Effects of Vegetation Belt Movement on Wildfire in the Mongolian Plateau over the Past 40 Years
Remote Sensing
Abstract and Figures
The frequency and intensity of fires are increasing because of warmer temperatures and increased droughts, as well as climate-change induced fuel distribution changes. Vegetation in environments, such as those in the mid-to-high latitudes and high elevations, moves to higher latitudes or elevations in response to global warming. Over the past 40 years, the Mongolian Plateau has been arid and semi-arid, with a decrease in growing season vegetation in the southwest and an increase in growing season vegetation in the northeast. The northward movement of vegetation has brought fires, especially in the Dornod, Sukhbaatar, and Kent provinces near the Kent Mountains, and has become more obvious in the past 20 years. The occurrence of a dead fuel index (DFI) with high probability is distributed in northern Mongolia, the border area between China and Mongolia, and the forest-side meadow-steppe region of the Greater Khingan Mountains. These findings suggest that vegetation is moving northward because of climate change and this presents a challenge of future warming spreading fire northward, adding material to the study of the relationship between the northward movement of global vegetation and fires.
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Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil–plant–atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.
Climate change has lengthened wildfire seasons and transformed fire regimes throughout the world. Thus, capturing fuel and fire dynamics is critical for projecting Earth system processes in warmer and drier future. Recent advances in fire regime modeling have linked land surface models with fire behavior models. Such models often rely on fine surface fuels to drive fire behavior and effects, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel accumulation), fuel loading estimates remain highly uncertain, largely due to uncertainties in the algorithms controlling decomposition. Uncertainties are often amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel decomposition as a key uncertainty in model systems. We review the current understanding of mechanisms controlling decomposition, describe how they are incorporated into models, and evaluate the uncertainties associated with different approaches. We also use three state‐of‐the‐art land surface fire regime models to demonstrate the sensitivity of decomposition and subsequent wildfire projections to both parameter and model structure uncertainty and show that sensitivity can increase substantially under future climate warming. Given that many of the governing decomposition equations are based on individual case studies from a single location, and because key parameters are often hard coded, critical uncertainties are currently ignored. It is essential to be transparent about these uncertainties as the domain of land surface models is expanded to include the evaluation of future wildfire regimes.
Fire frequency and intensity are increasing due to higher temperatures and more droughts. The distributions of fuels (vegetation in natural conditions) are also changing in response to climate change. The vegetation in cold environments such as high latitudes and high altitudes is found to move upward or northward due to global warming. However, few studies have investigated the distribution changes of fire adaptive species in warm environments. This study estimated and compared the distributions of a typical fuelwood, the Eucalyptus globulus , under different climate scenarios. The species distribution modeling techniques were adopted to estimate the current distributions of the Eucalyptus globulus and the future distributions under scenarios of both SSP245 and SSP585 in 2060. Results show that the probability of the existence of the Eucalyptus globulus in the Northern Hemisphere increases significantly ( p < 0.001) under both SSP245 and SSP585, especially in North America and Europe. However, the probability in the Southern Hemisphere decreases. The distribution of the Eucalyptus globulus expands in the mid-latitude (40 N–60 N) of the Northern Hemisphere. High carbon emissions contribute to the boost of the establishment of the Eucalyptus globulus in the Northern Hemisphere. These findings demonstrate that the fire adaptive species shows the tendency of shifting northward in response to climate change, highlighting the challenge of northward expansion of fires in future warming.
Escalating burned area in western US forests punctuated by the 2020 fire season has heightened the need to explore near-term macroscale forest-fire area trajectories. As fires remove fuels for subsequent fires, feedbacks may impose constraints on the otherwise climate-driven trend of increasing forest-fire area. Here, we test how fire-fuel feedbacks moderate near-term (2021–2050) climate-driven increases in forest-fire area across the western US. Assuming constant fuels, climate–fire models project a doubling of forest-fire area compared to 1991–2020. Fire-fuel feedbacks only modestly attenuate the projected increase in forest-fire area. Even models with strong feedbacks project increasing interannual variability in forest-fire area and more than a two-fold increase in the likelihood of years exceeding the 2020 fire season. Fuel limitations from fire-fuel feedbacks are unlikely to strongly constrain the profound climate-driven broad-scale increases in forest-fire area by the mid-21st century, highlighting the need for proactive adaptation to increased western US forest-fire impacts. Reduced fuel availability will only moderately diminish projected near-term increases in climate-driven forest fire area in the Western US, according to a macroscale climate–fire model.
Vegetation greenness dynamics in arid and semi-arid regions are sensitive to climate change, which is an important phenomenon in global climate change research. However, the driving mechanism, particularly for the longitudinal and latitudinal changes in vegetation greenness related to climate change, has been less studied and remains poorly understood in arid and semi-arid areas. In this study, we investigated changes in vegetation greenness and the vegetation greenness line (the mean growing season normalized difference vegetation index (NDVI) = 0.1 contour line) and its response to climate change based on AVHRR-GIMMS NDVI3g and the fifth and latest global climate reanalysis dataset from 1982 to 2015 in the arid and semi-arid transition zone of the Mongolian Plateau (ASTZMP). The results showed that the mean growing season NDVI increased from the central west to east, northeast, and southeast in ASTZMP. The vegetation greenness line migrated to the desert during 1982–1994, to the grassland during 1994–2005, and then to the desert during 2005–2015. Vegetation greenness was positively correlated with precipitation and negatively correlated with temperature. The latitudinal variation of the vegetation greenness line was mainly affected by the combination of precipitation and temperature, while the longitudinal variation was mainly affected by precipitation. In summary, precipitation was a key climatic factor driving rapid changes in vegetation greenness during the growing season of the transition zone. These results can provide meaningful information for research on vegetation coverage changes in arid and semi-arid regions.
Computational models of wildfires are necessary for operational prediction and risk assessment. These models require accurate spatial fuel data and remote sensing techniques have ability to provide high spatial resolution raster data for landscapes. We modelled a series of fires to understand and quantify the impact of the spatial resolution of fuel data on the behaviour of fire predictive model. Airborne laser scanning data was used to derive canopy height models and percentage cover grids at spatial resolutions ranging from 2 m to 50 m for Mallee heath fire spread model. The shape, unburnt area within the fire extent and extent of fire areas were compared over time. These model outputs were strongly affected by the spatial resolution of input data when the length scale of the fuel data is smaller than connectivity length scale of the fuel. At higher spatial resolutions breaks in the fuel were well resolved often resulting in a significant reduction in the predicted size of the fire. Our findings provide information for practitioners for wildfire modelling where local features may be important, such as operational predictions incorporating fire and fuel breaks, and risk modelling of peri-urban edges or assessment of potential fuel reduction mitigations.
Spatially-invariant land use and cover changes (LUCC) are not suitable for managing non-stationary drought conditions. Therefore, developing a spatially varying framework for managing land resources is necessary. In this study, the Dongjiang River Basin in South China is used to exemplify the significance of spatial heterogeneity in land planning optimization for mitigating drought risks. Using ERA5 that is the 5th major atmospheric reanalysis from the European Centre for Medium-Range Weather Forecast, we computed the Standardized Runoff Index (SRI) to quantify the hydrologic drought during 1992 to 2018. Also, based on Climate Change Initiative land use product, The Geographically Weighted Principal Component Analysis was used to identify the most dominant land types in the same period. Then, we used the Emerging Hot Spots Analysis to characterize the spatiotemporal evolution of historical LUCC and SRI. The spatially varying coefficients of Geographically and Temporally Weighted Regression models were used to reveal the empirical relationships between land types and the SRI. Results indicated that rainfed cropland with herbaceous cover, mosaic tress and shrub, shrubland, and grassland were four land types having statistical correlations with drought conditions over 27 years. Moreover, since 2003, the DRB was becoming drier, and the northern areas generally experienced severer hydrologic drought than the south. More importantly, we proposed region-specific land-use strategies for drought risk reductions. At a basin scale, we recommended to 1) increase rainfed herbaceous cropland and 2) reduce mosaic tree and shrub. At a sub-basin scale, the extents of shrub and grassland were suggested to increase in the northern DRB but to reduce in the south. Region-specific land use planning, including suitable locations, scales, and strategies, will contribute to handling current ‘one-size-fits-all’ LUCC. Planners are suggested to integrate spatial characteristics into future LUCC for regional hydrologic management.
Spartina alterniflora is an aggressive invasive plant spreading along the coastal China, spanning a latitudinal range of 20 • N-39 • N, and its invasion resulted in dramatic decline in both native plant diversity and ecosystem functioning. Phenology of S. alterniflora saltmarshes is a critical feature to elucidate the invasion dynamics over geographical regions but has not been well understood yet. In this study, we examined the variation of S. alterniflora saltmarsh phenology across coastal China during 2018-2020 by using time series Landsat 7/8 and Sentinel-2 images. Combined Landsat 7/8 and Sentinel-2 images provided more good-quality observations in a year, which could facilitate phenological retrieval. We applied and assessed three widely used phenology retrieval methods (i.e., NDVI-based pixel-specific statistical threshold, NDVI-based double logistic mathematical equation, and LSWI-based biological threshold) for retrieving the start and end of season (SOS and EOS) as well as the length of season (LOS) of S. alterniflora saltmarshes. The SOS and EOS dates derived from three phenology retrieval methods showed similar patterns in latitudinal phenology variation: SOS became later and LOS became shorter as latitude increased, and the latitudinal trend of EOS was not as large as that of SOS. This study shows the potential of Landsat 7/8 and Sentinel-2 to quantify land surface phenology of S. alterniflora saltmarshes, which not only enhances our understanding of the spatial-temporal dynamics of coastal saltmarshes in China but also improves the management of this plant invader that threatens native saltmarshes in the world.
An article in Communications Earth & Environment finds that reduced fuel availability will only moderately dampen projected increases in forest fire area in western USA.
Vegetation is highly sensitive to climate changes in arid regions. The relationship between vegetation and climate changes can be effectively characterized by vegetation phenology. However, few studies have examined the vegetation phenology and productivity changes in arid Central Asia (ACA). The vegetation phenological information of ACA was extracted using MODIS NDVI (Normalized Difference Vegetation Index) data, and the dynamics of vegetation phenological changes under spatiotemporal variations were quantitatively assessed. Moreover, the impacts of climate change on vegetation phenology and net primary productivity were analyzed by combining meteorological data with that of MODIS NPP (Net Primary Productivity) during the same period. The results demonstrated that the start of the season (SOS) of vegetation in the study was concentrated from mid-February to mid-April, while the end of the season (EOS) was concentrated from early October to mid-December. The length of growing season (LOS) ranged from 6 to 10 months. The SOS of vegetation was gradually postponed at a rate of 0.16 d·year⁻¹. The EOS advanced at a rate of 0.69 d·year⁻¹. The LOS was gradually shortened at a rate of 0.89 d·year⁻¹. For each per 1000 m increase in elevation, the SOS of vegetation was postponed by 12.40 d; the EOS advanced by 0.40 d, and the LOS was shortened by 11.70 d. For the impacts of climate changes on vegetation phenology and NPP, the SOS of vegetation phenology negatively correlated with temperature but positively correlated with precipitation and NPP. The EOS and LOS positively correlated with temperature but negatively with precipitation and NPP. Results indicated that the SOS was not moved ahead but was delayed, while the EOS advanced rather than being postponed under climate change. These results can offer new insights on the phenological response to climate change in arid regions and on non-systematic changes in phenology under global warming.