Articles | Volume 24, issue 2
https://doi.org/10.5194/nhess-24-445-2024
https://doi.org/10.5194/nhess-24-445-2024
Research article
 | 
09 Feb 2024
Research article |  | 09 Feb 2024

Improving the fire weather index system for peatlands using peat-specific hydrological input data

Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold

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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Cited articles

Ambadan, J. T., Oja, M., Gedalof, Z., and Berg, A. A.: satellite-observed soil moisture as an indicator of wildfire risk, Remote Sensing, 12, 1543, https://doi.org/10.3390/rs12101543, 2020. a, b
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, https://doi.org/10.5194/essd-11-529-2019, 2019a. a, b, c, d, e, f, g, h
Andela, N., Morton, D. C., Giglio, L., and Randerson, J. T.: Global Fire Atlas with Characteristics of Individual Fires, 2003–2016, ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1642, 2019b. a
Bechtold, M., De Lannoy, G. J., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., Bourgault, M. A., Brümmer, C., Burdun, I., Desai, A. R., Devito, K., Grünwald, T., Grygoruk, M., Humphreys, E. R., Klatt, J., Kurbatova, J., Lohila, A., Munir, T. M., Nilsson, M. B., Price, J. S., Röhl, M., Schneider, A., and Tiemeyer, B.: PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model, J. Adv. Model. Earth Sy., 11, 2130–2162, https://doi.org/10.1029/2018MS001574, 2019. a, b
Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., and Zarov, E. A.: Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework, Remote Sens. Environ., 246, 111805, https://doi.org/10.1016/j.rse.2020.111805, 2020a. a, b, c, d
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With global warming increasing the frequency and intensity of wildfires in the boreal region, accurate risk assessments are becoming more crucial than ever before. The Canadian Fire Weather Index (FWI) is a renowned system, yet its effectiveness in peatlands, where hydrology plays a key role, is limited. By incorporating groundwater data from numerical models and satellite observations, our modified FWI improves the accuracy of fire danger predictions, especially over summer.
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