scholarly journals Fire-Environment Analysis: An Example of Army Garrison Camp Williams, Utah

Fire ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 6
Author(s):  
Scott M. Frost ◽  
Martin E. Alexander ◽  
R. Justin DeRose ◽  
Michael J. Jenkins
Keyword(s):  
Vegetation Type ◽  
Fire Behavior ◽  
Weather Data ◽  
Climate Data ◽  
Classification Rate ◽  
Environment Analysis ◽  
Fire Environment ◽  
Area Of Concern ◽  
Utah Juniper ◽  
Wyoming Big Sagebrush

The planning of fuel treatments for ecological or societal purposes requires an in-depth understanding of the conditions associated with the occurrence of free-burning fire behavior for the area of concern. Detailed fire-environment analysis for Army Garrison Camp Williams (AGCW) in north-central Utah was completed as a prerequisite for fuel treatment planning, using a procedure that could be generally applied. Vegetation and fuels data, topographic and terrain features, and weather and climate data, were assessed and integrated into predictive fuel models to aid planning. A fire behavior fuel model map was developed from biophysical variables, vegetation type, and plot survey data using random forests, and resulted in an overall classification rate of 72%. The predominate vegetation type-fuel complex was grass, followed by lesser amounts of Gambel oak, Wyoming big sagebrush and Utah juniper. The majority of AGCW is mountainous in nature, characterized by slopes less than 40% in steepness with slightly more northerly and easterly aspects than south and west, and elevations that ranged from 1650 to 1950 m above mean sea level. Local fire weather data compiled from the three nearest remote automated weather stations indicated that average temperature maxima (32 °C) and relative humidity minima (12%) usually occurred between 1400 to 1500 h daily, and from July to August, seasonally. The semi-arid climate at AGCW, coupled with the corresponding preponderance of flashy fuel types and sloping terrain, constitutes a formidable fire environment in which to plan for mitigating against adverse fire behavior.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

<p>Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).</p><p>The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.</p><p>During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.</p>

Impact of Ventilation on Fire Behavior in Legacy and Contemporary Residential Construction

2010 ◽  
Author(s):  
Steve Kerber ◽  
Keyword(s):  
Homeland Security ◽  
Fire Behavior ◽  
The United States ◽  
Fire Service ◽  
United States Department ◽  
Department Of Homeland Security ◽  
Front Door ◽  
Fire Environment ◽  
Open Floor ◽  
The Impact

Under the United States Department of Homeland Security (DHS) Assistance to Firefighter Grant Program, Underwriters Laboratories examined fire service ventilation practices as well as the impact of changes in modern house geometries. There has been a steady change in the residential fire environment over the past several decades. These changes include larger homes, more open floor plans and volumes and increased synthetic fuel loads. This series of experiments examine this change in fire behavior and the impact on firefighter ventilation tactics. This fire research project developed the empirical data that is needed to quantify the fire behavior associated with these scenarios and result in immediately developing the necessary firefighting ventilation practices to reduce firefighter death and injury. Two houses were constructed in the large fire facility of Underwriters Laboratories in Northbrook, IL. The first of two houses constructed was a one-story, 1200 ft, 3 bedroom, 
bathroom house with 8 total rooms. The second house was a two-story 3200 ft, 4 bedroom, 2.5 bathroom house with 12 total rooms. The second house featured a modern open floor plan, two- story great room and open foyer. Fifteen experiments were conducted varying the ventilation locations and the number of ventilation openings. Ventilation scenarios included ventilating the front door only, opening the front door and a window near and remote from the seat of the fire, opening a window only and ventilating a higher opening in the two-story house. One scenario in each house was conducted in triplicate to examine repeatability. The results of these experiments provide knowledge for the fire service for them to examine their thought processes, standard operating procedures and training content. Several tactical considerations were developed utilizing the data from the experiments to provide specific examples of changes that can be adopted based on a departments current strategies and tactics.

Long-Term Effects of Fire on Vegetation Structure and Predicted Fire Behavior in Wyoming Big Sagebrush Ecosystems

Ecosystems ◽  
2018 ◽  
Vol 22 (2) ◽  
pp. 257-265 ◽  
Author(s):  
Schyler A. Reis ◽  
Lisa M. Ellsworth ◽  
J. Boone Kauffman ◽  
David W. Wrobleski
Keyword(s):  
Vegetation Structure ◽  
Fire Behavior ◽  
Long Term Effects ◽  
Big Sagebrush ◽  
Wyoming Big Sagebrush

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

<p>Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R² = 0.90 and RMSE about 3.4 Qt.ha<sup>-1</sup>.  When comparing the model’s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.</p>

scholarly journals Climate change impact – residential unit

2019 ◽  
Vol 279 ◽  
pp. 03007
Author(s):  
Ján Hollý ◽  
Adela Palková
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Weather Data ◽  
Emission Scenarios ◽  
Climate Data ◽  
Climate Conditions ◽  
Residential Unit ◽  
Data Application ◽  
Change Impact ◽  
Selection Of

The issue of climate change is undeniably demonstrating its presence. Consequently, there is a rising need to be prepared for upcoming threats by any means possible. One of the precautions includes obtaining the information characterizing the expected impact of global warming. This will allow authorities and other stakeholders to act accordingly in time. The article presents the assessment of the extent of impact of energy-related construction solutions in dwelling type unit situated in Central Europe region under the 21st century climate conditions. The findings represent eventual demands of energy for cooling and heating and its prospective savings. This is conducted by consecutively and automatically changing the parameters in individual simulation runs. As a basis for simulations, regionally scaled weather data of three different climate areas are used. These data are based on the emission scenarios by IPCC and are reaching to the year 2100. The selection of assessed parameters and climate data application are briefly explained in the article. The results of simulations are evaluated and recommended solutions are stated in regard to the specific energy-related construction changes. The aim is to successfully mitigate and adapt to the climate change phenomenon.

scholarly journals The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 180
Author(s):  
Amit Kumar Srivastava ◽  
Andrej Ceglar ◽  
Wenzhi Zeng ◽  
Thomas Gaiser ◽  
Cho Miltin Mboh ◽  
...  
Keyword(s):  
Crop Yield ◽  
Large Scale ◽  
Regional Scale ◽  
Maize Yield ◽  
Weather Data ◽  
Climate Data ◽  
Agricultural Planning ◽  
Input Variables ◽  
Different Sources ◽  
Grid Based

High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.

scholarly journals Evaluating the Temperature Difference Parameter in the SSEBop Model with Satellite-Observed Land Surface Temperature Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1947 ◽  
Author(s):  
Lei Ji ◽  
Gabriel B. Senay ◽  
Naga M. Velpuri ◽  
Stefanie Kagone
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Temperature Difference ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Data ◽  
Climate Data ◽  
Modis Data ◽  
South Central

The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (dT) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT (dTo) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dTo timeseries in the West Coast and South-Central regions. The comparison of dTo and modeled dT (dTm) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dTm estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.

Assessing the reliability of dynamical and historical climate forecasts in simulating hindcast pasture growth rates

2017 ◽  
Vol 57 (7) ◽  
pp. 1525 ◽  
Author(s):  
Matthew T. Harrison ◽  
Karen M. Christie ◽  
Richard P. Rawnsley
Keyword(s):  
Growth Rate ◽  
Growth Rates ◽  
Circulation Model ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Weather Data ◽  
Climate Data ◽  
Climate Forecasts ◽  
Farm Model ◽  
The Impact

A priori knowledge of seasonal pasture growth rates helps livestock farmers plan with pasture supply and feed budgeting. Longer forecasts may allow managers more lead time, yet inaccurate forecasts could lead to counterproductive decisions and foregone income. By using climate forecasts generated from historical archives or the global circulation model (GCM) called the Predictive Ocean Atmosphere Model for Australia (POAMA), we simulated pasture growth rates in a whole-farm model and compared growth-rate forecasts with growth-rate hindcasts (viz. retrospective forecasts). Hindcast pasture growth rates were generated using posterior weather data measured at two sites in north-western Tasmania, Australia. Forecasts were made on a monthly basis for durations of 30, 60 and 90 days. Across sites, forecasting approaches and durations, there were no significant differences between simulated growth-rate forecasts and hindcasts when our statistical inference was conducted using either the Kolmogorov–Smirnov statistic or empirical cumulative distribution functions. However, given that both of these tests were calculated by comparing growth-rate hindcasts with monthly distributions of forecasts, we also examined linear correlations between monthly hindcast values and median monthly growth-rate forecasts. Using this approach, we found a higher correlation between hindcasts and median monthly forecasts for 30 days than for 60 or 90 days, suggesting that monthly growth-rate forecasts provide more skilful predictions than forecast durations of 2 or 3 months. The range in monthly growth-rate forecasts at 30 days was less than that at 60 or 90 days, further reinfocing the aforementioned result. The strength of the correlation between growth-rate hindcasts and median monthly forecasts from the historical approach was similar to that generated using POAMA data. Overall, the present study found that (1) statistical methods of comparing forecast data with hindcast data are important, particularly if the former is a distribution whereas the latter is a single value, (2) 1-month growth-rate forecasts have less uncertainty than forecast durations of 2 or 3 months, and (3) there is little difference between pasture growth rates simulated using climate data from either historical records or from GCMs. To test the generality of these conclusions, the study should be extended to other dairy regions. Including more regions would both enable studies of sites with greater intra-seasonal climate variability, but also better highlight the impact of seasonal and regional variation in forecast skill of POAMA as applied in our forecasting methods.

Effects of inter-annual climate variability on grape harvest timing in rainfed hilly vineyards of Piedmont (NW Italy)

2020 ◽  
Author(s):  
Danilo Rabino ◽  
Marcella Biddoccu ◽  
Giorgia Bagagiolo ◽  
Guido Nigrelli ◽  
Luca Mercalli ◽  
...  
Keyword(s):  
Climate Variability ◽  
Weather Data ◽  
Maximum Temperature ◽  
Data Series ◽  
Positive Trend ◽  
Climate Variables ◽  
Climate Data ◽  
Historical Series ◽  
Nw Italy ◽  
Level Of Significance

&lt;p&gt;Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.&lt;/p&gt;&lt;p&gt;A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the &amp;#8220;Monferrato&amp;#8221; area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962&amp;#8211;2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.&lt;/p&gt;&lt;p&gt;The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P&lt; 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using the most complete dataset. The change ratios obtained using datasets including last 15 years were greater (in absolute terms) than results limited to the period 1962-2002, and also correlations have greater level of significance. The results indicated clearly the relationships between the temperature trend and the gradual anticipation of harvest and the importance of having long and continuous historical weather data series available.&lt;/p&gt;

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