highlight the importance of incorporating groundwater dynamics into assessments of climate extremes and land-surface processes.Publicationtype: Journal Article16 32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The Caspian Sea has lost an area nearly the size of Sicily: human activities are a major reason whyPublicationtype: Journal Article9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The shrinking Caspian Sea: eco-hydrological responses to human and climate pressures(Wiley-Blackwell, 2026-06-05); ; ; ; The Caspian Sea, the Earth's largest inland water body, faces water level decline, drawing comparisons to the collapse of the Aral Sea. Unlike the Aral Sea, the relative roles of climatic variability, hydrological changes, and anthropogenic pressures on the Caspian Sea remain poorly understood. Here, we integrate satellite observations, in situ hydrological records and reanalysis data to examine recent drivers of the Caspian water loss. We show that total river inflow to the Caspian Sea has declined significantly, primarily due to reduced discharge from the Volga River. At the same time, precipitation over the basin has remained broadly stable, while evaporation over the sea has shown a modest upward trend. These findings point to compound anthropogenic and climatic influences on the regional water balance. We also detect a long-term increase in chlorophyll-a concentrations in the shallow Northern Caspian, signaling growing ecological stress associated with ongoing hydrological change. Avoiding further ecological disruption requires coordinated international action and policies to mitigate shrinkage by optimizing water allocation and environmental releases, as well as prioritizing long-term ecosystem resilience. Without urgent intervention, the Caspian Sea risks following the trajectory of other desiccating inland water bodies, with long-lasting ecological and socioeconomic consequences.Publicationtype: Journal Article67 47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Accounting for extremes in modeling the size and likelihood of large fires in the United States(Wiley-Blackwell, 2026-05-12); ; ; ; Wildfires pose growing threats to ecosystems, infrastructure, and communities, yet fire size distributions are often modeled without sufficient attention to rare, high-impact events. Most statistical approaches emphasize the central body of the distribution, which can obscure the behavior of extreme wildfires (i.e., tail events) that have the most significant impact. Here, we introduce a framework that explicitly distinguishes between the statistical characteristics of body and tail fire size distributions. We analyzed 30,331 large fire perimeters from the Monitoring Trends in Burn Severity (MTBS) data set (1984–2024), disaggregated across 105 Level III ecoregions and 10 Geographic Area Coordination Centers (GACCs) across the United States. For each region, we fit three candidate distributions (Pareto Type II, lognormal, Weibull) to both the full data set and an exceedance-based tail sample. Results show that models calibrated only to the body can substantially misestimate the exceedance probability (by up to three orders of magnitude) and burned area (by hundreds of thousands of acres) for large fires. For the body of the distribution, the lognormal and Pareto Type II distributions generally outperform the Weibull. For the tail, the picture is more nuanced: the Pareto Type II and lognormal perform comparably at the ecoregion level, while the Weibull, despite being the weakest body model, provides the best tail fit at the GACC scale. These findings highlight the need to model body and tail behavior separately and show that optimal distributions vary across scales, underscoring the importance of region-specific tail models for suppression budgeting, fuel treatment, and resilience planning.Publicationtype: Journal Article18 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Iran’s policy priorities intensify water crisis(Springer, 2026-03-13); ; ; ; Mahoozi, SanamThe chronic water crisis in Iran stems from decades of water-intensive development, fragmented governance and national priorities that sidelined environmental protection. Without major governance reform and reprioritization, technical solutions alone cannot stop worsening water and environmental degradation.Publicationtype: Journal Article71 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A global analysis of the influence of shallow and deep groundwater tables on relationships between environmental parameters and heatwaves(Elsevier, 2026-01-15); ; ; ; Heatwaves increasingly impact ecosystems, human health, and economic activities worldwide. As their frequency and intensity rise, understanding the mechanisms driving heatwave dynamics and interactions with land surface processes becomes crucial. While numerous studies have examined atmospheric and land surface variables, the role of groundwater, through its effects on soil moisture and surface evaporative fluxes, remains less understood. Although modeling approaches at various scales have enhanced our understanding of groundwater-atmosphere coupling, machine learning (ML) enables capturing complex, nonlinear interactions and evaluating the relative importance of key drivers globally. We developed pixel-based ML models to estimate global summer heatwave frequency over the past 21 years. For each pixel, we considered data within a 1.5° radius (149 neighboring pixels), identified as the optimal scale through a saturation radius analysis. We used feature importance metrics to identify the dominant drivers among surface fluxes, land characteristics, atmospheric and hydrological variables, and interpreted these results in relation to contrasting groundwater depths (<10 m and >100 m). We ensured robustness using 10-fold cross-validation and confirmed that results were not driven by randomness with two additional validation runs on a subset of the data, with shuffled targets and randomized covariates. Our findings suggest that geopotential height showed the highest relative importance among predictors in regions with deep groundwater tables, while in areas with shallow groundwater, surface fluxes emerge as the key contributor. Incorporating groundwater-related processes may therefore improve understanding of land-atmosphere interactions and support more robust assessments of future heatwave risks.Publicationtype: Journal Article28 37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Water storage paradox of reservoir expansion and evaporative losses in the MENA region(Nature Publishing Group, 2025-12-01); ; ; ; Prolonged droughts and population growth have increased the demand for efficient water storage globally. Small agricultural reservoirs support local water demands, but high evaporation rates particularly in dry regions undermine their storage effectiveness. Integrating fine-resolution Sentinel-2 imagery and physical modeling, we created an annual dataset of small agricultural reservoirs (< 0.1 km2) in the Middle East and North Africa (MENA) and quantified their associated evaporative losses from 2016 to 2023. We identified over 133,700 reservoirs, peaking in 2020, providing a combined surface area of 1,408 km2. The largest cumulative areas are located in Türkiye (309 km2), Pakistan (234 km2), Iran (168 km2), Iraq (108 km2), and Egypt (64 km2). Small agricultural reservoirs offer a storage capacity of 1,243 million cubic meters, accounting for up to 16% of irrigation and livestock water use in most MENA countries. Annual evaporative losses from these reservoirs may potentially exceed 2,400 million cubic meters with hotspots of cumulative evaporation corresponding to regions with the highest reservoir surface area, including southern Pakistan, southwestern Iran, and southeastern Iraq. Our analysis suggests strong climatic and anthropogenic influences on the expansion of reservoirs and their storage efficiency emphasizing the need for mitigation strategies to improve agricultural water security in water-stressed regions.Publicationtype: Journal Article16 83 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A global perspective on endorheic lake shrinkage: Impacts of anthropogenic and atmospheric factorsEndorheic lakes, critical components of terrestrial hydrology in closed drainage basins, serve as sensitive indicators of environmental and anthropogenic changes (Hassani et al., 2020). This study analyzed 635 endorheic lakes globally using high-resolution satellite datasets to quantify changes in surface area from 2000 to 2021 and identify the underlying causes. Of these, 134 lakes showed noticeable surface area reductions, with the highest rates observed in water-stressed regions, particularly in Asia and Semi-Arid climates. We found that anthropogenic activities, including agricultural expansion, were key drivers of shrinkage in 89 lakes, whereas meteorological factors, such as increased aridity, primarily influenced 45 lakes. For example, irrigation significantly impacted water balance in places like Wadi Al Rayan in Egypt and Chenghai Lake in China, while industrial activities like lithium mining were particularly notable in the basin of the Dongtai Jiner Lake in China. Additionally, changes in climatic variables, including reduced precipitation and heightened evapotranspiration, further exacerbated lake surface reductions in many regions. These findings highlight the complex interplay between human and natural factors affecting lake dynamics often resulting in what is referred to as anthropogenic drought. They offer valuable insights for the sustainable management of endorheic lake ecosystems, emphasizing the need for strategies that address both direct anthropogenic pressures and changes in climatic and environmental factors.Publicationtype: Conference Paper25 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , How does groundwater table influence heatwave occurrences globally?Heatwaves pose a significant threat to ecosystems, human health and a wide range of socio-economic activities. Understanding the mechanisms that drive heatwave dynamics and their interactions with land surface processes is essential as the frequency and intensity of heatwaves are projected to increase in the future. While a number of studies have focused on the role of atmospheric and land surface variables on heatwaves, less is known about how groundwater depth might influence heatwave dynamics through its effects on soil moisture and surface evaporative fluxes. To address this, we examined past heatwave occurrences over a 21-year period, focusing on areas with shallow and deep groundwater levels in different climate and land cover regions. Furthermore, we examined the relationship between heatwave events and key variables across different water table levels on a global scale. For this purpose, we developed more than 200,000 localized artificial intelligence (AI) models to represent summer heatwave frequency across the globe. For each location (pixel), we considered data within a 1.5 degree radius (approximately 149 neighboring pixels) to identify key variables that contribute to heatwave occurrence. We used the model's built-in feature-importance metrics and tailored AI techniques to determine which variables, among surface fluxes and atmospheric, hydrological and local environmental variables, primarily govern heatwave occurrence in areas with different groundwater levels. Our results indicate that geopotential height, representing atmospheric drivers, dominates heatwave estimates in regions with deep groundwater levels (>100 m). In contrast, in areas with shallow groundwater (<10 m), surface fluxes emerge as an important contributor to heatwave occurrence. These findings shed light on the less discussed impact of groundwater depth on key environmental variables associated with heatwave events, and support sustainable climate adaptation and resource management.Publicationtype: Conference Presentation52 44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , How shallow and deep groundwater impact environmental parameters correlated with global heatwavesHeatwaves present serious challenges to ecosystems, human health, and a wide range of socioeconomic activities. As the frequency and intensity of heatwaves increase, understanding the mechanisms driving their dynamics and interactions with land surface processes become more important. While extensive research has investigated the influence of various land and atmospheric parameters on heatwaves, less is known about how groundwater depth influences heatwave dynamics through their effects on soil moisture and surface evaporative fluxes (Vogelbacher et al., 2024, Sadeghi et al., 2012). To address this knowledge gap, we investigated how the groundwater depth affects the key parameters controlling heatwave dynamics on a global scale. Specifically, we developed more than 200,000 localized Artificial Intelligence (AI) models to represent the spatial distribution of heatwave frequency over the past 21 years across the world. For each model, a radius of 1.5 degrees (approximately 149 neighboring pixels) is considered in the computation to identify key parameters contributing to heatwaves in that region. We analyzed surface fluxes, as well as atmospheric, hydrological, and local environmental variables, to understand their correlation to heatwaves. Our findings suggest that geopotential height representing atmospheric drivers, is the key predictor of heatwave events in regions with deep groundwater tables (>100 m). In contrast, in areas with shallow groundwater (<10 m), surface fluxes emerge as important contributor to the onset of heatwaves. These findings highlight the less-discussed impact of groundwater depth on atmospheric processes and the important role of soil in linking groundwater and the atmosphere. Our results have important implications for water and land management, emphasizing the need for integrated approaches to understand and address the increasing risks posed by heatwaves.Publicationtype: Conference Paper20 10