Global distribution of surface soil organic carbon in urban greenspaces

Domain: earth_science
Published: 2024-01-27
Editorial outcome: major_revision

Abstract
Urban greenspaces continue to grow with global urbanization. The global distribution and stock of soil organic carbon (SOC) in urban greenspaces remain largely undescribed and missing in global carbon (C) budgets. Here, we synthesized data of 420 observations from 257 cities in 52 countries to evaluate the global pattern of surface SOC density (0–20 cm depth) in urban greenspaces. Surface SOC density in urban greenspaces increases significantly at higher latitudes and decreases significantly with higher mean annual temperature, stronger temperature and precipitation seasonality, as well as lower urban greenness index. By mapping surface SOC density using a random forest model, we estimate an average SOC density of 55.2 (51.9–58.6) Mg C ha−1 and a SOC stock of 1.46 (1.37–1.54) Pg C in global urban greenspaces. Our findings present a comprehensive assessment of SOC in global urban greenspaces and provide a baseline for future urban soil C assessment under continuing urbanization.

Introduction
Urban areas cover 0.3–0.6% of the world’s land but account for more than 70% of global energy-related CO2 emissions. With continuing urban expansion, urban areas are becoming increasingly important in the global carbon (C) cycle. In parallel, urban greenspaces (e.g., urban forests and lawns from parks, residential areas, and gardens) are growing rapidly and store increasing amounts of C in vegetation and soils. Soil organic carbon (SOC) is essential for urban soil nutrient retention that both benefits plant growth and alleviates nutrient loss leaching to urban streams. Although an increasing number of studies have reported SOC densities (i.e., the amount of SOC to a certain depth per unit area) in urban greenspaces for individual cities or nationally, the global distribution, controlling factors, and magnitude of SOC stocks in urban greenspaces remain poorly characterized. A quantitative analysis of SOC density and stocks in global urban greenspaces is needed to fill this gap.

Global patterns of SOC density in natural terrestrial ecosystems have been well documented. In urban greenspaces, some early studies demonstrated a human-induced spatial convergence of urban soil properties among cities because of strong anthropogenic interventions and relatively similar management operations. At a much broader scale, recent observational analyses have suggested that there is nevertheless a strong latitudinal pattern and climate control of urban SOC density.

Theoretically, SOC density in urban greenspaces is co-regulated by natural and anthropogenic drivers. Urban greenspace SOC density can be affected by a city’s climate as well as potential legacy effects of pre-urban land use. The urban heat-island effect may increase SOC by accelerating the decomposition of soil organic matter. Vegetation conditions (e.g., vegetation type and coverage) and horticultural management (e.g., fertilization and irrigation) can also affect the spatial variation of SOC in urban greenspaces at large scales by modifying vegetation growth and soil biogeochemical cycles. However, the relative importance of the potential drivers of SOC density in urban greenspaces remains to our knowledge unquantified globally.

Similarly, despite rapid urbanization and consequent expansion of urban greenspaces, the total SOC stocks in global urban greenspaces remain unquantified. Several studies have reported SOC stocks of urban greenspaces at city or national scales, primarily in relatively wealthy countries (e.g., USA and UK). However, such studies are relatively lacking in many developing countries (e.g., China and India) with rapidly growing areas of young urban greenspaces. Overall, it is challenging to accurately evaluate the size of SOC pool in urban greenspaces globally.

Surface soils (e.g., 0–20 cm) provide major amounts of nutrients for plant growth and consequent ecosystem services. Surface soils are also strongly imprinted by plants and contain a large proportion of SOC in the whole soil profile. To fill the above mentioned research gaps, we constructed a global database of SOC concentration and density of greenspaces within urban built-up areas (SOC-U) to a depth of 20 cm by compiling data of 420 observations from 257 cities in 52 countries. We first conducted linear model selection analysis and random forest analysis to evaluate the relative importance of climatic, vegetational, social-economical, and topographical factors in explaining the spatial variation of the surface SOC density. We further mapped SOC density for global mid- and large cities (i.e., those with urban populations larger than 500,000 people) using the random forest model which was shown to perform better than linear models. Finally, we combined data on the area of urban greenspaces (reference year 2015) and SOC density to estimate the total SOC stocks in global urban greenspaces. We also compared our results with the estimates of SOC stocks for major natural terrestrial biomes.

Results
Spatial variations of SOC concentration and density
Observed values of surface SOC concentration in urban greenspaces varied considerably across cities, ranging from 3.6 to 101.0 g C kg−1 with a global geometric mean of 24.6 g C kg−1 (median=25.1 g C kg−1). Surface SOC density of urban greenspaces also varied greatly across cities, ranging from 8.8 to 112.0 Mg C ha−1 with a global geometric mean of 51.4 Mg C ha−1 (median=57.0 Mg C ha−1). Both SOC concentration and density increased significantly at higher latitudes on a global scale (p<0.001) and for both the northern and southern hemispheres (p<0.001). Among the climatic zones studied, mean values of surface SOC concentration and density were highest in boreal regions (SOC concentration, 53.3±3.6 g C kg−1; SOC density, 81.6±2.4 Mg C ha−1) and were three times and twice the values in tropical regions, respectively (SOC concentration, 17.9±1.5 g C kg−1; SOC density, 42.8±2.8 Mg C ha−1).

Variable importance and model selection
Both linear model selection analysis (Akaike weights) and random forest analysis (Mean Decrease Gini) were used to evaluate the relative importance of climatic (mean annual temperature, MAT; mean annual precipitation, MAP; temperature seasonality; precipitation seasonality), vegetational (urban greenness index, UGI; vegetation type, i.e., urban forest and urban lawn), social-economical (urban heat island index, UHI; GDP per capita, GDPP; population density, PD), and topographical variables (elevation). Results of both analytical approaches indicated that the spatial variation of surface SOC density was mainly explained by MAT, temperature seasonality, precipitation seasonality, and UGI. Specifically, conditional regression analysis showed that SOC density decreased significantly with higher MAT (p<0.001; variance explained 12.5%), stronger temperature seasonality (p<0.001; variance explained 2.5%) and precipitation seasonality (p<0.001; variance explained 15.5%). Moreover, SOC density increased significantly with UGI (p<0.001; variance explained 9.7%). Partial dependent plots indicated similar relationships between SOC density and these four important variables in comparison with the results of conditional regression analysis. Other potential predictors (i.e., MAP, vegetation type, UHI, GDPP, PD, and elevation), however, were less important in explaining the global patterns of SOC density in urban greenspaces.

Both linear model analysis and random forest analysis indicated that vegetation type (urban forest vs. urban lawn; available for 282 of 420 observations) had little importance for predicting the global distribution of SOC density compared with other explanatory variables. Considering the incomplete information on vegetation type for urban greenspaces in our dataset, we further conducted additional analyses using all 420 observations via the two approaches and confirmed the same important predictors for SOC density. In view of the fact that data on vegetation type for corresponding urban greenspaces were not available globally, we thus ignored vegetation type in following model training and global prediction of SOC density. Based on 10-fold cross-validation, the random forest model showed a better performance compared with the best linear mixed model (R2: 45.53% vs 40.19%; RMSE: 16.85 vs 17.64). We then mapped surface SOC density of global mid- and large cities using the random forest model combined with corresponding data on the nine predictors.

Global and national mapping of SOC density and stocks
Predicted SOC density of the surface soil layer (0-20 cm) generally exhibited a strong latitudinal pattern with a global average of 55.2 Mg C ha−1 (95% confidence interval: 51.9–58.6 Mg C ha−1). This value is 28 % higher than the global average for all natural soils (43 Mg C ha−1). Specifically, surface SOC density in urban greenspaces was on average lower than that in global boreal forests (93 Mg C ha−1) but higher than that in tropical/subtropical forests (38 Mg C ha−1), temperate forests (48 Mg C ha−1), and global grasslands and shrublands (26 Mg C ha−1). At the national scale, the top ten countries with the highest SOC density in urban greenspaces were all high-income European countries, with Ireland showing the highest value of SOC density (82.7 Mg C ha−1).

Based on the predicted SOC density and estimated areas of urban greenspaces, we calculated the total SOC stocks in urban greenspaces both at city and country levels. The sum of surface SOC stocks in global urban greenspaces was estimated to be 1.46 Pg C (95% confidence interval: 1.37-1.54 Pg C), which was 2.7‰ of the global terrestrial SOC stocks (543 Pg C) at the same soil depth. Notably, there were large differences in estimated SOC stocks across countries. The top ten countries with the largest SOC stocks were those having the largest areas of urban greenspaces. The United States had the largest SOC stocks in urban greenspaces (0.37 Pg C) that accounted for nearly one fourth of the global total, mainly attributable to its largest area of urban greenspaces (5.7×104 km2). In contrast, the total SOC stocks (0.27 Pg C) in urban greenspaces in China (0.15 Pg C) and India (0.12 Pg C) only accounted for less than one fifth of the global total, although they jointly had larger areas of urban greenspaces (6.4×104 km2) than the United States.

Methods
Data collection
By conducting a literature survey via ISI Web of Science (http://isiknowledge.com), Google Scholar (http://scholar.google.com) and China National Knowledge Infrastructure (http://www.cnki.net), we compiled a global database of surface SOC concentration and density in urban greenspaces (SOC-U) (Supplementary data 3, updated to June, 2023). The keywords ‘soil organic carbon’/‘SOC’ and ‘urban greenspaces’/‘urban parks’/‘urban forests’/‘urban lawns’ were used for literature search. Data were recorded according to the following criteria: (i) measured values of SOC concentration, SOC density or soil organic matter (SOM) concentration (further transformed to SOC concentration by multiplying SOM concentration with a factor of 0.58, which has been adopted by previous studies) were reported; (ii) soils were sampled in urban greenspaces within the built-up areas, while data were not recorded when field sampling was conducted in rural areas; (iii) soil samples were collected within the top 20 cm depth (as reported in original literature). We only considered the surface soil layer because (i) surface soils are strongly affected by vegetation and human activities, (ii) surface soils are essential for nutrient retention and supply for plant growth, and (iii) surface SOC data are more available in the literature. Reported data for surface sampling were averaged for further analysis. We also recorded geographic allocation (latitude and longitude), climatic condition (MAT, MAP, and climate zones), vegetation type (urban forest or urban lawn), land use type (parkland, residential area, or garden), elevation, and other soil properties (bulk density, clay fraction, pH, total nitrogen concentration, and total phosphorus concentration) when available from the original literature. Missing data for city geographic allocations were further derived using Google Earth (https://earth.google.com). Overall, the SOC-U database includes 420 and 155 observations of surface SOC concentration and density from 244 independent publications, across 257 cities in 52 countries. The SOC-U database has been deposited in figshare at: https://doi.org/10.6084/m9.figshare.24946002.

To explore the potential predictors for the global pattern of SOC in urban greenspaces, we further retrieved data on climatic, vegetational, social-economical, and topographical factors. Data on temperature seasonality and precipitation seasonality were derived from WorldClim database at 1 km × 1 km spatial resolution. Information on urban greenness index (UGI), calculated as the average of annual highest values of NDVI for all pixels within the built-up areas, was derived from Global Human Settlement Layer (GHSL) Data Package. We retrieved data on urban heat island index (UHI) from Global Urban Heat Island DataSet. Data on urban population density (PD) and GDP per capita (GDPP) were also derived from Gridded Population of the World Version 4 (GPWv4) and GHSL Data Package. In addition, we derived data on potential predictors for major cities to predict global SOC density in urban greenspaces. Data on the areas of urban greenspaces and urban built-up areas were derived from GHSL Data Package and used for upscaling (reference year 2015). Urban built-up areas were defined as regions dominated by continuous artificial impervious areas (>50%) or having a population density larger than 1,500 habitants per km2 regardless of political boundaries. Total urban built-up areas of all cities in each country were estimated based on GHSL Data Package and further used to calculate total SOC stocks in each country.

Data processing
When data on SOC concentration were reported in literature, SOC density (SOCD, Mg C ha-1) was calculated according to Eq. (1),

SOCD = SOCC × BD × T × 10

where SOCC is soil organic carbon concentration (g C kg−1), BD is soil bulk density (g cm−3), and T is soil thickness (cm). We established an empirical linear model between reported SOC concentration and soil bulk density. Missing values of soil bulk density were then estimated using the empirical model. We conducted an additional analysis only using data with reported values of soil bulk density (n=155) and found similar results as that using all observations (n=420), implying that our approach to estimate soil bulk density was reasonable. The calculated SOC densities were further combined with literature reported data for further analysis.

To ensure the comparability of data derived from different studies, the original SOC density was standardized to 20 cm depth according to Eqs. (2) and (3),

Y = 1 − βd

β = 1 − (X20 / Xd0)1/d0

where Y is the cumulative proportion of the SOC density from the soil surface to depth d (cm), β is the relative rate of decrease in the SOC density with soil depth, X20 is the SOC density in the upper 20 cm, d0 is the original soil depth in each study (cm), and Xd0 is the original SOC density. We adopted the value of β as 0.9786 which were commonly used in previous studies, including those for urban ecosystems.

Data analysis
Linear regression analysis was used to test the spatial trends of SOC concentration and density with latitude. To identify the important predictors for SOC density, we selected four climatic variables (mean annual temperature, MAT; mean annual precipitation, MAP; temperature seasonality; precipitation seasonality), two vegetational variables (urban greenness index, UGI; vegetation type, i.e., urban forest and urban lawn), three social-economical variables (urban heat island effect, UHI; GDP per capita, GDPP; population density, PD), and one topographical variable (elevation). We used two different statistical approaches (linear mixed model and random forest model) to evaluate the relative importance of these potential predictors and used the best model to conduct a global mapping of SOC density.

We first conducted a model selection analysis using linear mixed-effects models. In view of the measurements in different years or measurements by different researchers across cities, we used linear mixed-effects models by the ‘lme4’ package and tested possible random effects by the ‘lmerTest’ package. Model selection analysis was implemented based on the corrected Akaike information criterion using ‘glmulti’ package. The relative importance of each predictor was estimated as the sum of the Akaike weights for the linear mixed-effects models in which the predictor appeared, and a cut-off value of 0.8 was used to differentiate between the important and unimportant predictors. Conditional regression plots were created using the ‘visreg’ package to visualize the role of each important predictor on SOC density while holding all the other important variables constant (by default the median for numeric variables).

We also conducted an analysis using random forest models in combination with all ten potential predictors. We used a 10-fold cross-validated method to train the random forest models and obtained the optimal parameters (ntree, the number of decision trees; mtry, the number of input variables at each split) with the ‘caret’ package. Using the optimal parameters, we then tuned the random forest model. The variable importance was further evaluated by Mean Decrease Gini and partial dependence plots were created using the ‘pdp’ package. The determination coefficient (R2) and root mean square error (RMSE) were used to compare the performance of linear models and random forest models. The random forest model was then used to map SOC density for global urban greenspaces in view of its better performance over linear model.

To map and estimate the SOC densities and stocks in global urban greenspaces, we combined comprehensive spatial datasets for world cities. We first derived data on urban greenspaces for 1039 mid- and large cities (urban population > 500,000), defined based on UN population statistics in 2014. We also obtained data on the nine predictors from WorldClim database and Global GHSL Data Package. Vegetation type (urban forest vs. urban lawn) was not used in global prediction because it had a least importance in predicting the global distribution of SOC density and corresponding data were not available for global urban greenspaces. The random forest model was used to predict SOC density of urban greenspaces for global mid- and large cities. The relative uncertainty of the predicted SOC density was estimated by coefficient of variance (C.V., %) of the ten simulations when training random forest models. Note that 35 cities were excluded because of lacking data on predictors in Global GHSL Data Package.

The city-level SOC stocks (SOCSc, Tg C) was calculated as Eq. (4),

SOCSc = SOCDc × UGSAc × 10−6

where SOCDc (Mg C ha−1) is the predicted mean SOC density in that city and UGSAc (ha) is the corresponding area of urban greenspaces. To estimate the national SOC stocks in urban greenspaces, we first estimated the national urban greenspaces area (UGSAn, ha) according to Eq. (5),

UGSAn = UGSAc × UAn / UAc

where UGSAc (ha) is the total urban greenspaces area of mid- and large cities in a country, UAc (ha) is the corresponding total urban built-up area (ha) of these cities, and UAn (ha) is the total urban built-up area of all cities in that country. The national total SOC stocks of urban greenspaces (SOCSn, Tg C) was estimated as Eq. (6),

SOCSn = SOCSc × UGSAn / UGSAc

where SOCSc (Tg C) is the sum of the SOC stocks in the mid- and large cities in that country. The global total SOC pool of urban greenspaces was estimated as the sum of the SOC stocks in all countries. The area-weighted national and global SOC density was calculated based on SOC stocks divided by its area. Data used for creating maps of the SOC density and stock in global urban greenspaces were derived from the ‘rnaturalearth’ package. All analyses were conducted by R 4.0.5.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper compiles SOC concentrafion and density data globally and explores their relafion to a number of predictors, finding MAT, greenness and GDP per capita to be significant. The authors then create a stafisfical model to esfimate urban SOC globally, providing an estimate of the global stock and its distribufion across nafions.\nI was very excited to see data on urban soils globally being compiled and analysed as this is an overlooked SOC pool in the global budget and there is potenfial here to make an important contribufion to our picture of global carbon cycles and to helping steer urban development and ecosystem management.\nHowever, after considerafion, I have a number of reservafions and quesfions that lead me to recommend that the methods/supplementary need to be substanfially improved to provide a rigorous replicable dataset. This is also needed to be able to interpret whether the global analysis is well-founded. A clearer discussion of limitafions is also vital, and care needs to be taken with suggesfing that urban development could result in SOC accumulafion.\nI arrange my main comments by secfion below.\nSpafial variafions of SOC concentrafion and density\n1. The approach in theory this secfion seems sound and straight forward. However, I was surprised by how liftle data has been thrown up by the search. I know from our own studies and reading of literature nafionally that there are more than 6 observafions of SOC in the UK cifies in the dataset and on map figure S1. Perhaps I am reading figure S1 incorrectly? It suggests that this is the number of observafions but perhaps it shows the number of separate studies reporfing values? In which case, how are these averaged? Some studies will have very many samples from a wide variety of spaces, and others very few or focused on a limited range of sites depending on the quesfion being asked.\n2. Generally, not enough informafion is given in the methods or the supplementary to be able to understand the mean values, replicate the search that produced the dataset or to trace the data. The data in the database does not seem to be referenced. Apologies if I have missed this.\n3. Figure 1 would benefit from larger fonts on the map. The resolufion of the font is poor making it hard to disfinguish some cifies. Also, I find it hard to disfinguish the blue scale. For other figures, please avoid red and green combinafions as they are not friendly to those with colour vision issues.\n4. Apologies if I am misunderstanding, but where are the negafive lafitude sites on figures 1b and 1d?\n5. Did you record the ecosystem/habitat/greenspace type of for each data point compiled? I imagine this has a large effect on the SOC.\nModel selecfion and best predictors\n6. GDPP: What explanafion/rafionale is there for GDPP being a significant predictor? Cifies are not homogenous in their socioeconomic status – greenspaces across urban areas will be located across a range of socioeconomic contexts. Economic disparifies will also range greatly city to city. Can you comment on what bias might there be in the data collected in the studies in sampling high economic status areas where it is more likely that the area is ferfilized/irrigated/not as polluted/long established? This issue is important as it relates to how valid it is to assume that the relafions derived apply across all urban greenspace within a city and the validity of the global mapping approach.\nGlobal mapping of SOC density and stocks\n7. We absolutely need global maps and stocks of urban SOC, and I appreciate the aims of the authors here and acknowledge that there will be limitafions to any study that tries to do this. However, I have concerns that the approach here is a stretch of what the data can show us – this is unfortunately sfill a relafively small dataset of 161 points. The limitafions of the study need to be more clearly and plainly stated if this type of analysis is to be included in the paper. Can the authors please comment on potenfial bias in the underlying data, the potenfial influence of habitats, the potenfial effects of assuming that the proporfion of greenspace in medium/large cifies examined is representafive of all greenspaces nafionally, and the extent to which it is valid to esfimate this map given the paucity of data in the global south.\n8. More informafion on what data was used from the cited spafial datasets needs to be given – line 318 onwards.\n9. It is a counter-intuifive result that SOC stocks in China and India are so low, given the number of cifies over the vast regions. Do you think GDPP is playing too large a role in your model to allow the model to hold across these regions?\nDiscussion\n10. Line 184 – more nutrient addifions and irrigafion is not necessarily ‘befter’ management of vegetafion. I wonder whether what you are seeing here is the length of fime an urban greenspace has been established – young cifies are likely to have been recently developed, and during development topsoils and organic mafter are often stripped away and lost, and vegetafion has had a limited fime to establish and replenish SOC pools.\n11. Line 192 – good to see the esfimate being contextualised within the global carbon stocks. I worry about the statement that there is a large potenfial for SOC accumulafion given the rapid urban expansion expected in future though. This is a potenfially dangerous asserfion suggesfing urban development may be posifive for SOC and global carbon cycles. The amount of non-greenspace being created during expansion, the degree of topsoil removal/disturbance, import of soils from elsewhere to support urban greenspaces, etc. is not being considered in this statement.", "reviewer_2": "This paper aftempts to calculate the SOC stocks from urban areas across the globe. The authors are building on previous work on urban soils and notes the lack of global esfimates. A number of different data sets and models are used to esfimate and scale to the globe. In general, the wrifing is well structured and clear. However, I do have some major concerns with regard to the clarity and significance from this version.\nMajor Comments\n1. I found the informafion in the methods and approach to be lacking, including clear definifions that are key to the study. This lack of informafion and clarity made it very difficult to esfimate the validity and significance of the results. I think a lot of this could be addressed and explained within the methods of SI. Below the major omissions I think need to be addressed.\na. Greenspace – This term is very ambiguous and needs to be defined in a way that makes it clear what is being measured and scaled, as well as how literature SOC data supports it. The manuscript uses parks and residenfial gardens as examples, but that is insufficient. Greenspace could refer to parks, residenfial lawns, abandoned parcels, greenways, remnant forest, street trees, or any aggregafion or subset thereof. I suggest a more detailed operafional definifion that addresses the different land covers in the city and is true to the literature data supporfing it.\nb. Greenspace esfimates by Huang et al was used as a scaling step, which seems to be developed from vegetafion coverage. Key informafion about that step needs to be included in the methods. Another considerafion that is not addressed is that vegetafion and soil extents do not match. Cifies contain significant “green space” were vegetafion is either planted (street trees) or overhangs impervious surface. Again, depending on the operafional definifion of greenspace used in this study, this might be a significant area or not. To my knowledge, we know even less about C storage under impervious surfaces.\nc. The literature sources for each of the cifies to be included in the SI and data. The reference list included is not clear enough. The land uses/covers sampled in that literature needs to be idenfified. If most of the data is coming from parks and remnant forests, but being extrapolated to residenfial areas and lawns, that would be a significant mismatch. Residenfial spaces vary greatly and literature data may not be representafive. Addifional useful informafion like the number of sites, total area, and sample density (samples per area) within each city would help provide insight into uncertainty and scope.\nd. Lastly, a conceptual framework or illustrafion that conveys the data sources and scaling approach would be helpful. Cifies are notorious for substanfial heterogeneity across mulfiple scales. I am not convinced, perhaps due to some of the ambiguity, that the approach used yields valid results. In part, for some of the reasons in the discussion. Age is a really important factor in urban greenspace and SOC accumulafion that is not included here. It seems even the authors are suggesfing GDP might be a correlated and account for some of its importance.\n2. Given the lack of clarity, I am uncertain of the validity of the global esfimates produced and thus the contribufion this study makes to our understanding of SOC in urban landscapes. To be clear, I think the goal of generafing global scale esfimates of urban SOC concentrafions and stock is an interesfing contribufion. But the finding that urban SOC sfill is controlled by MAT and overall greenness at the global scale is not parficularly novel, nor is the esfimate very different from previous. The other feedback I will provide the authors regards to significance to the relevant fields. I think there are some interesfing findings for the urban soils and biogeochemistry discipline. But I am not sure I see a substanfial impact on the global carbon cycling community. There is so much uncertainty in soil carbon and fluxes, constraining the small contribufion of urban landscapes helps very liftle. The manuscript could provide a stronger argument for why this would be of interest outside of the discipline.\nMinor Comments\n1. Line 276 – The use of SOC to esfimate Bulk density is interesfing, but seems like it would introduce a lot of error. This is somewhat concerning though because SOC concentrafion is mulfiplied by BD to esfimate stocks and seems like there would be a potenfial propagafion of error. Is there enough data with measured density to determine how much difference in SOC stock may be generated using esfimates vs actual data?\n2. Equafion 5 – Unclear where the data is from and what the definifions are for total urban area. I assume the Ua is coming from Florczyk et al., 2019, but it is not clear. Also need to define “urban” is this any seftlement? Is there a lower populafion threshold? Not clear enough to judge.\n3. SI Many of the figures in the SI are too small to read. Please make maps that are large enough to see the color gradients and circles without having to zoom in 200%.", "reviewer_3": "Overview and Key Results\nThe authors present an important first esfimate of global carbon stores in the top 20 cm of soils in urban greenspaces. They note that SOC in these soils remains understudied and that urban soils are generally excluded from global analyses of soil carbon stores. This paper provides a remedy, offering an esfimate of these carbon stores that confirms that urban soil management has a significant role to play in global carbon budgets. The authors also idenfify per capita GDP and higher greenness indices as associated with higher SOC.\nValidity\nOverall, the methods are appropriate and thorough. However, because urban SOC data is sfill relafively scarce, the authors could provide befter esfimates of uncertainty. In addifion, a few conclusions (specifically those regarding the influence of higher per capita GDP and higher greenness) are made that are not fully supported by the data, in part because of unexplored uncertainty (see comments below and in Suggested Improvements for more specifics).\nData on urban soil C stores are inconsistent and incomplete. The authors use a number of steps (for example, esfimafing soil bulk density or converfing SOM to SOC or normalizing to 20 cm depth) to fill in some of these data gaps. While the methods used are appropriate, each esfimate introduces more potenfial for error, which is then propagated through the overall esfimate. In addifion, while the authors note some addifional factors (soil pH, N deposifion, landfills, etc.) that may introduce uncertainty into these esfimates, they ignore a number of other sources of error. For example, SOC data have not been collected uniformly across the globe. The U.S. and China, have more data points. Fig. 1 (b and d) depict regressions with quite low R2 values that also appear to heavily influenced by just a few data points. It would be relevant to know where these data were collected (it appears to be in equatorial Africa). Since these regressions feature prominently in the models used to esfimate SOC.\nAlso, the extent of urban land has always been difficult to quanfify. First, it changes rapidly. Second, there is not always agreement about what consfitutes urban land. Third, metropolitan areas define their polifical boundaries in different ways (some cifies include only the urban core, others annex rural land as it is developing). This also makes populafion counts in cifies a rather arbitrary way to categorize them. Can the authors clarify this part of the methodology a bit more? Finally, pafterns of urbanizafion take many different forms (see Schneider, A., & Woodcock, C. E. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cifies using remotely sensed data, paftern metrics and census informafion. Urban Studies, 45(3), 659-692.) The authors don’t really address these challenges and do not make any esfimates of how this might influence the uncertainty of results and in which direcfion. This topic merits considerably more discussion in the Uncertainfies secfion.\nFinally, I find it unconvincing that the global soil C baseline esfimate, or the models that were used to derive it, provide any real useful guidance for soil management in cifies, other than to underline that soils should be managed in the first place and not simply scraped away (a pracfice that releases large amounts of carbon). I think the authors could clarify this (line 32).\nSignificance\nThese are significant and important results that are essenfial for incorporafing urban areas (where the majority of humans live) into various earth system models. This was a challenging project and will make a significant contribufion to our understanding of the role of urban soils.\nData and methodology\nWhile the supplemental files present the data used in this study, there doesn’t seem to be any indicafion of the source of these data. I know numerous authors have compiled data from studies across the globe, but according to line 236-239, the authors did a literature search and compiled this data set. Perhaps I just missed it, but shouldn’t the data set include the name of the study whence the data came? Without a source, there is no way to verify these data.\nAnalyfical approach\nThe analyfical approach is sound, however, there are a few instances (already menfioned) where conclusions do not seem fully supported and where uncertainty is not fully described.\nSuggested improvements\nMany of the suggesfions below relate to topics described elsewhere in the review. They are presented in order of appearance in the manuscript, not in order of importance.\nTitle and elsewhere: The authors use “distribufions” in some cases in “distribufion” in others. In this context, “distribufion” makes more sense.\nL 21-23: This one of numerous instances where the comparison context is not clear. Is this total SOC? Or SOC per greenspace land area? Or SOC per city land area?\nL 24: SOC density is usually expected to include the top 1 m of soil. These are only for 0-20 cm (from Table 1, presumably) but it needs to be clarified here or it will be misinterpreted.\nL 27-30: The inclusion of “the top ten countries were all high income…” immediately before lisfing the U.S. SOC stocks gives the impression that this high SOC stock in the U.S. is associated with the large share of the world economy represented by the U.S. and conflates the density quesfion with the total. This should be rephrased by perhaps saying “the ten countries with the highest SOC density were all high-income countries”). I would think it most likely that the U.S. has high level of SOC in urban greenspaces because it has low density, sprawling cifies with low populafions and lots of greenspace.\nL 32: change “management” to “assessment”. This study doesn’t tell us much about management.\nL 35-37: Urban land has been quanfified as being up to 3% of global land. Can the authors qualify this a bit or give a range?\nL 38: “…and are generally…source” could be deleted? Seems redundant with previous sentence.\nl. 41: largest C pool in comparison to what? This is unclear.\nL 54-59: The authors aftempt seem to suggest that the urban convergence theory has been disproven by studies showing strong climate control of SOC. These aren’t really contradictory as urban convergence is more about social norms driving design and management and the outsize effects this somefimes has on plant communifies, N, and water, thus causing cifies to converge (not be the same, but move towards one another). Rephrase? Perhaps “However, especially at a broader scale, there is sfill strong…”\nL 78-79: Delete? Seems like this point has already been made?\nL 85 “urban built-up areas” isn’t really a very helpful definifion. Can the authors elaborate?\nL 90-94: the populafion cut-off is quite arbitrary. 500,000 is a small city in some regions and a large city in others. Can the authors jusfify this choice and explain the biases it might introduce? Can they indicate how the boundaries of these cifies were determined (central city, metro area)?\nl. 97-106: This is a context issue that occurs in several places. Are these figures for density for cifies? (as stated on line 100) or for urban greenspaces within cifies?\nL 110: I was surprised at the inclusion of soil depth here. Soil depth in cifies is often related to construcfion pracfices and prior land use. It is notoriously errafic. Individual studies tend to define depth in relafion to the quesfions of interest for that study and are thus inconsistent. Where did depth data come from and how was it defined?\nL121: The authors make much of the role of GDPP on SOC density here and elsewhere. I do not find this conclusion convincing. First GDPP only explains 8.7% of the variance. Second, I am not convinced it is not correlated with lafitude. In L 128 the authors say there is low mulficollinearity of variables, but it is difficult for me to see how lafitude and GDPP can be separated based on the cifies represented in the data. Many of the top ten cifies are presumably located on what was previously boreal forest, the forest cover with the highest SOC density in the world. Why did the authors not compare SOC density with the SOC density of surrounding nonurban land?\nL 184-5: I’m not really sure the “befter” vs “poor” management are appropriate terms. More irrigafion or N addifions may result in greater SOC density, but is it really “befter”management? I’m also not sure a broad generalizafion about developing countries can be made from the Zhang et al. 2022 paper. In addifion, papers regarding SOC accumulafion over fime are looking at small fime scales (say 30 years) and are not really applicable to comparisons between ancient urban cores and newer development where the fime scale could be 100s or 1000s of years. I think the authors need to rethink some of the generalizafions here.\nL 211-227: Addifional uncertainfies and their impacts should be included here. These include potenfial mismatches between the data sets used for the SOC vs the cifies studied, quesfions of landuse history, etc. The authors rightly point to high levels of HAHT soils in place in cifies. This trend is in fact increasing in many cifies and the potenfial for C sequestrafion in some of these soils is unknown and likely quite different from tradifional soils. In addifion, many soils with high organic mafter content are buried (buried A horizons) in some cifies. Can the authors provide some esfimates on the uncertainty? What was the range of SOC densifies reported from the actual data vs predicted results from the models. Perhaps I missed it, but I did not see this kind of analysis in this study.\nClarity and context\nIn general, the paper is clearly presented. However, there are numerous locafions where the scale or context is not clear, especially when comparisons are being made. While it can likely be puzzled out, a few addifional words would be helpful to readers. These are noted in the Suggested improvements secfion.\nReferences\nReferences seem appropriate, except as noted elsewhere."}, "author_response": "Reply to REVIEWERS’ COMMENTS\nReply to Reviewer #1:\nThe paper compiles SOC concentration and density data globally and explores their\nrelation to a number of predictors, finding MAT, greenness and GDP per capita to be\nsignificant. The authors then create a statistical model to estimate urban SOC globally,\nproviding an estimate of the global stock and its distribution across nations. I was very\nexcited to see data on urban soils globally being compiled and analysed as this is an\noverlooked SOC pool in the global budget and there is potential here to make an\nimportant contribution to our picture of global carbon cycles and to helping steer urban\ndevelopment and ecosystem management. I was surprised to learn that the previous manuscript focused on urban forests only. The expansion to other urban greenspace types is indeed valuable and necessary for the manuscript to claim to provide an analysis of urban greenspaces. Hence, I agree this is a good addition. I am very happy to see an updated database. This looks well organised and has helped to significantly improve the study overall. I am sure it will be useful to others in the community. Thank you for including the search string and details of where the searches were made. This is quite a short search string for a study of this nature. For example, garden isn't included or alternative words for 'urban'. Did you search for these terms in titles/abstracts/keywords? I also assume that you used syntax in the search that allowed it to deal with plurals or alternative word endings? I am not suggesting that you expand/improve the search at this stage, however, this is the level of detail needed to call this a systematic review. If it was a systematic review, I would expect to see a more robust search string, details on the number of articles returned, details of a process for screening and data abstraction, all following an accepted protocol for systemic review such as PRISMA. Hence, I suggest that you remove the term systematic from the manuscript to avoid misleading the reader. Thank you for including further detail regarding how SOC density was calculated. I also appreciate the inclusion of the test of the method for estimated bulk density requested by the other reviewer. Reply: Thanks for your insightful comments. We have carefully revised our manuscript according to your suggestions. We have now removed the term “systematic” to ensure the accuracy and clarity of descriptions according to your suggestions. We have substantially revised the manuscript according to your comments. First, we have included more details of the method to compile the database of SOC concentration and density in global urban greenspaces (SOC-U). We have further updated the database and our SOC-U database currently includes 420 observations from 257 cities in 52 countries. The sample size has been substantially increased in comparison to the earlier version of the database (n=420 vs. 161). Now the whole database and corresponding references have been provided as supplementary data and will be formally archived in Figshare (https://figshare.com/s/d03ab1150d644f2df301) when our manuscript is accepted for publication. We also conducted a reanalysis based on the updated database and revised the manuscript accordingly. Please see more details in the revised manuscript and our responses to your specific comments below. Second, we have now thoroughly discussed limitations of our global prediction of SOC density in the Section “Uncertainties and Implications”, such as i) not including physicochemical soil variables, parental materials, age of urban greenspaces, urban N deposition, CO enrichment, and anthropogenic disturbances in current analysis due to a lack of corresponding data, and ii)not assessing SOC in deeper layers mainly due to a lack of observed data and the complexity of different parental material sources. Please see more details in the revised manuscript and our responses to your specific comments below. Third, now we highlight that to our knowledge “Our findings present the first systematic assessment of surface-layer SOC in global urban greenspaces and provide a baseline for future urban soil C assessment in the context of continuing urbanization”. We didn’t intend to indicate that urban development could result in more SOC accumulation. We have now mentioned that “Greening efforts have a potential to increase topsoil C sequestration in established urban areas”. Sorry for any confusion in our earlier version of manuscript. We believe that we have now well addressed all your concerns. See more details below. Reply to Reviewer #2: This paper attempts to calculate the SOC stocks from urban areas across the globe. The authors are building on previous work on urban soils and notes the lack of global estimates. A number of different data sets and models are used to estimate and scale to the globe. In general, the writing is well structured and clear. However, I do have some major concerns with regard to the clarity and significance from this version. Reply: Thanks for your helpful comments. We have thoroughly revised the manuscript according to your input. We believe that all your concerns have been well addressed. Please see more details in our reply to your comments below. Reply to Reviewer #3: The authors have addressed all of my concerns. Reply: Many thanks for your helpful comments to improve our work. We are pleased that you are now satisfied with our revised manuscript."}]

Decision letter