The global significance of post fire soil erosion

The global significance of post fire soil erosion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The global significance of post fire soil erosion Diana Vieira, Pasquale Borrelli, Simone Scarpa, Leonidas Liakos, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5622658/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Nature Geoscience → Version 1 posted You are reading this latest preprint version Abstract Wildfires affect land surface and post-fire geomorphological activity worldwide, increasing surface runoff and soil erosion. Here, we present a global assessment of post-fire soil erosion, considering cumulative wildfire driven geomorphological changes over the last two decades. Stemmed from the largest database on wildfires occurrence and fire severity in the globe, this study estimates global trends of post fire soil erosion together with the recovery of those burned landscapes. Our results show that when considering multiple wildfire events, global post-fire soil erosion accounts for 8.1 ± 0.72 Pg annually, representing 19% of the global soil erosion budget, and additional 5.1 ± ± 0.56 Pg soil erosion annually in comparison to pre-fire conditions. Moreover, soil erosion attributed to the first post-fire year represents 31% of the total soil erosion, whereas the remaining share can be attributed to previous wildfires occurrences. In what concerns the spatial distribution, Africa is the continent that is impacted the most in terms of post-fire soil erosion, given its significantly larger burned area. The results of this study can illustrate the magnitude of post-fire soil erosion globally, and therefore support post-fire management actions towards the mitigation and restoration of affected areas, and policies towards Land Degradation Neutrality. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Solid Earth sciences/Geomorphology post-fire soil erosion RUSLE ecosystem services land management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction According to the Global Wildfire Information System 1 , on average, four million km 2 of land burn every year (2002–2023), an area almost equal to the surface of European Union. Wildfires are an integral part of many ecosystems around the globe, playing key roles in ecosystem dynamics and the retention of species that have evolved in response to fire 2 . However, this global phenomenon is often responsible for substantial environmental, social and economic losses, which combined with land abandonment, droughts, absence of appropriate land management and urban development, are expected to aggravate land degradation 3 – 5 . In addition, wildfires will become a persistent threat, since the fire risk is expected to increase in a context of a warmer and drier climate 6 . Led by the elevated temperatures reached in topsoil, fire induce changes in soil physical and chemical properties, loss of biodiversity, reduce water infiltration and ground cover protection, and alter soil aggregate stability 7 – 9 . When combined, these factors result in significant increases in soil erodibility after fire, which vary between geographical regions and burn severity levels 10 , 11 . High burn severity fires, not only increase on-site soil erosion, but may also lead to off-site impacts downstream of the burned area in the form of destructive floods and debris flows 7 , 12 , and the transport of ash and sediment loads onto downstream water bodies 13 – 15 . Moreover, the relative importance of post-fire soil erosion events is likely to increase in response to ongoing and anticipated increases in fire activity and rainfall intensity 12 . Despite wildfires have been regarded as an important hydrological and geomorphological agent 9 , until now post-fire soil erosion has mostly been assessed from plot to catchment scale by means of field measurements and from slope to regional scale using modelling approaches 16 . Current model-based soil erosion assessments at global scales 17 – 19 already gave significant steps forward, providing this knowledge for informed land management decisions, however, these have not yet considered the effects of wildfires in the equation. While the first steps for a large scale post-fire soil erosion estimation 10 provides an idea of the impact of wildfires in European soils, this approach still presents limitations by only accounting for a single disturbance event (1 fire year) and for a limited recovery period (5 years), evidencing that a global assessment considering the cumulative effect of several wildfires and a long term perspective are still missing 10 , 12 . Hence, the aim of this study is to provide the first estimation of post-fire soil erosion at global scale and analyse geomorphological changes driven by fire over the last 2 decades. To accomplish this, we estimated the global post-fire soil erosion using the largest global wildfires database available (MOSEV 20 ) for an 18-years period, and compared it against unburned conditions. In this way was possible to estimate the cumulative impact of several wildfire occurrence years in the global soil erosion budget and to identify the main trends for post-fire soil erosion. Additionally, this also allowed us to analyse the recovery of fire-affected ecosystems under systematic pressure, but also project these trends in combination with the anticipated changes in rainfall. 2. Results 2.1. Burned area, severity and erosion Over the study area (forest, shrubland and grassland) and period (2001–2019), annual burned areas averaged 2.9 million km 2 (about the area of India) over the globe. Throughout the two observed decades, the trend in burned areas have been declining globally, with the exception of North America (Fig. 1 ). Africa is the most affected continent, with 66% (366 thousand Km 2 ) of the total annual burned area identified mostly among low (67%, 244 thousand km 2 ) and moderate (33%, 122 thousand km 2 ) severity burns. High severity burns are more pronounced in North America (10%), Asia (5%), and Europe (4%), while in other continents such as South America (2%) tend to be rather limited or nearly absent as the case for Africa and Oceania (Fig. 1 ). When focusing on the soil erosion impacts, we have considered the cumulative effect of various wildfires to account for their long-term contribution to the global erosion budget. To do so, we have identified 6 years as the minimum time required for this cumulative effect to stabilize, based on the plateau provided by soil erosion under pre-fire conditions (Fig. 2 ), and within the range of expected effects as provided by the literature 7 , 9 , 10 . Using this metric, the post-fire affected areas targeted in this study (forest, shrubland and grassland) with at least 6 fire years are estimated to result in average 8.1 ± 0.72 Pg of soil losses annually over the 2006–2019 (Table 1 , Fig. 2 ). This represents an additional 5.1 ± 0.56 Pg of soil losses annually when compared to soil erosion from unburned conditions. The post-fire erosion rates in the globe are mostly driven by the burned areas from Africa (Table 1 ), to which corresponds 62% of the soil losses, followed then by Asia (12%), South America (11%), and Oceania (10%), with minor contributions from North America (4%) and the European continents (1%) (Fig. 2 , Fig. 3 ), resulting in a global soil erosion rate of 9.53 Mg ha − 1 year − 1 for the first post-fire year. This allowed us also to understand that from the total soil erosion estimated each year, only 31% (2.50 Pg y − 1 ) correspond to the last fire, whereas the remaining soil erosion results from previous fires events (Table 1 ). Table 1 Mean continental and global soil erosion estimations for pre- and post-fire conditions, share of global soil erosion, and share of soil erosion estimated for first post-fire year, over areas burned from 6 to 18 consecutive fire years (n = 14). Note SD for standard deviation. Pre-fire (6–18 years) Post-fire (6–18 years) Additional erosion (6–18 years) Share of global Share of 1st post-fire year Mean soil erosion SD Mean soil erosion SD Mean soil erosion SD (Pg y − 1 ) (Pg y − 1 ) (Pg y − 1 ) (%) (%) Africa 1.67 0.11 5.01 0.47 3.34 0.42 62 34 Asia 0.37 0.08 1.00 0.17 0.63 0.15 12 25 Europe 0.02 0.00 0.06 0.01 0.05 0.01 1 18 North America 0.12 0.04 0.29 0.06 0.16 0.03 4 18 Oceania 0.40 0.02 0.82 0.11 0.43 0.11 10 29 South America 0.46 0.08 0.92 0.17 0.46 0.15 11 26 Global 3.0 0.33 8.1 0.72 5.1 0.56 100 31 2.2. Post-fire recovery and long term soil erosion trends As expected, the overall recovery dynamics at continental scale reveal a complex pattern whereas factors such as burn severity, ecosystem resilience, post-fire climate, play major roles (McGuire et al. 2024). Within our study it was possible to assess recovery of the affected land through the RCOVER 10 , that represents the progress of vegetation recovery when compared to pre-fire conditions (see 4.3). Results show that following a single fire event, continents such as Oceania, and South and North America present the highest recovering performance, while the European and the African continents reveal more modest recovery rates (Fig. 4 ). Moreover, additional disturbances beyond wildfires might interfere with recovery, thus explaining why the maximum recovered areas after a single fire event was as much as 83% for Oceania, 75–76% for North and South America, 67% for Asia, 56% for Europe and 46% for Africa. These results however, should also include the effect of precise management actions, such as logging or post-fire mitigation measures 21 through visible changes in vegetation indices. When investigating the recovery considering cumulative impact over several fire years (Fig. 5 ), the complexity between affected land cover (Fig. 5 a), climate (Fig. 5 b) and biome (Fig. 5 c) emerge. At global level, recovered areas represent 39% of the 2019 baseline, whereas areas under high level of pressure (RCOVER [0.0-0.3[) stand in the 13% (Fig. 5 d). However, these values can be more positive for forest dominated landscapes (Recovered = 44%, [0-0.3[= 5%), or the contrary as the case mixed land cover (Recovered = 36%) or grassland areas ([0-0.3[= 17%). Notwithstanding, the most affected climate Tropical, savannah (Aw, 41%) or biome Tropical & Subtropical Grasslands, Savannas & Shrubland (TSGSS, 54%), seem to drive global RCOVER distribution (Recovered = 33–35%, [0-0.3[= 11%). Altogether these results further suggest that burned areas, not only are vulnerable immediately after the fire, but also a substantial portion of them never managed to recover until pre-fire conditions, as resulted by the 39% recovered areas when considered long and recent fire impacts (2019 baseline, Fig. 5 d), or of 48% recovery for areas burned 6 to 18 years before (Fig. 4 ). As a result, the estimated additional soil erosion ( post-fire minus pre-fire ) evidences a slight increasing trend (Fig. 6 ), contradicting thus with the overall burned area reduction trend (Fig. 1 ). Notwithstanding, at continental level, the increasing trend in annual post-fire soil erosion is only statistically significant for North America (Mann-Kendall trend test p < 0.05), while the increasing trend for Africa, Europe and Oceania are statistically non-significant. On the other hand, Asia and South America evidenced declining statistically non-significant trends for annual post-fire soil erosion. Unexpectedly, it was not possible to determine a standard recovery time within the areas affected by fire under the methodology applied in this study, despite the range of 2 to 7 years interval provided by the scientific community 7 , 9 , 12 . This analysis was explored under both vegetation indices as also in terms of soil erosion estimations, however, FCOVER (i.e. NDVI) observations from before and after the fire still evidenced changes even in the longest time series available (2001 to 2019) after a single fire event (38–69% recovery). Illustrating thus, the variability of time required to achieve full recovery for fire-affected ecosystems, which can also be a result from additional disturbances beyond the fire itself, such as land management 10 , pest outbreaks 24 , or droughts 12 , 25 , which haven’t been considered in this study. 2.3. Future projections Using as reference the year of 2019, the year with the most records of fire disturbances within our study, we have also assessed the impact of the expected changes in Rainfall Erosivity 26 over the identified burned areas (Fig. 7 , Fig. 8 ). The computation of all scenarios results in an increase in soil erosion globally from 11% (RCP2.6) to 23% (RCP8.5) for 2050, and from 23% (RCP2.6) to 28% (RCP8.5) for 2070. The greatest contribution for such increase comes from Africa where the burned areas have an overall larger surface, resulting between 54% (RCP8.5) and 60% (RCP2.6) of the total post-fire soil erosion globally in 2050, and between 54% and 55% for 2070. The increase in the Rainfall Erosivity in the burned areas in Asia however, results in the highest relative increases between 45–52% for 2050 and 49–67% for 2070 in relation to the area assessed in 2019. 3. Discussion The results from this study indicate that soil erosion following wildfires worldwide represent 19% of the latest global soil erosion estimation (2015), and 35% of the soil erosion generated in agricultural land 18 . This first global estimation can illustrate the dimension of the problem and trigger the development of solutions to prevent further impacts 12 , 27 . Despite the base for this modelling approach is set on post-fire field data 10 , the comparison and validation of post-fire soil erosion estimations of this study with field data is still challenging. Most of the post-fire soil erosion monitoring field studies are focused on short term assessments 10 , 21 , while over long(er)-term have only been done under non-continuous assessments 28 – 30 , and often targeting erosion processes that are scale (temporal and spatial) dependent 12 , 31 , 32 . Moreover, fewer studies consider the impacts of various fires in post-fire soil erosion rates 33 , 34 , or do not consider the history of prior disturbances in the post-fire experimental design 35 . In what concerns the predicted soil erosion rates for the first post-fire year, our results (9.53 Mg ha − 1 y − 1 ) are within the range of field measurements compiled by Shakesby and Doerr (2006) for bounded plots at hillslope scale (0.5–197 Mg ha − 1 y − 1 ) and tracer and sediments traps from open plots (0.1–70 Mg ha − 1 y − 1 ), or within the range provided from interrill, to rill erosion or gully erosion at hillslope scale (0.001-25.00 Mg ha − 1 y − 1 ) 12,21 . It should be highlighted, however, that field measurements in Africa and South America are underrepresented or absent in these reviews 9 , 16 , 21 . The limited vegetation recovery trend observed in this study, is in line with Patacca et al. (2023) results on European forests evidencing increasing trends of forest disturbances, but also by the global analysis of Forzieri et al. (2022), which resulted in a reduction of forest resilience under managed and intact forests. In fact, Forzieri et al. (2022) hypothesized that current (2000–2020) observed changes in forest globally are already a result of climate-induced changes. Nonetheless, it was not possible to find a comprehensive study integrating multiple local forest disturbances, and how such pressures interact altogether globally 24 . On the other hand, our observations are not aligned with those of Xu et al. (2024) who, using EVI and kNDVI indices, determined that 87% of burned vegetation regained pre-fire productivity levels within 2 years. Such inconsistencies might be related to differences in the definition of recovery, differences in the indicators since FCOVER targets ground cover, but also to the coarser scale used (10 km) in comparison with the present study (500 m). The concept of window of disturbance model 38 already have been extensively investigated by the post-fire community 7 , 9 , 34 , 39 , 40 , and still drives important research questions on the post-fire hydrological and erosive response 41 , and post-fire recovery for soil properties 42 . However, and similar to what was found in this study, no clear standard recovery time has been found yet. In fact, the results of this study are in line with the geomorphic resilience framework as suggested by McGuire et al. (2024), whereas after wildfires the ecosystem response can persist, change and recover, or lead to an alternate state. Based on our results, or either 19 years of observations do not allow us to fully capture the change and recovery of fire-affected areas, or the share of alternate state might be greater than anticipated (> 50%). In addition, the variability in climate, vegetation type, burn severity, and history of past disturbances are known to affect post-fire recovery, and thus explain the observed recovery variability. Concerning trends, the results of this study evidence a non-significant statistical trend of increasing post-fire soil erosion globally despite the decreasing burned area trend observed from 2001–2019. Moreover, post-fire soil erosion is estimated to aggravate in the future, as 2050–2070 projections indicate an increase of rainfall erosivity 26 . These considerations, however, do not include the anticipated changes in fire activity 43 , which when combined with the anticipated rainfall erosivity changes 44 , were projected to increase by 68% for post-fire debris flows events, in locations where those events already took place in the past 12 . The results of this study combined with the limited number of countries (U.S.A, Spain) undertaking post-fire management actions globally 21 , highlight the need for further knowledge on forest disturbances to plan and implement adequate land management in the mitigation and restoration of burned areas 24 . Field studies have shown that pre-fire fuel treatments 45 , post-fire soil mitigation treatments 21 , 46 , 47 , or post-fire restoration 48 have shown positive results in the recovery of vegetation, but also for soil condition 49 after wildfire. Despite the limitations, this study provides a first estimation of the post-fire soil erosion globally, thus contributing with two new blocks (forest and fire) to the existing high resolution global soil erosion estimates and projections (GLoSEM) 18 , 50 . However, further research on how additional disturbances impact post-fire soil erosion and recovery is still required, namely, for how long wildfire affects soil properties 42 , and what is the role of those fire-induced changes limiting forest recovery. Moreover, further investigations on off-site impacts due to the post-fire soil erosion increase, including the Carbon sink ability of Soil Organic Carbon redistribution by erosion after fire 51 , or the impact of the transport of sediments and ash to the downstream waterbodies 13 , 52 are necessary. 4. Conclusions This is the first study that assesses soil erosion in post-fire conditions at the global scale following wildfires. The main findings of this modelling exercise with respect to the 2001–2019 burned area are: Our study estimates global post-fire soil erosion to be around the 8.1 Pg y - 1 , considering the cumulative effect of 18 years of global fire disturbances. Post-fire soil erosion as estimated in this study, corresponds to 19% of the latest global estimation for 2015 for all land, and to 35% of the erosion produced in agricultural land 18 . Africa is by far the continent that is impacted the most in terms of post-fire soil erosion (62%), given its significantly larger burned area (67% total). The estimated soil erosion from the first post-fire year corresponds to 31% of the total soil erosion produced in a single year, whereas the remaining erosion losses can be attributed to previous wildfires occurrences. The average time until full recovery could not be determined under the methodology of this study, given the significant proportion of areas (> 50%) presenting less vegetation cover in comparison to pre-fire condition. Trends assessed during the 2001–2019 period reveal an increasing post-fire soil erosion trend, and when using projections for 2050–2070 rainfall erosivity globally, post-fire soil erosion is predicted to further increase, with 11–23% and 23–28% post-fire soil erosion increase, for 2050 and 2070 respectively. The results of this assessment reinforce previous evidences that limited attention has been given to the prevention and mitigation of wildfire impacts to forest soils. Moreover, the improvement of global forest resilience is urgent, in order also to stop land degradation and efficiently adapt to climate change. 5. Methods 5.1. Methodological approach To accomplish a Global post-fire soil erosion assessment, this study used as main data sources land and climate data products from previous soil erosion assessments at European 10 , 53 and Global scale 19 , 50 , and remote sensing data for the determination of wildfires impact on ecosystems and the correspondent vegetation dynamics from pre-fire conditions to post-fire recovery (Fig. 9 ). The data is used to feed the Revised Universal Soil Loss Equation (RUSLE) 54 , 55 for forest, shrubland and grasslands 56 , following up the the latest EU post-fire soil erosion assessment 10 methodology. However, this study gives a step further by upscaling the previous methodology to the global spatial scale, and by increasing the assessment temporal scale from 5 to 18 years. In addition, also looks into the cumulative impacts of wildfires on soil erosion for several fire years, instead of following the recovery of a single year of fires which was highlighted as a major underestimations aspect. With the increase in the spatial and temporal scale, several adaptations were required as a result from data availability restrictions over two main data inputs, the biophysical parameter Fraction of Vegetation Cover (FCOVER) and the Normalized Burn Ratio (NBR). The methodological approach presents a balance between keeping a reasonable input resolution and the increase in data processing for a global study (Fig. 10 ). Now, the FCOVER determination was required for each individual fire throughout the 18 years since FCOVER products from Copernicus were not available prior 2014. As a result, this indicator had to be estimated for all burned areas in the entire globe following Song et al. (2022) methodology (30m resolution), which also gave us the opportunity to compensate the resolution loss from now using MOSEV (500m) database 20 . The decision to use MOSEV database was driven by being the only open access product compiling burned area delineation at global scale, and by the fact that such dataset already contained NBR classification. Moreover, a recent study has also evidenced the use of FCOVER as having a good performance assessing burn severity 58 . Since such changes could lead to new uncertainties, and because it was possible to replicate the assessment made by Vieira et al. (2023) under this new methodology, the soil erosion results under both methodologies were compared to assess quality (Supplementary material S1). 5.2. RUSLE model and pre-fire conditions This study used the Revised Universal Soil Loss Equation (RUSLE) 54 , 55 , in order to model the average soil erosion potential in the absence of wildfire by including ground cover changes described by 59 . RUSLE model uses the following factors to estimate soil loss: A = R · K · LS · C · P (1) where, A = soil loss (Mg ha − 1 yr − 1 ), R = rainfall and runoff factor (MJ mm h − 1 ha − 1 yr − 1 ), K = soil erodibility factor (Mg h MJ − 1 mm − 1 ), LS = slope length and the slope steepness factor (dimensionless), and CP = cover management practice factor (dimensionless). The K and R factors were expressed spatially using the most maps developed by the Joint Research Centre of the European Commission 50 . Since the C - cover and management - factor was originally developed for agricultural croplands, it required an adjustment for forest, shrub and grasslands ground cover characteristics 10 , 53 . Considering the size of the study area, the C-factor used to predict the soil loss potential was estimated as Vieira et al. (2023) whereas the vegetation density was quantified by manipulating the biophysical parameter Fraction of Vegetation Cover (FCOVER 60 ). This provided an estimation of the fraction of the vegetation that is visible vertically, allowing to differentiate a C factor for undisturbed conditions (C U ) between bare or protected soil for forest, shrub, and grasslands (Fig. 10 ). Management practices (P) were not considered under this assessment. 5.3. Adaptation for post-fire conditions According to the state-of-the-art post-fire soil erosion rates and vegetation recovery, are closely related to burnt severity and bare soil cover 11 , 12 , 21 . Notwithstanding, as evidenced by Johansen et al. (2001), forests, shrubland, and grasslands are distinctively impacted by wildfires, whereas contrary to the undisturbed condition, burned forests present much higher post-fire soil erosion rates in comparison to shrubland and grasslands due to higher fire residence times and burn severities. Therefore, the C factor for burned conditions (Fig. 2 ) was approximated as follows: $$\:{Cmax}_{B\_Forest}={Cmax}_{U\_Forest}\times\:{B}_{severity}\times\:10\:\:\:\:\:\:\:\:for\:FCOVER=0.2,\:max=0.5\:$$ 2 $$\:{Cmax}_{B\_Shrub}={Cmax}_{U\_Shrub}\times\:{B}_{severity}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:for\:FCOVER=0.2,\:max=0.3\:$$ 3 $$\:{Cmax}_{B\_Grass}={Cmax}_{U\_Grass}\times\:{B}_{severity}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:for\:FCOVER=0.2,\:max=0.3$$ 4 where, for equations 2 , 3 and 4 , the maximum C factor for burned conditions (Cmax B ) was estimated by multiplying the maximum C factor for undisturbed conditions (Cmax U ) and the burn severity anomaly factor (B severity ) as derived by Vieira et al. (2023). Such maximum value was determined for ground cover (FCOVER) values of 0.2, similarly to the approach taken by Wischmeier and Smith (1978). Regarding the individual cover types, for forest (2) the C factor was increased one order of magnitude for all the severities and limited to a maximum of 0.5, while shrubland (3) and grasslands (4) were represented by low and moderate severities and limited to a maximum of 0.3. In this way was possible to accommodate the impacts determined by Vieira et al. (2015) for distinctive severities, as also the conceptual model of Johansen et al. (2001) for distinct cover types (Fig. 2 ). The recovery of the burned areas was addressed by using vegetation cover indicators as proxies, which consisted in merging two criteria: 1) a burned area is considered recovered once the ground cover reaches 100%; 2) a burned area is considered recovered once the ground cover reaches the pre-fire conditions. In this way, it was possible to determine erosion reduction with the increase of the protective cover, but also consider the initial characteristics of the local vegetation before the wildfires. For the first point, a minimum C factor was estimated: $$\:{Cmin}_{B}={Cmin}_{U}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:for\:FCOVER=1.0$$ 5 Being the minimum C factor for burned conditions (Cmin B ) approximated to the (Cmin U 53 ) for each land cover (forest, shrub, and grassland) whenever the ground cover (FCOVER) reached its maximum value of 1. This formulation allowed to include the impact of the burn severity in the soil erosion estimations for when protective cover presented the minimum value according to the burn severity impact, and approximate the recovery of the burned areas under an exponential curve in function of FCOVER over a 5-years’ time period (Fig. 2 ). The choice of an exponential base for such estimations goes in line with 61 conceptual model after observing a non-linear relationship between sediment yields and bare soil cover, and a sharp increase of the sediment yields on the 60–70% bare soil cover. The second point of the recovery to the background levels, was motivated by the FCOVER distribution sample for pre-fire conditions. For the 3 years preceding the wildfires, the mean FCOVER value rounded 0.43, thus representing the mixture of Forest, Shrubs and Grasslands within our AOI. This sample presented a normal distribution among the 3 pre-fire years, and the mean FCOVER value obtained for this period was used to assess the rate of recovery. Therefore a recovery rate (RCOVER) was calculated, and several classes were defined as follows: $$\:{RCOVER}_{t}=\frac{{FCOVER}_{t}}{{FCOVER}_{pre-fire}}$$ 6 No Recovery: 0.0 ≤ RCOVER < 0.3 Ongoing Recovery: 0.3 ≤ RCOVER < 0.6 Near Recovery: 0.6 ≤ RCOVER < 1.0 Recovered: RCOVER ≥ 1.0 The consideration of a RCOVER class allowed us to adopt a succession of C-factor equations according to the status of the vegetation recovery progress. For the first 2 classes ‘No Recovery’ and ‘Ongoing Recovery’ the C factor would be calculated as described for equations ( 2 ) to ( 5 ) according to the initial burn severity classification and land cover attributes. In the case of ‘Near Recovery” class, this would led to the adoption of the equation with one burn severity level lower than the original one, likewise the ‘Recovered’ class, would led to adoption of the equation corresponding to unburned conditions. This formulation allow us to use FCOVER as constant input for C estimations, while reducing the impact of severity proportionally to the pre-fire conditions. 5.4. Spatial data processing The burned area delineation, wildfire date, and burn severity (NBR) was retrieved from MOSEV database 20 with resolution of 500m and global coverage, for the 2000–2020 period. Given the incompleteness of the dataset for the years of 2000 and 2020, these were removed from our analysis, while burn severity was then converted to low, moderate and high burn severity classes, according to Key and Benson (2006). To target the land cover types affected by wildfires, the reference dataset for land cover factions the Dynamic Land Cover map at 100 m resolution (CGLS-LC100 56 ) was used, which allowed to estimate the individual contributions of soil erosion for forest, shrub, and grassland with reference year of 2015. Despite more recent versions of this dataset exists (2019), the choice to keep 2015 was based to keep the comparability with the previous assessment 10 , but also due to the inexistence of comparable datasets prior to this date which would allow to accommodate land cover changes throughout the entire temporal scale of this study. Moreover, cropland was also excluded from this analysis due to the lack of consolidated knowledge on the cumulative effect from intensive soil management operations and wildfire 10 , while at the same time soil erosion in these areas have already been assessed extensively 18 . The ground cover changes were assessed with FCOVER 60 , which provides a measure of the fraction of ground covered by green vegetation. The change in vegetation cover at 30m resolution was determined by NDVI 57 , using as reference date the first Landsat imagery available following each fire record. The repeated measurement of NDVI was performed annually (2001–2019), in order to follow-up the recovery of each burned area. Whenever a repeated wildfire occurred in the same location, the procedure would start again considering the latest fire as reference, and assuming the recovery was sufficient in order to allow the most recent fire to occur. Declarations Data availability The data for post-fire soil erosion following the 2001-2019 wildfires will be available in the European Soil Data Centre (ESDAC) 64 . References San-Miguel-Ayanz, J. et al. Global Wildfire Information System - Country Profile. (2023). Pausas, J. G. & Keeley, J. E. Wildfires as an ecosystem service. Front. Ecol. Environ. 17, 289–295 (2019). Ferreira, C. S. S., Seifollahi-Aghmiuni, S., Destouni, G., Ghajarnia, N. & Kalantari, Z. Soil degradation in the European Mediterranean region: Processes, status and consequences. Sci. Total Environ. 805, 150106 (2022). 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Universal Soil Loss Equation and Revised Universal Soil Loss Equation. in Handbook of Erosion Modelling 135–167 (John Wiley & Sons, Ltd, 2010). doi: 10.1002/9781444328455.ch8 . Buchhorn, M. et al. Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015–2019: Product User Manual . https://zenodo.org/records/3938963 (2020) doi: 10.5281/zenodo.3938963 . Song, D.-X., Wang, Z., He, T., Wang, H. & Liang, S. Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data. Sci. Remote Sens. 6, 100058 (2022). Fernández-Guisuraga, J. M., Calvo, L., Quintano, C., Fernández-Manso, A. & Fernandes, P. M. Fractional vegetation cover ratio estimated from radiative transfer modeling outperforms spectral indices to assess fire severity in several Mediterranean plant communities. Remote Sens. Environ. 290, 113542 (2023). Hansen, M. C. et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853 (2013). Fuster, B. et al. Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service. Remote Sens. 12, 1017 (2020). Johansen, M. P., Hakonson, T. E. & Breshears, D. D. Post-fire runoff and erosion from rainfall simulation: contrasting forests with shrublands and grasslands. Hydrol. Process. 15, 2953–2965 (2001). Wischmeier, W. H. & Smith, D. Predicting rainfall erosion losses: a guide to conservation planning. in (1978). Key, C. & Benson, N. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. in (2006). Panagos, P. et al. European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies. Eur. J. Soil Sci. 73, e13315 (2022). Additional Declarations There is NO Competing Interest. Supplementary Files 02SMVieiraetalDv17.docx Quality assessment of global upscaling Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Nature Geoscience → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5622658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432813548,"identity":"139388b9-059d-46e6-bcdb-902feb965fce","order_by":0,"name":"Diana Vieira","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2213-3798","institution":"European Commission, Joint Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Diana","middleName":"","lastName":"Vieira","suffix":""},{"id":432813549,"identity":"6744ef95-ba48-4bd1-8136-9978b50d9907","order_by":1,"name":"Pasquale Borrelli","email":"","orcid":"https://orcid.org/0000-0002-4767-5115","institution":"University of Roma Tre","correspondingAuthor":false,"prefix":"","firstName":"Pasquale","middleName":"","lastName":"Borrelli","suffix":""},{"id":432813550,"identity":"cfc032d8-8e4e-4864-850b-fed72b7a0cbb","order_by":2,"name":"Simone Scarpa","email":"","orcid":"","institution":"EUROPEAN DYNAMICS","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Scarpa","suffix":""},{"id":432813551,"identity":"781df3cb-7038-4478-accd-e4fd79ba4179","order_by":3,"name":"Leonidas Liakos","email":"","orcid":"","institution":"UNISYSTEMS","correspondingAuthor":false,"prefix":"","firstName":"Leonidas","middleName":"","lastName":"Liakos","suffix":""},{"id":432813552,"identity":"a5f5075b-6d58-465c-bca8-49fc8de32382","order_by":4,"name":"Cristiano Ballabio","email":"","orcid":"https://orcid.org/0000-0001-7452-9271","institution":"Joint Research Centre - European Comission","correspondingAuthor":false,"prefix":"","firstName":"Cristiano","middleName":"","lastName":"Ballabio","suffix":""},{"id":432813553,"identity":"87ede468-2085-4196-92a2-0964d181af35","order_by":5,"name":"Panos Panagos","email":"","orcid":"https://orcid.org/0000-0003-1484-2738","institution":"European Commission, Joint Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Panos","middleName":"","lastName":"Panagos","suffix":""}],"badges":[],"createdAt":"2024-12-11 09:05:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5622658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5622658/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41561-025-01876-0","type":"published","date":"2026-01-05T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79182095,"identity":"7752c120-00bd-4687-9c94-388a38d1fad2","added_by":"auto","created_at":"2025-03-25 10:39:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":827646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlobal burned area trends (Million km2) and burn severity distribution (adapted from MOSEV \u003c/em\u003e\u003csup\u003e20\u003c/sup\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/836170f794cae27313ec2ca2.png"},{"id":79180085,"identity":"93b5da7a-8696-420e-af27-f639d773cd74","added_by":"auto","created_at":"2025-03-25 10:22:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":247147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlobal soil erosion estimations for areas burned from 2001 to 2019 for pre- and post-fire soil conditions. Note the pre-fire soil erosion estimations in light green, the additional post-fire soil erosion with distinctive color for each continent, and box identifying data for at least 6 consecutive fire years.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/5047a1b80e824c72a09d1087.png"},{"id":79181056,"identity":"43843997-c2f1-4a19-a15d-20a7fe693ff8","added_by":"auto","created_at":"2025-03-25 10:30:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2291471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlobal a) pre and b) post-fire soil erosion for 2019, considering burned areas and cumulative impacts since 2001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/cd790d9a846292f0b53d97ac.png"},{"id":79179438,"identity":"a694a414-997f-4118-8f9e-238a8f04c1e1","added_by":"auto","created_at":"2025-03-25 10:14:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":520957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRecovered areas (%) (RCOVER≥1) \u0026nbsp;by time since fire. RCOVER determined for each continent following a single fire event and thought the study period. Recurrent fires in the same pixel were removed from long term assessment, and reclassified as another event.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/e8586500d35090e585bff469.png"},{"id":79181060,"identity":"2682e754-030a-4296-aee5-b32bfc8bf333","added_by":"auto","created_at":"2025-03-25 10:30:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":450276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCharacterization of 2019 cumulative impacts regarding burned area distribution (%) over a) target Land Cover (Forest, Shrubland, Grassland), b) top 12 affected Climates\u003c/em\u003e\u003csup\u003e22\u003c/sup\u003e\u003cem\u003e, c) affected Biomes\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e\u003cem\u003e, and RCOVER classification (%) for d) target Land Cover, e) top 7 affected Climates\u003c/em\u003e\u003csup\u003e22\u003c/sup\u003e\u003cem\u003e, and f) top 7 affected Biomes\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e\u003cem\u003e. Note Land Cover for forest, shrubland, and grassland correspond to areas dominated by these land cover (\u0026gt;50%), otherwise classified as mixed. RCOVER classes: [0-0.3[- No Recovery, [0.3-0.6[- Ongoing Recovery, [0.6-1.0[-Near recovery, and RCOVER\u0026gt;= 1 – Recovered following \u003c/em\u003e\u003csup\u003e10\u003c/sup\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/b38d9539983792afd6fec656.png"},{"id":79182355,"identity":"22c0d798-7a20-41ee-be13-e51817c74d9f","added_by":"auto","created_at":"2025-03-25 10:47:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":464368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdditional post-fire soil erosion (post-fire minus pre-fire) and trend per continent for 6 to 18 consecutive fire years (2006-2019).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/e3e86bd5d10e24ed1dd64750.png"},{"id":79179431,"identity":"e4e48658-aa15-4e14-b62d-e779caeb2a64","added_by":"auto","created_at":"2025-03-25 10:14:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":154447,"visible":true,"origin":"","legend":"\u003cp\u003eContinental post-fire soil erosion estimation for 2050 (left) and 2070 (right) under the 2.6, 4.5, and 8.5 RCP scenarios. Note the post-fire soil erosion estimation for 2019 as reference.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/bba2f1d2f9a1737a324d7354.png"},{"id":79179447,"identity":"b8ed39e0-07ee-41de-930d-3fe156134a12","added_by":"auto","created_at":"2025-03-25 10:14:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9248097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlobal soil erosion projections in burned areas for a) c) e) 2050 and b) d) f) 2070 using as reference the accumulated burned areas until 2019.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/b8708bca6cb6894bbb4f29a3.png"},{"id":79180096,"identity":"ae2fad7b-1bec-4e11-8aa2-b79e68887d33","added_by":"auto","created_at":"2025-03-25 10:22:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2383057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOverall methodological modelling approach.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/e56bc7697d6f71a7c59bdf15.png"},{"id":79179432,"identity":"da81a81c-1655-416d-8136-f48f43501623","added_by":"auto","created_at":"2025-03-25 10:14:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":461355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodological adaptation to new spatial and temporal scales focusing in FCOVER and NBR dataset. Major changes are the alteration of NBR from 25m to 500m resolution, and the FCOVER from 300m to 30m. C factors derived in Vieira et al. (2023) for unburned and burned conditions for forest, shrubland, grassland, and cropland, according to low, moderate and high burn severity are kept.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/e4c8ac08fbeb1ec7a3c7b7d8.png"},{"id":99588315,"identity":"2a6e7e34-9972-4c11-bedb-81776834066d","added_by":"auto","created_at":"2026-01-06 08:13:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16774162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/f6d2fc6a-0ba9-4ad7-a965-10f93973d995.pdf"},{"id":79179422,"identity":"3be0579d-c00e-49c5-a28e-6af9c00711a5","added_by":"auto","created_at":"2025-03-25 10:14:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":250914,"visible":true,"origin":"","legend":"Quality assessment of global upscaling","description":"","filename":"02SMVieiraetalDv17.docx","url":"https://assets-eu.researchsquare.com/files/rs-5622658/v1/4dbfd6dc22d2398e102c1261.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The global significance of post fire soil erosion","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the Global Wildfire Information System\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, on average, four million km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of land burn every year (2002\u0026ndash;2023), an area almost equal to the surface of European Union. Wildfires are an integral part of many ecosystems around the globe, playing key roles in ecosystem dynamics and the retention of species that have evolved in response to fire\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, this global phenomenon is often responsible for substantial environmental, social and economic losses, which combined with land abandonment, droughts, absence of appropriate land management and urban development, are expected to aggravate land degradation\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In addition, wildfires will become a persistent threat, since the fire risk is expected to increase in a context of a warmer and drier climate\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLed by the elevated temperatures reached in topsoil, fire induce changes in soil physical and chemical properties, loss of biodiversity, reduce water infiltration and ground cover protection, and alter soil aggregate stability\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. When combined, these factors result in significant increases in soil erodibility after fire, which vary between geographical regions and burn severity levels\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. High burn severity fires, not only increase on-site soil erosion, but may also lead to off-site impacts downstream of the burned area in the form of destructive floods and debris flows\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and the transport of ash and sediment loads onto downstream water bodies\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, the relative importance of post-fire soil erosion events is likely to increase in response to ongoing and anticipated increases in fire activity and rainfall intensity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite wildfires have been regarded as an important hydrological and geomorphological agent\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, until now post-fire soil erosion has mostly been assessed from plot to catchment scale by means of field measurements and from slope to regional scale using modelling approaches\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Current model-based soil erosion assessments at global scales\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e already gave significant steps forward, providing this knowledge for informed land management decisions, however, these have not yet considered the effects of wildfires in the equation. While the first steps for a large scale post-fire soil erosion estimation \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e provides an idea of the impact of wildfires in European soils, this approach still presents limitations by only accounting for a single disturbance event (1 fire year) and for a limited recovery period (5 years), evidencing that a global assessment considering the cumulative effect of several wildfires and a long term perspective are still missing\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHence, the aim of this study is to provide the first estimation of post-fire soil erosion at global scale and analyse geomorphological changes driven by fire over the last 2 decades. To accomplish this, we estimated the global post-fire soil erosion using the largest global wildfires database available (MOSEV \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e) for an 18-years period, and compared it against unburned conditions. In this way was possible to estimate the cumulative impact of several wildfire occurrence years in the global soil erosion budget and to identify the main trends for post-fire soil erosion. Additionally, this also allowed us to analyse the recovery of fire-affected ecosystems under systematic pressure, but also project these trends in combination with the anticipated changes in rainfall.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Burned area, severity and erosion\u003c/h2\u003e \u003cp\u003eOver the study area (forest, shrubland and grassland) and period (2001\u0026ndash;2019), annual burned areas averaged 2.9\u0026nbsp;million km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (about the area of India) over the globe. Throughout the two observed decades, the trend in burned areas have been declining globally, with the exception of North America (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Africa is the most affected continent, with 66% (366 thousand Km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) of the total annual burned area identified mostly among low (67%, 244 thousand km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and moderate (33%, 122 thousand km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) severity burns. High severity burns are more pronounced in North America (10%), Asia (5%), and Europe (4%), while in other continents such as South America (2%) tend to be rather limited or nearly absent as the case for Africa and Oceania (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen focusing on the soil erosion impacts, we have considered the cumulative effect of various wildfires to account for their long-term contribution to the global erosion budget. To do so, we have identified 6 years as the minimum time required for this cumulative effect to stabilize, based on the plateau provided by soil erosion under pre-fire conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and within the range of expected effects as provided by the literature\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Using this metric, the post-fire affected areas targeted in this study (forest, shrubland and grassland) with at least 6 fire years are estimated to result in average 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 Pg of soil losses annually over the 2006\u0026ndash;2019 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This represents an additional 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 Pg of soil losses annually when compared to soil erosion from unburned conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe post-fire erosion rates in the globe are mostly driven by the burned areas from Africa (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), to which corresponds 62% of the soil losses, followed then by Asia (12%), South America (11%), and Oceania (10%), with minor contributions from North America (4%) and the European continents (1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), resulting in a global soil erosion rate of 9.53 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the first post-fire year. This allowed us also to understand that from the total soil erosion estimated each year, only 31% (2.50 Pg y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) correspond to the last fire, whereas the remaining soil erosion results from previous fires events (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean continental and global soil erosion estimations for pre- and post-fire conditions, share of global soil erosion, and share of soil erosion estimated for first post-fire year, over areas burned from 6 to 18 consecutive fire years (n\u0026thinsp;=\u0026thinsp;14). Note SD for standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePre-fire\u003c/p\u003e \u003cp\u003e(6\u0026ndash;18 years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePost-fire\u003c/p\u003e \u003cp\u003e(6\u0026ndash;18 years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAdditional erosion\u003c/p\u003e \u003cp\u003e(6\u0026ndash;18 years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShare of global\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShare of 1st post-fire year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean soil erosion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean soil erosion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean soil erosion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(Pg y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(Pg y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(Pg y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Post-fire recovery and long term soil erosion trends\u003c/h2\u003e \u003cp\u003eAs expected, the overall recovery dynamics at continental scale reveal a complex pattern whereas factors such as burn severity, ecosystem resilience, post-fire climate, play major roles (McGuire et al. 2024). Within our study it was possible to assess recovery of the affected land through the RCOVER\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, that represents the progress of vegetation recovery when compared to pre-fire conditions (see 4.3).\u003c/p\u003e \u003cp\u003eResults show that following a single fire event, continents such as Oceania, and South and North America present the highest recovering performance, while the European and the African continents reveal more modest recovery rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, additional disturbances beyond wildfires might interfere with recovery, thus explaining why the maximum recovered areas after a single fire event was as much as 83% for Oceania, 75\u0026ndash;76% for North and South America, 67% for Asia, 56% for Europe and 46% for Africa. These results however, should also include the effect of precise management actions, such as logging or post-fire mitigation measures \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e through visible changes in vegetation indices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen investigating the recovery considering cumulative impact over several fire years (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the complexity between affected land cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), climate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and biome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) emerge. At global level, recovered areas represent 39% of the 2019 baseline, whereas areas under high level of pressure (RCOVER [0.0-0.3[) stand in the 13% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). However, these values can be more positive for forest dominated landscapes (Recovered\u0026thinsp;=\u0026thinsp;44%, [0-0.3[=\u0026thinsp;5%), or the contrary as the case mixed land cover (Recovered\u0026thinsp;=\u0026thinsp;36%) or grassland areas ([0-0.3[=\u0026thinsp;17%). Notwithstanding, the most affected climate Tropical, savannah (Aw, 41%) or biome Tropical \u0026amp; Subtropical Grasslands, Savannas \u0026amp; Shrubland (TSGSS, 54%), seem to drive global RCOVER distribution (Recovered\u0026thinsp;=\u0026thinsp;33\u0026ndash;35%, [0-0.3[=\u0026thinsp;11%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAltogether these results further suggest that burned areas, not only are vulnerable immediately after the fire, but also a substantial portion of them never managed to recover until pre-fire conditions, as resulted by the 39% recovered areas when considered long and recent fire impacts (2019 baseline, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), or of 48% recovery for areas burned 6 to 18 years before (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As a result, the estimated additional soil erosion (\u003cem\u003epost-fire minus pre-fire\u003c/em\u003e) evidences a slight increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), contradicting thus with the overall burned area reduction trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notwithstanding, at continental level, the increasing trend in annual post-fire soil erosion is only statistically significant for North America (Mann-Kendall trend test p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the increasing trend for Africa, Europe and Oceania are statistically non-significant. On the other hand, Asia and South America evidenced declining statistically non-significant trends for annual post-fire soil erosion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnexpectedly, it was not possible to determine a standard recovery time within the areas affected by fire under the methodology applied in this study, despite the range of 2 to 7 years interval provided by the scientific community\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This analysis was explored under both vegetation indices as also in terms of soil erosion estimations, however, FCOVER (i.e. NDVI) observations from before and after the fire still evidenced changes even in the longest time series available (2001 to 2019) after a single fire event (38\u0026ndash;69% recovery). Illustrating thus, the variability of time required to achieve full recovery for fire-affected ecosystems, which can also be a result from additional disturbances beyond the fire itself, such as land management\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, pest outbreaks\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, or droughts \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, which haven\u0026rsquo;t been considered in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Future projections\u003c/h2\u003e \u003cp\u003eUsing as reference the year of 2019, the year with the most records of fire disturbances within our study, we have also assessed the impact of the expected changes in Rainfall Erosivity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e over the identified burned areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The computation of all scenarios results in an increase in soil erosion globally from 11% (RCP2.6) to 23% (RCP8.5) for 2050, and from 23% (RCP2.6) to 28% (RCP8.5) for 2070. The greatest contribution for such increase comes from Africa where the burned areas have an overall larger surface, resulting between 54% (RCP8.5) and 60% (RCP2.6) of the total post-fire soil erosion globally in 2050, and between 54% and 55% for 2070. The increase in the Rainfall Erosivity in the burned areas in Asia however, results in the highest relative increases between 45\u0026ndash;52% for 2050 and 49\u0026ndash;67% for 2070 in relation to the area assessed in 2019.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe results from this study indicate that soil erosion following wildfires worldwide represent 19% of the latest global soil erosion estimation (2015), and 35% of the soil erosion generated in agricultural land \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This first global estimation can illustrate the dimension of the problem and trigger the development of solutions to prevent further impacts\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the base for this modelling approach is set on post-fire field data\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, the comparison and validation of post-fire soil erosion estimations of this study with field data is still challenging. Most of the post-fire soil erosion monitoring field studies are focused on short term assessments\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, while over long(er)-term have only been done under non-continuous assessments\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and often targeting erosion processes that are scale (temporal and spatial) dependent\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Moreover, fewer studies consider the impacts of various fires in post-fire soil erosion rates\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, or do not consider the history of prior disturbances in the post-fire experimental design\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In what concerns the predicted soil erosion rates for the first post-fire year, our results (9.53 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) are within the range of field measurements compiled by Shakesby and Doerr (2006) for bounded plots at hillslope scale (0.5\u0026ndash;197 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and tracer and sediments traps from open plots (0.1\u0026ndash;70 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), or within the range provided from interrill, to rill erosion or gully erosion at hillslope scale (0.001-25.00 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003e12,21\u003c/sup\u003e. It should be highlighted, however, that field measurements in Africa and South America are underrepresented or absent in these reviews\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe limited vegetation recovery trend observed in this study, is in line with Patacca et al. (2023) results on European forests evidencing increasing trends of forest disturbances, but also by the global analysis of Forzieri et al. (2022), which resulted in a reduction of forest resilience under managed and intact forests. In fact, Forzieri et al. (2022) hypothesized that current (2000\u0026ndash;2020) observed changes in forest globally are already a result of climate-induced changes. Nonetheless, it was not possible to find a comprehensive study integrating multiple local forest disturbances, and how such pressures interact altogether globally \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. On the other hand, our observations are not aligned with those of Xu et al. (2024) who, using EVI and kNDVI indices, determined that 87% of burned vegetation regained pre-fire productivity levels within 2 years. Such inconsistencies might be related to differences in the definition of recovery, differences in the indicators since FCOVER targets ground cover, but also to the coarser scale used (10 km) in comparison with the present study (500 m).\u003c/p\u003e \u003cp\u003eThe concept of window of disturbance model\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e already have been extensively investigated by the post-fire community\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and still drives important research questions on the post-fire hydrological and erosive response\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and post-fire recovery for soil properties\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, and similar to what was found in this study, no clear standard recovery time has been found yet. In fact, the results of this study are in line with the geomorphic resilience framework as suggested by McGuire et al. (2024), whereas after wildfires the ecosystem response can persist, change and recover, or lead to an alternate state. Based on our results, or either 19 years of observations do not allow us to fully capture the change and recovery of fire-affected areas, or the share of alternate state might be greater than anticipated (\u0026gt;\u0026thinsp;50%). In addition, the variability in climate, vegetation type, burn severity, and history of past disturbances are known to affect post-fire recovery, and thus explain the observed recovery variability.\u003c/p\u003e \u003cp\u003eConcerning trends, the results of this study evidence a non-significant statistical trend of increasing post-fire soil erosion globally despite the decreasing burned area trend observed from 2001\u0026ndash;2019. Moreover, post-fire soil erosion is estimated to aggravate in the future, as 2050\u0026ndash;2070 projections indicate an increase of rainfall erosivity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These considerations, however, do not include the anticipated changes in fire activity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, which when combined with the anticipated rainfall erosivity changes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, were projected to increase by 68% for post-fire debris flows events, in locations where those events already took place in the past\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of this study combined with the limited number of countries (U.S.A, Spain) undertaking post-fire management actions globally\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, highlight the need for further knowledge on forest disturbances to plan and implement adequate land management in the mitigation and restoration of burned areas\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Field studies have shown that pre-fire fuel treatments\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, post-fire soil mitigation treatments\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, or post-fire restoration\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e have shown positive results in the recovery of vegetation, but also for soil condition\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e after wildfire.\u003c/p\u003e \u003cp\u003eDespite the limitations, this study provides a first estimation of the post-fire soil erosion globally, thus contributing with two new blocks (forest and fire) to the existing high resolution global soil erosion estimates and projections (GLoSEM)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. However, further research on how additional disturbances impact post-fire soil erosion and recovery is still required, namely, for how long wildfire affects soil properties\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, and what is the role of those fire-induced changes limiting forest recovery. Moreover, further investigations on off-site impacts due to the post-fire soil erosion increase, including the Carbon sink ability of Soil Organic Carbon redistribution by erosion after fire\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, or the impact of the transport of sediments and ash to the downstream waterbodies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e are necessary.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis is the first study that assesses soil erosion in post-fire conditions at the global scale following wildfires. The main findings of this modelling exercise with respect to the 2001\u0026ndash;2019 burned area are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOur study estimates global post-fire soil erosion to be around the 8.1 Pg y\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, considering the cumulative effect of 18 years of global fire disturbances.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePost-fire soil erosion as estimated in this study, corresponds to 19% of the latest global estimation for 2015 for all land, and to 35% of the erosion produced in agricultural land \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAfrica is by far the continent that is impacted the most in terms of post-fire soil erosion (62%), given its significantly larger burned area (67% total).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe estimated soil erosion from the first post-fire year corresponds to 31% of the total soil erosion produced in a single year, whereas the remaining erosion losses can be attributed to previous wildfires occurrences.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe average time until full recovery could not be determined under the methodology of this study, given the significant proportion of areas (\u0026gt;\u0026thinsp;50%) presenting less vegetation cover in comparison to pre-fire condition.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTrends assessed during the 2001\u0026ndash;2019 period reveal an increasing post-fire soil erosion trend, and when using projections for 2050\u0026ndash;2070 rainfall erosivity globally, post-fire soil erosion is predicted to further increase, with 11\u0026ndash;23% and 23\u0026ndash;28% post-fire soil erosion increase, for 2050 and 2070 respectively.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe results of this assessment reinforce previous evidences that limited attention has been given to the prevention and mitigation of wildfire impacts to forest soils. Moreover, the improvement of global forest resilience is urgent, in order also to stop land degradation and efficiently adapt to climate change.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Methodological approach\u003c/h2\u003e \u003cp\u003eTo accomplish a Global post-fire soil erosion assessment, this study used as main data sources land and climate data products from previous soil erosion assessments at European \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and Global scale \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and remote sensing data for the determination of wildfires impact on ecosystems and the correspondent vegetation dynamics from pre-fire conditions to post-fire recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data is used to feed the Revised Universal Soil Loss Equation (RUSLE)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e for forest, shrubland and grasslands\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, following up the the latest EU post-fire soil erosion assessment\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e methodology. However, this study gives a step further by upscaling the previous methodology to the global spatial scale, and by increasing the assessment temporal scale from 5 to 18 years. In addition, also looks into the cumulative impacts of wildfires on soil erosion for several fire years, instead of following the recovery of a single year of fires which was highlighted as a major underestimations aspect.\u003c/p\u003e \u003cp\u003eWith the increase in the spatial and temporal scale, several adaptations were required as a result from data availability restrictions over two main data inputs, the biophysical parameter Fraction of Vegetation Cover (FCOVER) and the Normalized Burn Ratio (NBR). The methodological approach presents a balance between keeping a reasonable input resolution and the increase in data processing for a global study (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNow, the FCOVER determination was required for each individual fire throughout the 18 years since FCOVER products from Copernicus were not available prior 2014. As a result, this indicator had to be estimated for all burned areas in the entire globe following Song et al. (2022) methodology (30m resolution), which also gave us the opportunity to compensate the resolution loss from now using MOSEV (500m) database\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The decision to use MOSEV database was driven by being the only open access product compiling burned area delineation at global scale, and by the fact that such dataset already contained NBR classification. Moreover, a recent study has also evidenced the use of FCOVER as having a good performance assessing burn severity \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Since such changes could lead to new uncertainties, and because it was possible to replicate the assessment made by Vieira et al. (2023) under this new methodology, the soil erosion results under both methodologies were compared to assess quality (Supplementary material S1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.2. RUSLE model and pre-fire conditions\u003c/h2\u003e \u003cp\u003eThis study used the Revised Universal Soil Loss Equation (RUSLE)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, in order to model the average soil erosion potential in the absence of wildfire by including ground cover changes described by\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. RUSLE model uses the following factors to estimate soil loss:\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;R \u0026middot; K \u0026middot; LS \u0026middot; C \u0026middot; P (1)\u003c/p\u003e \u003cp\u003ewhere, A\u0026thinsp;=\u0026thinsp;soil loss (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), R\u0026thinsp;=\u0026thinsp;rainfall and runoff factor (MJ mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), K\u0026thinsp;=\u0026thinsp;soil erodibility factor (Mg h MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e mm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), LS\u0026thinsp;=\u0026thinsp;slope length and the slope steepness factor (dimensionless), and CP\u0026thinsp;=\u0026thinsp;cover management practice factor (dimensionless).\u003c/p\u003e \u003cp\u003eThe K and R factors were expressed spatially using the most maps developed by the Joint Research Centre of the European Commission\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Since the C - cover and management - factor was originally developed for agricultural croplands, it required an adjustment for forest, shrub and grasslands ground cover characteristics\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Considering the size of the study area, the C-factor used to predict the soil loss potential was estimated as Vieira et al. (2023) whereas the vegetation density was quantified by manipulating the biophysical parameter Fraction of Vegetation Cover (FCOVER\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e). This provided an estimation of the fraction of the vegetation that is visible vertically, allowing to differentiate a C factor for undisturbed conditions (C\u003csub\u003eU\u003c/sub\u003e) between bare or protected soil for forest, shrub, and grasslands (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Management practices (P) were not considered under this assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Adaptation for post-fire conditions\u003c/h2\u003e \u003cp\u003eAccording to the state-of-the-art post-fire soil erosion rates and vegetation recovery, are closely related to burnt severity and bare soil cover \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Notwithstanding, as evidenced by Johansen et al. (2001), forests, shrubland, and grasslands are distinctively impacted by wildfires, whereas contrary to the undisturbed condition, burned forests present much higher post-fire soil erosion rates in comparison to shrubland and grasslands due to higher fire residence times and burn severities. Therefore, the C factor for burned conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was approximated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Cmax}_{B\\_Forest}={Cmax}_{U\\_Forest}\\times\\:{B}_{severity}\\times\\:10\\:\\:\\:\\:\\:\\:\\:\\:for\\:FCOVER=0.2,\\:max=0.5\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Cmax}_{B\\_Shrub}={Cmax}_{U\\_Shrub}\\times\\:{B}_{severity}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:for\\:FCOVER=0.2,\\:max=0.3\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Cmax}_{B\\_Grass}={Cmax}_{U\\_Grass}\\times\\:{B}_{severity}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:for\\:FCOVER=0.2,\\:max=0.3$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, for equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the maximum C factor for burned conditions (Cmax\u003csub\u003eB\u003c/sub\u003e) was estimated by multiplying the maximum C factor for undisturbed conditions (Cmax\u003csub\u003eU\u003c/sub\u003e) and the burn severity anomaly factor (B\u003csub\u003eseverity\u003c/sub\u003e) as derived by Vieira et al. (2023). Such maximum value was determined for ground cover (FCOVER) values of 0.2, similarly to the approach taken by Wischmeier and Smith (1978). Regarding the individual cover types, for forest (2) the C factor was increased one order of magnitude for all the severities and limited to a maximum of 0.5, while shrubland (3) and grasslands (4) were represented by low and moderate severities and limited to a maximum of 0.3. In this way was possible to accommodate the impacts determined by Vieira et al. (2015) for distinctive severities, as also the conceptual model of Johansen et al. (2001) for distinct cover types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe recovery of the burned areas was addressed by using vegetation cover indicators as proxies, which consisted in merging two criteria:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) a burned area is considered recovered once the ground cover reaches 100%;\u003c/h3\u003e\n\n\u003ch3\u003e2) a burned area is considered recovered once the ground cover reaches the pre-fire conditions.\u003c/h3\u003e\n\u003cp\u003eIn this way, it was possible to determine erosion reduction with the increase of the protective cover, but also consider the initial characteristics of the local vegetation before the wildfires.\u003c/p\u003e \u003cp\u003eFor the first point, a minimum C factor was estimated:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{Cmin}_{B}={Cmin}_{U}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:for\\:FCOVER=1.0$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBeing the minimum C factor for burned conditions (Cmin\u003csub\u003eB\u003c/sub\u003e) approximated to the (Cmin\u003csub\u003eU\u003c/sub\u003e\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e) for each land cover (forest, shrub, and grassland) whenever the ground cover (FCOVER) reached its maximum value of 1.\u003c/p\u003e \u003cp\u003eThis formulation allowed to include the impact of the burn severity in the soil erosion estimations for when protective cover presented the minimum value according to the burn severity impact, and approximate the recovery of the burned areas under an exponential curve in function of FCOVER over a 5-years\u0026rsquo; time period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The choice of an exponential base for such estimations goes in line with \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e conceptual model after observing a non-linear relationship between sediment yields and bare soil cover, and a sharp increase of the sediment yields on the 60\u0026ndash;70% bare soil cover.\u003c/p\u003e \u003cp\u003eThe second point of the recovery to the background levels, was motivated by the FCOVER distribution sample for pre-fire conditions. For the 3 years preceding the wildfires, the mean FCOVER value rounded 0.43, thus representing the mixture of Forest, Shrubs and Grasslands within our AOI. This sample presented a normal distribution among the 3 pre-fire years, and the mean FCOVER value obtained for this period was used to assess the rate of recovery.\u003c/p\u003e \u003cp\u003eTherefore a recovery rate (RCOVER) was calculated, and several classes were defined as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{RCOVER}_{t}=\\frac{{FCOVER}_{t}}{{FCOVER}_{pre-fire}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eNo Recovery: 0.0\u0026thinsp;\u0026le;\u0026thinsp;RCOVER\u0026thinsp;\u0026lt;\u0026thinsp;0.3\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOngoing Recovery: 0.3\u0026thinsp;\u0026le;\u0026thinsp;RCOVER\u0026thinsp;\u0026lt;\u0026thinsp;0.6\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNear Recovery: 0.6\u0026thinsp;\u0026le;\u0026thinsp;RCOVER\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRecovered: RCOVER\u0026thinsp;\u0026ge;\u0026thinsp;1.0\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe consideration of a RCOVER class allowed us to adopt a succession of C-factor equations according to the status of the vegetation recovery progress. For the first 2 classes \u0026lsquo;No Recovery\u0026rsquo; and \u0026lsquo;Ongoing Recovery\u0026rsquo; the C factor would be calculated as described for equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) according to the initial burn severity classification and land cover attributes. In the case of \u0026lsquo;Near Recovery\u0026rdquo; class, this would led to the adoption of the equation with one burn severity level lower than the original one, likewise the \u0026lsquo;Recovered\u0026rsquo; class, would led to adoption of the equation corresponding to unburned conditions. This formulation allow us to use FCOVER as constant input for C estimations, while reducing the impact of severity proportionally to the pre-fire conditions.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Spatial data processing\u003c/h2\u003e \u003cp\u003eThe burned area delineation, wildfire date, and burn severity (NBR) was retrieved from MOSEV database \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e with resolution of 500m and global coverage, for the 2000\u0026ndash;2020 period. Given the incompleteness of the dataset for the years of 2000 and 2020, these were removed from our analysis, while burn severity was then converted to low, moderate and high burn severity classes, according to Key and Benson (2006).\u003c/p\u003e \u003cp\u003eTo target the land cover types affected by wildfires, the reference dataset for land cover factions the Dynamic Land Cover map at 100 m resolution (CGLS-LC100\u003csup\u003e56\u003c/sup\u003e) was used, which allowed to estimate the individual contributions of soil erosion for forest, shrub, and grassland with reference year of 2015. Despite more recent versions of this dataset exists (2019), the choice to keep 2015 was based to keep the comparability with the previous assessment \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, but also due to the inexistence of comparable datasets prior to this date which would allow to accommodate land cover changes throughout the entire temporal scale of this study. Moreover, cropland was also excluded from this analysis due to the lack of consolidated knowledge on the cumulative effect from intensive soil management operations and wildfire\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, while at the same time soil erosion in these areas have already been assessed extensively\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ground cover changes were assessed with FCOVER\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, which provides a measure of the fraction of ground covered by green vegetation. The change in vegetation cover at 30m resolution was determined by NDVI\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, using as reference date the first Landsat imagery available following each fire record. The repeated measurement of NDVI was performed annually (2001\u0026ndash;2019), in order to follow-up the recovery of each burned area. Whenever a repeated wildfire occurred in the same location, the procedure would start again considering the latest fire as reference, and assuming the recovery was sufficient in order to allow the most recent fire to occur.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data for post-fire soil erosion following the 2001-2019 wildfires will be available in the European Soil Data Centre (ESDAC)\u003csup\u003e64\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSan-Miguel-Ayanz, J. \u003cem\u003eet al.\u003c/em\u003e Global Wildfire Information System - Country Profile. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePausas, J. G. \u0026amp; Keeley, J. E. Wildfires as an ecosystem service. \u003cem\u003eFront. Ecol. 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Soil Sci.\u003c/em\u003e 73, e13315 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"post-fire, soil erosion, RUSLE, ecosystem services, land management","lastPublishedDoi":"10.21203/rs.3.rs-5622658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5622658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWildfires affect land surface and post-fire geomorphological activity worldwide, increasing surface runoff and soil erosion. Here, we present a global assessment of post-fire soil erosion, considering cumulative wildfire driven geomorphological changes over the last two decades. Stemmed from the largest database on wildfires occurrence and fire severity in the globe, this study estimates global trends of post fire soil erosion together with the recovery of those burned landscapes.\u003c/p\u003e \u003cp\u003eOur results show that when considering multiple wildfire events, global post-fire soil erosion accounts for 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 Pg annually, representing 19% of the global soil erosion budget, and additional 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 Pg soil erosion annually in comparison to pre-fire conditions. Moreover, soil erosion attributed to the first post-fire year represents 31% of the total soil erosion, whereas the remaining share can be attributed to previous wildfires occurrences. In what concerns the spatial distribution, Africa is the continent that is impacted the most in terms of post-fire soil erosion, given its significantly larger burned area.\u003c/p\u003e \u003cp\u003eThe results of this study can illustrate the magnitude of post-fire soil erosion globally, and therefore support post-fire management actions towards the mitigation and restoration of affected areas, and policies towards Land Degradation Neutrality.\u003c/p\u003e","manuscriptTitle":"The global significance of post fire soil erosion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 10:14:54","doi":"10.21203/rs.3.rs-5622658/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-geoscience","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ngeo","sideBox":"Learn more about [Nature Geoscience](http://www.nature.com/ngeo/)","snPcode":"","submissionUrl":"","title":"Nature Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f2119607-0ade-4e96-ade7-501bc8d39e2f","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46089477,"name":"Earth and environmental sciences/Natural hazards"},{"id":46089478,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":46089479,"name":"Earth and environmental sciences/Solid Earth sciences/Geomorphology"}],"tags":[],"updatedAt":"2026-01-06T08:12:58+00:00","versionOfRecord":{"articleIdentity":"rs-5622658","link":"https://doi.org/10.1038/s41561-025-01876-0","journal":{"identity":"nature-geoscience","isVorOnly":false,"title":"Nature Geoscience"},"publishedOn":"2026-01-05 05:00:00","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-03-25 10:14:54","video":"","vorDoi":"10.1038/s41561-025-01876-0","vorDoiUrl":"https://doi.org/10.1038/s41561-025-01876-0","workflowStages":[]},"version":"v1","identity":"rs-5622658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5622658","identity":"rs-5622658","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}