Earthquakes act as a capacitor for terrestrial organic carbon

Domain: neuroscience
Published: 2026-01-23
Editorial outcome: minor_revision

Abstract
Earthquakes and seismically-induced landslides accelerate carbon export from mountains by eroding hillslope soil carbon. However, a quantitative understanding of their net contribution to carbon cycling remains incomplete. Using the 2008 Mw 7.9 Wenchuan Earthquake which generated the largest landslide volume in recent history, we quantify its carbon mass balance accounting for storage, loss, and transport within the ensuing sediment cascade. Thanks to post-event revegetation and extensive intermontane sediment storage, we show that the earthquake boosted Longmenshan carbon mass by ~10%. Given the stability of these deposits and low rates of carbon export, we anticipate this landslide carbon will persist for centuries to millennia before gradually declining. In effect, we demonstrate that earthquakes and landslides function as capacitors, regulating carbon storage and discharge across mountain belts over time. This suggests frequent landslides in seismically-active mountains may lead to a net carbon sequestration, providing a critical and direct link between tectonics and the carbon cycle.

Introduction
Large earthquakes play a dual role in the terrestrial organic carbon system: they emit carbon to the atmosphere by destroying vegetation that subsequently decays, and they store particulate organic carbon in the ensuing sediment cascade. Landslides (hereafter explicitly coseismic) are the main players perturbing the carbon mass balance of a mountain belt during and following an earthquake. Organic carbon eroded by landslides can take a number of pathways through mountain catchments; some fraction is stored on hillslopes and in headwater channels, while the rest is exported via drainage networks and carried ultimately into long-term sinks. The amount of carbon temporarily stored in these landslide deposits, and the timescale over which it is released and exported, effectively act as a “carbon capacitor”, buffering the interval between carbon production and its eventual transfer downstream. While erosion of petrogenic (i.e., rock-derived) organic carbon also plays an important role in the long-term carbon cycle, our focus here is on the larger fluxes associated with production, burial, and loss of terrestrial organic carbon (hereafter explicitly OC) within mountain belts following large earthquakes.

The largest continental earthquakes generate up to ~50 km3 of landslide deposits composed of soil and fragmented rock. These earthquake-triggered slope failures entrain soil OC and live biomass, creating an OC store whose magnitude over time depends on the volume of landslide deposits and the rate at which they are eroded. Such deposits largely rest on hillslopes, where typically less than 10% are eroded immediately after the earthquake, while the majority remain stable for hundreds or even thousands of years. The rate of sediment and OC export is a function of the density of the newly developed drainage network on the landslide itself, together with its connectivity to the river below and the efficiency of hillslope erosion processes such as debris flows. Where this connectivity has been measured, for instance following the Chi-Chi, Wenchuan and Gorkha earthquakes, 5–60% of landslides are shown to be connected to a drainage network, though erosion is likely to be less efficient in the smallest headwater channels. The highest post-event sediment yields measured historically are reported from the 1970 Madang Earthquake (Papua New Guinea), where up to 50% of the mobilised sediment was exported in the first year. And yet, much lower yields are more typical, such as in the Matiri River (New Zealand), where at least 50–75% of the sediment generated by the 1929 Murchison Earthquake remained in the catchment 50 years later. Evacuation of the 1999 Chi-Chi Earthquake debris filling the Peikang River is predicted to take centuries, just as 70% still remains intact of the 5–7 km3 of sediment injected into the Pokhara Valley (Nepal) by medieval earthquakes.

In line with sediment flux, OC export from seismically active mountain belts varies widely: 0.12–0.28 teragrams (TgC) per year following the Gorkha Earthquake and up to 35–40 TgC per year to the offshore of Taiwan, while a sediment record from Lake Paringa indicates that 14±5 TgC was eroded during a single earthquake on New Zealand’s Alpine Fault. Nevertheless, existing research on post-earthquake carbon dynamics has tended to focus on sediment fluxes and revegetation, whereas the role of transient OC storage held within landslides has been overlooked. We propose that the observed worldwide variation in carbon export reflects either (1) differences in the primary mass of OC mobilised during an earthquake, or (2) the potential for landslides to function as transient stores (a “capacitor effect”) that mediate the release of sediment and OC beyond the mountain belt. These two hypotheses serve as the focus for our study.

The 2008 Mw 7.9 Wenchuan Earthquake is a large historical landslide event of between 3 and 15 km3 (6000–30,000 Tg of sediment, assuming an average density of landslide deposits of 2000 kg/m3) of erosion. Thanks to the intensive study of the resultant sediment cascade, Wenchuan is one of the best understood examples of post-earthquake erosional response. The earthquake struck at a time of rapid expansion of satellite technologies, which have allowed researchers to constrain the frequency and intensity of landsliding, the reworking of sediment from landslides into the drainage network, and the changes in vegetation coverage after the event. As the largest earthquake-triggered landsliding event of the past 50 years, the Wenchuan example provides a unique opportunity to understand the carbon dynamics at scale. Our analysis therefore aims to constrain the potential upper bound of landslide-driven carbon storage and transient carbon transfer in mountain systems, rather than to represent the average behaviour across all earthquakes. Well-established river gauging in the affected region revealed a doubling of particulate OC transport via streams in the Zagunao catchment in the first four years, but the post-earthquake analysis remains incomplete. Here, we present a comprehensive OC budget for the period, 2008–2020, by integrating fieldwork, laboratory analyses, remote sensing and numerical modelling. Mapping of carbon stocks pre- and post-earthquake allows us to quantify the hillslope carbon mass balance for this earthquake with unprecedented precision. This framework further enables us to assess the long-term trajectory of carbon storage within mountainous landscapes.

Results
Spatial distribution of carbon and its erosion by landslides
We set out to identify and measure carbon stocks directly in the field at 123 sites comprising landslide deposits and areas undisturbed by earthquakes across the Longmenshan study area. These field data are augmented with data extracted from previous studies. A subset of our dataset, scored for high quality, was incorporated into a machine-learning model that aims to simulate retrodictively the spatial distribution of carbon stocks prior to the Wenchuan Earthquake based on a wider range of parameters (Table S1) (see Methods).

Prior to the Wenchuan Earthquake, we estimate the average OC stock across the Longmenshan study area at 136±25 megagrams carbon per hectare (Mg C ha–1), an average that embraces local variability of 50–260 Mg C ha–1 (note that all uncertainties are given as ±1 standard deviation, unless otherwise stated; units for carbon stock are presented as Mg C ha–1 and carbon flux is expressed as Mg C km2 yr–1). The spatial distribution of pre-earthquake OC stock correlates with topography and climate. The OC (within surface soil and vegetation) increases with elevation between 500–2000 metres above sea level (masl) but varies little outside that range (Figs. S1, S2). The highest concentration of total OC is found on the steepest mountains close to the range front where average precipitation rates are also highest (Fig. 1A, B).

Wenchuan landslides eroded 5.5±0.01 Tg C, amounting to 2–6 weight percent (wt%) of global annual terrestrial carbon erosion. The spatial distribution of landsliding relates to topography, lithology, and position relative to the fault rupture, and the majority (2.5±0.6 km3) of the landslide volume occurred within the Minjiang and Tuojiang basins (Fig. 1B). Corresponding erosion of OC by landslides in these two catchments equalled 4.5±0.01 Tg C, which accounts for 82±0.2% of the overall OC denuded by landslides associated with this event. The large OC stock in the Fujiang catchment remained largely intact, consistent with the spatial distribution of pre- and post-earthquake net primary production (NPP), which saw little change over this area (Fig. S3). These eroded OC are rapidly mobilised and widely deposited along hillslopes and channels, awaiting further transport or transformation.

Recovery of carbon stocks following landslides
A decade after the Wenchuan Earthquake, landslides stored 33±21 Mg C ha–1 of OC in the study area (Fig. 2B). The recovery rate (i.e., growth) of OC was 475±110 Mg C km–2 yr–1 on landslide surfaces and 5.5±0.02 Mg C km–2 yr–1 when applied to the overall affected area, reflecting that landslides account for only about 8% of the total landscape area. The major components of the OC storage at these sites were soil OC (55±29%) and tree biomass (32±29%) (Fig. S4). Soil OC content is positively correlated with total soil nitrogen content (TN) for all sites (Fig. S5). New vegetation on landslide surfaces (including both deposit and scar areas) accounts for a quarter of the pre-earthquake OC stock (Fig. S6). Recovery was characterised by a major change in the species composition from a pre-earthquake community of Pinus tabuliformis, Cupressus funerbri and Cryptomeria fortunei to a recovery community of Alnus cremastogyne, Populus alba and Ziziphus jujuba (Fig. S7).

Vegetation coverage on landslide surfaces had recovered to 80–90% of pre-earthquake levels within a decade, as inferred from metrics including normalised difference vegetation index, enhanced vegetation index, leaf area index. This observation is consistent with time series of net primary productivity (NPP) changes in our study area (Fig. 2B). Here, NPP was used solely to assess the temporal recovery of vegetation productivity, not as a direct measure of total carbon storage. NPP of both the whole landscape and landslide areas decreased immediately after the earthquake from 760±110 Mg C km–2 yr–1 (in 2007) to a low point of 580±120 Mg C km–2 yr–1 (in 2010), after which NPP recovered rapidly and reached pre-earthquake levels around 2015. Despite the rapid increase in NPP during this initial stage of recovery, total carbon storage within the landscape remained deficient due to the slower return of soil carbon and vegetation biomass (Fig. 2). Landslide surfaces sequestered 3.5±0.01 Tg C via NPP, referring to the amount of OC fixed by plant photosynthesis minus losses from plant respiration. The upscaled OC, derived from field-measured OC, for landslide surfaces is 2.2±0.01 Tg C, which is much smaller than the pre-earthquake level of 5.5±0.01 Tg C. The field-measured OC represents the combined carbon storage in soil and vegetation biomass. The measured OC was about 10% lower than the NPP-derived estimate, which could partly reflect measurement uncertainty, in addition to potential carbon loss via fluvial transport, decomposition, and soil respiration.

Hillslope carbon budget after an earthquake
Using our estimates of burial and recovery of OC after the Wenchuan Earthquake, we calculated the hillslope carbon budget for the decade after the earthquake (Table S2). We determined the total volume of carbon stored in landslides (Fig. 3) based on the sediment export flux to the fluvial system from prior work, together with our results showing that pre-earthquake soil and vegetation OC are well mixed in landslide deposits—allowing us to assume that the proportional removal of OC is similar to that of sediment. On this basis, we estimate that 12% of the carbon stored in landslide deposits has been evacuated, primarily by debris flows. The OC yield implied in this calculation falls within the uncertainty range of field measurements from the Zagunao catchment in the study area. As an upper bound for erosion, we can also assume that all of the material in landslide deposits that are connected to the fluvial network has been evacuated, resulting in the removal of 43% of carbon mobilised by landslides. Considering this range, hillslope landslide deposits apparently retained between 3.1 and 4.8 Tg C of the mass that was previously held in soils and vegetation. Based on field observations and the detailed sediment budget for this event, storage is likely to be closer to the upper bound of 4.8 Tg C. Meanwhile, 2.2±0.01 Tg C was restored on the hillslopes. Overall, the Wenchuan Earthquake has stored up to 7.0 Tg C within landslide-affected areas between 2008 and 2020 (Fig. 3).

Methods
Sampling sites
Our study area covers 33,005 km2 of the region most severely affected by the 2008 Mw 7.9 Wenchuan Earthquake. Landslides occurred primarily in mountainous terrain within four large river systems: the Minjiang, Tuojiang, Fujiang and Bailongjiang (Fig. S8). We selected 123 sampling sites for field investigation, of which 59 were on coseismic landslide deposits (disturbed sites) and 64 were on adjacent undisturbed areas with no clear slope failure (since 2008) based on aerial and satellite imagery. The disturbed sites were randomly distributed along the major faults of the Longmenshan that ruptured during the earthquake.

Plot design
The plot design for sampling followed previous studies in the Luoquanwan catchment. Measured OC is composed of soil, tree, understorey plant and litter OC. The simplified vegetation OC represents the tree, understorey plant and litter OC. Within a 5-m radius plot (after slope correction), all trees with a diameter at breast height (DBH) >5 cm were recorded for species, DBH and total height (Table S2). Heights of trees were measured in each plot using a Vertex III height meter (Haglöf, Sweden). Plants with DBH <5 cm were considered as understorey. To estimate biomass of the understorey and litter, three 1-m radius circular subplots were set at three directions (0°, 120°, 240°, 3 m from the plot centre) within the 5-m radius plots. Understorey and litter within each subplot were dried and weighed. Within the three subplots, soil samples were collected in cloth bags down to 50 cm in three layers: 0–10 cm, 10–30 cm, and 30–50 cm, respectively. Soil samples from the same layer were mixed for each given sub-plot location. Soil samples were collected using standard ring cores with a diameter of 70 cm for each layer to analyse soil physical properties in undisturbed areas. Due to the high gravel content of landslide deposits, a circular bucket with fixed volume was used to obtain the wet density for different layers. A detailed list of collected samples is shown in Table S4.

Landslide inventory
We used a published landslide inventory to calculate the total carbon budget. This dataset covers over 90% of the surface rupture area and the zones of most concentrated landslide density. The approach for constructing the carbon budget is described in detail in the Supplementary Information. Estimates of carbon storage in coseismic landslides include only failures >5800 m2, consistent with the 30×30 m resolution of the prediction model described in the method of modelling of carbon stock. Landslides >10,000 m2 were selected for field sampling as they were easily recognized from satellite images. The mass of total OC, as determined through gridded data, is presented in Table S3 pre-earthquake, along with the mobilisation and recovery post-earthquake.

Landslide volume and area
To model the carbon balance following the 2008 Wenchuan Earthquake, it is essential to estimate landslide volumes, which directly determine the timescale of sediment and carbon transport. We used a common approach of applying a power-law volume-area relationship to convert the landslide inventory to landslide volumes. There is significant uncertainty with this approach, but the satellite imagery available to us precludes a more accurate volume-based approach. Instead, we use a range of different area-volume scaling parameters and scar-deposit correction methods, as detailed in Supplementary Information. None of the available landslide inventories distinguishes between landslide types or between source and deposit areas making direct inferences about volume a challenge. Here we have compared different approaches to estimate the potential range of volume uncertainty (Tables S11 S12).

Soil physical properties, organic carbon and total nitrogen
All soil samples were dried in an oven at 85°C to a constant weight. Bulk density, water content, and gravel fraction (>2 mm) were analysed following standard methods. For the disturbed sites, wet density was measured in the field. Identifiable plant residues and roots were removed manually, and samples were sieved at 0.15, 0.25, 0.5, 1.0, 2.0, and 5.0 mm intervals. Soil OC content was analysed using the potassium dichromate and sulphuric acid (K2Cr2O7–H2SO4) wet oxidation method. Total nitrogen (TN) content was determined by the automatic Kjeldahl apparatus method following the Chinese Agriculture Industry Standard (NY/T1121.24-2012). Both soil OC and TN analyses were repeated three times for each sample.

Vegetation biomass calculation
Tree biomass was calculated using a dataset of Chinese allometric equations. We estimated above-ground and below-ground biomass by selecting the appropriate allometric relationship based on the tree species, diameter and height range, mean annual precipitation (MAP), mean annual temperature (MAT), and location. For species not found in the database, we applied the general allometry by plant functional group (e.g., deciduous broad-leaved forest or evergreen broad-leaved forest). We included all major biomass components, including stem, branch, leaf, and below-ground biomass. Tree species and related allometric models are given in Table S5. All sampled understorey vegetation and litter samples within three 1-m radius circular subplots were dried at 70°C to a constant weight. Carbon content of 0.5 g g–1 was used to convert biomass to C stock.

Soil OC stock
The soil organic C stock (Mg ha–1) for each layer was calculated using the following formula:

SOCstock = SOC × BD × (1 – RC) × D / 10

where total SOC stock (Mg C ha–1) is the soil OC concentration (g kg–1); BD is bulk density (g cm–3); RC is gravel (>2 mm) content; D is soil layer depth (cm).

Data compiled from previous studies
We compiled previously published OC data from the study area (194 entries derived from 25 publications), focusing on studies that report field sampling data in undisturbed forest and shrub ecosystems, excluding those based on models or satellite data, as well as planted forests. For inclusion data must include geographical coordinates, and at least one of the following: soil organic carbon, above-ground and below-ground biomass, or total carbon (see Fig. S9). Data quality was evaluated by assigning scores, 0–2, based on eight categories (namely data type, data information, climate information, geographic information, soil information, soil organic carbon, vegetation organic carbon and total terrestrial organic carbon), with the maximum cumulative score being 16 (see Table S6). This dataset will serve as a reference for further research conducted in the Longmenshan Range or the Tibetan Plateau (see Source Data 1).

For the training of the machine-learning predictive model, we utilized previous study datasets scoring >5, together with our own field-measured dataset. Soil C stock was normalised to a depth of 50 cm; for soil profiles <50 cm, extrapolation was performed by averaging the C content and dry bulk density from known depths and then applying it to 50 cm. The augmented data helps to overcome the spatial limitations inherent in our field-measured dataset.

Modelling of carbon stock
During preliminary screening, we trialled twenty different predictive models to identify the most suitable for total OC stock; these included: random forest, gradient boosting decision trees (GBDT), extreme gradient boost, decision tree, support vector regression, k-nearest neighbour regression, linear weight regression, etc. Twenty covariates derived from remote sensing data, geographic data and landslide inventory were applied. The prediction models were built using representative topographic and soil characteristics, climatic, and vegetation indices across the study area, and include landslide area, slope, aspect, elevation, lithology, MAP, MAT, land surface temperature, TN, clay content, bulk density, Landsat derived enhanced vegetation index, normalised difference vegetation index, red band and modified soil adjusted vegetation index, MODIS product (MOD17A3) derived enhance vegetation index, normalized difference vegetation, red band and modified soil adjusted vegetation index and gross primary production, as specified in Table S1. The raster maps of these variables were resampled to 10 m resolution for spatial calculation, consistent with DEM resolution.

To ensure that our model was applied only to forest ecosystems, we used the 30 m Landsat-derived annual land-cover dataset for China to define land use types (with categories: forest, shrub, grassland, wetland, farmland, urban and other), and areas not identified as forest or shrub were excluded.

We trained the machine-learning prediction model using the total carbon stock data from our field-measured data and the twenty covariates noted above. We developed models for disturbed and undisturbed sites trained using 70% of the field sampling data, leaving the remaining 30% for testing. After training and testing, we found that the GBDT method was the best performer (see Table S7). Figure S10 presents the importance of covariates within the training samples as identified by the GBDT.

Modelling of carbon balance
To quantify the temporal trend of major carbon pools under the impact of the 2008 Wenchuan Earthquake, our carbon balance model adopts a pool–flux structure, employing a set of differential equations to simplify the connection between carbon flux and mass movements. We derived the parameters for the carbon balance model by integrating subsequent research and data collected since the Wenchuan event (particularly the carbon stock distribution maps obtained from empirical data noted above). This approach ensures that the parameter settings are tailored to our study area and effectively reflect complex environmental interactions. The carbon balance modelling separates into three main parts: constructing the carbon balance framework, introducing earthquake-triggered landslides and implementing carbon balance model. The model is based on two key assumptions: (1) following the earthquake, the entire aboveground biomass carbon pool (B) is converted into debris (D) that is fully mixed with soil carbon and buried within landslide deposits. Because this exact initial B–D portioning lacks empirical constraint, it represents a source of uncertainty in the estimated long-term carbon storage. This assumption represents an endmember maximum potential carbon storage after the earthquake; (2) ecosystems outside the landslide surface areas are assumed to have remained undisturbed, regions showing no bare surfaces or vegetation loss are assumed to have the same stock before and after the earthquake. The major carbon pools and fluxes are shown in Fig. S11.

Statistical analyses
For our field data, descriptive statistical analyses were conducted to characterise the spatial variations of soil organic carbon (SOC) content, soil total nitrogen (TN) content, and soil OC stock with depth, as well as tree, understory, litter, and total ecosystem organic carbon stocks in both undisturbed areas and landslide deposits (Table S8). Prior to model training, multicollinearity among covariates was evaluated using Pearson correlation analysis (Fig. S12). For the modelling of carbon stock analysis, we applied the Shapiro–Wilk test to examine the representativeness and normality of twenty selected covariates (see Table S9). Subsequently, a one-way analysis of variance (ANOVA, F test) was used to test whether the effect of coseismic landslides on OC stock is distinguishable (p<0.05) when comparing undisturbed areas and landslide deposits (see Table S10).

Data availability
The data generated or analysed during this study are included in the paper, Supplementary Information and the Source Data 1. Source data are provided with this paper.

Code availability
The codes to produce the carbon balance model are available at https://github.com/liujie1408/carbon-dynamics-model

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents a novel quantification of earthquake-induced terrestrial organic carbon (OC) dynamics using the 2008\nWenchuan Earthquake as a case study. By integrating field data, remote sensing, and numerical modelling, the authors\npresent a comprehensive carbon mass balance that accounts for storage, loss, and transport of OC during and after the\nearthquake-induced sediment cascade. This work bridges tectonics and the carbon cycle, offering a fresh perspective on\nseismic events as net carbon sinks over millennial timescales. However, some issues remain that require further clarification\nand explanation by the authors before publication.\n1. L56-57: What is the empirical basis for the claim that landslide deposits can remain stable for “hundreds or even\nthousands of years,” given the dynamic nature of mountainous terrain and climate variability?\n2. L62-70: Why do some earthquake-induced landslide deposits remain stable for centuries while others are rapidly\nevacuated?\n3. The “capacitor effect” is not clearly defined, which could mislead readers. Should “capacitor” be defined earlier for broader\naccessibility?\n4. L130-131: Why recovery rates differ by 100x between landslide surfaces and overall affected area?\n5. L133–135: This reports no clear change in soil carbon content with depth in landslide deposits. Was this finding\nconsistent across all landslide types and locations, or are there exceptions that should be discussed?\n6. L136–143: Could the authors quantify or elaborate more on the planted vs. natural regrowth proportions?\n7. L154-156: Would briefly noting potential reasons for the NPP vs. measured OC discrepancy?\n8. L353–362: Twenty covariates were used for machine learning. Please clarify whether predictor importance or\nmulticollinearity was evaluated, and whether any variables were removed due to redundancy.\n9. Supplementary Information: Was the normal distribution assumption for Monte Carlo validated?\n10. Supplementary Information: The authors apply both Marc et al. and Li et al. correction methods for scar area estimation.\nCould they clarify how the choice between these approaches affects the resulting landslide volume and the downstream\ncarbon budget?\n11. Supplementary Information: The estimation of total landslide volume pools multiple published empirical parameters.\nHow do the authors select which parameter sets to use for the Wenchuan region?\n12. OC recovery is compared to NPP using remote sensing. How do the authors account for root biomass and belowground\nOC, which may be underestimated by NPP-based approaches?\n13. The carbon balance model pools all disturbance-derived carbon into three pools (B, D, S). How sensitive are the model’s\nlong-term predictions to the initial partitioning of carbon among these pools, and what empirical data constrain these\nchoices?\n14. Could a few key abbreviations (e.g., NPP, TN) be defined at first use?\n(Remarks on code availability)\nThe code appears to be well-structured and comprehensive for modeling carbon dynamics following earthquake-triggered\nlandslides. The modular organization into separate files for different analysis aspects (landslide info, earthquake info, carbon\ndynamics, etc.) is logical and makes the codebase more maintainable. The README provides a clear overview of each\nscript's purpose, though it could benefit from more detailed installation instructions and dependency requirements.", "reviewer_2": "This paper does a very good job of condensing a huge amount of work into a short form article. The authors should be\ncommended. Overall, it would make a good contribution to the science exploring how large mountainous EQ’s, and the\nlandslides they generate, influence worldwide variations in carbon export.\nA general comment:\nThere is a large amount of additional information in the supplementary materials, some of which might be good to bring into\nthe main body of the paper, especially when it would help to describe the uncertainties on the estimates of mobilised,\nstored and distributed OC. These points are discussed below.\nMain points:\nIn summary, landslide volume is a key parameter in determining the amount of OC mobilised, buried and stored, and\neventually released, making it central to the study's carbon budget calculations.\nThe estimates of landslide volume will, therefore, significantly impact the calculation of the mobilised and buried organic\ncarbon (OC), because landslide volume determines the amount of soil and vegetation mobilised and buried, which directly\ncorrelates with the OC mobilized and stored.\nAs the paper currently stands, I don’t believe there is enough discussion on how the landslide source volumes have been\nderived and their uncertainties estimated, and what these uncertainties mean wrt., the two main hypotheses stated. The\nsupplementary section has a section on this, but it doesn’t currently provide enough detail on or adequately addresses, the\ncalculation of landslide source volumes for the different types of landslides.\nModelling and Uncertainty:\nAccurate landslide volume estimates are critical for modelling OC budgets. The study uses volume-area scaling methods to\nconvert landslide ‘area’ inventories into volume estimates, which are then used to calculate OC stocks.\nRecent work by Jones et al. (2024), highlights the limitations of existing landslide volume-area scaling relationships, and\ntheir use to estimate landslide volume by orders of magnitude. These limitations and uncertainties should be explored in the\npaper and their impact on the results quantified, for example:\n• Estimating the area of the landslide, why use the scar and not the source area only? This could lead to an overestimate of\nlandslide source volume when using volume-area scaling? A landslide scar in the context of this study is assumed to be the\ncombined source area and debris trail of the landslide? Whilst the scar area is appropriate to account for source area OC,\nand buried OC together, using the scar to estimate volume with lead to an overestimate of the source volume and its\nassociated OC.\n• The volume-area scaling relationships used and how these relate to different landslide types in different materials – these\nhave huge uncertainties associated with them. Would incorporating other uncertainties adopting different methods of volume\nestimation (as per those shown by Jones et al. 2025) impact the results?\n• Why use landslide volume, what about using the landslide ‘scar’ area (which is less uncertain) – noting the difference\nbetween source and scar – to calculate soil OC and live biomass depleted from source versus entrained/buried along trail.\nFor example, is soil depth easier to calculate then landslide volume? Could soil depth and veg type within the landslide scar\narea provide a better estimate of removed and buried OC?\nThese uncertainties and their impact on the estimated OC generated and stored by coseismic landslides, could be used to\naddress whether there is too much ‘noise’ versus ‘signal’ in the estimates, to be able to substantiate the studies initial\nhypotheses. Currently I can’t tell.\nIn addition, the Wenchuan EQ is thought to be an outlier wrt., the number and area affected by coseismic landslides. Is it\ntherefore, representative of other EQ events and the landslides and OC they generate and store? Wenchuan could be\nanomalous wrt., number of landslides generated, but was less OC mobilised and buried in the area (per unit area of\nlandslide scar?) when compared to the landslides triggered in other recent coseismic events (listed) thus making it less\nanomalous? How does this anomalous event support the studies hypotheses?\nOther points include:\nOC Mobilisation:\nMobilised OC by coseismic landslides is estimated to be within the range reported for other landslide events. However, the\nWenchuan EQ and the landslides it generated are reported to be an outlier wrt., to other EQ events and the landslides they\ngenerated? Please discuss this in more detail.\nLandslides mix soil OC and live biomass into their deposits, creating an OC store. The magnitude of OC mobilised depends\non the initial coseismic landslide volume and any entrainment of additional debris as the landslide mass moves downslope\nand buried by the debris.\nHow do different landslide types affect the mixing of soil OC and live biomass into their deposits? Will coherent landslides –\nwhere the displacing debris/rock largely remains intact – do this differently to disrupted ones – where the debris/rock is\nbroken down due to movement? Some commentary on the importance of landslide type and OC mobilisation, entrainment\nand burial would be good to include (note I’ve adopted the terminology of Keefer (1984) for coherent versus disrupted\nlandslides).\nOC Storage:\nThe proportional removal and storage of OC are assumed to be similar to sediment removal and storage. Larger landslide\nvolumes result in greater OC storage in deposits. How do different landslide types affect this? Would more disrupted\nlandslides incorporate and bury more OC into the debris thus making it more difficult to remove and as a result\nstore it for longer? Versus coherent landslides, where soil OC and live biomass might remain largely on the surface of the debris\n(limited mixing) and not buried by it, thus making it easier to subsequently erode? If most landslides were disrupted in\nnature, are such differences important, please state?\nSpatial Distribution:\nThe spatial distribution of OC pre-EQ, is stated as correlating with topography and climate. The OC (within surface soil and\nvegetation) increases with elevation between 500–2000 metres above sea level (masl). Does OC correlate with soil\nthickness? If it does, then does soil thickness correlate with elevation. In NZ, Larsen et al. (2023) found that soils on lower\nelevation slopes tend to be generally thicker than those at higher elevations, due to increased chemical weathering at lower\nelevations compared to increased physical weathering at higher elevations. This appears to the opposite case in\nWenchuan? Some more discussion on this, but in the Wenchuan context, would be useful, and compared to other contexts\nlike Taiwan and NZ.\nThe spatial distribution of coseismic landslides, are influenced by topography and lithology, and the level of ground shaking,\nwhich affects the OC stock calculations. Areas with higher landslide volumes tend to have higher OC mobilization and\nstorage. Areas of higher landslide density might relate to lots of shallower landslides over large areas, versus many larger\ndeeper landslides. Is this distinction important? Some more commentary on the landslide types, their depths and source\nmaterials are needed to give the OC estimates some context?\nRate of landslide sediment and OC export:\nThe rate of sediment and OC export is a function of the density of the newly developed drainage network on the landslide\nitself together with its connectivity to the river below. But what about the erodibility of the landslide debris? This will in some\ncases be a function of the type of landslide (coherent versus disrupted) and landslide source material (finer-grained versus\nblocky rock sizes etc), and the stream power. How is connectivity defined wrt., stream power? Is it important in this context?\nPlease describe.\nRefs cited:\nJones et al. (2025) Landslides (2025) 22:2219–2236 DOI 10.1007/s10346-025-02479-x\nKeefer (1984) GSA Bulletin (1984) 95 (4): 406–421. DOI 10.1130/0016-7606(1984)\nLarsen et al. (2023) The influence of erosion and vegetation on soil production and chemical weathering rates in the\nSouthern Alps, New Zealand. Earth and Planetary Science Letters. Volume 608. 2023. 118036, ISSN 0012-821X\n(Remarks on code availability)"}, "author_response": "Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThis study presents a novel quantification of earthquake-induced terrestrial organic\ncarbon (OC) dynamics using the 2008 Wenchuan Earthquake as a case study. By\nintegrating field data, remote sensing, and numerical modelling, the authors present a\ncomprehensive carbon mass balance that accounts for storage, loss, and transport of OC\nduring and after the earthquake-induced sediment cascade. This work bridges tectonics\nand the carbon cycle, offering a fresh perspective on seismic events as net carbon sinks\nover millennial timescales.\nWe thank the reviewer for taking the time to read and provide very constructive comments on our\nmanuscript. We appreciate that the reviewer recognises the value of this high-quality and original\ndataset. We have provided responses below to the general concerns and line-by-line changes.\nHowever, some issues remain that require further clarification and explanation by the\nauthors before publication.\n1. L56-57: What is the empirical basis for the claim that landslide deposits can remain\nstable for “hundreds or even thousands of years,” given the dynamic nature of\nmountainous terrain and climate variability?\nThere is strong empirical evidence demonstrating that landslide deposits can persist for centuries\nto millennia, even within highly dynamic mountain environments. We have revised the the\nparagraph starting at line 56 to more strongly identify the evidence. It now reads as follows:\n“The largest continental earthquakes generate up to ~ 50 km3 of landslide deposits, which are\ncomposed of a mixture of soil and fragmented rock (10). Landslides mix soil OC and live biomass\ninto these deposits, creating an OC store whose magnitude over time depends on the volume of\nlandslide deposits and the rate at which they are eroded (11). These deposits largely rest on\nhillslopes, where typically less than 10% of these deposits are eroded immediately after the\nearthquake, while the majority of deposits remain stable for hundreds or even thousands of years\n(12-16). The rate of sediment and OC export is a function of the density of the newly developed\ndrainage network on the landslide itself together with its connectivity to the river below (17, 18)\nand the efficiency of hillslope erosion processes such as debris flow generation (Francis). Where\nthis connectivity has been measured, for instance following the Chi-Chi, Wenchuan and Gorkha\nearthquakes, 5–60% of landslides are shown to be connected to a drainage network (19), though\nerosion is likely to be less efficient in the smallest headwater channels (20). The highest post-\nevent sediment yields measured historically are reported from the 1970 Madang Earthquake\n(Papua New Guinea), where up to 50% of the mobilised sediment was exported in the first year\n(21). And yet, much lower yields are more typical, such as in the Matiri River (New Zealand),\nwhere at least 50–75% of the sediment generated by the 1929 Murchison Earthquake remained in\nthe catchment 50 years later (13). Evacuation of the 1999 Chi-Chi Earthquake debris filling the\nPeikang River is predicted to take centuries (18, 22), just as 70% still remains intact of the 5–7\nkm3 of sediment injected into the Pokhara Valley (Nepal) by medieval earthquakes (23).”\n2. L62-70: Why do some earthquake-induced landslide deposits remain stable for\ncenturies while others are rapidly evacuated?\nThank you very much for this insightful question. We have revised and clarified the current\nunderstanding of this process in the paragraph starting on line 56, noting that it is still and open\nquestion.\n3. The “capacitor effect” is not clearly defined, which could mislead readers. Should\n“capacitor” be defined earlier for broader accessibility?\nThank you for this helpful comment. We agree that the term “capacitor effect” required a clearer\nand earlier definition for broader accessibility, as introducing it later in the manuscript could\npotentially mislead readers. In response, we have revised the Introduction at Lines 48–51 to\nexplicitly define this concept. We now clarify that the volume of carbon temporarily stored in\nlandslide deposits, and the timescale over which it is subsequently released and exported,\neffectively act as a “carbon capacitor”, buffering the interval between carbon production and its\neventual downstream transfer. Meanwhile, we then emphasized the carbon capacitor in the\nstatement of hypotheses at Line 105.\n4. L130-131: Why recovery rates differ by 100x between landslide surfaces and overall\naffected area?\nThe difference in recovery rates reflects how we have averaged the data, with one estimate based\non the entire affected area and the other is limited to the landslide surfaces. To clarify this point,\nwe have revised the text at Lines 174-176 to read: “…475±110 Mg C km-2 y--1 on landslide\nsurfaces and 5.5±0.02 Mg C km-2 yr-1 when applied to the overall affected area, reflecting that\nlandslides account for only about 8% of the total landscape area.”\n5. L133–135: This reports no clear change in soil carbon content with depth in landslide\ndeposits. Was this finding consistent across all landslide types and locations, or are there\nexceptions that should be discussed?\nOur statement that soil carbon content with depth in landslides shows no clear trend immediately\nfollowing the earthquake was based on limited field observations. These field samples were taken\nonly to a depth of 50 cm, as illustrated in the Supplementary Material (Fig. S4). We acknowledge\nthat this limited dataset is not representative across all landslide types and locations, and we\ncurrently lack sufficient information to generalize this trend. To avoid potential confusion for\nreaders, we have therefore removed the description at Line 178: “Our excavated soil profiles\nshow no clear change in carbon content with depth, suggesting vertical mixing for landslide\ndeposits”. We provide Fig.S4 and Fig.S5 showing the soil organic carbon stock and soil organic\ncarbon content changing trend in the Supplementary Materials, but we do not think it requires\nfurther discussion in the main text.\n6. L136–143: Could the authors quantify or elaborate more on the planted vs. natural\nregrowth proportions?\nWe agree that quantifying the proportions of planted versus naturally regenerated vegetation\nwould be valuable for understanding post-landslide recovery. However, distinguishing these\nproportions was not the main focus of our study, which primarily aimed to quantify the carbon\nmass balance associated with earthquake-induced landslides and post-seismic sediment dynamics.\nVegetation recovery was considered mainly in terms of its contribution to post-earthquake carbon\nstorage, rather than as an ecological succession process.\nIn our field surveys, we found that only three landslide sites showed possible influence from\nlocally dispersed crop or tree seeds associated with nearby villages. Given the limited occurrence\nand our focus on the carbon cycle rather than on vegetation origin, we removed the following\nsentence (Line 183) to avoid overinterpretation: “Some of these new species are distinguished by\ntheir rapid growth rates and resilient capacity for colonizing disturbed sites, while others\nespecially those of economic importance may have been planted as part of conservation policies\nand projects in southwest China, rather than resulting from natural regrowth”.\n7. L154-156: Would briefly noting potential reasons for the NPP vs. measured OC\ndiscrepancy?\nWe have revised the text to clarify the relationship between NPP-derived and field-measured\norganic carbon (OC). The field measurement represents the total OC stored in soil and vegetation\nbiomass, while NPP refers to the amount of OC fixed by plant photosynthesis minus plant\nrespiration losses. Given their different definitions and timescales, they are not directly\ncomparable. The observed ~10% lower field-measured OC compared to the NPP-derived estimate\nis within the typical range of measurement uncertainty (Turner et al., 2005); however, it may also\nreflect real carbon losses through fluvial transport, decomposition, and soil respiration.\nAccordingly, the previous sentences in the manuscript has been revised at Lines 199-202: “The\nfield-measured OC represents the combined carbon storage in soil and vegetation biomass. The\nmeasured OC was about 10% lower than the NPP-derived estimate, reflecting carbon losses\nthrough fluvial transport, decomposition, and soil respiration. The field-measured OC represents\nthe combined carbon storage in soil and vegetation biomass. The measured OC was about 10%\nlower than the NPP-derived estimate, which could partly reflect measurement uncertainty , in\naddition to potential carbon losses through fluvial transport, decomposition, and soil\nrespiration.”\n8. L353–362: Twenty covariates were used for machine learning. Please clarify whether\npredictor importance or multicollinearity was evaluated, and whether any variables were\nremoved due to redundancy.\nWe have clarified in the revised Methods section that multicollinearity among covariates was\nevaluated prior to model training. Specifically, we computed Pearson correlation matrices and\ngenerated pairwise correlation plots for field sampling data across undisturbed areas (UD) and\nlandslide deposits (LD) in Fig. S12. In addition, both univariate and multivariate regression\nanalyses were performed to assess the relationships between each covariate and total organic\ncarbon (TOC).\nDuring model training, variable importance was further evaluated based on the GBDT algorithm,\nas illustrated in Figure S10. Nearly all initially selected covariates were retained, as they\nrepresent distinct topographic, climatic, soil, and vegetation attributes and contribute\nmeaningfully to model performance. Although redundancy removal is often recommended in large\ndatasets, our relatively short dataset favored maintaining these representative covariates to avoid\noverfitting while preserving interpretability and ensuring robust prediction accuracy.\nAccordingly, we have modifed and added the following clarification to the Methods section at\nLines 510-520.\n9. Supplementary Information: Was the normal distribution assumption for Monte Carlo\nvalidated?\nIn the original submission, the uncertainty analysis was based on a deterministic propagation of\nuncertainty, where bothα and γ were assumed to follow normal distributions as reported in\nprevious studies (Guzzetti et al., 2009; Larsen et al., 2010; Parker et al., 2011). The reviewer\ncorrectly noted that this assumption was not explicitly validated in the earlier version.\nIn the revised manuscript (Lines 45-66 of Supplementary Note), we have replaced the analytical\napproach with a Monte Carlo simulation framework, where α and γ are treated as normally\ndistributed variables following the same empirical parameterizations. The resulting ensemble of\ntotal volumes allows us to characterize the output distribution directly. To provide a more\ndistribution-independent representation of uncertainty, we now report the interquartile range\n(25th–75th percentiles) rather than the standard deviation. This modification effectively relaxes\nthe requirement for normally distributed inputs and outputs and makes the uncertainty\nquantification more robust. We note that there has not been a significant change in our estimates\nof uncertainty with this new method.\n10. Supplementary Information: The authors apply both Marc et al. and Li et al. correction\nmethods for scar area estimation. Could they clarify how the choice between these\napproaches affects the resulting landslide volume and the downstream carbon budget?\nApplying the scale‐factor method of Li et al. (2014) generally yields larger scar areas—and\ntherefore greater total landslide volumes—than the geometry-based correction of Marc et al.\n(2018). The difference arises because Li’s approach employs an empirical scale factor (fₛₐᵣ ≈ 0.70)\ncalibrated specifically for the Wenchuan inventory, whereas Marc’s method explicitly accounts\nfor the shape of each landslide polygon through its aspect ratio (K). Consequently, Li’s method\nmay overestimate scar areas when fₛₐᵣ is not regionally representative, while Marc’s approach\ntends to reduce estimates for elongated or irregular landslides.\nGiven that volume scales nonlinearly with area (𝑉 =𝛼𝐴 𝛾 with 𝛾 >1), even moderate\ns\ndifferences in scar area can amplify into substantial discrepancies in total volume. Considering\nthis sensitivity and the limited spatial resolution of the available Wenchuan landslide inventory,\nwe were required to use the area–volume scaling relationship to estimate landslide volumes.\nMeanwhile, given that the correction parameters proposed by Li et al. (2014) were derived\nprimarily from the Longmenshan fault zone, we adopted their correction method to compute the\nsource areas, providing the most reasonable estimate of total landslide volume across the study\narea.\nWe agree with the reviewer that the choice of correction method can propagate through the\ncarbon balance model by influencing the derived landslide volumes and, in turn, the downstream\ncarbon budget. However, our analysis indicates that while the two methods affect the magnitude\nof carbon fluxes, they do not alter the qualitative trends or the overall direction of post-seismic\ncarbon system evolution. Thus, averaging the results from both correction schemes provides a\nbalanced and robust representation under current data constraints.\nIn addition, we have revised the Supplementary Materials section titled “Uncertainty of\nLandslide Volume” (Line 45-73) to include a dedicated discussion clarifying the uncertainty and\nthe additional variability arising from different parameter settings and correction methods.\n11. Supplementary Information: The estimation of total landslide volume pools multiple\npublished empirical parameters. How do the authors select which parameter sets to use\nfor the Wenchuan region?\nIn our study, we included several published parameter sets, comprising both locally derived\nvalues from the Longmenshan region (Parker et al., 2011) and global or regional compilations\n(Guzzetti et al., 2009; Larsen et al., 2010). This choice ensures both regional relevance and\ncomparability with previous work. As the different parameter sets yield slightly different total\nlandslide volumes (Table S11), we reported all results and then used their average as the\nrepresentative estimate for subsequent calculations of sediment mass and carbon budgets. This\napproach avoids reliance on any single parameterization, reflects the uncertainty among\npublished values, and provides a robust basis for our conclusions.\n12. OC recovery is compared to NPP using remote sensing. How do the authors account\nfor root biomass and belowground OC, which may be underestimated by NPP-based\napproaches?\nIn our study, vegetation biomass and corresponding organic carbon (OC) were estimated using\nspecies-specific allometric equations compiled for Chinese forest types (see Lines 414–417 in the\nMethods section). These equations explicitly include both above-ground and below-ground\ncomponents, thereby accounting for root biomass and belowground OC in the total vegetation\ncarbon stock. For species without specific equations, we used generalized allometric relationships\naccording to plant functional type (e.g., deciduous broad-leaved or evergreen broad-leaved\nforests).\nTherefore, root and belowground carbon were fully considered in our field-based OC estimates.\nThe remote-sensing–derived NPP data were used only to evaluate the temporal recovery trends of\nvegetation productivity following the earthquake, not as a direct measure of total carbon storage.\nSo we clarified in manuscript at Lines 189-190:”..NPP was used solely to assess the temporal\nrecovery of vegetation productivity, not as a direct measure of total carbon storage.”\n13. The carbon balance model pools all disturbance-derived carbon into three pools (B,\nD, S). How sensitive are the model’s long-term predictions to the initial partitioning of\ncarbon among these pools, and what empirical data constrain these choices?\nWe are not aware of any empirical evidence that provides guidance on how to partition carbon\nbetween these three pools after an earthquake or landslide. Consistent with our broader goal of\ncontrainin the upper bounds of carbon storage by an earthquake, we assume that after an\nearthquake all of the B pool in areas where landslides occurs becomes the D pool. We have\nupdated the wording to note that this is a maximum estimate. Hence, following a landslide, the\nbiomass carbon (B pool) on affected slopes is entirely transferred to the buried debris (D pool),\nwhile the soil carbon(S pool) retains its total mass but undergoes changes in density and spatial\ndistribution.\nAs noted in the Supplementary Note section “Modelling of Carbon Balance” (Lines 144–289),\nthe initial partitioning of carbon among the three pools following landslides is described in the\nmodel equations. However, we agree that this assumption required clearer explanation.\nTherefore, we have revised the text and added the following clarification at Lines 249–253 in\nthe Supplementary Materials: “…This setting represents an end-member assumption in which the\nB pool is instantaneously converted to the D pool after landslides, corresponding to a 100%\ntransfer of B to D. This approach provides a maximum estimate of post-seismic carbon\nredistribution, as no empirical data are currently available to constrain the actual B–D\nconversion ratio.”\n14. Could a few key abbreviations (e.g., NPP, TN) be defined at first use?\nWe thank the reviewer for this helpful suggestion. We have carefully reviewed the entire\nmanuscript to ensure that all abbreviations are defined at their first appearance. Specifically, net\nprimary productivity (NPP) is now defined at Line 169, and total nitrogen (TN) is defined at\nLine 179. Similar adjustments have been made for other abbreviations where necessary to\nimprove clarity and consistency throughout the text.\nReviewer #1 (Remarks on code availability):\nThe code appears to be well-structured and comprehensive for modeling carbon\ndynamics following earthquake-triggered landslides. The modular organization into\nseparate files for different analysis aspects (landslide info, earthquake info, carbon\ndynamics, etc.) is logical and makes the codebase more maintainable. The README\nprovides a clear overview of each script's purpose, though it could benefit from more\ndetailed installation instructions and dependency requirements.\nWe have updated the README file to include more detailed installation instructions and a\ncomplete list of software dependencies required to run the code. These additions should make the\ncodebase easier to install and reproduce for other users.\nReviewer #2 (Remarks to the Author):\nThis paper does a very good job of condensing a huge amount of work into a short form\narticle. The authors should be commended. Overall, it would make a good contribution to\nthe science exploring how large mountainous EQ’s, and the landslides they generate,\ninfluence worldwide variations in carbon export.\nWe sincerely thank the reviewer for their positive and encouraging comments. Below we provide a\npoint-by-point response, with corresponding changes made in the revised manuscript. Line\nnumbers refer to the revised version with tracks.\nA general comment:\nThere is a large amount of additional information in the supplementary materials, some of\nwhich might be good to bring into the main body of the paper, especially when it would\nhelp to describe the uncertainties on the estimates of mobilised, stored and distributed\nOC. These points are discussed below.\nWe agree that several important details were previously confined to the Supplementary\nMaterials. In response, we have moved selected sections into the main text to provide clearer\ncontext and better explain the uncertainties. In addition, the Supplementary Materials have been\nreorganized to improve clarity and coherence, ensuring that the supporting information more\ndirectly complements the corresponding sections of the manuscript.\nMain points:\nIn summary, landslide volume is a key parameter in determining the amount of OC\nmobilised, buried and stored, and eventually released, making it central to the study's\ncarbon budget calculations.\nThe estimates of landslide volume will, therefore, significantly impact the calculation of\nthe mobilised and buried organic carbon (OC), because landslide volume determines the\namount of soil and vegetation mobilised and buried, which directly correlates with the OC\nmobilized and stored.\nAs the paper currently stands, I don’t believe there is enough discussion on how the\nlandslide source volumes have been derived and their uncertainties estimated, and what\nthese uncertainties mean wrt., the two main hypotheses stated. The supplementary\nsection has a section on this, but it doesn’t currently provide enough detail on or\nadequately addresses, the calculation of landslide source volumes for the different types\nof landslides.\nWe have revised the Supplementary Materials to make the derivation of landslide source volumes\nand their uncertainties clearer and more traceable. Specifically, Lines 23–73 of the revised\nSupplementary Materials now (i) describe how the landslide source areas are obtained,\n(ii) explain the conversion from source area to volume using the adopted area–volume scaling,\nand (iii) detail how uncertainty is quantified based on Monte Carlo approach.\nRegarding the hypotheses, we made several modifications to the manuscript to clarify their\npurpose and connection to the overall research framework. We introduced the term “carbon\ncapacitor” in the first paragraph of the Introduction at Lines 48-51 to clearly define this concept.\nWe then emphasized the carbon capacitor in the statement of hypotheses at Line 105. In addition,\nwe elaborated on the assumptions underlying the carbon balance model at Lines 498–508,\nspecifying that the biomass carbon pool (B) is assumed to be entirely converted into the debris\ncarbon pool (D) to represent the maximum potential carbon storage following the earthquake. A\nbrief explanation of this rationale is also provided in the main text at Lines 116–119. These\nrevisions make the conceptual basis of the hypotheses more explicit and better aligned with the\nobjectives of our study.\nRegarding landslide types, we note that for the 2008 Wenchuan Earthquake there are 5\ninventories available; however, to our knowledge none of the publicly accessible inventories\nseparates landslides by type or distinguishes source vs. deposit polygons at the level needed for\ntype-specific volume calculations. Accordingly, we consistently use Li’s inventory and apply Li’s\ncorrection method to infer source areas and then estimate volumes (Li et al., 2014). Given the lack\nof type labels across the inventory, type-resolved volume calculations are presently not feasible;\nour approach provides a uniform and internally consistent treatment of source areas and volumes\nfor the entire inventory. We have clarified this constraint and rationale in the Supplementary\n(Lines 40–44) and cross-referenced it in the Methods (Lines 390-399).\nModelling and Uncertainty:\nAccurate landslide volume estimates are critical for modelling OC budgets. The study\nuses volume-area scaling methods to convert landslide ‘area’ inventories into volume\nestimates, which are then used to calculate OC stocks.\nRecent work by Jones et al. (2024), highlights the limitations of existing landslide volume-\narea scaling relationships, and their use to estimate landslide volume by orders of\nmagnitude. These limitations and uncertainties should be explored in the paper and their\nimpact on the results quantified, for example:\n• Estimating the area of the landslide, why use the scar and not the source area only?\nThis could lead to an overestimate of landslide source volume when using volume-area\nscaling? A landslide scar in the context of this study is assumed to be the combined\nsource area and debris trail of the landslide? Whilst the scar area is appropriate to\naccount for source area OC, and buried OC together, using the scar to estimate volume\nwith lead to an overestimate of the source volume and its associated OC.\nWe thank the reviewer for drawing our attention to the recent work by Jones et al. (2025), which\nprovides an important discussion on the limitations of existing volume–area scaling relationships.\nIn the revised manuscript, we have clarified the definition and terminology related to landslide\narea. Specifically, throughout the main text and Supplementary Materials, we now consistently\nuse the term “landslide source area” to refer to the portion of the slope where material is initially\nmobilized, and we have removed any ambiguous references to “scar area”. We use the term\n“landslide surface” to refer to the denuded surface after landsliding, which included the source,\nrunout, and deposit zones.\nIn addition, we have expanded the description of the landslide volume and area estimation in the\nMethods (Lines 390–399). Although we applied the empirical power-law volume–area\nrelationship to estimate landslide volumes, we corrected the mapped areas using the method\nproposed by Li et al. (2016) to better represent the source area and reduce potential\noverestimation. We also acknowledge, as noted by Jones et al. (2025), that such empirical scaling\nmay still lead to systematic underestimation of the true landslide volume when large and deep-\nseated failures dominate the inventory.\nGiven the limitations of currently available inventories for the Wenchuan Earthquake, which do\nnot provide a consistent separation of source and deposit polygons, the adopted correction\nremains the most reliable and regionally calibrated approach available. We have now added a\nreference to Jones et al. (2025) and discussed this limitation and its potential effect on volume and\nOC estimation in the revised manuscript and Supplementary Materials to make this uncertainty\nexplicit.\n• The volume-area scaling relationships used and how these relate to different landslide\ntypes in different materials – these have huge uncertainties associated with them. Would\nincorporating other uncertainties adopting different methods of volume estimation (as per\nthose shown by Jones et al. 2025) impact the results?\nWe appreciate the reviewer’s valuable comment. We fully agree that large uncertainties are\ninherent in landslide volume estimation due to the variability in scaling relationships and the\ndiversity of landslide types and materials. As discussed in our previous responses, our current\napproach represents the most reliable and regionally calibrated method available given the\nlimitations of existing inventories for the Wenchuan Earthquake, which do not provide consistent\ntype or material classifications.\nWe acknowledge that incorporating alternative volume estimation methods, such as those\npresented by Jones et al. (2025)), and accounting for landslide typologies (shallow and deep-\nseated) through typology-specific volume-scaling relationships could more effectively constrain\nthese uncertainties and improve future carbon budget estimates. We plan to incorporate such\napproaches in future work, particularly by applying higher-resolution models at the catchment\nscale to better quantify and propagate the uncertainties associated with landslide volume\nestimation.\n• Why use landslide volume, what about using the landslide ‘scar’ area (which is less\nuncertain) – noting the difference between source and scar – to calculate soil OC and live\nbiomass depleted from source versus entrained/buried along trail. For example, is soil\ndepth easier to calculate then landslide volume? Could soil depth and veg type within the\nlandslide scar area provide a better estimate of removed and buried OC?\nWe thank the reviewer for this insightful comment. As discussed above, we have unified the\nterminology of landslide scar area and landslide source area throughout the manuscript and\nSupplementary Materials. The landslide source area represents the primary input to the sediment\nbudget, while landslide volume is used in our carbon cycle model mainly to approximate the\ninitial sediment thickness that governs soil (S) and debris (D) export. Although incorporating soil\ndepth and vegetation type could, in principle, refine local estimates of OC loss and burial, soil\ndepth are not available at the regional scale. Therefore, in this study, we first fixed the soil depth\nwhen calculating field-based OC stock distributions. In the carbon cycle model, we adopted a\nvolume-based approach and incorporated sediment transport processes as a consistent and\nparsimonious means of representing carbon transfer across the study area..\nThese uncertainties and their impact on the estimated OC generated and stored by\ncoseismic landslides, could be used to address whether there is too much ‘noise’ versus\n‘signal’ in the estimates, to be able to substantiate the studies initial hypotheses.\nCurrently I can’t tell.\nWe thank the reviewer for this valuable comment. We agree that incorporating multiple layers of\nuncertainty could, in principle, introduce significant variability into the results. However, as\nshown in Fig. 4, the overall impact remains limited, and even under extreme parameter settings,\nthe model outcomes do not deviate substantially from the mean estimates. This stability arises\nbecause the uncertainties associated with landslide volume estimation are effectively moderated\nthrough the formulation of h(t) in our carbon cycle model (Supplementary Note), which constrains\ntheir propagation into the pool dynamics. Consequently, while uncertainty inevitably exists, the\nmodelled signal remains strong enough to substantiate the study’s initial hypotheses.\nIn addition, the Wenchuan EQ is thought to be an outlier wrt., the number and area\naffected by coseismic landslides. Is it therefore, representative of other EQ events and\nthe landslides and OC they generate and store? Wenchuan could be\nanomalous wrt., number of landslides generated, but was less OC mobilised and buried in the area (per\nunit area of landslide scar?) when compared to the landslides triggered in other recent\ncoseismic events (listed) thus making it less anomalous? How does this anomalous\nevent support the studies hypotheses?\nWe thank the reviewer for this valuable comment. The Wenchuan Earthquake represents one of\nthe largest and well-documented events associated with widespread coseismic landsliding. In this\nstudy, we assume that all mobilized organic carbon is immediately buried within landslide\ndeposits, allowing us to quantify the maximum potential carbon “capacitor effect” of such a\nmega-earthquake. This does not imply that a larger number or total area of landslides necessarily\nleads to higher carbon mobilization, as the primary OC stock density also plays a critical role. In\nthis context, the Wenchuan event supports our first hypothesis—that worldwide variations in\ncarbon export reflect differences in the primary mass of OC mobilised during earthquakes.\nAlthough the Wenchuan earthquake generated an exceptionally large number of landslides, the\namount of OC mobilized and subsequently buried per unit area is comparable to, or even lower\nthan, in some other seismically active regions, like in Southern Alps. This highlights that carbon\nresponse is controlled not only by the extent of landslides disturbance but also by fluvial systems,\ntopographic and climatic factors. The carbon cycle model we developed further aims to capture\nthese dependencies, allowing us to explore both the intensity and timescale of post-seismic carbon\nrecovery.\nOther points include:\nOC Mobilisation:\nMobilised OC by coseismic landslides is estimated to be within the range reported for\nother landslide events. However, the Wenchuan EQ and the landslides it generated are\nreported to be an outlier wrt., to other EQ events and the landslides they generated?\nPlease discuss this in more detail.\nWe thank the reviewer for this thoughtful comment. As shown in Table 1, our comparison focuses\non the total amount of organic carbon mobilised, transported, and subsequently recovered across\ndifferent earthquake-induced landslide events. These estimates vary among studies because of\ndifferences in measurement approaches, data sources, and observation timescales. Moreover,\nseveral factors—including primary productivity, microbial respiration, sediment yield, and fluvial\ntransport capacity—can substantially influence the magnitude of observed OC mobilisation and\naccumulation.\nFor example, the exceptionally high OC mobilisation reported for the Windbag Basin in the\nSouthern Alps partly reflects the use of lake sediment records that integrate carbon accumulation\nover a long period (approximately 250–350 years), rather than short-term coseismic fluxes. In this\ncontext, the Wenchuan Earthquake, though often regarded as an outlier in terms of the total\nnumber and extent of landslides, exhibits higher rates of post-seismic OC accumulation than many\nother reported events. To clarify this point, we have revised the text at Lines 289–292 and\nupdated the Table 1 caption to better highlight these distinctions.\nLandslides mix soil OC and live biomass into their deposits, creating an OC store. The\nmagnitude of OC mobilised depends on the initial coseismic landslide volume and any\nentrainment of additional debris as the landslide mass moves downslope and buried by\nthe debris. How do different landslide types affect the mixing of soil OC and live biomass\ninto their deposits? Will coherent landslides – where the displacing debris/rock largely\nremains intact – do this differently to disrupted ones – where the debris/rock is\nbroken down due to movement? Some commentary on the importance of landslide type and OC\nmobilisation, entrainment and burial would be good to include (note I’ve adopted the\nterminology of Keefer (1984) for coherent versus disrupted landslides).\nWe thank the reviewer for this insightful comment. As mentioned in our response to the previous\nquestion, the five available landslide inventories used in this study do not distinguish between\ndifferent landslide types. Therefore, we were not able to explicitly evaluate the influence of\nlandslide type on OC mobilisation and storage.\nHowever, we are currently conducting a follow-up study based on the present model framework to\nexplore how total landslide area and the size distribution of individual landslides affect OC\naccumulation. Similar to the current study, this ongoing work does not yet incorporate differences\namong landslide types. We fully agree that the role of landslide type (coherent versus disrupted) in\ncontrolling OC mixing, entrainment, and burial is an important and interesting question.\nAddressing it will require extensive field data and more detailed landslide classification, which we\nplan to consider in future research.\nOC Storage:\nThe proportional removal and storage of OC are assumed to be similar to sediment\nremoval and storage. Larger landslide volumes result in greater OC storage in deposits.\nHow do different landslide types affect this? Would more disrupted landslides incorporate\nand bury more OC into the debris thus making it more difficult to remove and as a result\nstore it for longer? Versus coherent landslides, where soil OC and live biomass might\nremain largely on the surface of the debris (limited mixing) and not buried by it, thus\nmaking it easier to subsequently erode? If most landslides were disrupted in nature, are\nsuch differences important, please state?\nWe thank the reviewer for this insightful question. We fully agree that different landslide types\nmay influence the extent to which organic carbon (OC) is incorporated into, mixed within, and\ntemporarily retained by the landslide debris. However, disrupted landslides do not necessarily\nlead to greater long-term OC storage. Shallow or highly fragmented landslides may generate finer\nsediments incorporating dead biomass and soil carbon are easily transported through the channel\nsystem. The increased surface exposure of these materials also promotes oxidation and carbon\nrelease to the atmosphere. In contrast, more coherent landslides may preserve a larger fraction of\nintact surface vegetation, while OC mixed within landslide deposits could be buried more deeply\nand become less susceptible to remobilisation. Due to the lack of field data or empirical\nconstraints that distinguish landslide types across the Wenchuan region, it is not currently\npossible to quantitatively evaluate these differences in this study.\nBased on our current understanding, the influence of landslide type on carbon storage capacity\nshould be viewed as a continuous and dynamic process involving both carbon inputs and outputs.\nSuch dynamics are jointly regulated by vegetation composition and productivity, debris and\ndeposit respiration, sediment transport rates, climatic factors and etc.\nSpatial Distribution:\nThe spatial distribution of OC pre-EQ, is stated as correlating with topography and\nclimate. The OC (within surface soil and\nand vegetation) increases with elevation between 500–2000 metres above sea level (masl). Does OC correlate with soil\nthickness? If it does, then does soil thickness correlate with elevation. In NZ, Larsen et al. (2023) found that soils on lower\nelevation slopes tend to be generally thicker than those at higher elevations, due to increased chemical weathering at lower\nelevations compared to increased physical weathering at higher elevations. This appears to the opposite case in\nWenchuan? Some more discussion on this, but in the Wenchuan context, would be\nuseful, and compared to other contexts like Taiwan and NZ.\nWe thank the reviewer for this valuable comment. In our study area, the observed increase in\norganic carbon (OC) stock with elevation between 500–2000 m.a.s.l. primarily reflects the\ninfluence of climatic gradients rather than soil thickness. We did not directly measure soil\nthickness in the field, and therefore cannot quantitatively constrain its relationship with elevation\nin this study.\nThe spatial distribution of coseismic landslides, are influenced by topography and\nlithology, and the level of ground shaking, which affects the OC stock calculations. Areas\nwith higher landslide volumes tend to have higher OC mobilization and storage. Areas of\nhigher landslide density might relate to lots of shallower landslides over large areas,\nversus many larger deeper landslides. Is this distinction important? Some more\ncommentary on the landslide types, their depths and source materials are needed to give\nthe OC estimates some context?\nWe thank the reviewer for this thoughtful comment. In the case of the 2008 Wenchuan Earthquake,\nthe spatial distribution of coseismic landslides was primarily controlled by the level of ground\nshaking (Gorum & Carranza, 2015), with lithology and topography acting as secondary factors.\nThe observed pattern of decreasing landslide density with increasing distance from the fault zone\nis consistent for both large and small landslides. Therefore, the distinction between numerous\nshallow failures and fewer deep-seated landslides does not significantly affect our estimation of\nOC mobilization and storage at the regional scale. Moreover, areas with high overall landslide\ndensity generally coincide with zones of large landslide occurrence, supporting the robustness of\nour volume-based OC estimates.\nRate of landslide sediment and OC export:\nThe rate of sediment and OC export is a function of the density of the newly developed\ndrainage network on the landslide itself together with its connectivity to the river below.\nBut what about the erodibility of the landslide debris? This will in some\ncases be a function of the type of landslide (coherent versus disrupted) and landslide source material\n(finer-grained versus blocky rock sizes etc), and the stream power. How is connectivity\ndefined wrt., stream power? Is it important in this context? Please describe.\nWe agree this is a major source of uncertainty in the calculations. We have modified the\nparagraph starting at Line 57 to better reflect the processes governing the export of sediment and\ncarbon from these catchments. We better reflect the observations from the sediment budgeting\nwork of Francis et al. (2022) which demonstrated the important role of debris flows in the\nstochastic delivery of landslide sediment to channels. As a result we have softened the language\naround stream power, as “connectivity” reflects a combination of hillslope and weak stream\npower. In terms of the sediment budget, the use of the Li et al connectivity values provides a\nmaximum estimate for the erodibility, which highlights that the flux of carbon from landslide\ndebris is likely an overestimate, and that landslide derived carbon may persist in the landscape.\nRefs cited:\nJones et al. (2025) Landslides (2025) 22:2219–2236 DOI 10.1007/s10346-025-02479-x\nKeefer (1984) GSA Bulletin (1984) 95 (4): 406–421. DOI 10.1130/0016-7606(1984)\nLarsen et al. (2023) The influence of erosion and vegetation on soil production and\nchemical weathering rates in the Southern Alps, New Zealand. Earth and Planetary\nScience Letters. Volume 608. 2023. 118036, ISSN 0012-821X\nFrancis, O., Fan, X., Hales, T., Hobley, D., Xu, Q., & Huang, R. (2022).\nThe Fate of Sediment After a Large Earthquake. Journal of\nGeophysical Research: Earth Surface, 127(3).\nhttps://doi.org/10.1029/2021jf006352\nGorum, T., & Carranza, E. J. M. (2015). Control of style-of-faulting on\nspatial pattern of earthquake-triggered landslides. International\nJournal of Environmental Science and Technology, 12(10), 3189-3212.\nhttps://doi.org/10.1007/s13762-015-0752-y\nGuzzetti, F., Ardizzone, F., Cardinali, M., Rossi, M., & Valigi, D. (2009).\nLandslide volumes and landslide mobilization rates in Umbria,\ncentral Italy. Earth and Planetary Science Letters, 279(3-4), 222-\n229. https://doi.org/10.1016/j.epsl.2009.01.005\nJones, K. E., Howarth, J. D., Massey, C. I., Luković , B., Sirguey, P.,\nSingeisen, C., Gasston, C., Morgenstern, R., & Ries, W. (2025). An\nalternative to landslide volume-area scaling relationships: an\nensemble approach adopting a difference model to estimate the total\nvolume of landsliding triggered by the 2016 Kaikōura earthquake, New\nZealand. Landslides, 22(7), 2219-2236.\nhttps://doi.org/10.1007/s10346-025-02479-x\nLarsen, I. J., Montgomery, D. R., & Korup, O. (2010). Landslide erosion\ncontrolled by hillslope material. Nature Geoscience, 3(4), 247-251.\nhttps://doi.org/10.1038/ngeo776\nLi, G., West, A. J., Densmore, A. L., Hammond, D. E., Jin, Z., Zhang, F.,\nWang, J., & Hilton, R. G. (2016). Connectivity of earthquake-\ntriggered landslides with the fluvial network: Implications for\nlandslide sediment transport after the 2008 Wenchuan earthquake.\nJournal of Geophysical Research: Earth Surface, 121(4), 703-724.\nhttps://doi.org/10.1002/2015jf003718\nLi, G., West, A. J., Densmore, A. L., Jin, Z., Parker, R. N., & Hilton, R.\nG. (2014). Seismic mountain building: Landslides associated with the\nearthquake volume balance. Geochemistry, Geophysics, Geosystems,\n15(4), 833-844. https://doi.org/10.1002/2013gc005067\nMarc, O., Stumpf, A., Malet, J.-P., Gosset, M., Uchida, T., & Chiang, S.-H.\n(2018). Initial insights from a global database of rainfall-induced\nlandslide inventories: the weak influence of slope and strong\ninfluence of total storm rainfall. Earth Surface Dynamics, 6(4),\n903-922. https://doi.org/10.5194/esurf-6-903-2018\nParker, R. N., Densmore, A. L., Rosser, N. J., de Michele, M., Li, Y.,\nHuang, R., Whadcoat, S., & Petley, D. N. (2011). Mass wasting\ntriggered by the 2008 Wenchuan earthquake is greater than orogenic\ngrowth. Nature Geoscience, 4(7), 449-452.\nhttps://doi.org/10.1038/ngeo1154\nTurner, D. P., Ritts, W. D., Cohen, W. B., Maeirsperger, T. K., Gower, S.\nT., Kirschbaum, A. A., Running, S. W., Zhao, M., Wofsy, S. C., Dunn,\nA. L., Law, B. E., Campbell, J. L., Oechel, W. C., Kwon, H. J.,\nMeyers, T. P., Small, E. E., Kurc, S. A., & Gamon, J. A. (2005).\nSite-level evaluation of satellite-based global terrestrial gross\nprimary production and net primary production monitoring. Global\nChange Biology, 11(4), 666-684.\nhttps://doi.org/https://doi.org/10.1111/j.1365-2486.2005.00936.x"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done a great job revising the manuscript. They have fully addressed the previous comments. The changes\nto the main text and supplement make the study clearer, and more transparent. Key method aspects, such as landslide\nvolume uncertainty, machine learning covariate selection, and carbon balance model assumptions, are now much better\nexplained. The manuscript is much improved and makes a valuable contribution. I therefore recommend acceptance\npending the following minor revisions:\n1. Supplementary Note (L65-71): The carbon budget equation lists OCoxidation but states it is not considered. For formal\ncorrectness, please adjust the equation to reflect the actual budget used, or add a brief note stating that the OCoxidation is\nomitted from the quantitative balance as justified.\n2. Supplementary Note (L249-253): the clarification regarding the assumption for the B to D pool transfer (100%) is excellent.\nTo further strengthen this, consider adding a single sentence in the Discussion section (or Methods section) acknowledging\nthat this represents a maximum storage scenario and that the model’s long-term predictions are sensitive to this initial\npartitioning, the exact value of which lacks empirical constraint.\n3. Supplementary Note (L273-274): The parameter calculation flowchart contains several equations with subscripts (e.g.,\nk_B, C_1) that are not fully defined in the surrounding text. Please ensure all variables in this flowchart are defined in the\ntext.\n(Remarks on code availability)", "reviewer_2": "The authors have responded to the comments the other reviewer and I have made. Thank you.\nMy only outstanding comment relates to how they estimate the volumes of the landslides using the ‘common’ approach of\narea-to-volume ‘scaling’ relationships from Guzzetti et al., Larsen et al., and Parker et al. Whilst the updated approach\nadopts a Monte Carlo method (which is an established method), it still assumes that the landslide volumes relate to their\nsource areas via ‘scaling’ relationships. I acknowledge that they cannot do any better given the data sets available for this\nhistorical earthquake event. However, given the recent advances shown by Jones et al., such scaling relationships do not\nrepresent the true values, or uncertainties, on the estimates of landslide volume, when compared to methods where direct\ndifferencing is possible (e.g., Jones et al.). Therefore, whilst a Monte Carlo approach is better, their volume estimates still\nrely on scaling relationships, and so they still do not represent the true estimates of the landslide volumes used or their\nuncertainties.\nI would like to see a statement in e.g., the Discussion section, where these uncertainties and their impacts on the results,\nare discussed. Given the volume ranges shown in Tables S11 and S12, I assume these volumes estimates are where the\nlargest uncertainties exist within the presented results?\nWhilst outside the scope of this study, it would be interesting to apply their methods to the\nKaikoura EQ landslide volumes of Jones et al., to compare the differences that might result from using the different methods to\nestimate landslide volumes.\n(Remarks on code availability)"}, "author_response": "Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThe authors have done a great job revising the manuscript. They have fully addressed the\nprevious comments. The changes to the main text and supplement make the study clearer,\nand more transparent. Key method aspects, such as landslide volume uncertainty, machine\nlearning covariate selection, and carbon balance model assumptions, are now much better\nexplained. The manuscript is much improved and makes a valuable contribution. I therefore\nrecommend acceptance pending the following minor revisions:\n1. Supplementary Note (L65-71): The carbon budget equation lists OCoxidation but states it is\nnot considered. For formal correctness, please adjust the equation to reflect the actual budget\nused, or add a brief note stating that the OCoxidation is omitted from the quantitative balance\nas justified.\nFor formal correctness, we have revised the description of OC oxidation to clarify that this term is\nnot omitted, but instead represents a lumped atmospheric loss of ecosystem OC during\ntransportation, including respiration- and oxidation-related processes. Because this flux is not\ndirectly observable in our data, it is accounted for indirectly rather than parameterized as an\nindependently measured term in the carbon budget. The revised text is shown at Lines 86-89 in\nSupplementary Information.\n2. Supplementary Note (L249-253): the clarification regarding the assumption for the B to D\npool transfer (100%) is excellent. To further strengthen this, consider adding a single\nsentence in the Discussion section (or Methods section) acknowledging that this represents a\nmaximum storage scenario and that the model’s long-term predictions are sensitive to this\ninitial partitioning, the exact value of which lacks empirical constraint.\nWe have further clarified this assumption on both Supplementary Information and the Methods.\nIn\nSupplementary Information (Lines 243-247), we explicitly state that “the assumed 100% transfer\nfrom the B to D pool represents an end-member assumption corresponding to a maximum estimate\nof post-seismic carbon redistribution, and that no empirical data are currently available to\nconstrain the actual B-D conversion ratio”. In addition, we have added a sentence in the\nMethods\nsection as shown at Lines 410-412. Together, these revisions clarify the nature, implications, and\nlimitations of this assumption without altering the model structure.\n3. Supplementary Note (L273-274): The parameter calculation flowchart contains several\nequations with subscripts (e.g., k_B, C_1) that are not fully defined in the surrounding text.\nPlease ensure all variables in this flowchart are defined in the text.\nWe have revised the Supplementary Information to improve the clarity of variable definitions.\nSpecifically, a typographical error in C₁ has been corrected to C . The parameter k is already\nB B\ndefined in the main text (Line 179). For intermediate variables (k , k , C , and C ) that are\nD S D S\nintroduced for computational convenience rather than for explicit physical interpretation, we have\nreplaced the equality sign “=” with “:=” in the flowchart to clearly indicate their initial\ndefinition at that step. We believe these revisions clarify the notation and address the reviewer’s\nconcern.\nReviewer #2 (Remarks to the Author):\nThe authors have responded to the comments the other reviewer and I have made. Thank\nyou.\nMy only outstanding comment relates to how they estimate the volumes of the landslides\nusing the ‘common’ approach of area-to-volume ‘scaling’ relationships from Guzzetti et al.,\nLarsen et al., and Parker et al. Whilst the updated approach adopts a Monte Carlo method\n(which is an established method), it still assumes that the landslide volumes relate to their\nsource areas via ‘scaling’ relationships. I acknowledge that they cannot do any better given\nthe data sets available for this historical earthquake event. However, given the recent\nadvances shown by Jones et al., such scaling relationships do not represent the true values,\nor uncertainties, on the estimates of landslide volume, when compared to methods where\ndirect differencing is possible (e.g., Jones et al.). Therefore, whilst a Monte Carlo approach is\nbetter, their volume estimates still rely on scaling relationships, and so they still do not\nrepresent the true estimates of the landslide volumes used or their uncertainties.\nWe fully agree that, although the Monte Carlo framework improves the propagation of\nuncertainty, our estimates of carbon burial remain fundamentally dependent on the estimation of\nlandslide volumes. In this study, the amount of carbon buried is determined by the volume of\nlandslide deposits generated. As demonstrated by recent work (e.g., Jones et al.), area–volume\nscaling relationships may not fully capture true landslide volumes when compared with methods\nbased on direct topographic differencing. Consequently, uncertainties in sediment volume\nestimates propagate into the associated carbon estimates.\nI would like to see a statement in e.g., the Discussion section, where these uncertainties and\ntheir impacts on the results, are discussed. Given the volume ranges shown in Tables S11\nand S12, I assume these volumes estimates are where the largest uncertainties exist within\nthe presented results?\nIn response, we have added an explicit statement in the Discussion section acknowledging this\nlimitation. Specifically, we now state (Discussion, Lines 251–254) that “estimates of mobilised\nsediment remain fundamentally constrained by landslide volume calculations derived from area–\nvolume scaling relationships. As a result, uncertainties associated with landslide volume\nestimation likely represent one of the dominant sources of uncertainty in our results and in the\ninferred carbon budget (Jones et. al., 2025). Further details are provided in the Supplementary\nMaterials.”\nWhilst outside the scope of this study, it would be interesting to apply their methods to the\nKaikoura EQ landslide volumes of Jones et al., to compare the differences that might result\nfrom using the different methods to estimate landslide volumes.\nWe further agree that applying our framework to landslide inventories for which volumes are\nconstrained by high-resolution pre- and post-event topographic differencing—such as the\nKaikōura earthquake dataset of Jones et al.—would provide a valuable opportunity to assess the\nsensitivity of our results to different landslide volume estimation methods. While such an analysis\nlies beyond the scope of the present study, we consider it a promising direction for future work.\nRefs cited:\nJones et al. (2025) Landslides (2025) 22:2219–2236 DOI 10.1007/s10346-025-02479-x"}]

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