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... This study uses a data set of quality controlled hourly rainfall observations from the Remote Automated Weather Station (RAWS) network (Brown et al. 2011) produced by Oakley et al. (2018). The data set contains observations from 137 RAWS stations that have at least 80% complete October-May data between 1995-2016. ...
California is regularly impacted by floods and droughts, primarily as a result of too many or too few atmospheric rivers (ARs). This study analyzes a two-decade-long hourly precipitation dataset from 176 California weather stations and a 3-hourly AR chronology to report variations in rainfall events across California and their association with ARs. On average, 10-40 and 60-120 hours of rainfall in southern and northern California, respectively, are responsible for more than half of annual rainfall accumulations. Approximately 10-30% of annual precipitation at locations across the state is from only one large storm. On average, northern California receives 25-45 rainfall events annually (40-50% of which are AR-related). These events typically have longer durations and higher event-precipitation totals than those in southern California. Northern California also receives more AR landfalls with longer durations and stronger Integrated Vapor Transport (IVT). On average, ARs contribute 79%, 76%, and 68% of extreme-rainfall accumulations (i.e., top 5% events annually) in the north coast, northern Sierra, and Transverse Ranges of southern California, respectively. The San Francisco Bay Area terrain gap in the California Coast Range allows more AR water vapor to reach inland over the Delta and Sacramento Valley, and thus, influences precipitation in the Delta’s catchment. This is particularly important for extreme precipitation in the northern Sierra Nevada, including river basins above Oroville Dam and Shasta Dam. This study highlights differences between rainfall and AR characteristics in coastal versus inland northern California, differences that largely determine the regional geography of flood risks and water-reliability. These analyses support water resource, flood, levee, wetland, and ecosystem management within the catchment of the San Francisco estuary system by describing regional characteristics of ARs and their influence on rainfall on an hourly timescale.
... Stations (RAWS), the interagency guidelines and standards should be followed, but it also recommended that fire agencies also review the Brown et al. (2011) report for assessing potential station locations. ...
... This study result suggested that RAWS should be no more than 50 miles apart in the Great Basin, but highlighted that elevation should also be factored such that the 50 mile radius applies within each of three elevation bands (<5,000 feet, ≥5,000 feet and ≤7,000 feet, and >7,000 feet). Brown et al. (2011) conducted a more general study on the entire RAWS network covering all of the United States. The primary purpose of this report was to examine RAWS and non-RAWS (Automated Surface Observing System [ASOS)]) observations with regard to their influence on gridded depictions of model initializations. ...
... For example, the RAWS network was initially established to provide fire danger and weather information. However, given a longterm climatology for a number of RAWS and an emerging variety of land management decision-making needs, RAWS data are now used for more than just wildfire (Brown et al. 2011). ...
Hydrological models require complete and accurate weather data time series to represent watershed‐scale responses adequately. The Global Historical Climatology Network (GHCN) is the most comprehensive weather database used in hydrological modelling studies globally. Since higher‐density, lower‐reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modellers in the United States have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for the estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW) and Closest Station), creating a seamless time series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) and Thornthwaite‐Mather models for 21 US Department of Agriculture‐Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010–2021. Except for three watersheds, all of the SWAT models had Nash‐Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from −25% to 25% with a satisfactory performance rating. IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using novel combined GHCN data with all Closest Station, IDW and IDEW methods.
This chapter assesses the current state of the science regarding the composition, intensity, and drivers of wildland fire emissions in the USA and Canada. Globally and in the USA wildland fires are a major source of gases and aerosols which have significant air quality impacts and climate interactions. Wildland fire smoke can trigger severe pollution episodes with substantial effects on public health. Fire emissions can degrade air quality at considerable distances downwind, hampering efforts by air regulators to meet air standards. Fires are a major global source of aerosols which affect the climate system by absorbing and scattering radiation and by altering optical properties, coverage, and lifetime of clouds. A thorough understanding of fire emissions is essential for effectively addressing societal and climate consequences of wildland fire smoke.
Smoke from wildland fire is a significant concern to resource managers who need tools, knowledge, and training to analyze, address, and minimize potential impacts; follow relevant rules and regulations; and inform the public of possible effects. Successful navigation of competing pressures to appropriately use fire on the landscape to manage fire-adapted and fire-dependent ecosystems, while protecting public health and other air quality values, depends on credible science and tools conceived of and developed in partnership between managers and the research community. Fire and smoke management are made even more complex by the current condition of ecosystems as a result of fire exclusion and the future implications of a changing climate. This chapter describes the scope of smoke management, social and regulatory contexts, and pathways through which scientific information and tools can improve the accuracy and timeliness of management and communication with the public.
Smoke plume dynamic science focuses on understanding the various smoke processes that control the movement and mixing of smoke. A current challenge facing this research is providing timely and accurate smoke information for the increasing area burned by wildfires in the western USA. This chapter synthesizes smoke plume research from the past decade to evaluate the current state of science and identify future research needs. Major advances have been achieved in measurements and modeling of smoke plume rise, dispersion, transport, and superfog; interactions with fire, atmosphere, and canopy; and applications to smoke management. The biggest remaining gaps are the lack of high-resolution coupled fire, smoke, and atmospheric modeling systems, and simultaneous measurements of these components. The science of smoke plume dynamics is likely to improve through development and implementation of: improved observational capabilities and computational power; new approaches and tools for data integration; varied levels of observations, partnerships, and projects focused on field campaigns and operational management; and new efforts to implement fire and stewardship strategies and transition research on smoke dynamics into operational tools. Recent research on a number of key smoke plume dynamics has improved our understanding of coupled smoke modeling systems, modeling tools that use field campaign data, real-time smoke modeling and prediction, and smoke from duff burning. This new research will lead to better predictions of smoke production and transport, including the influence of a warmer climate on smoke.
Modeling smoke dispersion from wildland fires is a complex problem. Heat and emissions are released from a fire front as well as from post-frontal combustion, and both are continuously evolving in space and time, providing an emission source that is unlike the industrial sources for which most dispersion models were originally designed. Convective motions driven by the fire’s heat release strongly couple the fire to the atmosphere, influencing the development and dynamics of the smoke plume. This chapter examines how fire events are described in the smoke modeling process and explores new research tools that may offer potential improvements to these descriptions and can reduce uncertainty in smoke model inputs. Remote sensing will help transition these research tools to operations by providing a safe and reliable means of measuring the fire environment at the space and time scales relevant to fire behavior.
Smoke chemistry (i.e., chemical transformations taking place within smoke plumes) can alter the composition and toxicity of smoke on time scales from minutes to days. Air quality agencies need better information on and better models of smoke chemistry to more accurately characterize the contributions of smoke to ambient ozone and particulate matter, and to better predict good windows for prescribed burning. The ability of these agencies to quantify the contributions of wildland fires to air pollutants and the ability of forest and burn managers to both predict and mitigate these impacts are limited by how current models represent smoke chemistry. This limitation is interconnected with uncertainties in smoke emissions, plume dynamics, and long-range transport. Improving predictive models will require a combination of laboratory, field, and modeling studies focused on enhancing our knowledge of smoke chemistry, including when smoke interacts with anthropogenic emissions and enters indoors.
At a fundamental level, smoke from wildland fire is of scientific concern because of its potential adverse effects on human health and social well-being. Although many impacts (e.g., evacuations, property loss) occur primarily in proximity to the actual fire, smoke can end up having a significant social impact far from the source. This dynamic, combined with lengthening fire seasons, suggests that understanding how wildland fire smoke affects diverse social values will be increasingly critical. This chapter reviews the existing scientific knowledge related to wildland fire smoke with respect to four topic areas: human health, economics, social acceptability, and risk communication. The broadest existing knowledge base, regarding the health effects attributed to wildland fire smoke exposure, stems from decades of research on the health effects of exposures to ambient fine particulate matter (PM 2.5 ). Despite the potential consequences, scientific knowledge about chronic health effects, economic impacts, and effectiveness of protective actions in response to wildfire smoke risk communication is fairly limited. The chapter concludes with identification of (1) key areas where the need for more empirical information is most critical, and (2) challenges that inhibit an improved scientific understanding.
Recent large wildfires in the USA have exposed millions of people to smoke, with major implications for health and other social and economic values. Prescribed burning for ecosystem health purposes and hazardous fuel reduction also adds smoke to the atmosphere, in some cases affecting adjacent communities. However, we currently lack an appropriate assessment framework that looks past the planned versus unplanned nature of a fire and assesses the environmental conditions under which particular fires burn, their socio-ecological settings, and implications for smoke production and management. A strong scientific foundation is needed to address wildland fire smoke challenges, especially given that degraded air quality and smoke exposure will likely increase in extent and severity as the climate gets warmer. It will be especially important to provide timely and accurate smoke information to help communities mitigate potential smoke impacts from ongoing wildfires, as well as from planned prescribed fires. This assessment focuses on primary physical, chemical, biological, and social considerations by documenting our current understanding of smoke science and how the research community can collaborate with resource managers and regulators to advance smoke science over the next decade.
