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The theory, direction, and magnitude of ecosystem fire probability as constrained by precipitation and temperature.

ABSTRACT: The effects of climate on wildland fire confronts society across a range of different ecosystems. Water and temperature affect the combustion dynamics, irrespective of whether those are associated with carbon fueled motors or ecosystems, but through different chemical, physical, and biological processes. We use an ecosystem combustion equation developed with the physical chemistry of atmospheric variables to estimate and simulate fire probability and mean fire interval (MFI). The calibration of ecosystem fire probability with basic combustion chemistry and physics offers a quantitative method to address wildland fire in addition to the well-studied forcing factors such as topography, ignition, and vegetation. We develop a graphic analysis tool for estimating climate forced fire probability with temperature and precipitation based on an empirical assessment of combustion theory and fire prediction in ecosystems. Climate-affected fire probability for any period, past or future, is estimated with given temperature and precipitation. A graphic analyses of wildland fire dynamics driven by climate supports a dialectic in hydrologic processes that affect ecosystem combustion: 1) the water needed by plants to produce carbon bonds (fuel) and 2) the inhibition of successful reactant collisions by water molecules (humidity and fuel moisture). These two postulates enable a classification scheme for ecosystems into three or more climate categories using their position relative to change points defined by precipitation in combustion dynamics equations. Three classifications of combustion dynamics in ecosystems fire probability include: 1) precipitation insensitive, 2) precipitation unstable, and 3) precipitation sensitive. All three classifications interact in different ways with variable levels of temperature.

SUBMITTER: Guyette R

PROVIDER: S-EPMC5509281 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:There is currently a gap in satellite observations of the moisture structure during heavy precipitation conditions, since infrared and microwave sounders cannot sense water-vapour structure near the surface in the presence of intense precipitation. Conversely, Global Navigation Satellite System (GNSS) radio occultations (RO) can profile the moisture structure with high precision and vertical resolution, but cannot indicate the presence of precipitation directly. Polarimetric RO (PRO) measurements have been proposed as a method to characterize heavy rain in GNSS RO, by measuring the polarimetric differential phase delay induced by large size hydrometeors. Previous studies have shown that the PRO polarimetric phase shift is sensitive to the path-integrated rain rate under intense precipitation scenarios, but there is no current method to invert PRO measurements into quantitative estimates of the path-averaged rain rate. In this manuscript, a probabilistic inversion approach to the GNSS PRO observables is proposed, where the GPM precipitation products are used for the construction of an a priori look-up table (LUT) database. The performance of the LUTs is assessed for use in the inversion of satellite-based GNSS PRO observations, based on synthetically generated PRO data of actual events, which correspond to co-locations between GNSS RO profiles and the TRMM observations. The synthetic data include end-to-end propagation effects of the polarimetric observables and a simple separation algorithm to isolate the hydrometeor component of the observation. The assessment results in agreement better than ±1 mm/hr between the reference LUT and the actual rain statistics of the synthetic data, proving the suitability of the GPM-based probabilistic inversion tool. These findings indicate that the GNSS PRO products are capable of extending the current GNSS RO ones by associating indications of rain-rate probabilities at different altitudes, at ?250 m vertical resolution and under intense precipitation scenarios with the standard vertical thermodynamic profiles.

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The theory, direction, and magnitude of ecosystem fire probability as constrained by precipitation and temperature.

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