scholarly journals A data-driven prediction model for Fennoscandian wildfires

2021 ◽  
Author(s):  
Sigrid Jørgensen Bakke ◽  
Niko Wanders ◽  
Karin van der Wiel ◽  
Lena Merete Tallaksen
Keyword(s):  
Soil Water ◽  
Prediction Models ◽  
Terrestrial Ecosystems ◽  
Fire Danger ◽  
Future Climate ◽  
Data Driven ◽  
Separate Analysis ◽  
Fire Occurrence ◽  
Fire Weather Index ◽  
Volumetric Soil Water

Abstract. Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia, and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly 2001–2019 satellite-based fire occurrence dataset at a 0.25° spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian fire weather index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This improvement shows the potential of developing reliable data-driven prediction models for regions with a high quality fire occurrence record, and the limitation of using satellite-based fire occurrence data in regions subject to small fires not picked up by satellites. We conclude that data-driven fire prediction models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and its compound predictors can further be used to assess potential changes in fire danger probability under future climate scenarios.

scholarly journals Assessment of Forest Fire Rating Systems in Typical Mediterranean Forest, Crete, Greece

2017 ◽  
Author(s):  
Mohamed Elhag ◽  
Slivena Boteva
Keyword(s):  
Moisture Content ◽  
Forest Fire ◽  
Pinus Halepensis ◽  
Water Status ◽  
Fire Danger ◽  
Forest Understory ◽  
Fire Season ◽  
Fire Occurrence ◽  
Fire Weather Index ◽  
Area Burned

Abstract. The Fire Weather Index (FWI) module was tested under the Mediterranean- type conditions of Crete (Greece) for the two fire seasons 2008–2009. High correlations were found between the Fine Fuel Moisture Code (FFMC) and the Duff Moisture Code (DMC. The Drought Code (DC) was insignificantly correlated with the soil moisture content. No significant correlation was found between the area burned by wildfires and any component of the FWI system during the studied period, unlike fire occurrence with which most of the components were highly correlated. Meanwhile, the Keetch-Byram Drought Index (KBDI) of the American Forest Fire Danger Rating System (NFFDRS) was also examined under the same conditions. It provided a useful means of monitoring general wetting and drying cycles, but is inadequate for indicating daily fire danger throughout the fire season in our region. Weak correlations between the KBDI- the fire occurrence and the area burned were found for the two fire seasons studied-2008–2009. Correlations between the KBDI and litter, duff and soil did not give statistically sound results. On the contrary, the KBDI seemed to predict with high accuracy the moisture content of three annual plants (Piplatherum miliaceum, Parietaria diffusa, Avena sterillis) with a shallow rooting system of Pinus halepensis forest understory in the region. This indicated that the index was adequate, to a certain extent, to represent the upper soil layers' water status, while it is unsuitable to predict needles moisture content of Pinus halepensis, which has a deep rooting system.

Future Climate Change Impact on Wildfire Danger over the Mediterranean: the case of Greece

2021 ◽  
Author(s):  
Anastasios Rovithakis ◽  
Apostolos Voulgarakis ◽  
Manolis Grillakis ◽  
Christos Giannakopoulos ◽  
Anna Karali
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Fire Risk ◽  
Fire Danger ◽  
Future Climate ◽  
Future Climate Change ◽  
Fire Weather Index ◽  
The Mediterranean ◽  
The Impact ◽  
Of Greece

<p>The Canadian Fire Weather Index (FWI) is a meteorologically based index designed initially to be used in Canada but it can also be used worldwide, including the Mediterranean, to estimate fire danger in a generalized fuel type based solely on weather observations. The four weather variables are measured and used as inputs to the FWI (rain accumulated over 24 h, temperature, relative humidity, and wind speed) are generally taken daily at noon local standard time.</p><p>Recent studies have shown that temperature and precipitation in the Mediterranean, and more specifically in Greece are expected to change, indicating longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity is on the rise. In the present study, the FWI index is used in order to assess changes in future fire danger conditions.</p><p>To represent meteorological conditions, regional EURO-CORDEX climate model simulations over the Mediterranean and mainly Greece at a spatial resolution of 11 km, were utilized. In order to assess the impact of future climate change, we used two Representative Concentration Pathway (RCP) scenarios consisting of an optimistic emission scenario where emissions peak and decline beyond 2020 (RCP2.6) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5).  We compare the FWI projections for two future time periods, 2021-2050 and 2071-2100 with reference to the historical time period 1971-2000. Based on the critical fire risk threshold values that have been established in previous studies for the area of Greece, the days with critical fire risk were calculated for different Greek domains.</p>

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

Assessing the capacity of Earth System Models to simulate spatiotemporal variability in fire weather indicators

2021 ◽  
Author(s):  
Carolina Gallo Granizo ◽  
Jonathan Eden ◽  
Bastien Dieppois ◽  
Matthew Blackett
Keyword(s):  
Climate Change ◽  
Spatial Scales ◽  
Fire Danger ◽  
Spatiotemporal Variability ◽  
Global Evaluation ◽  
Fire Weather ◽  
Earth System ◽  
Fire Weather Index ◽  
System Models ◽  
Earth System Models

<p>Weather and climate play an important role in shaping global fire regimes and geographical distributions of burnable areas. At the global scale, fire danger is likely to increase in the near future due to warmer temperatures and changes in precipitation patterns, as projected by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). There is a need to develop the most reliable projections of future climate-driven fire danger to enable decision makers and forest managers to take both targeted proactive actions and to respond to future fire events.</p><p>Climate change projections generated by Earth System Models (ESMs) provide the most important basis for understanding past, present and future changes in the climate system and its impacts. ESMs are, however, subject to systematic errors and biases, which are not fully taken into account when developing risk scenarios for wild fire activity. Projections of climate-driven fire danger have often been limited to the use of single models or the mean of multi-model ensembles, and compared to a single set of observational data (e.g. one index derived from one reanalysis).</p><p>Here, a comprehensive global evaluation of the representation of a series of fire weather indicators in the latest generation of ESMs is presented. Seven fire weather indices from the Canadian Forest Fire Weather Index System were generated using daily fields realisations simulated by 25 ESMs from the 6<sup>th</sup> Coupled Model Intercomparison Project (CMIP6). With reference to observational and reanalysis datasets, we quantify the capacity of each model to realistically simulate the variability, magnitude and spatial extent of fire danger. The highest-performing models are identified and, subsequently, the limitations of combining models based on independency and equal performance when generating fire danger projections are discussed. To conclude, recommendations are given for the development of user- and policy-driven model evaluation at spatial scales relevant for decision-making and forest management.</p>

scholarly journals Effects of volumetric soil water content and fertilizer rate on growth and baicalin accumulation in two species of Scutellaria

2018 ◽  
Vol 10 (6) ◽  
pp. 97-105 ◽  
Author(s):  
Morgan Amanda ◽  
Joseph Pearson Brian ◽  
Shad Ali Gul ◽  
Moore Kimberly ◽  
Osborne Lance
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Fertilizer Rate ◽  
Volumetric Soil Water Content ◽  
Volumetric Soil Water

scholarly journals Temporal variations and change in forest fire danger in Europe for 1960–2012

2014 ◽  
Vol 14 (6) ◽  
pp. 1477-1490 ◽  
Author(s):  
A. Venäläinen ◽  
N. Korhonen ◽  
O. Hyvärinen ◽  
N. Koutsias ◽  
F. Xystrakis ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
National Level ◽  
Temporal Variations ◽  
Fire Danger ◽  
Fire Season ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Current Season ◽  
Forest Fire Danger

Abstract. Understanding how fire weather danger indices changed in the past and how such changes affected forest fire activity is important in a changing climate. We used the Canadian Fire Weather Index (FWI), calculated from two reanalysis data sets, ERA-40 and ERA Interim, to examine the temporal variation of forest fire danger in Europe in 1960–2012. Additionally, we used national forest fire statistics from Greece, Spain and Finland to examine the relationship between fire danger and fires. There is no obvious trend in fire danger for the time period covered by ERA-40 (1960–1999), whereas for the period 1980–2012 covered by ERA Interim, the mean FWI shows an increasing trend for southern and eastern Europe which is significant at the 99% confidence level. The cross correlations calculated at the national level in Greece, Spain and Finland between total area burned and mean FWI of the current season is of the order of 0.6, demonstrating the extent to which the current fire-season weather can explain forest fires. To summarize, fire risk is multifaceted, and while climate is a major determinant, other factors can contribute to it, either positively or negatively.

scholarly journals Development of a Global Fire Weather Database

2015 ◽  
Vol 15 (6) ◽  
pp. 1407-1423 ◽  
Author(s):  
R. D. Field ◽  
A. C. Spessa ◽  
N. A. Aziz ◽  
A. Camia ◽  
A. Cantin ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Large Scale ◽  
Prediction Models ◽  
Dry Season ◽  
Weather Data ◽  
Data Sets ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Mato Grosso ◽  
Global Database

Abstract. The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

scholarly journals Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach

2020 ◽  
Author(s):  
Al-Ekram Elahee Hridoy ◽  
Mohammad Naim ◽  
Nazim Uddin Emon ◽  
Imrul Hasan Tipo ◽  
Safayet Alam ◽  
...  
Keyword(s):  
Social Life ◽  
Inflection Point ◽  
Prediction Models ◽  
Short Term Memory ◽  
Data Driven ◽  
World Health ◽  
Estimation Methods ◽  
Atypical Pneumonia ◽  
List Type ◽  
Logistic Curve

AbstractOn December 31, 2019, the World Health Organization (WHO) was informed that atypical pneumonia-like cases have emerged in Wuhan City, Hubei province, China. WHO identified it as a novel coronavirus and declared a global pandemic on March 11th, 2020. At the time of writing this, the COVID-19 claimed more than 440 thousand lives worldwide and led to the global economy and social life into an abyss edge in the living memory. As of now, the confirmed cases in Bangladesh have surpassed 100 thousand and more than 1343 deaths putting startling concern on the policymakers and health professionals; thus, prediction models are necessary to forecast a possible number of cases in the future. To shed light on it, in this paper, we presented data-driven estimation methods, the Long Short-Term Memory (LSTM) networks, and Logistic Curve methods to predict the possible number of COVID-19 cases in Bangladesh for the upcoming months. The results using Logistic Curve suggests that Bangladesh has passed the inflection point on around 28-30 May 2020, a plausible end date to be on the 2nd of January 2021 and it is expected that the total number of infected people to be between 187 thousand to 193 thousand with the assumption that stringent policies are in place. The logistic curve also suggested that Bangladesh would reach peak COVID-19 cases at the end of August with more than 185 thousand total confirmed cases, and around 6000 thousand daily new cases may observe. Our findings recommend that the containment strategies should immediately implement to reduce transmission and epidemic rate of COVID-19 in upcoming days.HighlightsAccording to the Logistic curve fitting analysis, the inflection point of the COVID-19 pandemic has recently passed, which was approximately between May 28, 2020, to May 30, 2020.It is estimated that the total number of confirmed cases will be around 187-193 thousand at the end of the epidemic. We expect that the actual number will most likely to in between these two values, under the assumption that the current transmission is stable and improved stringent policies will be in place to contain the spread of COVID-19.The estimated total death toll will be around 3600-4000 at the end of the epidemic.The epidemic of COVID-19 in Bangladesh will be mostly under control by the 2nd of January 2021 if stringent measures are taken immediately.

scholarly journals Comparison of Methods for the Assessment of Fire Danger in the Czech Republic

2019 ◽  
Vol 67 (5) ◽  
pp. 1285-1295 ◽  
Author(s):  
František Jurečka ◽  
Martin Možný ◽  
Jan Balek ◽  
Zdeněk Žalud ◽  
Miroslav Trnka
Keyword(s):  
Czech Republic ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Danger ◽  
Special Focus ◽  
Fire Weather ◽  
Fire Event ◽  
Fire Weather Index ◽  
The Czech Republic ◽  
Fire Danger Indices

The performance of fire indices based on weather variables was analyzed with a special focus on the Czech Republic. Three fire weather danger indices that are the basis of fire danger rating systems used in different parts of the world were assessed: the Canadian Fire Weather Index (FWI), Australian Forest Fire Danger Index (FFDI) and Finnish Forest Fire Index (FFI). The performance of the three fire danger indices was investigated at different scales and compared with actual fire events. First, the fire danger indices were analyzed for different land use types during the period 1956–2015. In addition, in the analysis, the three fire danger indices were compared with wildfire events during the period 2001–2015. The fire danger indices were also analyzed for the specific locality of the Bzenec area where a large forest fire event occurred in May 2012. The study also focused on the relationship between fire danger indices and forest fires during 2018 across the area of the Jihomoravský region. Comparison of the index values with real fires showed that the index values corresponded well with the occurrence of forest fires. The analysis of the year 2018 showed that the highest index values were reached on days with the greater fire occurrence. On days with 5 or 7 reported fires per day, the fire danger indices reached values between 3 and 4.

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