Soil moisture as an indicator of growing-season herbaceous fuel moisture and curing rate in grasslands

Abstract

Soil moisture depletion during the growing season can induce plant water stress, thereby driving declines in grassland fuel moisture and accelerating curing. These drying and curing dynamics and their dependencies on soil moisture are inadequately represented in fire danger models. To elucidate these relationships, grassland fuelbed characteristics and soil moisture were monitored in nine patches of tallgrass prairie under patch-burn management in Oklahoma, USA, during two growing seasons. This study period included a severe drought (in 2012), which resulted in a large wildfire outbreak near the study site. Fuel moisture of the mixed live and dead herbaceous fuels (MFM) clearly tracked soil moisture, expressed as fraction of available water capacity (FAW). MFM decreased with decreasing soil moisture below an FAW threshold of 0.59 and fell below 30% only when FAW fell below 0.30. Likewise, the curing rate increased linearly as FAW declined below 0.30, while Normalized Difference Vegetation Index (NDVI) readings failed to adequately respond to rapid drying and curing of the fuelbed. Incorporating soil moisture observations into grassland fuelbed models could result in more accurate fuel moisture and curing estimates, contributing to improved wildfire danger assessments and reduced losses of life and property due to wildfire outbreaks.

Calibration and validation of soil water reflectometers

“…Using a subset of the current dataset, Sharma et al. (2020) also reported a negative partial slope, but Caldwell et al. (2018) and Kargas and Soulis (2019) reported positive slopes for some soils, The interpretation of this parameter is complicated by the fact that the permittivity values reported by the CS655 are internally adjusted in an attempt to account for the effects of EC b .…”

“…The latest family of improved soil water reflectometers based on transmission line oscillators is represented by the CS65x series consisting of the CS650 (30‐cm rods), CS655 (12‐cm rods), CS658 (20‐cm rods, hand‐held use), and CS659 (12‐cm rods, hand‐held use) sensors. In recent years, soil water reflectometers from the CS65x series have been adopted in core calibration–validation sites of the NASA Soil Moisture Active Passive satellite mission (Caldwell et al., 2018), in core calibration–validation sites of a new near‐surface soil moisture product for northern boreal forests developed by the European Space Agency's (ESA) Climate Change Initiative (Ikonen et al., 2018), in small catchment‐scale networks aimed at better understanding the role of soil moisture in herbaceous fuel moisture and curing rates in a tallgrass prairie in the U.S. Great Plains (Sharma et al., 2020), and have been adopted for long‐term monitoring of rootzone soil water storage by mesoscale environmental monitoring networks like the Kansas Mesonet (Patrignani et al., 2020). Similarly, portable soil water reflectometers have been used to delineate soil moisture‐based field management zones in agricultural fields (Rossini et al., 2021), explore the hydrologic connection between snowmelt runoff and streamflow (Kampf et al., 2015), and to calibrate and validate proximal soil moisture sensors like impulse radar sensors (Tan et al., 2014) and cosmic‐ray neutron detectors (Patrignani et al., 2021; Vather et al., 2019).…”

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

“…Pixels were masked when the DL was less than 0 • C, suggesting snow coverage or frozen soil at that time and location. Strong positive relationships between RSM and the vegetation index (e.g., NDVI) were found for Australia [92], tallgrass prairies in the USA [93,94], croplands in North China [95], and East Africa [96]. Surface reflectance products (including seven bands) were chosen to calculate the five vegetation indices: EVI, DVI, RVI, NDVI, EVI2, and MSAVI [34].…”

Using Soil Moisture Information to Better Understand and Predict Wildfire Danger

“…We now know that in situ soil moisture relates differently to wildfire probability during the growing season than during the dormant season (Krueger et al., 2016) and that in situ soil moisture measurements can better predict large growing‐season wildfires than the Keetch–Byram Drought Index (Krueger et al., 2017). We also learned how soil moisture observations can provide an effective indicator of fuel moisture content and curing rate for grassland vegetation (Sharma et al., 2020) and dead fuel moisture content in Sierra Nevada forests (Rakhmatulina et al., 2021). In situ soil moisture measurements have also proven valuable for improving predictions of grassland fuel loads (Krueger et al., 2021), which is significant given the fact that grasslands account for a large portion of the area burned worldwide each year.…”