Weather Events Research Articles

Home environments in an age of precarity

Environment-behavior research prior to the 2000s often portrayed homes as relatively self-contained, insular settings that provided residents refuge from the demands and distractions of the outside world—a kind of safe haven for domestic activities and family life. Residential security was viewed largely in relation to personal assets and the nearby environment, including one's capacity to afford high quality housing, defensible space design of the dwelling, and the absence of nearby threats such as fire and flood hazards, seismic risks, and undesirable land uses like oil drilling sites, landfills, and congested roadways. These proximal sources of residential precarity still play a role in people's lives, but their impacts on people are amplified nowadays by a variety of increasingly pervasive threats situated at societal and global levels such as climate change and extreme weather events, disease pandemics, rampant cybercrime, and growing worries about the spread of nuclear weapons. Also, the modern home has become a polyfunctional hub for both household and non-domestic activities, owing to the infusion of work, educational, and recreational activities into residences via their online connections to the outside world. We offer a social ecological analysis of the changing meanings and functions of home environments in the early 21st Century, and a conception of domestic precarity that highlights its links to broader existential concerns driven by societal and global forces. Future policies and environmental interventions to effectively curb residential precarity will require collaboration among individuals from multiple fields and between diverse organizations and institutions working at municipal, state, national and international levels.

Enhancing Olive Cultivation Resilience: Sustainable Long-Term and Short-Term Adaptation Strategies to Alleviate Climate Change Impacts

Olive cultivation, an icon of Mediterranean agriculture, economy, and cultural heritage, faces significant challenges due to climate change and soil degradation. Climate projections indicate that altered precipitation patterns, rising temperatures, and increased frequency of extreme weather events will adversely affect olive tree growth, fruit quality, and yield. This review provides a novel perspective on addressing these challenges through both long-term and short-term adaptation strategies, emphasizing innovative products, advanced technologies, and practical solutions that must work synergistically and be tailored to regional conditions. Long-term practices refer to proactive strategies for enduring climate resilience, including cover cropping, mulching, soil amendments, and breeding programs which enhance soil health, improve water retention, and increase the trees’ resilience. Short-term strategies focus on immediate impacts, offering immediate stress relief and enhanced plant physiological responses, including optimized irrigation systems, pruning management, particle coating films, biostimulants, and plant growth regulators. The review underscores the importance of aligning agricultural practices with sustainability goals and evolving environmental policies and the education of farmers and policymakers. By integrating adaptive practices and technological advancements, the olive sector can better address climate challenges, contribute to global food security, and advance environmental sustainability.

Climatological Tracking and Lifecycle Characteristics of Mesoscale Convective Systems in Northwestern South America

AbstractMesoscale convective systems (MCSs) are crucial in shaping large‐scale tropical circulation and the hydrological cycle, particularly in Northwestern South America (NwSA), a region marked by complex terrain and significant MCS activity. Understanding MCSs in NwSA is vital due to their impact on precipitation patterns and potential for severe weather events. To enhance this understanding, the ATRACKCS algorithm was developed for tracking convective systems, utilizing precipitation and brightness temperature data sets. This research focuses on documenting the spatiotemporal variability of MCS occurrence, life cycle, and movement. Notably, MCS hotspots were identified to the west of the major orographic features in the region, with maximum occurrences at night, contrasting with the region's typical afternoon peak in land convection. MCS movement is also heavily influenced by topography, with higher velocities on the eastern (windward) side of the Andes compared to velocities on the western (leeward) side. MCSs generally move westward, driven by easterly winds, but this pattern is not consistent throughout the year or region. Northward movement is predominant to the west of the Andes, while southward movement is observed to the east. These seasonal and regional movement variations are linked to factors such as the intertropical convergence zone position, moisture availability, topography, and low‐level jets. This research underscores the complexity of MCSs in NwSA and emphasizes the need for detailed studies on the atmospheric environment shaping these systems. Additionally, it provides a robust 21‐year MCS database for NwSA and an advanced tracking tool for research in various geographic contexts and impact areas.

Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention

Transportation systems experience significant disruptions and loss during hazardous weather events, exhibiting great needs of timely intervention to effectively improve the resilience of the affected traffic systems. An informed and science-based proactive intervention strategy depends on accurate forecasting of the resilience performance of traffic systems with essential lead time during hazards. A new resilience performance forecasting methodology at both global and local scales is proposed for traffic networks under natural hazards by addressing unique challenges such as scarcity and time-evolving nature of hazard-specific data. The proposed methodology consists of two modules: the local traffic resilience performance short-term forecasting module based on the modified diffusion convolutional recurrent neural network (DCRNN) and transfer learning techniques, and the global traffic resilience performance forecasting module integrating percolation-based robustness assessment and SIR-based congestion propagation modeling. A case study of an urban traffic network during a major snowstorm hazard is conducted as a demonstration, followed by the feasibility investigation to guide proactive intervention during hazards. It is found the proposed methodology can forecast the time-evolving traffic resilience performance with good accuracy at both global and local scales. With sufficient lead time for the forecast, it bears promising potential to assist the stakeholders to make informed and timely decision about possible proactive intervention by providing key information to help identify the optimal moments and individual strategic links for possible intervention.

Diel metatranscriptomes capture cyanobacteria-dominated Lake Erie community response to episodic events.

Here, we report on the raw and coassembled metatranscriptomes of 39 Lake Erie surface (1.0 m) water samples collected over a 2-day diel period encompassing episodic weather and bloom events. Preliminary taxonomic annotations and read mappings revealed that Microcystis spp. accounted for up to ~47% of the transcriptionally active community.

Challenges and initiatives regarding the extension of operating hours at Osaka International Airport

Osaka International Airport is an urban airport located near the city center, and its operating hours are set at 14 hours, from 7:00 a.m. to 9:00 p.m. The airport extends its hours under unavoidable circumstances and allows takeoffs and landings after 9:00 p.m. However, the airport has long conflicted with local communities over aircraft noise at night, and now, the airport receives harsh opinions especially regarding noise at night and takeoffs and landings after 9:00 p.m. And the number of flights taking-off and landing outside of the operating hours has been on the rise, reaching a record high in FY2022, despite all the countermeasures described later. This is caused not only by the congestion of air traffic flow seen for the last several years, but also by severe weather events around the airport, such as frequent typhoons and thunderclouds triggered by the recent intensifying climate changes. It is becoming even more critical to ensure on-time performance and to gain the understanding of local communities about the circumstances above. The presentation will introduce many initiatives to assess the situation accurately, ensure punctuality in cooperation with airlines, and engage in regular dialogues with residents in the surrounding area.

Environmental salinity differentiates responses to acute hypothermal stress in milkfish

Global warming has led to an increase in the frequency of cold extremes, causing significant economic losses in aquaculture, particularly in subtropical regions. Milkfish (Chanos chanos) holds significant importance in aquaculture within subtropical Asian regions. Despite milkfish's ability to tolerate varying salinity levels, frequent cold snaps associated with extreme weather events have caused substantial mortality. Understanding the molecular and cellular mechanisms underlying cold stress-induced cell death is crucial for developing effective strategies to mitigate such losses. Given the pivotal role of the liver in fish physiology, we established a primary milkfish hepatocyte culture demonstrating robust proliferation and expressing a unique marker leptin A. The molecular characterization of cold-treated hepatocytes at 18 °C showed that the mRNA levels of superoxide dismutase (sod1) and catalase (cat), responsible for neutralizing reactive oxygen species (ROS), were downregulated in freshwater (FW) conditions, while cat expression was upregulated in seawater (SW) conditions. This differential modulation of ROS signaling implies distinct responses in FW and SW, leading to higher ROS levels and increased cell death in FW condition compared to those in SW. Transcriptomic analysis of liver tissues from milkfish reared in FW or SW, with or without cold stress, revealed distinct gene expression patterns. Although cold stress affected a common set of genes in both FW and SW conditions, SW-specific cold responsive genes are associated with metabolic pathways while FW-specific genes were linked to cell proliferation pathways. Notably, gene set enrichment analysis highlighted the downregulation of cytochrome-related genes, a major source of ROS production, in response to cold stress in SW. In summary, our study unveils an insightful mechanism whereby the salinity of SW counteracts cold stress-induced ROS signaling, emphasizing the feasibility and practicality of preconditioning fish in SW as a preventive measure against cold stress-induced mortality.

The Dynamics of Vegetation Evapotranspiration and Its Response to Surface Meteorological Factors in the Altay Mountains, Northwest China

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change.

Flammable futures—storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

Abstract Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must consider the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (>5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global Biosphere Management Model (GLOBIOM) to predict resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic fire model used to reproduce and project wildfires based on various criteria and input variables; GLOBIOM is a global economic land use model, which assesses land use competition and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

How outdoor and indoor green spaces affect human health: a literature review.

The analysis of the complex interactions between outdoor or indoor greenness and the health of individuals and ecosystems is a topic of current and growing interest. This review aims to examine and summarise the results of studies conducted to evaluate the effects of exposure to greenness on various aspects of human health and the natural environment. For this purpose, in April 2024 we searched PubMed, Web of Science, and Scopus databases, Google Scholar and specialised books. Evidence gathered demonstrates a remarkable correlation between exposure to outdoor and indoor greenness and the improvement of mental health, including the reduction of stress, anxiety, and depression. Contact with greenness is also associated with improvements in physical health, such as reductions in blood pressure, heart rate, and inflammation, as well as in cognitive abilities, concentration, and overall recovery. These benefits are recognisable both in outdoor spaces, such as urban parks, oases, and public gardens, and in indoor spaces, through the introduction of plants and nature-evoking elements in living and working environments. The presence of vegetation in indoor environments, such as offices, schools, healthcare facilities, prisons, and others, can contribute to improving the quality of social spaces, fostering communication, and collaboration, and attenuating aggressiveness and inequalities, thereby increasing employee satisfaction and work efficiency. The combination of outdoor/indoor green spaces and the well-being of the living environment includes exposure to greater biodiversity, mitigation of extreme weather events, absorption of atmospheric pollutants, attenuation of urban background noise, and increased privacy. The presence of vegetation in urban areas has a positive impact on social cohesion, promoting interpersonal interaction and facilitating the development of more cohesive and inclusive communities, thus supporting a sense of belonging and collective identity. In conclusion, these results underline the importance of considering contact with greenness as a fundamental element in promoting the psychophysical health and well-being of individuals and ecosystems, suggesting the adoption of nature-based therapies and interventions in public health policies and urban planning practices.

Predicting herbage biomass on small‐scale farms by combining sward height with different aggregations of weather data

AbstractAccurate predictions of herbage biomass are important for efficient grazing management. Small‐scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning‐based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on‐farm and nearby public stations. Prediction models were learned on a training set (n = 291) and evaluated on a test set (n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha−1, where DM is dry matter) and increased the R2 by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% (R2 = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH‐based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH‐based models for small‐scale farms.

Design of a High-Power Nanosecond Electromagnetic Pulse Radiation System for Verifying Spaceborne Detectors

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics.

LiveWire: Horizontal Geoelectric Field Prediction With 1‐hr Lead‐Time Using Multi‐Fidelity Boosted Neural Networks

AbstractGeomagnetically Induced Currents (GICs) are electrical currents generated by rapid changes in the geomagnetic field during space weather events, posing risks to power grids and pipelines. Traditional approaches predict GICs indirectly by forecasting , the temporal variation of the geomagnetic field, which is proportional to the induced electric field via Faraday's law. However, current physics‐based models driven by in situ solar wind measurements offer only 10–30 min lead times, insufficient for power grid operators to take mitigating actions. Additionally, grid operators prefer direct forecasts of the geoelectric field, which directly influences GICs, rather than relying on intermediate predictions of that require complex and time‐consuming calculations. We present a novel approach that directly forecasts the horizontal geoelectric field with a one‐hour lead time, bypassing predictions. Our method combines magnetometer data, magnetotelluric survey data, and solar wind inputs into a new probabilistic multi‐fidelity machine learning technique, ProBoost, resulting in the LiveWire model. Using data from the Boulder Geomagnetic Observatory (BOU) since 2002, we trained and validated LiveWire on the top 50 geoelectric field events during geomagnetic storms. Our results show that LiveWire outperforms both a persistence forecast and the operational Space Weather Modeling Framework (SWMF) by at least 31% and 23%, respectively. This advancement in geoelectric field forecasting promises more accurate GIC predictions, helping enhance the resilience of critical infrastructure to space weather.

Occupational Burnout: Florida Public Health Workforce during the COVID-19 Pandemic

Abstract Objective Florida’s public health workforce faced unprecedented challenges during the compounding COVID-19 pandemic and record-setting hurricane seasons. The objective of this study was to understand burnout within this essential workforce and identify strategies to better support them. Methods A cross-sectional survey addressing burnout, job satisfaction, turnover intention, job demands, job resources, and organizational support in 2021 was completed by 891 Florida Department of Health workers and analyzed by the research team. Results Approximately half of the respondents reported experiencing burnout, which is linked to job dissatisfaction and turnover intention. Strategies to effectively support and retain this essential workforce include improving staffing, salaries, flexibility, trainings, resources, and leadership. Conclusions As infectious disease outbreaks and extreme weather events continue to occur, effectively supporting the public health workforce is critical to protecting and promoting the health and well-being of all Floridians.

Earth’s Interconnected Climate: Understanding Our Changing World

Have you ever thought about what causes the weather conditions that you experience where you live? Maybe you are thinking, “Sure, when there are storm clouds overhead it rains or snows, and when the sun shines strongly in the summer, we have hot weather”. This is true, but the “big” story is more complicated: the conditions you experience in your area can actually be caused by weather and climate events that occur thousands of kilometers away. These long-distance effects are called teleconnections, and they are a natural part of Earth’s climate system. In this article, we provide examples of teleconnections and describe how scientists study them using powerful computer programs. We also explain how climate change might affect these invisible forces that stretch across the Earth, and how resulting changes in teleconnections could cause extreme weather events, like powerful rainstorms or droughts, in certain areas. The more scientists understand about teleconnections, the better we can protect areas that are vulnerable to the dangerous effects of climate change.

Inter-comparison of GNSS- IPWV with ERA-5 IPWV and monitoring of convective events over the Indian region

The present study deals with (i) An Inter-comparison study of ground based Global Navigation Satellite System (GNSS) derived Integrated Precipitable Water Vapour (IPWV) with 5th generation global climate reanalysis data of European Centre for Medium Range Weather Forecast (ERA-5) (ii) IPWV thresholds of GNSS data (iii) case studies of IPWV analysis. It is found that both the datasets (GNSS and ERA-5) are strongly correlated & the correlation coefficient ranging between 0.97 and 0.99. Monthly Thresholds of IPWV (MTI) are generated with 2017 -2020 data sets from Indian GNSS station having continuous data and found very useful input as value addition to know the possibility of building up /decaying of convection in day to day daily forecast issued to the public. Case studies shows an increase of IPWV, 3 to 4 hour prior to the occurrence of convective event around the GNSS station and found very useful for nowcasting. Therefore, utilization of IPWV (model as wells GNSS based) have incremented value in enriching the understanding about the weather event and its forecasting. This supplement information along with other analysis products (model, surface, upper air, satellite or radar etc) can support further in appropriate decision making to the forecasters, decision makers and the end users of the society.

Role of Polyhouse Technology in Mitigating Climate Risks for Floriculture

Climate change poses significant challenges to agricultural productivity, particularly in sectors like floriculture that rely heavily on favorable environmental conditions. In this context, polyhouse technology emerges as a promising solution for mitigating climate risks faced by floriculture farmers. Polyhouses provide a controlled environment that shields crops from extreme weather events, temperature fluctuations, and excessive rainfall, thereby reducing the vulnerability of floriculture production to climate variability. This paper reviews the role of polyhouse technology in climate risk mitigation for floriculture farmers. It examines how polyhouses help in maintaining optimal growing conditions, including temperature, humidity, and light intensity, throughout the year. By regulating these environmental factors, polyhouses enable year-round cultivation of high-value flower crops, offering farmers a stable income stream despite external climate fluctuations. Furthermore, the study explores the economic and environmental benefits associated with polyhouse-based floriculture systems. It analyses the cost-effectiveness of polyhouse construction and operation, considering factors such as energy consumption, water usage, and crop yield. Additionally, the environmental sustainability of polyhouse technology, including its potential to reduce pesticide and fertilizer usage, is discussed. The paper also highlights the challenges and limitations of adopting polyhouse technology in floriculture farming, including initial investment costs, technical expertise requirements, and market access issues. Strategies for overcoming these barriers, such as government subsidies, capacity building programs, and market linkages, are proposed. The polyhouse technology can play a climate-resilient solution for floriculture farmers, offering opportunities for increased productivity, income stability, and environmental sustainability in the face of climate change.

Evaluating Machine Learning–Based Probabilistic Convective Hazard Forecasts Using The HRRR: Quantifying Hazard Predictability and Sensitivity to Training Choices

Abstract The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts. Significance Statement Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.

A flexible data‐driven approach to co‐producing drought vulnerability assessments

AbstractIntensifying weather events are key characteristics of climate change that are fundamentally changing ecological disturbance regimes. Intensifying drought is a particular threat to species, ecosystems, and ecosystem services worldwide. Proactive drought adaptation measures are acutely needed, but without a better understanding of drought vulnerability at the appropriate scale and geography, such measures may not be effective, or even anticipated as potential options. A recent conceptual framework for ecological drought aligns a holistic suite of potential drivers with the key components of climate change vulnerability (exposure, sensitivity, and adaptive capacity). We leverage the ecological drought framework and components of vulnerability to introduce a six‐step process for developing a drought vulnerability assessment (DVA) that (1) is place‐based and avoids mismatches between assessment geography and management action, (2) uses existing empirical datasets and leverages machine learning techniques and remotely sensed data from a recent drought, (3) emphasizes the inclusion of stakeholders and the importance of data visualization and science communication, and (4) is flexible and adaptable to a wide range of planning contexts. We illustrate the DVA process with a case study for forested watersheds in the Missouri Headwaters (MH), Montana, USA, that is focused on the impact of an early 2000s drought event on forest health. We show how the DVA provides insights on drought vulnerability that are helpful starting points for co‐developing region‐specific management actions to prepare for the next drought, including strategies to enhance ecologically available water, reduce competition for water, promote ecosystem persistence under drought conditions, and prioritize sites for forest restoration, transition, or protection. The work described here provides a model for developing a DVA in other places that, when used in a participatory adaptation planning process, supports the implementation of effective adaptation strategies.

Droughts Amplify Soil Moisture Losses in Burned Forests of Southeastern Amazonia

AbstractSoil moisture is a crucial variable mediating soil‐vegetation‐atmosphere water exchange. As climate and land use change, the increased frequency and intensity of extreme weather events and disturbances will likely alter feedbacks between ecosystem functions and soil moisture. In this study, we evaluated how extreme drought (2015/2016) and postfire vegetation regrowth affected the seasonality of soil water content (0–8 m depth) in a transitional forest in southeastern Amazonia. The experiment included three treatment plots: an unburned Control, an area burned every three years (B3yr), and an area burned annually (B1yr) between 2004 and 2010. We hypothesized that (a) soil moisture at B1yr and B3yr would be higher than the Control in the first years postfire due to lower transpiration rates, but differences between burned plots would decrease as postfire vegetation regrew; (b) during drought years, the soil water deficit in the dry season would be significantly greater in all plots as plants responded to greater evaporative demand; and (c) postfire recovery in the burned plots would cause an increase in evapotranspiration over time, especially in the topsoil. Contrary to the first expectation, the burned plots had lower volumetric water content than the Control plot. However, we found that droughts significantly reduced soil moisture in all plots compared to non‐drought years (15.6%), and this effect was amplified in the burned plots (19%). Our results indicate that, while compounding disturbances such as wildfires and extreme droughts alter forest dynamics, deep soil moisture is an essential water source for vegetation recovery.