scholarly journals The importance of antecedent vegetation and drought conditions as global drivers of burnt area

2021 ◽  
Vol 18 (12) ◽  
pp. 3861-3879
Author(s):  
Alexander Kuhn-Régnier ◽  
Apostolos Voulgarakis ◽  
Peer Nowack ◽  
Matthias Forkel ◽  
I. Colin Prentice ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Fire Season ◽  
Random Forest Regression ◽  
Burnt Area ◽  
Antecedent Conditions ◽  
Antecedent Moisture ◽  
Out Of Sample ◽  
Fuel Accumulation ◽  
Moisture Conditions ◽  
The Impact

Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

scholarly journals Quantifying the Importance of Antecedent Fuel-Related Vegetation Properties for Burnt Area using Random Forests

2020 ◽  
Author(s):  
Alexander Kuhn-Régnier ◽  
Apostolos Voulgarakis ◽  
Peer Nowack ◽  
Matthias Forkel ◽  
I. Colin Prentice ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Fire Season ◽  
Burnt Area ◽  
Antecedent Conditions ◽  
Antecedent Moisture ◽  
Out Of Sample ◽  
Fuel Accumulation ◽  
Moisture Conditions ◽  
The Impact ◽  
Antecedent Moisture Conditions

Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month, and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. We showed that the inclusion of antecedent vegetation conditions on timescales > 1 yr had no impact on burnt area, but inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.567 to 0.686. The inclusion of antecedent moisture conditions also improved the simulation of burnt area through its influence on fuel build-up, which is additional to the influence of current moisture levels on fuel drying. The length of the period which needs to be considered to account for fuel build-up varies across biomes; fuel-limited regions are sensitive to antecedent conditions over longer time periods (~4 months) and moisture-limited regions are more sensitive to current conditions.

scholarly journals Understanding the Impact of Different Landscape-Level Fuel Management Strategies on Wildfire Hazard in Central Portugal

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 522
Author(s):  
Akli Benali ◽  
Ana C. L. Sá ◽  
João Pinho ◽  
Paulo M. Fernandes ◽  
José M. C. Pereira
Keyword(s):  
Management Strategies ◽  
Treatment Strategies ◽  
Fire Season ◽  
Burned Area ◽  
Treatment Area ◽  
Fuel Treatment ◽  
The North ◽  
Wildfire Hazard ◽  
Central Portugal ◽  
The Impact

The extreme 2017 fire season in Portugal led to widespread recognition of the need for a paradigm shift in forest and wildfire management. We focused our study on Alvares, a parish in central Portugal located in a fire-prone area, which had 60% of its area burned in 2017. We evaluated how different fuel treatment strategies may reduce wildfire hazard in Alvares through (i) a fuel break network with different extents corresponding to different levels of priority and (ii) random fuel treatments resulting from a potential increase in stand-level management intensity. To assess this, we developed a stochastic wildfire simulation system (FUNC-SIM) that integrates uncertainties in fuel distribution over the landscape. If the landscape remains unchanged, Alvares will have large burn probabilities in the north, northeast and center-east areas of the parish that are very often associated with high fireline intensities. The different fuel treatment scenarios decreased burned area between 12.1–31.2%, resulting from 1–4.6% increases in the annual treatment area and reduced the likelihood of wildfires larger than 5000 ha by 10–40%. On average, simulated burned area decreased 0.22% per each ha treated, and cost-effectiveness decreased with increasing area treated. Overall, both fuel treatment strategies effectively reduced wildfire hazard and should be part of a larger, holistic and integrated plan to reduce the vulnerability of the Alvares parish to wildfires.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Simulating Antecedent Moisture Conditions to Calibrate Rainfall Derived Inflow and Infiltration for Sanitary Systems

2012 ◽  
Vol 2012 (16) ◽  
pp. 1296-1317
Author(s):  
Ben Gamble ◽  
Eric Saylor ◽  
Joseph Koran ◽  
Susan Moisio ◽  
Nancy Schultz ◽  
...  
Keyword(s):  
Antecedent Moisture ◽  
Moisture Conditions ◽  
Antecedent Moisture Conditions

scholarly journals Multi-Scale Spatiotemporal Change Characteristics Analysis of High-Frequency Disturbance Forest Ecosystem Based on Improved Spatiotemporal Cube Model

2021 ◽  
Vol 13 (13) ◽  
pp. 2537
Author(s):  
Yangcen Zhang ◽  
Xiangnan Liu ◽  
Meiling Liu ◽  
Xinyu Zou ◽  
Qian Zhang ◽  
...  
Keyword(s):  
High Frequency ◽  
Forest Ecosystems ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Anthropogenic Factors ◽  
Residential Areas ◽  
Short Term ◽  
Spatiotemporal Changes ◽  
Spatiotemporal Scales ◽  
The Impact

High-frequency disturbance forest ecosystems undergo complex and frequent changes at various spatiotemporal scales owing to natural and anthropogenic factors. Effectively capturing the characteristics of these spatiotemporal changes from satellite image time series is a powerful and practical means for determining their causes and predicting their trends. Herein, we combined the spatiotemporal cube and vegetation indices to develop the improved spatiotemporal cube (IST-cube) model. We used this to acquire the spatiotemporal dynamics of forest ecosystems from 1987 to 2020 in the study area and then classified it into four spatiotemporal scales. The results showed that the cube-core only exists in the increasing IST-cubes, which are distributed in residential areas and forests. The length of the IST-cube implies the duration of triggers. Human activities result in long-term small-scope IST-cubes, and the impact in the vicinity of residential areas is increasing while there is no change within. Meteorological disasters cause short-term, large scope, and irregular impacts. Land use type change causes short-term small scope IST-cubes and a regular impact. Overall, we report the robustness and strength of the IST-cube model in capturing spatiotemporal changes in forest ecosystems, providing a novel method to examine complex changes in forest ecosystems via remote sensing.

scholarly journals Empirical methods for the estimation of Southern Ocean CO<sub>2</sub>: support vector and random forest regression

2017 ◽  
Vol 14 (23) ◽  
pp. 5551-5569 ◽  
Author(s):  
Luke Gregor ◽  
Schalk Kok ◽  
Pedro M. S. Monteiro
Keyword(s):  
Random Forest ◽  
Southern Ocean ◽  
Synthetic Data ◽  
Good Effect ◽  
Support Vector ◽  
Co2 Uptake ◽  
Empirical Methods ◽  
Random Forest Regression ◽  
Proxy Variables ◽  
The Impact

Abstract. The Southern Ocean accounts for 40 % of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate ΔpCO2 in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschützer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude (14.84 and 16.45 µatm, where 1 atm  =  101 325 Pa) where the SVR method has a larger RMSE (24.40 µatm). However, the larger errors for SVR and RFR are, in part, due to an increase in coastal observations from SOCAT v2 to v3, where the SOM-FFN method used v2 data. The success of both SOM-FFN and RFR depends on the ability to adapt to different modes of variability. The SOM-FFN achieves this by having independent regression models for each cluster, while this flexibility is intrinsic to the RFR method. Analyses of the estimates shows that the SVR and RFR's respective sensitivity and robustness to outliers define the outcome significantly. Further analyses on the methods were performed by using a synthetic dataset to assess the following: which method (RFR or SVR) has the best performance? What is the effect of using time, latitude and longitude as proxy variables on ΔpCO2? What is the impact of the sampling bias in the SOCAT v3 dataset on the estimates? We find that while RFR is indeed better than SVR, the ensemble of the two methods outperforms either one, due to complementary strengths and weaknesses of the methods. Results also show that for the RFR and SVR implementations, it is better to include coordinates as proxy variables as RMSE scores are lowered and the phasing of the seasonal cycle is more accurate. Lastly, we show that there is only a weak bias due to undersampling. The synthetic data provide a useful framework to test methods in regions of sparse data coverage and show potential as a useful tool to evaluate methods in future studies.

Spatial distribution and temporal variability of open fire in China

2017 ◽  
Vol 26 (2) ◽  
pp. 122 ◽  
Author(s):  
Kunpeng Yi ◽  
Yulong Bao ◽  
Jiquan Zhang
Keyword(s):  
Meteorological Data ◽  
Fire Season ◽  
Spatial And Temporal Patterns ◽  
Burnt Area ◽  
The North ◽  
Temperature And Precipitation ◽  
North Eastern ◽  
Vegetation Fires ◽  
Precipitation Variation ◽  
Two Peaks

This study presents the spatial and temporal patterns of vegetation fires in China based on a combination of national fire records (1950–2010) and satellite fire data (2001–12). This analysis presents the first attempt to understand existing patterns of open fires and their consequences for the whole of China. We analysed inter- and intra-annual fire trends and variations in nine subregions of China as well as associated monthly meteorological data from 130 stations within a 50-year period. During the period 2001–12, an average area of 3.2 × 106 ha was consumed by fire per year in China. The Chinese fire season has two peaks occurring in the spring and autumn. The profiles of the burnt area for each subregion exhibit distinct seasonality. The majority of the vegetation fires occurred in the north-eastern and south-western provinces. We analysed quantitative relationships between climate (temperature and precipitation) and burnt area. The results indicate a synchronous relationship between precipitation variation and burnt area. The data in this paper reveal how climate and human activities interact to create China’s distinctive pyrogeography.

scholarly journals Inclusion of biomass burning in WRF-Chem: impact of wildfires on weather forecasts

2011 ◽  
Vol 11 (11) ◽  
pp. 5289-5303 ◽  
Author(s):  
G. Grell ◽  
S. R. Freitas ◽  
M. Stuefer ◽  
J. Fast
Keyword(s):  
Biomass Burning ◽  
Cloud Model ◽  
Cloud Condensation Nuclei ◽  
Cloud Microphysics ◽  
Fire Season ◽  
Strong Impact ◽  
Emission Rates ◽  
Weather Forecasts ◽  
High Concentrations ◽  
The Impact

Abstract. A plume rise algorithm for wildfires was included in WRF-Chem, and applied to look at the impact of intense wildfires during the 2004 Alaska wildfire season on weather simulations using model resolutions of 10 km and 2 km. Biomass burning emissions were estimated using a biomass burning emissions model. In addition, a 1-D, time-dependent cloud model was used online in WRF-Chem to estimate injection heights as well as the vertical distribution of the emission rates. It was shown that with the inclusion of the intense wildfires of the 2004 fire season in the model simulations, the interaction of the aerosols with the atmospheric radiation led to significant modifications of vertical profiles of temperature and moisture in cloud-free areas. On the other hand, when clouds were present, the high concentrations of fine aerosol (PM2.5) and the resulting large numbers of Cloud Condensation Nuclei (CCN) had a strong impact on clouds and cloud microphysics, with decreased precipitation coverage and precipitation amounts during the first 12 h of the integration. During the afternoon, storms were of convective nature and appeared significantly stronger, probably as a result of both the interaction of aerosols with radiation (through an increase in CAPE) as well as the interaction with cloud microphysics.

scholarly journals Reconstructing burnt area during the Holocene: an Iberian case study

2021 ◽  
Author(s):  
Yicheng Shen ◽  
Luke Sweeney ◽  
Mengmeng Liu ◽  
Jose Antonio Lopez Saez ◽  
Sebastián Pérez-Díaz ◽  
...  
Keyword(s):  
Fire Regimes ◽  
Weighted Averaging ◽  
Pollen Data ◽  
Burnt Area ◽  
The Holocene ◽  
Quantitative Estimates ◽  
In Fire ◽  
The Impact ◽  
Pollen Records

Abstract. Charcoal accumulated in lake, bog or other anoxic sediments through time has been used to document the geographical patterns in changes in fire regimes. Such reconstructions are useful to explore the impact of climate and vegetation changes on fire during periods when the human influence was less prevalent than today. However, charcoal records only provide semi-quantitative estimates of change in biomass burning. Here we derive quantitative estimates of burnt area from vegetation data in two stages. First, we relate the modern charcoal abundance to burnt area using a conversion factor derived from a generalized linear model of burnt area probability based on eight environmental predictors. Then, we establish the relationship between fossil pollen assemblages and burnt area using Tolerance-weighted Weighted Averaging Partial Least-Squares with sampling frequency correction (fxTWA-PLS). We test this approach using the Iberian Peninsula as a case study because it is a fire-prone region with abundant pollen and charcoal records covering the Holocene. We derive the vegetation-burnt area relationship using the 29 records that have both modern and fossil charcoal and pollen data, and then reconstruct palaeo-burnt area for the 114 records with Holocene pollen records. The pollen data predict charcoal abundances through time relatively well (R2 = 0.47) and the changes in reconstructed burnt area are synchronous with known climate changes through the Holocene. This new method opens up the possibility of reconstructing changes in fire regimes quantitatively from pollen records, which are far more numerous than charcoal records.

scholarly journals A case study on the use of appropriate surrogates for antecedent moisture conditions (AMCs)

2010 ◽  
Vol 7 (3) ◽  
pp. 3329-3363 ◽  
Author(s):  
G. A. Ali ◽  
A. G. Roy
Keyword(s):  
Soil Moisture ◽  
Point Scale ◽  
Nonlinear Regression Models ◽  
Terrain Attributes ◽  
Antecedent Moisture ◽  
Triggering Conditions ◽  
Linear And Nonlinear ◽  
Active Processes ◽  
Moisture Conditions ◽  
Antecedent Moisture Conditions

Abstract. While a large number of non-linear hillslope and catchment rainfall-runoff responses have been attributed to the temporal variability in antecedent moisture conditions (AMCs), two problems emerge: 1) the difficulty of measuring AMCs, and 2) the absence of explicit guidelines for the choice of surrogates or proxies for AMCs. This paper aims at determining whether or not multiple surrogates for AMCs should be used in order not to bias our understanding of a system hydrological behaviour. We worked in a small forested catchment, the Hermine, where soil moisture has been measured at 121 different locations at four depths on 16 occasions. Without making any assumption on active processes, we used various linear and nonlinear regression models to evaluate the point-scale temporal relations between actual soil moisture contents and selected meteorological-based surrogates for AMCs. We then mapped the nature of the "best fit" model to identify 1) spatial clusters of soil moisture monitoring sites whose hydrological behaviour was similar, and 2) potential topographic influences on these behaviours. Two conclusions stood out. Firstly, it was shown that the sole reference to AMCs indices traditionally used in catchment hydrology, namely antecedent rainfall amounts summed over periods of seven or ten days, would have led to an incomplete understanding of the Hermine catchment dynamics. Secondly, the relationships between point-scale soil moisture content and surrogates for AMCs were not spatially homogeneous, thus revealing a mosaic of linear and nonlinear catchment "active" and "contributing" sources whose location was often controlled by surface terrain attributes or the topography of a soil-confining layer interface. These results represent a step forward in developing a hydrological conceptual model for the Hermine catchment as they indicate depth-specific processes and spatially-variable triggering conditions. Further investigations are, however, necessary in order to derive general guidelines for the choice of the best surrogates for AMCs in a catchment.

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