Search

In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur-one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450% compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.

BackgroundModeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers.ObjectiveThe objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions.MethodsAn expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies.ResultsThe panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting.DiscussionThis checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745.

Objective: To synthesize the methodologies of studies that evaluate the impacts of heat exposure on morbidity and mortality. Methods: Embase, MEDLINE, Web of Science, and Scopus were searched from date of inception until 1 March 2023 for English language literature on heat exposure and health outcomes. Records were collated, deduplicated and screened, and full texts were reviewed for inclusion and data abstraction. Eligibility for inclusion was determined as any article with climate‐related heat exposure and an associated morbidity/mortality outcome. Results: Of 13,136 records initially identified, 237 articles were selected for analysis. The scope of research represented 43 countries, with most studies conducted in China (62), the USA (44), and Australia (16). Across all studies, there were 141 unique climate data sources, no standard threshold for extreme heat, and 200 unique health outcome data sources. The distributed lag non‐linear model (DLNM) was the most common analytic method (48.1% of studies) and had high usage rates in China (68.9%) and the USA (31.8%); Australia frequently used conditional logistic regression (50%). Conditional logistic regression was most prevalent in case‐control studies (5 of 8 studies, 62.5%) and in case‐crossover studies (29 of 70, 41.4%). DLNMs were most common in time series studies (64 of 111, 57.7%) and ecological studies (13 of 20, 65.0%). Conclusions: This review underscores the heterogeneity of methods in heat impact studies across diverse settings and provides a resource for future researchers. Underrepresentation of certain countries, health outcomes, and limited data access were identified as potential barriers. Plain Language Summary: Climate change and global warming are major threats to public health, leading to increased illness and death worldwide due to rising temperatures. Heatwaves are becoming more frequent and severe, causing various health problems, particularly affecting low‐income and minority communities. Studying the link between heat and health requires diverse methods due to regional differences. Different study designs and analytical techniques have been used, but there is no consensus on the best approach. Factors like heat severity, duration, air quality, and humidity must be considered. However, defining extreme heat and determining the best metric for heat exposure remain debated. With climate change worsening, understanding previous research on heat‐related health impacts is crucial for future policies and adaptation strategies. This scoping review aims to address gaps in knowledge by examining the range of epidemiological approaches used to study the effects of extreme heat on health outcomes. Key Points: Distributed lag non‐linear models were widely used across various health outcomes and study designs, especially in time series studiesThere were disparities in research focus across different regions with regions like Africa and Oceania being underrepresentedAccess to public health data and standardized definitions for extreme heat remain significant challenges in heat impact studies [ABSTRACT FROM AUTHOR]

Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.

The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We found that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. These results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases.

During the winters of 2013-2014 and 2014-2015, anomalously warm temperatures in western North America and anomalously cool temperatures in eastern North America resulted in substantial human and environmental impacts. Motivated by the impacts of these concurrent temperature extremes and the intrinsic atmospheric linkage between weather conditions in the western and eastern United States, we investigate the occurrence of concurrent "warm-West/cool-East" surface temperature anomalies, which we call the "North American winter temperature dipole." We find that, historically, warm-West/cool-East dipole conditions have been associated with anomalous mid-tropospheric ridging over western North America and downstream troughing over eastern North America. We also find that the occurrence and severity of warm-West/cool-East events have increased significantly between 1980 and 2015, driven largely by an increase in the frequency with which high-amplitude "ridge-trough" wave patterns result in simultaneous severe temperature conditions in both the West and East. Using a large single-model ensemble of climate simulations, we show that the observed positive trend in the warm-West/cool-East events is attributable to historical anthropogenic emissions including greenhouse gases, but that the co-occurrence of extreme western warmth and eastern cold will likely decrease in the future as winter temperatures warm dramatically across the continent, thereby reducing the occurrence of severely cold conditions in the East. Although our analysis is focused on one particular region, our analysis framework is generally transferable to the physical conditions shaping different types of extreme events around the globe.

Characterizing changes in precipitation patterns over time is critical for hydrologically dependent fields like water resource management and agriculture. Here, we explore observed trends in interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in situ Global Historical Climatology Network precipitation data from 1970 to present over 17 internally consistent sub‐national United States domains using a regional Mann‐Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north‐central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments. Plain Language Summary: While many studies have examined how annual precipitation totals and precipitation frequency have changed, few examine the variability, or consistency, of year‐over‐year precipitation. We test for these trends in daily observations across 17 regions within the United States. We find changes in yearly precipitation variability for most regions, though results in the central U.S. are mixed. We also identify rising average annual precipitation and precipitation frequency for the central and eastern U.S. and falling average annual precipitation and frequency for the western U.S. Our results are important for agriculture and water resource management and can be compared against historical climate model simulations to determine how well they reproduce observations. Key Points: We find widespread robust changes in two measures of interannual precipitation variability across the United StatesWe detect increases (decreases) in annual mean precipitation and wet day frequency across the eastern (western) United StatesWe explore the interaction of changes in precipitation frequency and wet day precipitation intensity on interannual variability [ABSTRACT FROM AUTHOR]

Author(s): Daniel E. Horton (corresponding author) [1, 2]; Nathaniel C. Johnson [3, 4, 5]; Deepti Singh [1]; Daniel L. Swain [1]; Bala Rajaratnam [1, 2, 6]; Noah S. Diffenbaugh [1, [...]