scholarly journals TOPOFIRE: A Topographically Resolved Wildfire Danger and Drought Monitoring System for the Conterminous United States

2019 ◽  
Vol 100 (9) ◽  
pp. 1607-1613 ◽  
Author(s):  
Zachary A. Holden ◽  
W. Matt Jolly ◽  
Alan Swanson ◽  
Dyer A. Warren ◽  
Kelsey Jencso ◽  
...  
Keyword(s):  
United States ◽  
Snow Water Equivalent ◽  
Wildland Fire ◽  
Early Warning Systems ◽  
The United States ◽  
Snow Accumulation ◽  
Assessment System ◽  
Shortwave Radiation ◽  
Mountainous Regions ◽  
Proactive Management

AbstractPatterns of energy and available moisture can vary over small (<1 km) distances in mountainous terrain. Information on fuel and soil moisture conditions that resolves this variation could help to inform fire and drought management decisions. Here, we describe the development of TOPOFIRE, a web-based mapping system designed to provide finely resolved information on soil water balance, drought, and wildfire danger information for the contiguous United States. We developed 8-arc-second-resolution (~250 meter) daily historical, near real-time, and 4-day forecast radiation, temperature, humidity, and snow water equivalent data and used these grids to calculate a suite of drought and wildfire danger indices. Large differences in shortwave radiation and surface air temperature with aspect contribute to greater snow accumulation and delays in melt timing on north-facing slopes, delaying fuel conditioning on shaded slopes. These datasets will help advance our understanding of the role of topography in wildland fire spread and ecological effects. Integration with national programs like the Wildland Fire Assessment System, the Wildland Fire Decision Support System, and drought early warning systems could support more proactive management of wildland fires and refine the characterization of drought in mountainous regions of the United States.

Effects of wildfire on national park visitation and the regional economy: a natural experiment in the Northern Rockies

2013 ◽  
Vol 22 (8) ◽  
pp. 1155 ◽  
Author(s):  
John W. Duffield ◽  
Chris J. Neher ◽  
David A. Patterson ◽  
Aaron M. Deskins
Keyword(s):  
United States ◽  
National Park ◽  
Net Present Value ◽  
Wildland Fire ◽  
The United States ◽  
Management Policy ◽  
Demand Models ◽  
Value Loss ◽  
Park Service ◽  
Observed Behaviour

Federal wildland fire management policy in the United States directs the use of value-based methods to guide priorities. However, the economic literature on the effect of wildland fire on nonmarket uses, such as recreation, is limited. This paper introduces a new approach to measuring the effect of wildfire on recreational use by utilising newly available long-term datasets on the location and size of wildland fire in the United States and observed behaviour over time as revealed through comprehensive National Park Service (NPS) visitor data. We estimate travel cost economic demand models that can be aggregated at the site-landscape level for Yellowstone National Park (YNP). The marginal recreation benefit per acre of fire avoided in, or proximate to, the park is US$43.82 per acre (US$108.29 per hectare) and the net present value loss for the 1986–2011 period is estimated to be US$206 million. We also estimate marginal regional economic impacts at US$36.69 per acre (US$90.66 per hectare) and US$159 million based on foregone non-resident spending in the 17-county Great Yellowstone Area (GYA). These methods are applicable where time-series recreation data exist, such as for other parks and ecosystems represented in the 397-unit NPS system.

Desert Traffic: The Dynamics of the Drug Trade in Northwestern Mexico

2004 ◽  
Author(s):  
Eric P. Perramond
Keyword(s):  
United States ◽  
Urban Areas ◽  
Cannabis Sativa ◽  
The United States ◽  
The State ◽  
Drug Industry ◽  
Total Output ◽  
Northern Mexico ◽  
Mountainous Regions ◽  
Northwestern Mexico

The semiarid expanses of northern Mexico have long been a haven for drug trafficking and shipment into the southwestern United States. During the past 3 decades, a more specialized and dedicated drug industry has used the long U.S.-Mexican border to move illicit narcotics. Northern Mexico is not a heavily indigenous zone, and yet some native populations have been adversely affected by this recent industry, and not just a few have taken a role in it. Two states in northern Mexico that still have indigenous peoples are Sonora and Chihuahua. Both of these semiarid states are more sparsely populated than the rest of Mexico, yet both share a long, expansive border with the United States. Thus, neither state has escaped the activities of the drug industry, and some of the major drug cartels are located in this region (figure 8.1), the largest in urban areas such as Ciudad Juarez in the state of Chihuahua and Culiacán in the state of Sinaloa. Although these urban areas are the economic and logistical centers of two large cartels, an aspect frequently ignored in the literature, and certainly in policy circles, is the variety of scales of production in this industry. Aside from these giant cartels, drug cultivation, production, and transportation are also common at lesser scales, and the difficulties and dangers associated with drug production and trafficking extend to these small farmers. Small plots of marijuana (Cannabis sativa) and poppies (Papaver somniferum) dot the northern Mexican landscape, especially in the foothills and high peaks of the Sierra Madre. Most of the poppy production lies further south, in the states of Michoacan, Guerrero, and Oaxaca. Marijuana (Cannabis) is by far the more common of the two illicit crops grown in Mexico, partly because of its longer history of cultivation in the country’s mountainous regions and partly because of its greater ease of integration into agriculture. Poppy fields are a lot harder to hide, both from neighbors and from more interested authorities. Marijuana is also more easily intercropped with more common agricultural crops. Intercropping is the practice of growing two or more crops in the same field or parcel of land, and it is common when farmers need to maximize total output per unit of area (Wilken 1987: 248). I have seen marijuana integrated with corn, bean, squash, sunflower, and tomato plants.

scholarly journals Projecting Medicinal Plant Trade Volume and Value in Deciduous Forests of the Eastern United States

Forests ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 74 ◽  
Author(s):  
Steve D. Kruger ◽  
John F. Munsell ◽  
James L. Chamberlain ◽  
Jeanine M. Davis ◽  
Ryan D. Huish
Keyword(s):  
United States ◽  
Multinomial Logistic Regression ◽  
The United States ◽  
Black Cohosh ◽  
Raw Material ◽  
Similar Data ◽  
Trade Volume ◽  
Eastern United States ◽  
Medicinal Species ◽  
Mountainous Regions

The volume, value and distribution of the nontimber forest product (NTFP) trade in the United States are largely unknown. This is due to the lack of systematic, periodic and comprehensive market tracking programs. Trade measurement and mapping would allow market actors and stakeholders to improve market conditions, manage NTFP resources, and increase the sustainable production of raw material. This is especially true in the heavily forested and mountainous regions of the eastern United States. This study hypothesized that the tendency to purchase medicinal NTFPs in this region can be predicted using socioeconomic and environmental variables associated with habitat and trade, and those same variables can be used to build more robust estimates of trade volume. American ginseng (Panax quinquefolius L.) dealers were surveyed (n = 700), because by law they must acquire a license to legally trade in this species, and therefore report a business address. They also record purchase data. Similar data are not reported for other medicinal species sold to the same buyers, known colloquially as ‘off-roots’. Ginseng buyers were queried about trade activity in eleven commonly-harvested and previously untracked medicinal NTFP species in 15 states. Multinomial logistic regression comprised of socioeconomic and environmental predictors tied to business location was used to determine the probability that a respondent purchased off-roots. Significant predictors included location in a particular subregion, population and percentage of employment in related industries. These variables were used in a two-step cluster analysis to group respondents and nonrespondents. Modeled probabilities for off-root purchasing among respondents in each cluster were used to impute average off-root volumes for a proportion of nonrespondents in the same cluster. Respondent observations and nonrespondent estimations were summed and used to map off-root trade volume and value. Model functionality and estimates of the total volume, value and spatial distribution are discussed. The total value of the species surveyed to harvesters was 4.3 million USD. We also find that 77 percent of the trade value and 73 percent of the trade volume were represented by two species: black cohosh (Actaea racemosa L.) and goldenseal (Hydrastis canqdensis L.)

scholarly journals Modeling Regional-Scale Wildland Fire Emissions with the Wildland Fire Emissions Information System*

2014 ◽  
Vol 18 (16) ◽  
pp. 1-26 ◽  
Author(s):  
Nancy H. F. French ◽  
Donald McKenzie ◽  
Tyler Erickson ◽  
Benjamin Koziol ◽  
Michael Billmire ◽  
...  
Keyword(s):  
United States ◽  
Information System ◽  
North America ◽  
Fuel Consumption ◽  
Carbon Emissions ◽  
Wildland Fire ◽  
The United States ◽  
Fire Emissions ◽  
The U.S ◽  
The Web

Abstract As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products.

An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

2018 ◽  
Vol 33 (1) ◽  
pp. 301-315 ◽  
Author(s):  
Wesley G. Page ◽  
Natalie S. Wagenbrenner ◽  
Bret W. Butler ◽  
Jason M. Forthofer ◽  
Chris Gibson
Keyword(s):  
United States ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Wildland Fire ◽  
Fire Behavior ◽  
The United States ◽  
Mean Bias Error ◽  
Bias Error ◽  
Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

scholarly journals Shortwave Radiative Fluxes on Slopes

2016 ◽  
Vol 55 (7) ◽  
pp. 1513-1532
Author(s):  
Yingtao Ma ◽  
Rachel T. Pinker ◽  
Margaret M. Wonsick ◽  
Chuan Li ◽  
Laura M. Hinkelman
Keyword(s):  
United States ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
The United States ◽  
Spatial And Temporal Variability ◽  
Remotely Sensed Data ◽  
Radiative Fluxes ◽  
Attractive Option ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractSnow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.

scholarly journals Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
Author(s):  
Naoki Mizukami ◽  
Sanja Perica
Keyword(s):  
United States ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Western United States ◽  
Densification Rate ◽  
Snow Density ◽  
Mountainous Regions ◽  
Spatiotemporal Characteristics ◽  
Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

scholarly journals Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas*

2015 ◽  
Vol 16 (5) ◽  
pp. 2169-2186 ◽  
Author(s):  
Stefanie Jörg-Hess ◽  
Nena Griessinger ◽  
Massimiliano Zappa
Keyword(s):  
Snow Water Equivalent ◽  
Similarity Score ◽  
Snow Accumulation ◽  
Ensemble Prediction ◽  
Regional Patterns ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Runoff Volume ◽  
Probabilistic Forecasts ◽  
Snow Water

Abstract Good initial states can improve the skill of hydrological ensemble predictions. In mountainous regions such as Switzerland, snow is an important component of the hydrological system. Including estimates of snow cover in hydrological models is of great significance for the prediction of both flood and streamflow drought events. In this study, gridded snow water equivalent (SWE) maps, derived from daily snow depth measurements, are used within the gridded version of the conceptual hydrological model Precipitation Runoff Evapotranspiration Hydrotope (PREVAH) to replace the model SWE at initialization. The ECMWF Ensemble Prediction System (ENS) reforecast is used as meteorological input for 32-day forecasts of streamflow and SWE. Experiments were performed in several parts of the Alpine Rhine and the Thur River. Predictions where modeled SWE estimates were replaced with SWE maps could successfully enhance the predictability of SWE up to a lead time of 25 days, especially at the beginning and the end of the snow season. Additionally, the prediction of the runoff volume was improved, particularly in catchments where the snow accumulation, and thus the runoff volume, had been greatly overestimated. These improvements in predictions have been made without affecting the ability of the forecast system to discriminate between the different runoff volumes observed. A spatial similarity score was first used in the context of SWE forecast verification. This confirmed the findings of the time series analysis and yielded additional insight on regional patterns of extended range SWE predictability.

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