The sensitivity of US wildfire occurrence to pre-season soil moisture conditions across ecosystems

Importance of soil moisture conservation in mitigating climate change

A troubling feedback loop, where drier soil contributes to hotter climates, has been widely recognized. This study, drawing on climate model simulations, reveals that maintaining current global soil moisture levels could significantly alleviate 32.9% of land warming under low-emission scenarios. This action could also postpone reaching critical warming thresholds of 1.5 °C and 2.0 °C by at least a decade. Crucially, preserving soil moisture at current levels could prevent noticeable climate change impacts across 42% of the Earth's land, a stark deviation from projections suggesting widespread impacts before the 2060s. To combat soil drying, afforestation in mid-to-low latitude regions within the next three decades is proposed as an effective strategy to increase surface water availability. This underscores the substantial potential of nature-based solutions for managing soil moisture, benefiting both climate change mitigation and ecological enhancement.

High-resolution mapping of wildfire drivers in California based on machine learning

Wildfires are important natural disturbances of ecosystems; however, they threaten the sustainability of ecosystems, climate and humans worldwide. It is vital to quantify and map the controlling drivers of wildfires for effective wildfire prediction and risk management. However, high-resolution mapping of wildfire drivers remains challenging. Here we established machine-learning (Random Forests) models using 23 climate and land surface variables as model inputs to reconstruct the spatial variability and seasonality of wildfire occurrence and extent in California. The importance of individual drivers was then quantified based on the Shapley value method. Thus, we provided spatially resolved maps of wildfire drivers at high resolutions up to 0.004° × 0.004°. The results indicated that precipitation and soil moisture are the major drivers dominating 37% of the total burnt area for large and extreme wildfires in summer and 63% in autumn, while elevation plays a major role for 15-58% of burnt areas in small wildfires in all seasons. Winds are also an important contributor to summer wildfires, accounting for 41% of large and extreme burnt areas. This study enhanced our knowledge of spatial variability of wildfire drivers across diverse landscapes in a fine-scale mapping, providing valuable perspectives and case studies for other regions of the world with frequently occurred wildfire.

Sea level rise and climate change acting as interactive stressors on development and dynamics of tropical peatlands in coastal Sumatra and South Borneo since the Last Glacial Maximum

Southeast Asian peatlands, along with their various important ecosystem services, are mainly distributed in the coastal areas of Sumatra and Borneo. These ecosystems are threatened by coastal development, global warming and sea level rise (SLR). Despite receiving growing attention for their biodiversity and as massive carbon stores, there is still a lack of knowledge on how they initiated and evolved over time, and how they responded to past environmental change, that is, precipitation, sea level and early anthropogenic activities. To improve our understanding thereof, we conducted multi-proxy paleoecological studies in the Kampar Peninsula and Katingan peatlands in the coastal area of Riau and Central Kalimantan, Indonesia. The results indicate that the initiation timing and environment of both peatlands are very distinct, suggesting that peat could form under various vegetation as soon as there is sufficient moisture to limit organic matter decomposition. The past dynamics of both peatlands were mainly attributable to natural drivers, while anthropogenic activities were hardly relevant. Changes in precipitation and sea level led to shifts in peat swamp forest vegetation, peat accumulation rates and fire regimes at both sites. We infer that the simultaneous occurrence of El Niño-Southern Oscillation (ENSO) events and SLR resulted in synergistic effects which led to the occurrence of severe fires in a pristine coastal peatland ecosystem; however, it did not interrupt peat accretion. In the future, SLR, combined with the projected increase in frequency and intensity of ENSO, can potentially amplify the negative effects of anthropogenic peatland fires. This prospectively stimulates massive carbon release, thus could, in turn, contribute to worsening the global climate crisis especially once an as yet unknown threshold is crossed and peat accretion is halted, that is, peatlands lose their carbon sink function. Given the current rapid SLR, coastal peatland managements should start develop fire risk reduction or mitigation strategies.

Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate

The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in the Eastern Mediterranean. In the ecosystems of this area, prolonged droughts and heat waves create preconditions in the land surface state that increase the frequency and intensity of landscape fires. The relationships between the spatial–temporal variability of LST and fire activity modulated by land cover types and Soil Moisture Availability (SMA) are quantified. Long-term (2007–2018) datasets derived from geostationary MSG satellite observations are used: LST retrieved by the LSASAF LST product; fire activity assessed by the LSASAF FRP-Pixel product. All fires in the period of July–September occur in days associated with positive LST anomalies. Exponential regression models fit the link between LST monthly means, LST positive anomalies, LST-T2 (as a first proxy of sensible heat exchange with atmosphere), and FRP fire characteristics (number of detections; released energy FRP, MW) at high correlations. The values of biophysical drivers, at which the maximum FRP (MW) might be expected at the corresponding probability level, are identified. Results suggest that the biophysical index LST is sensitive to the changes in the dynamics of vegetation fire occurrence and severity. Dependences are found for forest, shrubs, and cultivated LCs, which indicate that satellite IR retrievals of radiative temperature is a reliable source of information for vegetation dryness and fire activity.

Significant light absorption of brown carbon during the 2020 California wildfires

In this study, the contribution of brown carbon (BrC) to the absorption aerosol optical depth (AAOD) during the August to October 2020 California wildfires in Fresno, Monterey, and the University of California Santa Barbara (UCSB) was investigated using Aerosol Robotic Network (AERONET) column measurements with Moderate Resolution Imaging Spectroradiometer (MODIS) fire pixel counts. There was an approximate three to five times increase in AAOD and fine-mode aerosols during intensive wildfires in August-October 2020 compared to the wildfires in the previous 18 years (2002-2019). Substantial daily variation in the contribution of BrC to AAOD was correlated with the fire pixel counts (correlation coefficients of 0.63, 0.40, and 0.57 at Fresno, Monterey, and UCSB, respectively). This variation was influenced by regional topography, atmospheric conditions, and distance from the fire. Between August and October 2020, the average contribution of BrC to AAOD at 440 nm due to wildfires was 35.3 ± 5.6, 35.1 ± 6.8, and 40.6 ± 9.5% at Fresno, Monterey, and UCSB, respectively. This was approximately twice as high as for those sites without a direct wildfire influence. The BrC contribution with direct wildfire influence over the period of January-December 2020 at Fresno, Monterey, and UCSB (32.8 ± 7.5, 31.6 ± 7.9, and 40.0 ± 3.5%, respectively) and from 2002 to 2019 (30.7 ± 8.3, 28.5 ± 4.8, and 35.7 ± 14.6%, respectively) was approximately 20% greater than other BrC sources including vehicles, fossil fuel combustion, and residential heating.

Plant-water sensitivity regulates wildfire vulnerability

Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity; R² = 0.71). This has led to rapid increases in human exposure to wildfire risk, both because the population living in areas with high plant-water sensitivity grew 50% faster during 1990–2010 than in other wildland–urban interfaces and because VPD has risen most rapidly in these vulnerable areas. As plant-water sensitivity is strongly linked to wildfire vulnerability, accounting for ecophysiological controls should improve wildfire forecasts. If recent trends in VPD and demographic shifts continue, human wildfire risk will probably continue to increase.

The authors show that an ecosystem’s sensitivity to drought, measured as the amount of change in vegetation moisture content for a given change in background moisture, predicts the fire hazard in that location.

Investigation of Forest Fire Activity Changes Over the Central India Domain Using Satellite Observations During 2001-2020

Recurrent and large forest fires negatively impact ecosystem, air quality, and human health. Moderate Resolution Imaging Spectroradiometer fire product is used to identify forest fires over central India domain, an extremely fire prone region. The study finds that from 2001 to 2020, ∼70% of yearly forest fires over the region occurred during March (1,857.5 counts/month) and April (922.8 counts/month). Some years such as 2009, 2012, and 2017 show anomalously high forest fires. The role of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is comprehensively investigated. Warmer period from 2006 to 2020 showed doubling and tripling of forest fire activity during forest fire (February-June; FMAMJ) and non-fire (July-January; JASONDJ) seasons, respectively. From 2015 JASONDJ to 2018 FMAMJ, central India experienced a severe heatwave, a rare drought and an extremely strong El Niño, the combined effect of which is linked to increased forest fires. Further, the study assesses quinquennial spatiotemporal changes in forest fire characteristics such as fire count density and average fire intensity. Deciduous forests of Jagdalpur-Gadchiroli Range and Indravati National Park in Chhattisgarh state are particularly fire prone (>61 fire counts/grid) during FMAMJ and many forest fires are of high intensity (>45 MW). Statistical associations link high near surface air temperature and low precipitation during FMAMJ to significantly high soil temperature, low soil moisture content, low evapotranspiration and low normalized difference vegetation index. This creates a significantly drier environment, conducive for high forest fire activity in the region.

2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities

Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1 °C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires.

Wildfire Trend Analysis over the Contiguous United States Using Remote Sensing Observations

Understanding the evolution of wildfire regimes throughout the United States (US) is crucial in the preparation, mitigation, and planning for national wildfires. Recent wildfire trajectories demonstrating an increase in both frequency and size across the US have made documenting the changes in wildfire regimes a topic of growing importance. While previous studies have examined wildfire regimes using ecoregions, this study analyzes wildfire regimes through the Geographic Area Coordination Center (GACC) regions across the Contiguous US over 34 years, 1984–2017. GACCs are geopolitical boundaries designed by wildfire agencies to promote an efficient way to distribute resources during emergencies such as wildfires. Wildfire observations originate from the Monitoring Trends in Burn Severity (MTBS) database which records large fire events that are 1000(500) acres or greater in the Western (Eastern) US. Using GACCs and MTBS data, this study examines wildfire regimes across the Contiguous US through the following three parameters: total burned area, frequency, and average burned area. This study characterizes the trend direction of the wildfire parameters and which are statistically significant. Results demonstrate that most GACC regions display statistically significant trends, including wildfire regimes that are beyond the Western US (e.g., Southern GACC). The Northwest and Southwest GACCs demonstrate statistically significant positive trends in every parameter observed. The California and Great Basin GACCs demonstrate statistically significant positive trends in the average burned area. The Eastern GACC is the only region to not display any significant trends. Determining significant wildfire regimes and their trend direction can help wildfire agencies to minimize the negative impacts on the environment, society, and economy.

Observational evidence of wildfire-promoting soil moisture anomalies

Wildfires can destroy property and vegetation, thereby threatening people’s livelihoods and food security. Soil moisture and biomass are important determinants of wildfire hazard. Corresponding novel satellite-based observations therefore present an opportunity to better understand these disasters globally and across different climate regions. We sampled 9,840 large wildfire events from around the globe, between 2001 and 2018, along with respective surface soil moisture and biomass data. Using composites across fire events in similar climate regions, we show contrasting soil moisture anomalies in space and time preceding large wildfires. In arid regions, wetter-than-average soils facilitate sufficient biomass growth required to fuel large fires. In contrast, in humid regions, fires are typically preceded by dry soil moisture anomalies, which create suitable ignition conditions and flammability in an otherwise too wet environment. In both regions, soil moisture anomalies continuously decrease in the months prior to fire occurrence, often from above-normal to below-normal. These signals are most pronounced in sparsely populated areas with low human influence, and for larger fires. Resolving natural soil moisture–fire interactions supports fire modelling and facilitates improved fire predictions and early warning.

Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States

Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.

A New Coupled Modeling Approach to Simulate Terrestrial Water Storage in Southern California

The study introduces a new atmosphere-land-aquifer coupled model and evaluates terrestrial water storage (TWS) simulations for Southern California between 2007 and 2016. It also examines the relationship between precipitation, groundwater, and soil moisture anomalies for the two primary aquifer systems in the study area, namely the Coastal Basin and the Basin and Range aquifers. Two model designs are introduced, a partially-coupled model forced by reanalysis atmospheric data, and a fully-coupled model, in which the atmospheric conditions were simulated. Both models simulate the temporal variability of TWS anomaly in the study area well (R2 ≥ 0.87, P < 0.01). In general, the partially-coupled model outperformed the fully-coupled model as the latter overestimated precipitation, which compromised soil and aquifer recharge and discharge. Simulations also showed that the drought experienced in the area between 2012 and 2016 caused a decline in TWS, evapotranspiration, and runoff of approximately 24%, 65%, and 11%, and 20%, 72% and 8% over the two aquifer systems, respectively. Results indicate that the models first introduced in this study can be a useful tool to further our understanding of terrestrial water storage variability at regional scales.

Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations

Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representative of ground measurements by reproducing the scale invariance properties of soil moisture derived from hydrologic simulations at hyperresolutions of less than 100 m. Specifically, we find that surface soil moisture is scale invariant over regimes extending from a satellite footprint to 100 m. We use this evidence to calibrate a statistical downscaling algorithm that reproduces the scale invariance properties of soil moisture and test the approach against 1-km aircraft remote sensing products and through comparisons of downscaled satellite products to ground observations. We demonstrate that hyperresolution hydrologic models can close the loop of satellite soil moisture downscaling for local applications such as agricultural irrigation, flood event prediction, and drought and fire management.

Contributions of GRACE to understanding climate change

Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations and understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends and improve service applications such as the U.S. Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi decadal record of mass variability in the Earth system is within reach.