Evaluating the effects of forest fire on water balance using fire susceptibility maps

Venkatesh, Karnam; Preethi, K.; Ramesh, H.

doi:10.1016/j.ecolind.2019.105856

Cited by 58 publications

(27 citation statements)

References 25 publications

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“…The MODIS Collection 6 fire archive was used in this paper for wildfire susceptibility model construction [57,59]. MODIS hotspots have been adopted by many researchers for susceptibility mapping [60,61]. The fire products were downloaded from Fire Information for Resource Management System (FIRMS).…”

Section: Hazard Inventoriesmentioning

confidence: 99%

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Jiang

Wang

et al. 2021

Remote Sensing

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Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA.

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Section: Hazard Inventoriesmentioning

confidence: 99%

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Jiang

Wang

et al. 2021

Remote Sensing

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“…Topographic variables also have a strong influence on RFFO, such as the relief inclination and orientation (MOTA et al, 2019;NADERPOUR et al, 2019;VENKATESH et al, 2020;ÇOLAK et al, 2020). Thus, it is possible to notice from Figure 3 that the study area does not present high slopes, with 30.93% being the highest inclination found.…”

Section: Discussionmentioning

confidence: 87%

Geotechnology applied to predict the risk of occurrence of fire in the Atlantic Forest

Ramalho

Neder

Fiedler

et al. 2020

RICA

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The maintenance of biodiversity is a global concern in economic, social and environmental terms. Thus, conservation units for the protection of natural environments were created. Despite the importance of these areas, forest fires have caused immeasurable and constant damage to Conservation Units. In view of this, the objective of this study was to determine the risk areas of forest fire occurrence through Fuzzy logic modeling in the Córrego Grande Biological Reserve, located in the Mata Atlântica Brazilian biome. In order to prospect for areas at risk of forest fires, the following variables were inserted in the model: land use, road network, slope and relief orientation. Finally, the model was validated by comparing the location of fire occurrences between the years 2008 and 2018, and a layout of the risk classes in the study area. In doing so, it was found that 65.87% of the area is between the 'moderate' and 'very high' range of fire risk classes, and that 70.22% of the fires which occurred in the studied period also occurred in that class range. The study concluded that the most influential variable on the risk level of fire occurrence is the forest road network. In this way, the proposed methodology can be applied to any other areas and types of land cover.

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“…Our results are in parallel with the conclusions made by [79], stating that the bias of rainfall products determines the accuracy of runoff simulations by a model. e reason specified for the conclusion (streamflow simulations are affected by the bias of a product) as per [16,42] is related to the nonlinearity of the hydrologic process, where moderate rainfall bias can be transmuted into large PBias in discharge simulations. In correspondence to the above conclusion, the RMSE and bias values of SM2RAIN-CCI and GPCP-CDR v1.3 might have adversely affected the hydrologic performance and contributed to unsatisfactory discharge simulations.…”

Section: From Gauge-adjusted Dataset Resultsmentioning

confidence: 99%

“…e performance of Princeton datasets (PGF 0.5°and PGF 0.25°) was moderate in both statistical and hydrological analyses because of high bias and less precipitation detection capabilities. From the results of PGF and CHIRPS SPPs that have multiple spatial resolutions, the datasets with finer resolution (PGF 0.25°and CHIRPS 0.05°) proved effective compared to coarse 16 Advances in Meteorology resolution products (PGF 0.5°and CHIRPS 0.25°). One possible reason for the better performance of fine scale products can be attributed to the size of the catchment considered in the study.…”

Section: From Reanalysis Datasetmentioning

confidence: 99%

“…Reviews related to the SPPs evaluation through various hydrological framework models can also be found in different climatic and geographical regions [9][10][11][12]. Most of these studies focused on either analyzing a single rainfall productʼs performance in hydrological modeling or evaluating the efficiency of a few rainfall products in runoff simulations, thus restricting their analysis to specific products [7,[13][14][15][16][17][18][19][20]. Most of the studies have not considered reanalysis products during their evaluation or have not recalibrated each rainfall dataset, thus missing to differentiate the in situ corrected and uncorrected dataset efficiencies [15,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India

Kolluru

Krakauer

Sharifi

et al. 2020

Advances in Meteorology

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This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.

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Evaluating the effects of forest fire on water balance using fire susceptibility maps

Cited by 58 publications

References 25 publications

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Geotechnology applied to predict the risk of occurrence of fire in the Atlantic Forest

Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India

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