Carbon emissions from a temperate coastal peatland wildfire: contributions from natural plant communities and organic soils

Background One of the scientific challenges of understanding climate change has been determining the important drivers and metrics of global carbon (C) emissions and C cycling in tropical, subtropical, boreal, subarctic, and temperate peatlands. Peatlands account for 3% of global land cover, yet contain a major reservoir of 550 gigatons (Gt) of soil C, and serve as C sinks for 0.37 Gt of carbon dioxide (CO2) a year. In the United States, temperate peatlands are estimated to store 455 petagrams of C (PgC). There has been increasing interest in the role of wildfires in C cycling and altering peatlands from C sinks to major C sources. We estimated above- and below-ground C emissions from the Pains Bay Fire, a long-duration wildfire (112 days; 18,329 ha) that burned a coastal peatland in eastern North Carolina, USA. Results Soil C emissions were estimated from pre- and post-burn Light Detection and Ranging (LIDAR) soil elevation data, soils series and C content mapping, remotely sensed soil burn severity, and post-burn field surveys of soil elevation. Total above-ground C emissions from the fire were 2,89,579 t C and 214 t C ha−1 for the 10 vegetation associations within the burn area perimeter. Above-ground sources of C emissions were comprised of litter (69,656 t C), shrub (1,68,983 t C), and foliage (50,940 t C). Total mean below-ground C emissions were 5,237,521 t C, and ranged from 2,630,529 to 8,287,900 t C, depending on organic matter content of different soil horizons within each of the 7 soil series. The mean below-ground C emissions within the burn area were 1,595.6 t C ha−1 and ranged from 629.3 to 2511.3 t C ha−1. Conclusions In contrast to undisturbed temperate peatlands, human induced disturbances of the natural elevation gradient of the peatland has resulted in increased heterogeneity of floristic variation and assemblages that are a product of the spatial and temporal patterns of the water table level and the surface wetness across peatlands. Human induced changes in surface hydrology and land use influenced the fuel characteristics of natural vegetation and associated soils, thus influencing wildfire risk, behavior, and the resulting C emissions.

Wildfire ash mobilization by splash under simulated rainfall in controlled laboratory conditions

<p>Recently burnt areas across the world have been documented to produce strong to extreme runoff and erosion responses. At the same time, they are well known to lose their typically blackish colour due to wildfire ashes (<em>sensu latu</em>, including char) relatively quickly during the early phases of the window-of-disturbance. The contribution of wildfire ash to post-fire erosion rates, however, remains poorly quantified. Arguably, this is first and foremost due to the difficulties of separating the ash and char fractions from the mineral soil fractions, at least at the routinely basis that is required for field erosion studies with high temporal resolution (say, less than 1 month) and an absolute minimum of three replicate plots per slope or treatment. To this end, the national ASHMOB project (CENTRO-01-0145-FEDER-029351) is trying to advance the knowledge of the mobilization of wildfire ash by wind and water erosion by studying it first under controlled laboratory conditions. The present study concerns the first phase of wildfire ash erosion by water, using Morgan cups to quantify the splash erosion of wildfire ash by high-intensity simulated rainfall in the Laboratory of Hydraulics, Water Resources and Environment of the University of Coimbra. More specifically, this study assessed the importance of the following factors in ash splash erosion: (1) extreme rainfall intensities, ranging from 150 to 450 mm/h; (2) source of the ash, from recently burnt woodlands dominated by maritime <em>Pinus pinaster</em>, <em>Eucalyptus globulus</em>, and <em>Arbutus unedo</em>; (3) ash depth or load. Preliminary analysis of the obtained results suggested that splash erosion of wildfire ash: (1) varied strongly with the applied rainfall intensity, increasing in a linear manner with increasing intensity; (2) differed markedly with the dominant tree cover, being clearly lower for the pine and eucalypt stands than for the strawberry tree stands, possibly due to the differences in soil burn severity as indicated by blackish and whitish ashes, respectively; (3) depended noticeably on ash depth, decreasing clearly with increasing ash depth and, arguably, with a greater damping capacity.</p>

Debris Yield Modeling Application under Post-Wildfire Conditions with the Hydrologic Modeling System (HEC-HMS)

<p>Predicting debris yield under post-wildfire conditions is important for hazard mitigation and flood risk planning. Current prediction efforts aim to reduce the amount and impacts of debris flows that minimizes environmental and economic impacts for communities. However, recovery efforts are difficult and costly. Debris flows and excess runoff block access roads and bridges, inhibiting emergency responses. It also effects the surrounding community's water supply and property. Therefore, having a debris flow sediment management plan is crucial. Predicting debris yield volume, estimating debris basin capabilities, and developing yield mitigation alternatives will mitigate future debris yield disasters. In previous versions of the Hydrologic Engineering Center, Hydrologic Modeling System (HEC-HMS) contains no capacity to simulate debris yield. However, the need for debris yield modeling exists throughout the Corps of Engineers, especially mountainous in arid and semi-arid regions. The HEC has added empirical models for prediction debris yield volumes under post-wildfire conditions. The goal is to develop tools within HEC-HMS that provide outputs necessary for developing debris yield mitigation strategies for managing debris yields within the burned watershed. This research discusses the addition of debris yield methods under post-wildfire situations within the watershed available in HEC-HMS 4.5. The new debris yield modeling capabilities will increase the application of HEC-HMS for debris yield modeling studies by directly computing yields from burn watersheds. Additionally, the model was coupled with the Hydrologic Engineering Center, River Analysis System (HEC-RAS) to ensure that debris yield output from HEC-HMS could be easily used as boundary conditions for predicting the hydraulic non-Newtonian debris flow runout and inundation.  The new debris yield methods use precipitation, topography, and soil burn severity information within the watershed to model debris yield. Reach and reservoir debris routing methods are being further developed, meanwhile existing sediment flow routing methods in reach and reservoir elements can be used with certain limitations.</p><p> </p><p>Keywords: Debris Yield Prediction; Post-Wildfire; Hazard Mitigation; Hydrology Modeling System</p>

Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.

Exploring the use of spectral indices to assess alterations in soil properties in pine stands affected by crown fire in Spain

Background Forest fires have increased in extent and intensity in the Mediterranean area in recent years, threatening forest ecosystems through loss of vegetation, changes in soil properties, and increased soil erosion rates, particularly in severely burned areas. However, establishing the relationships between burn severity and soil properties that determine infiltration remain challenging. Determining where soil burn severity evaluation should be carried out is critical for planning urgent measures to mitigate post-fire soil erosion. Although previous research has indicated that spectral indices are suitable for assessing fire severity, most of the classifications used consider combined effects in vegetation and soil. Moreover, the relationship between spectral indices and soil burn severity has scarcely been explored until now. Results We selected three pine stands in Spain for study immediately after being burned by wildfires. We analyzed various soil properties (soil saturated hydraulic conductivity, mean weight diameter of soil aggregates, and soil organic carbon) in relation to six levels of soil burn severity in all three stands. In addition, we established 25 field plots in the burned areas. We computed ten spectral indices for each plot by using Sentinel-2 satellite data. The soil burn severity categories indicated the degree of degradation of important soil properties related to soil erosion susceptibility. Of the spectral indices considered, the relativized burn ratio (RBR) was the best predictor of cumulative infiltration and mean weight diameter of soil aggregates. The differenced mid-infrared bispectral index (dMIRBI) was most closely correlated with soil organic carbon content. Conclusions The findings demonstrate the potential applicability of remote sensing to determining changes in soil properties after fire.

Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.

Testing a novel technique, geotubes with mycotechnosoil, to mitigate post-fire erosion and enhance ecosystem recovery

<p>Recently burnt areas have frequently been documented to produce strong to extreme catchment-scale hydrological and erosion responses to major rainfall events, even if these responses have rarely been quantified. These responses have raised important concerns, both among forest owners and managers on the on-site implications of soil (fertility) loss and among water resources managers for the off-side impacts on downstream values-at-risk such as road and hydraulic infrastructures, flood zones, and surface water quality in reservoirs or at river intake points. State-of-the-art emergency stabilization management, as practiced in the USA and Galicia, aims at reducing the hydrological and erosion response at its main source, i.e. the hillslopes. Based on years and decades of experience and pain-staking field monitoring in both the USA and Galicia, mulching is typically preferred over barrier-based methods, especially for being more effective in the case of high-intensity rainfall storms. Even so, the LIFE-REFOREST consortium (LIFE17 ENV/ES/000248) has developed an innovative barrier-based technique that is designed to be implemented easier and faster than log and shrub barriers and, at the same time, to improve vegetation recovery, using seeds of plant species that establish vegetation strips against runoff and erosion and/or seeds of tree and shrub species for re- or afforestation. The REFOREST barriers consists of geotubes containing, besides seeds, a mycotechnosoil as well as straw. The effectiveness of the LIFE-REFOREST geotubes is current being tested under field conditions in summer-2019 burnt areas in north-central Portugal and Galicia, in contrasting forest types (eucalypt vs. pine) on contrasting parent materials (schist vs. granite). Both field trials involve, besides 3 control plots and 3 plots with geotubes, also 3 plots mulched with either eucalypt logging residues or pine needles. The present poster will show preliminary results of the field trial in north-central Portugal, in a second-rotation eucalypt stand where tree crowns were scorched by the fire and soil burn severity was classified as moderate. These results concern the initial monitoring period till early spring 2020. However, this monitoring period has been quite rainy so far, arguably providing rather ideal conditions for testing the effectiveness of barrier-based solutions such as that of LIFE-REFOREST.</p>

Testing the Use of Forest Soil δ13C Shifts as a Post-Fire Index for Soil Burn Severity Estimation

Due to the increasing number and virulence of forest wildfires recently observed around the world, the establishment of a simple, accurate and reliable index that would correctly evaluate the fire effects on soil quality as a support for a suitable forest recovery management is becoming progressively more necessary. This objective is addressed here by using both δ13C isotope ratio mass spectrometry and traditional solvent fractionation methods (widely used to assess soil biogenic components or humus fractions) to quantify the temperature-induced changes in soil chemical and isotopic composition. Soil samples from the upper 5 cm layer of two Cambisols developed over granite under pine forest in the NW of Spain were heated in an oven under controlled conditions to attain moderate or intense soil burn severity levels by using two different temperatures (220 °C or 350 °C). Biochemical changes induced by the heating process appreciably differed according to the intensity of the temperature applied. Multilinear regression modelling not only showed a significant relationship between soil C isotopic signature shifts (Δsoil δ13C) with temperature increases but also revealed other key outcomes: i.e., >96 or >81% of its total variance can be predicted by changes in lignin or non-humified organic matter, respectively. Indeed, Δsoil δ13C explained by itself ≈60% of thermal variance, pointing to the aptness of using 13C shifts as a valid index for soil burn severity estimation in wildfires.