Explore the vast range of titles available.

\"Barret Baumgart's literary debut presents a haunting and deeply personal portrait of civilization poised at the precipice, a picture of humanity caught between its deepest past and darkest future. In the fall of 2013, during the height of California's historic drought, Baumgart toured the remote military base, NAWS China Lake, near Death Valley, California. His mother, the survivor of a recent stroke, decided to come along for the ride. She hoped the alleged healing power of the base's ancient Native American hot springs might cure her crippling headaches. Baumgart sought to debunk claims that the military was spraying the atmosphere with toxic chemicals to control the weather. What follows is a discovery that threatens to sever not only the bonds between mother and son but between planet Earth and life itself. Stalking the fringes of Internet conspiracy, speculative science, and contemporary archaeology, Baumgart weaves memoir, military history, and investigative journalism in a dizzying journey that carries him from the cornfields of Iowa to drought-riddled California, from the Vietnam jungle to the caves of prehistoric Europe and eventually the walls of the US Capitol, the sparkling white hallways of the Pentagon, and straight into the contradicted heart of a worldwide climate emergency\"-- Provided by publisher.

We quantify the impact of the Wuhan Covid-19 lockdown on concentrations of four air pollutants using a two-step approach. First, we use machine learning to remove the confounding effects of weather conditions on pollution concentrations. Second, we use a new augmented synthetic control method (Ben-Michael et al. in The augmented synthetic control method. University of California Berkeley, Mimeo, 2019. https://arxiv.org/pdf/1811.04170.pdf) to estimate the impact of the lockdown on weather normalised pollution relative to a control group of cities that were not in lockdown. We find NO2 concentrations fell by as much as 24 μg/m3 during the lockdown (a reduction of 63% from the pre-lockdown level), while PM10 concentrations fell by a similar amount but for a shorter period. The lockdown had no discernible impact on concentrations of SO2 or CO. We calculate that the reduction of NO2 concentrations could have prevented as many as 496 deaths in Wuhan city, 3368 deaths in Hubei province and 10,822 deaths in China as a whole.

ContextAs the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by weather, fuels, topography, and management.ObjectivesIdentify the biophysical and management drivers of severity patterns and their spatial variability across the 2013 Rim Fire, Sierra Nevada, California, USA.MethodsRandom forest models were developed separately for reburned and fire-excluded (> 80 year) areas within Yosemite National Park (NP) and Stanislaus National Forest (NF). Models included biophysical, past disturbance, and spatial autocorrelation (SA) predictors. Variable importance was assessed globally and locally. Variance partitioning was used to assess pure and shared variance among predictors.ResultsHigh spatial variability in the relative dominance of predictors existed across burn days and between land ownerships. Fire weather was a dominant top-down control during plume-dominated fire spread days. However, bottom-up controls from fuels and topography created local, fine-scale heterogeneity throughout. Reburn severity correlated with previous severity suggesting strong landscape memory, particularly in Yosemite NP. SA analysis showed broad-scale spatial dependencies and high shared variance among predictors.ConclusionsWildfires are inherently a multi-scaled process. Spatial structure in environmental variables create broad-scale patterns and dependencies among drivers leading to regions of similar fire behavior, while local bottom-up drivers generate fine-scaled heterogeneity. Identifying the conditions under which top-down factors overwhelm bottom-up controls can help managers monitor and manage wildfires to achieve both suppression and restoration goals. Restoration targeting both surface and ladder fuels can mediate future fire severity even under extreme weather conditions.

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Extreme heat events have adverse effects on population health, causing heat-related illnesses, such as heat exhaustion and heat stroke, but also exacerbating underlying medical conditions, such as cardiac and respiratory diseases, through various mechanisms.1 In the United States, from 2000 to 2010 there were approximately 28 000 recorded heat-related hospitalizations, and between 2004 and 2018, an average of about 700 people died because of heat-related illnesses, making heat the deadliest weather-related hazard in the United States.2,3 These figures do not represent heat morbidity and mortality that were not attributable by International Classification of Diseases (Geneva, Switzerland: World Health Organization) Ninth Revision (1980) or 10th Revision (1992) code to a confirmed diagnosis of heat-related illnesses, which likely results in underreporting.4 Additionally, the health consequences of extreme heat are amplified by sociodemographic vulnerabilities and our built environment. As extreme heat events continue to increase in frequency and intensity, individuals, communities, and the municipalities in which they live will need to prepare and adapt.Health impacts from high ambient temperatures have led many municipalities to develop plans to respond to extreme heat events. These plans are sometimes referred to as excessive heat emergency plans, heat-health response plans, or heat action plans (HAPs). Many European countries implemented HAPs following the 2003 European heat wave.5 In the United States, a number of cities have developed HAPs,6,7 although the vast majority of US cities and regions rely only on local National Weather Service offices to issue heat advisories based on heat index forecasts that may not be linked to local HAPs.8In 2020, the US Centers for Disease Control and Prevention (CDC) released a technical report on the summary and strategies for HAPs and ascribed their focus to emergency response planning or long-term planning for extreme heat. The report identifies that plans can stand alone or be an annex to an all-hazards plan and specifically identifies emergency preparedness and management activities when coordinating plans.9 Although the CDC report is not a step-by-step guide or an all-inclusive approach to how to specifically prepare or coordinate a HAP, the reference to emergency operations plans and the location of HAPs in all-hazards mitigation plans suggest that extreme heat is an event that consistently requires an emergency response and is best understood in that context. However, climate change will increase the likelihood and frequency of extreme weather events, such as extreme heat, and these events have increased substantially over the past decades and will continue to affect regions of the globe regularly.10 We argue that the increasing frequency and regularity of these events move them from emergencies to an issue to be planned for with preventive health plans.

Temperature readings observed at surface weather stations have been used for detecting changes in climate due to their long period of observations. The most common temperature metrics recorded are the daily maximum (TMax) and minimum (TMin) extremes. Unfortunately, influences besides background climate variations impact these measurements such as changes in (1) instruments, (2) location, (3) time of observation, and (4) the surrounding artifacts of human civilization (buildings, farms, streets, etc.) Quantifying (4) is difficult because the surrounding infrastructure, unique to each site, often changes slowly and variably and is thus resistant to general algorithms for adjustment. We explore a direct method of detecting this impact by comparing a single station that experienced significant development from 1895 to 2019, and especially since 1970, relative to several other stations with lesser degrees of such development (after adjustments for the (1) to (3) are applied). The target station is Fresno, California (metro population ~ 15,000 in 1900 and ~ 1 million in 2019) situated on the eastern side of the broad, flat San Joaquin Valley in which several other stations reside. A unique component of this study is the use of pentad (5-day averages) as the test metric. Results indicate that Fresno experienced + 0.4 °C decade−1 more nighttime warming (TMin) since 1970 than its neighbors—a time when population grew almost 300%. There was little difference seen in TMax trends between Fresno and non-Fresno stations since 1895 with TMax trends being near zero. A case is made for the use of TMax as the preferred climate metric relative to TMin for a variety of physical reasons. Additionally, temperatures measured at systematic times of the day (i.e., hourly) show promise as climate indicators as compared with TMax and especially TMin (and thus TAvg) due to several complicating factors involved with daily high and low measurements.

Optimizing water management has become one of the biggest challenges for grapevine growers in California, especially during drought conditions. Monitoring grapevine water status and stress level across the whole vineyard is an essential step for precision irrigation management of vineyards to conserve water. We developed a unified machine learning model to map leaf water potential (ψleaf), by combining high-resolution multispectral remote sensing imagery and weather data. We conducted six unmanned aerial vehicle (UAV) flights with a five-band multispectral camera from 2018 to 2020 over three commercial vineyards, concurrently with ground measurements of sampled vines. Using vegetation indices from the orthomosaiced UAV imagery and weather data as predictors, the random forest (RF) full model captured 77% of ψleaf variance, with a root mean square error (RMSE) of 0.123 MPa, and a mean absolute error (MAE) of 0.100 MPa, based on the validation datasets. Air temperature, vapor pressure deficit, and red edge indices such as the normalized difference red edge index (NDRE) were found as the most important variables in estimating ψleaf across space and time. The reduced RF models excluding weather and red edge indices explained 52–48% of ψleaf variance, respectively. Maps of the estimated ψleaf from the RF full model captured well the patterns of both within- and cross-field spatial variability and the temporal change of vine water status, consistent with irrigation management and patterns observed from the ground sampling. Our results demonstrated the utility of UAV-based aerial multispectral imaging for supplementing and scaling up the traditional point-based ground sampling of ψleaf. The pre-trained machine learning model, driven by UAV imagery and weather data, provides a cost-effective and scalable tool to facilitate data-driven precision irrigation management at individual vine levels in vineyards.

Fuel and wildfire management decisions related to fuel break construction, maintenance, and use in fire suppression suffer from limited information on fuel break success rates and drivers of effectiveness. We built a dataset of fuel break encounters with recent large wildfires in Southern California and their associated biophysical, suppression, weather, and fire behavior characteristics to develop statistical models of fuel break effectiveness with boosted regression. Our results suggest that the dominant influences on fuel break effectiveness are suppression, weather, and fire behavior. Variables related to fuel break placement, design, and maintenance were less important but aligned with manager expectations for higher success with wider and better maintained fuel breaks, and prior research findings that fuel break success increases with accessibility. Fuel breaks also held more often if burned by a wildfire during the previous decade, supporting the idea that fuel breaks may be most effective if combined with broader fuel reduction efforts.

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM 2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM 2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM 2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM 2.5 to 9 μg/m 3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM 2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM 2.5 levels that exceeded 9 μg/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.

The US Army Corps of Engineers (USACE) prescribes flood control operations for reservoirs it regulates in watershed‐specific water control manuals (WCMs), which can be decades‐old and may not capture changed conditions in the watersheds or include the benefit of state‐of‐the‐science weather and streamflow prediction. Considering the specific characteristics of a reservoir, forecast‐informed reservoir operations (FIRO) may be used to enhance flood risk reduction, improve water availability, and achieve other benefits. The first FIRO pilot project at Lake Mendocino in California focused on determining if water supply reliability could be improved using FIRO without increasing flood risk. The final report concluded that FIRO concepts could indeed improve water supply reliability while enhancing flood risk reduction. Subsequently, USACE chose additional reservoir systems in California with different characteristics as additional pilot study locations to further investigate FIRO concepts. These successful FIRO efforts have provided justification to continue its expansion beyond the initial pilot sites. The lessons learned from the FIRO pilot projects are being used to inform the development of the FIRO Screening Process, a screening level framework intended to scale up the implementation of FIRO. The lessons learned could support FIRO implementation at suitable USACE reservoirs by updating WCMs.