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Open AccessArticle

Hydrological Impacts of Large Fires and Future Climate: Modeling Approach Supported by Satellite Data

CIBIO-InBIO—Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Universidade do Porto, Campus Agrário Vairão, 4485-661 Vila do Conde, Portugal
CBMA—Centre of Molecular and Environmental Biology, University of Minho, 4710–057 Braga, Portugal
Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, FC4-Biologia, 4169-007 Porto, Portugal
CE3C—Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande 016, 1749-016 Lisboa, Portugal
Departamento de Zooloxía, Xenética e Antropoloxía Física, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
Institute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), Corso Fiume 4, 10133 Torino, Italy
Institute of Geography and Spatial Planning- MOPT unit, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2832;
Received: 4 October 2019 / Revised: 12 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Remote Sensing in Ecosystem Modelling)
Fires have significant impacts on soil erosion and water supply that may be exacerbated by future climate. The aims of this study were: To simulate the effects of a large fire event in the SWAT (Soil and Water Assessment Tool) hydrological model previously calibrated to a medium-sized watershed in Portugal; and to predict the hydrological impacts of large fires and future climate on water supply and soil erosion. For this, post-fire recovery was parametrized in SWAT based on satellite information, namely, the fraction of vegetation cover (FVC) calculated from the normalized difference vegetation index (NDVI). The impact of future climate was based on four regional climate models under the stabilization (RCP 4.5) and high emission (RCP 8.5) scenarios, focusing on mid-century projections (2020–2049) compared to a historical period (1970–1999). Future large fire events (>3000 ha) were predicted from a multiple linear regression model, which uses the daily severity rating (DSR) fire weather index, precipitation anomaly, and burnt area in the previous three years; and subsequently simulated in SWAT under each climate model/scenario. Results suggest that time series of satellite indices are useful to inform SWAT about vegetation growth and post-fire recovery processes. Different land cover types require different time periods for returning to the pre-fire fraction of vegetation cover, ranging from 3 years for pines, eucalypts, and shrubs, to 6 years for sparsely vegetated low scrub. Future climate conditions are expected to include an increase in temperatures and a decrease in precipitation with marked uneven seasonal distribution, and this will likely trigger the growth of burnt area and an increased frequency of large fires, even considering differences across climate models. The future seasonal pattern of precipitation will have a strong influence on river discharge, with less water in the river during spring, summer, and autumn, but more discharge in winter, the latter being exacerbated under the large fire scenario. Overall, the decrease in water supply is more influenced by climate change, whereas soil erosion increase is more dependent on fire, although with a slight increase under climate change. These results emphasize the need for adaptation measures that target the combined hydrological consequences of future climate, fires, and post-fire vegetation dynamics. View Full-Text
Keywords: future climate; fire; hydrological impacts; post-fire recovery; satellite data; SWAT model future climate; fire; hydrological impacts; post-fire recovery; satellite data; SWAT model
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Carvalho-Santos, C.; Marcos, B.; Nunes, J.P.; Regos, A.; Palazzi, E.; Terzago, S.; Monteiro, A.T.; Honrado, J.P. Hydrological Impacts of Large Fires and Future Climate: Modeling Approach Supported by Satellite Data. Remote Sens. 2019, 11, 2832.

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