Social vulnerability of the people exposed to wildfires in U.S. West Coast states.

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000-2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000-2010 to 2011-2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (>40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans.

Elevation-dependent intensification of fire danger in the western United States.

Studies have identified elevation-dependent warming trends, but investigations of such trends in fire danger are absent in the literature. Here, we demonstrate that while there have been widespread increases in fire danger across the mountainous western US from 1979 to 2020, trends were most acute at high-elevation regions above 3000 m. The greatest increase in the number of days conducive to large fires occurred at 2500-3000 m, adding 63 critical fire danger days between 1979 and 2020. This includes 22 critical fire danger days occurring outside the warm season (May-September). Furthermore, our findings indicate increased elevational synchronization of fire danger in western US mountains, which can facilitate increased geographic opportunities for ignitions and fire spread that further complicate fire management operations. We hypothesize that several physical mechanisms underpinned the observed trends, including elevationally disparate impacts of earlier snowmelt, intensified land-atmosphere feedbacks, irrigation, and aerosols, in addition to widespread warming/drying.

Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques.

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information.

Anthropogenic Decline of Ancient, Sustainable Water Systems: Qanats.

Qanat is an ancient underground structure to abstract groundwater without the need for external energy. A recognized world heritage, Qanat has enabled civilization in arid and semi-arid regions that lack perennial surface water resources. These important structures, however, have faced significant challenges in recent decades due to increasing anthropogenic pressures. This study uses remote sensing to investigate land-use changes and the loss of 15,983 Qanat shafts in the Mashhad plain, northeast of Iran, during the past six decades. This entails obtaining a rare aerial imagery from 1961, as well as recent satellite imagery, over a region with the highest density of Qanats in Iran, the birthplace of Qanat. Results showed that only 5.59% of the Qanat shafts in 1961 remained intact in 2021. The most prominent Qanat-impacting land-use changes were agriculture and urban areas, that accounted for 42.93 and 31.81% Qanat shaft destruction in the study area, respectively. This study also showed that groundwater table decline, demographic changes, and reduction in the appeal of working in the Qanat maintenance and construction industry among the new generation are existential threats to Qanats, and may result in the demise of these ancient structures in the future. Findings of this study can be used for urban planning in arid and semi-arid areas with the aim of protecting these historic water structures.

Anthropogenic stressors compound climate impacts on inland lake dynamics: The case of Hamun Lakes.

Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system - the terminal Hamun Lakes on the Iran-Afghanistan border - that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km2 versus 410 km2), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes' dynamics indicate that the average lakes' surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes' desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region.

Unconventional water resources: Global opportunities and challenges.

Water is of central importance for reaching the Sustainable Development Goals (SDGs) of the United Nations. With predictions of dire global water scarcity, attention is turning to resources that are considered to be unconventional, and hence called Unconventional Water Resources (UWRs). These are considered as supplementary water resources that need specialized processes to be used as water supply. The literature encompasses a vast number of studies on various UWRs and their usefulness in certain environmental and/or socio-economic contexts. However, a recent, all-encompassing article that brings the collective knowledge on UWRs together is missing. Considering the increasing importance of UWRs in the global push for water security, the current study intends to offer a nuanced understanding of the existing research on UWRs by summarizing the key concepts in the literature. The number of articles published on UWRs have increased significantly over time, particularly in the past ten years. And while most publications were authored from researchers based in the USA or China, other countries such as India, Iran, Australia, and Spain have also featured prominently. Here, twelve general types of UWRs were used to assess their global distribution, showing that climatic conditions are the main driver for the application of certain UWRs. For example, the use of iceberg water obviously necessitates access to icebergs, which are taken largely from arctic regions. Overall, the literature review demonstrated that, even though UWRs provide promising possibilities for overcoming water scarcity, current knowledge is patchy and points towards UWRs being, for the most part, limited in scope and applicability due to geographic, climatic, economic, and political constraints. Future studies focusing on improved documentation and demonstration of the quantitative and socio-economic potential of various UWRs could help in strengthening the case for some, if not all, UWRs as avenues for the sustainable provision of water.

Anthropogenic depletion of Iran's aquifers.

Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran's widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km3/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km3/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating "very high salinity hazard" for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.

Warming enabled upslope advance in western US forest fires.

Increases in burned area and large fire occurrence are widely documented over the western United States over the past half century. Here, we focus on the elevational distribution of forest fires in mountainous ecoregions of the western United States and show the largest increase rates in burned area above 2,500 m during 1984 to 2017. Furthermore, we show that high-elevation fires advanced upslope with a median cumulative change of 252 m (-107 to 656 m; 95% CI) in 34 y across studied ecoregions. We also document a strong interannual relationship between high-elevation fires and warm season vapor pressure deficit (VPD). The upslope advance of fires is consistent with observed warming reflected by a median upslope drift of VPD isolines of 295 m (59 to 704 m; 95% CI) during 1984 to 2017. These findings allow us to estimate that recent climate trends reduced the high-elevation flammability barrier and enabled fires in an additional 11% of western forests. Limited influences of fire management practices and longer fire-return intervals in these montane mesic systems suggest these changes are largely a byproduct of climate warming. Further weakening in the high-elevation flammability barrier with continued warming has the potential to transform montane fire regimes with numerous implications for ecosystems and watersheds.

Design of a high-coverage ground-based CO2 monitoring layout using a novel information theory-based optimization model.

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO2 monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory-in terms of transinformation-is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO2 monitoring layout for Iran, the eighth-ranked country emitting CO2 worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns.

Multi-objective conflict resolution optimization model for reservoir's selective depth water withdrawal considering water quality.

This paper develops a multi-objective conflict resolution simulation-optimization model based on a leader-follower game to resolve conflicts between different water users while optimizing water quality in the river through selective depth water withdrawal from the reservoir. Iran Water Resources Management Company (IWRMC), given the nature of the power distribution in this region, is selected as leader, and agricultural, domestic, and industrial water users are selected as followers. Nash-Harsanyi bargaining theory is used as a nested model in this general framework to model competition between followers. The proposed selective withdrawal approach considers four reservoir outlets, located at 120, 145, 163, and 181 m above sea level. Water withdrawal from multiple outlets addresses reservoir thermal stratification and water quality. Temperature and water quality are simulated based on different possible scenarios of reservoir inflow and release using a calibrated CE-QUAL-W2 model. Five artificial neural network (ANN) surrogate/meta models are then trained and validated based on CE-QUAL-W2 model results for each water quality variable. Subsequently, these validated surrogate models are coupled with the NSGA-II optimization model, which along with the utility functions of different stakeholders, constitute the building blocks of our conflict resolution multi-objective optimization model. Finally, three decision-making methods, namely AHP, PROMETHEE, and TOPSIS, are utilized to choose the superior compromise solution. Our results show that water withdrawal from multiple reservoir outlets ensures optimal water allocation to different stakeholders while satisfying the desired water quality criteria. In this study, the top outlet (181 m) has desirable quality, and the IRWQISC water quality criterion at the top and deepest outlets are highest and lowest, respectively.

A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes.

Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes.

Probabilistic hazard assessment of contaminated sediment in rivers.

We propose a probabilistic framework rooted in multivariate and copula theory to assess heavy metal hazard associated with contaminated sediment in freshwater rivers that provide crucial ecosystem services such as municipal water source, eco-tourism, and agricultural irrigation. Exploiting the dependence structure between suspended sediment concentration (SSC) and different heavy metals, we estimate the hazard probability associated with each heavy metal at different SSC levels. We derive these relationships for warm (spring-summer) and cold (fall-winter) seasons, as well as stormflow condition, to unpack their nonlinear associations under different environmental conditions. To demonstrate its efficacy, we apply our proposed generic framework to Fountain Creek, CO, and show heavy metal concentration in warm season and under stormflow condition bears a higher hazard likelihood compared to the cold season. Under both warm season and stormflow conditions, probability of exceeding maximum allowable threshold for all studied heavy metals (Cu, Zn, and Pb, in recoverable form) at a standard hardness of 100 mg/lCaCo3 and at a high level of SSC (95th percentile) is consistently more than 80% in our study site. Moreover, a longitudinal study along the Fountain Creek demonstrates that urban and agricultural land use considerably increase likelihoods of violating water quality standards compared to natural land cover. The novelty of this study lies in introducing a probabilistic hazard assessment framework that enables robust risk assessment with important policy implications about the likelihood of different heavy metals violating water quality standards under various SSC levels.

A dataset on human perception of and response to wildfire smoke.

Wildfire smoke presents a growing threat in the Western U.S.; and human health, transportation, and economic systems in growing western communities suffer due to increasingly severe and widespread fires. While modelling wildfire activity and associated wildfire smoke distributions have substantially improved, understanding how people perceive and respond to emerging smoke hazards has received little attention. Understanding and incorporating human perceptions of threats from wildfire smoke is critical, as decision-makers need such information to mitigate smoke-related hazards. We surveyed 614 randomly selected people (in-person) across the Boise Metropolitan Area in Idaho and 1,623 Boise State University affiliates (online), collecting information about their level of outside activity during smoke event(s), knowledge about the source of air quality information and effective messaging preference, perception of wildfire smoke as a hazard, and smoke-related health experiences. This relatively large dataset provides a novel perspective of people's perception of smoke hazards, and provides crucial policy-relevant information to decision-makers. Dataset is available to the public and can be used to address a wide range of research questions.

Heat wave Intensity Duration Frequency Curve: A Multivariate Approach for Hazard and Attribution Analysis.

Atmospheric warming is projected to intensify heat wave events, as quantified by multiple descriptors, including intensity, duration, and frequency. While most studies investigate one feature at a time, heat wave characteristics are often interdependent and ignoring the relationships between them can lead to substantial biases in frequency (hazard) analyses. We propose a multivariate approach to construct heat wave intensity, duration, frequency (HIDF) curves, which enables the concurrent analysis of all heat wave properties. Here we show how HIDF curves can be used in various locations to quantitatively describe the likelihood of heat waves with different intensities and durations. We then employ HIDF curves to attribute changes in heat waves to anthropogenic warming by comparing GCM simulations with and without anthropogenic emissions. For example, in Los Angeles, CA, HIDF analysis shows that we can attribute the 21% increase in the likelihood of a four-day heat wave (temperature > 31 °C) to anthropogenic emissions.

A fuzzy multi-objective optimization approach for treated wastewater allocation.

In face of the new climate and socio-environmental conditions, conventional sources of water are no longer reliable to supply all water demands. Different alternatives are proposed to augment the conventional sources, including treated wastewater. Optimal and objective allocation of treated wastewater to different stakeholders through an optimization process that takes into account multiple objectives of the system, unlike the conventional ground and surface water resources, has been widely unexplored. This paper proposes a methodology to allocate treated wastewater, while observing the physical constraints of the system. A multi-objective optimization model (MOM) is utilized herein to identify the optimal solutions on the pareto front curve satisfying different objective functions. Fuzzy transformation method (FTM) is utilized to develop different fuzzy scenarios that account for potential uncertainties of the system. Non-dominated sorting genetic algorithm II (NSGA-II) is then expanded to include the confidence level of fuzzy parameters, and thereby several trade-off curves between objective functions are generated. Subsequently, the best solution on each trade-off curve is specified with preference ranking organization method for enrichment evaluation (PROMETHEE). Sensitivity analysis of criteria's weights in the PROMETHEE method indicates that the results are highly dependent on the weighting scenario, and hence weights should be carefully selected. We apply this framework to allocate projected treated wastewater in the planning horizon of 2031, which is expected to be produced by wastewater treatment plants in the eastern regions of Tehran province, Iran. Results revealed the efficiency of this methodology to obtain the most confident allocation strategy in the presence of uncertainties.

A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation.

This study proposes a fuzzy multi-stakeholder socio-optimal methodology for joint water and waste load allocation (WWLA) in river systems while addressing upstream flow uncertainty and different social choice rules (SCRs). QUAL2Kw, as the numerical river water quality model, is executed for various scenarios of water and waste loads to construct a comprehensive dataset of plausible settings, which is in turn used to train a meta-model in the form of multivariate linear regressions. The river upstream flow as the main uncertain parameter is assessed by fuzzy transformation method (FTM). Then, for different confidence levels of fuzzy uncertain input, the meta-model is linked with the non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization model to generate trade-off curves among the stakeholders' utility functions. Subsequently, five SCRs are utilized at each confidence level to determine the fuzzy interval solutions for each objective. Next, the possibility degree method is applied to rank the fuzzy interval solutions in each α-cut level. Finally, considering the priorities of all stakeholders, the fallback bargaining method is used to specify the most appropriate SCR in each confidence level. Application of the proposed methodology in Kor River, Iran, shows its efficacy to realize the socio-optimal WWLA scenario(s) among different stakeholders.

GHWR, a multi-method global heatwave and warm-spell record and toolbox.

Heatwaves are extended periods of unusually high temperatures with significant societal and environmental impacts. Despite their significance, there is not a generalized definition for heatwaves. In this paper, we introduce a multi-method global heatwave and warm-spell data record and analysis toolbox (named GHWR). In addition to a comprehensive long-term global data record of heatwaves, GHWR allows processing and extracting heatwave records for any location efficiently. We use traditional constant temperature threshold methods, as well as spatially and temporally localized threshold approaches to identify heatwaves. GHWR includes binary (0/1) occurrence records of heatwaves/warm-spells, and annual summary files with detailed information on their frequency, duration, magnitude and amplitude. GHWR also introduces the standardized heat index (SHI) as a generalized statistical metric to identify heatwave/warm-spells. SHI has direct association with the probability distribution function of long-term daily temperatures for any given calendar day and spatial grid. Finally, GHWR offers a unique opportunity for users to select the type of heatwave/warm-spell information from a plethora of methods based on their needs and applications.

Optimal and objective placement of sensors in water distribution systems using information theory.

Optimization-based deployment of contamination warning system in water distribution systems has been widely used in the literature, due to their superior performance compared to rule- and opinion-based approaches. However, optimization techniques impose an excessive computational burden, which in turn is compensated for by shrinking the problem's decision space and/or using faster optimization algorithms with less accuracy. This imposes subjectivity in interpretation of the system and associated risks, and undermines model's accuracy by not exploring the entire feasible space. We propose a framework that uses information theoretic techniques, including value of information and transinformation entropy, for optimal sensor placement. This can be used either as pre-selection, i.e. pinpointing best potential locations of sensors to be in turn used in optimization framework, or ultimate selection, i.e. single-handedly selecting sensor locations from the feasible space. The proposed framework is then applied to Lamerd water distribution system, in Fars province, Iran, and the results are compared to the suggested potential locations of sensors in previous studies and results of TEVA-SPOT model. The proposed information theoretic scheme enhances the decision space, provides more accurate results, significantly reduces the computational burden, and warrants objective selection of sensor placement.

Increasing probability of mortality during Indian heat waves.

Rising global temperatures are causing increases in the frequency and severity of extreme climatic events, such as floods, droughts, and heat waves. We analyze changes in summer temperatures, the frequency, severity, and duration of heat waves, and heat-related mortality in India between 1960 and 2009 using data from the India Meteorological Department. Mean temperatures across India have risen by more than 0.5°C over this period, with statistically significant increases in heat waves. Using a novel probabilistic model, we further show that the increase in summer mean temperatures in India over this period corresponds to a 146% increase in the probability of heat-related mortality events of more than 100 people. In turn, our results suggest that future climate warming will lead to substantial increases in heat-related mortality, particularly in developing low-latitude countries, such as India, where heat waves will become more frequent and populations are especially vulnerable to these extreme temperatures. Our findings indicate that even moderate increases in mean temperatures may cause great increases in heat-related mortality and support the efforts of governments and international organizations to build up the resilience of these vulnerable regions to more severe heat waves.