Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Domain: earth_science
Published: 2024-03-18
Editorial outcome: minor_revision

Abstract
Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emissions scenario, but spatio-temporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscore the importance of emission mitigation to sustaining wetland ecosystems in the future.

Introduction
Inland wetlands are important freshwater resources and one of the most productive ecosystems on Earth1. Besides the importance for biodiversity2, inland wetlands play a critical role in global water, energy, and carbon cycles3. Specifically, inland wetlands are the largest natural source of methane (CH ), the second most important atmospheric greenhouse gases4–7, and can sequester soil organic carbon at a rate much higher than many other ecosystems8. Inland wetlands also function as a buffer zone that delays and mitigates the runoff/streamflow peaks9,10. They further act as a natural filter that reduces the sediments, nutrients, and pollutants entering groundwater and downstream waterways, thereby improving water quality11. Wetlands also significantly impact land-atmosphere interactions because of the enhanced evaporation from the open water or saturated soil12. As a result, any changes of inland wetlands may cause cascading consequences to biogeochemical and hydrological cycles at different spatial scales13.

Climate change can impact the spatiotemporal distribution of inland wetlands14–17, but the direction and magnitude of the changes remain uncertain18. Periodically to permanently inundated by water, wetland regime is strongly controlled by surface water dynamics (hereafter used interchangeably with wetland) that are influenced by variations over small spatial scales. However, some wetlands may not be necessarily inundated by water, as saturated soils are sufficient to create wetland ecosystems. Previous studies using satellite observations at the sub-kilometer scale14,15,19–21 found that annual precipitation is the dominant factor controlling large-scale surface water dynamics20,22. However, temperature can also affect wetlands through its influence on several processes including evapotranspiration, snowmelt, infiltration, soil thawing and freezing, and precipitation23. When considered individually, these processes may have very dissimilar outcomes. In addition, the surface water dynamics also depend on the regime of groundwaters which are controlled by climate, vegetation dynamics, and human extraction. Given the complexity and nonlinearity of the climate system and related hydrological responses24,25, data-driven approaches could be useful for deriving the relationships between wetland dynamics and their hydroclimatic drivers26. However, statistical relationships derived from data-driven methods trained using historical observations only may not be transferable to the future warmer climate as wetlands are sensitive to nonstationary climate trajectories27,28. Besides, the accuracy of such approaches depends on the quantity and quality of training data, which are still limited for surface water dynamics over large areas. Although numerous studies derived surface water dynamics from satellite datasets at regional or national scales20,29–32, such data are limited at the integrating continental and global scales14,15, and the spatial coverage of monthly wetland dynamics is very poor (Supplementary Fig. 1).

Earth system models (ESMs) are alternative tools for understanding the large-scale wetland changes induced by external forcings and uncovering the driving mechanisms33. ESMs are physically based models that couple atmosphere, land, ocean, land ice, sea ice, and river processes at large scales. Typically applied at spatial resolutions of ~100 km or coarser, ESMs parameterize smaller-scale processes that are not explicitly resolved by the models. Compared to data-driven methods, ESMs do not require a large amount of data for training and can well capture the wetland evolution under changing conditions if the related processes are appropriately parameterized. However, the current ESMs usually represent the wetland hydrology in over-simplified ways33. Additionally, they are highly uncertain in representing the two inundation processes that form inland wetlands: fluvial and pluvial processes. Fluvial inundation occurs when river flow accumulated from upstream exceeds the channel capacity and generates overbank flow flooding into the neighboring floodplain wetlands34–37. Pluvial inundation typically occurs in low-lying areas, when excess water from precipitation, overland flow, and groundwater discharge cannot infiltrate into the soil or drain away with surface flow. Both fluvial and pluvial inundation mechanisms of formation are needed to explicitly model wetland dynamics as wetlands interact with rivers, runoff generation process, surface-subsurface interaction, and evaporation process. While most ESMs can reasonably capture the dynamics of fluvial inundation34,36, the pluvial inundation process is generally inferred using a diagnostic scheme. For example, a wetland diagnostic scheme uses the simulated groundwater depth to estimate the wetland areas, but the inferred wetland areas do not impact other processes in the model4,33,38–41. This widely used diagnostic schemes ignore the soil freeze-thaw cycle, which is critical for the wetlands dynamics in cold regions39. Accurately representing soil freeze-thaw process is necessary for understanding the interactions between wetland dynamics and groundwater under climate change conditions, as over half of the global wetlands are located in the northern high latitudes (e.g., north of 50° N)42. Furthermore, parameter calibration is commonly needed to constrain ESMs’ uncertainty, which is computationally intensive for applications at large scales. As a result, the parameters of ESMs related to fluvial and pluvial inundation are usually not well constrained. The process simplification and inherent parametric uncertainty could result in significant biases in wetland simulations, especially when regional understanding of their regime is sought.

In this study, we aim to understand the drivers and future trajectories of inland wetland area changes over North America from 25° N to 53° N) using a state-of-the-science ESM, Energy Exascale Earth System Model (E3SM), based on the most up-to-date climate change projections. We implemented a modified infiltration scheme to improve modeling of the pluvial inundation process (see Methods) in E3SM43. Simulations were performed using the coupled land and river components of E3SM, and calibrated against an upscaled global surface water dynamics dataset from Global Land Analysis & Discovery (GLAD, see details in the Methods)15. Another global satellite dataset14 is available for use in model calibration as well, and it has been used to benchmark GLAD15. The Shared Socioeconomic Pathways (SSP)44 scenarios for lower emissions (SSP126) and higher emission (SSP585) were used along with five global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to provide a multi-model ensemble capturing a range of warming trajectories45,46. Compared to previous studies of large-scale wetland projections, this work improves the representation of wetland dynamics in an ESM to increase the confidence in wetland projections by (1) relying on a process-based inundation process to identify wetland dynamics; (2) running the simulation at a relatively higher spatial resolution; and (3) calibrating the model parameters against satellite dataset. We hypothesized that climate change will significantly impact wetland area (i.e., defined as the sum of fluvial and pluvial inundated areas excluding rivers, lakes, and reservoirs) and wetland habitats (i.e., defined as the inundated area for at least 1 month during the growing season with surface temperature above 5°C). Additional definition details can be found in Methods. We further uncover the driving mechanisms for the wetland changes from different emissions scenarios.

Results
Wetland characteristics during the historical period
The E3SM model with refined wetland hydrology and reduced parametric uncertainty closely captures the upscaled surface water extent retrieved from satellite data over 1999–2020. At an annual timescale, the E3SM simulations demonstrate good performance when benchmarked against upscaled satellite observations at the model spatial resolution of ~12.5 km × 12.5 km (Fig. 1a, b). The model also adequately captures the observed surface water seasonality (Supplementary Fig. 2), despite the weaker performance during winter. The latter can be attributed to data gaps in the GLAD dataset (Supplementary Fig. 3a) and challenges in model representation of snow melting processes47. Importantly, the calibrated model captures the positive trend of annual wetland changes from 1999 to 2020 (Fig. 1c), which was also reported in a previous study20. The performance of capturing inter-annual variability is demonstrated by the high evaluation metrics in Supplementary Fig. 4. The simulated surface water dynamics closely follow the benchmark for selected zoomed-in regions (e.g., with an averaged correlation coefficient of 0.83), though substantial underestimation can be found around the Great Salt Lake (Supplementary Fig. 5). The model performance is further validated using the Global Inundation Extent from Multi-Satellite19 during an independent period (1993–2007), showing consistency with our simulated spatiotemporal variation and negative annual trend of wetland area (Supplementary Fig. 6). The high fidelity of the refined E3SM in simulating the inland wetland area changes is largely due to the improved soil water infiltration scheme (Supplementary Fig. 7) that resolves the difference of infiltration between saturated soils and unsaturated soils within a grid cell (see more details in Methods). In contrast, the surface water extent is significantly underestimated without improvements in representing pluvial inundation process, except in snow-dominated regions during cold season (Supplementary Fig. 8) when there is likely significant overestimation of surface water extent.

Pluvial inundation is the dominant wetland generation mechanism over North America. Aggregated from model grid cells to basin scale (see Methods), the pluvial process accounts for more than 70% of the annual surface water area over 2282 of the total 2478 selected basins (Fig. 1d). Fluvial inundation is important for surface water dynamics in only 37 basins, where fluvial process explain more than 70% of the annual surface water area. When averaged at the continental scale, inundated areas due to pluvial mechanism represent ~90% of surface water extent. Pluvial inundation is not only important for the averaged wetland areas, but also explains a large fraction of their temporal variation (Fig. 1e), while fluvial inundation is mostly significant along major rivers (e.g., Mississippi river, Colorado river, etc.). However, a time-invariant wetland area is routinely removed from satellite-observed surface water to derive fluvial inundation in previous studies36,48, assuming the variability of pluvial inundation is negligible in explaining surface water dynamics. Such assumption can bias benchmark datasets towards higher importance of fluvial inundation mechanism. Overall, the inclusion of pluvial inundation in land processes of ESMs emerges as crucial for accurate understanding wetland area dynamics.

Future changes in wetland area
At the continental scale, wetland area decreases in all seasons in the future with the exception of winter (Fig. 2). These projected seasonal changes result in a decreasing annual averaged wetland area under both lower and higher emission scenarios (Fig. 2a). Specifically, the median annual wetland area in 2071–2100 decreases by 5.2% (4.2–7.0%) and 10.6% (5.9–13.5%) based on our multi-model ensemble projections under SSP126 and SSP585, respectively, relative to the historical period (1971–2000). The 25th percentile of annual wetland area decreases by 5–20%, suggesting that climatologically drier wetland environments become even drier in the future. Further, wetland seasonality is significantly altered in both SSP126 and SSP585, with increased wetland area during winter (Fig. 2b) and decreased wetland area in other seasons (Fig. 2c–e). Such differences of seasonal wetland changes are driven by the changes in water supply to wetlands (i.e., rainfall and snowmelts) which increases more in winter than other seasons in the warmer future (Supplementary Fig. 9). In SSP126, the increase in wetland areas during winter offsets their decrease in the other three seasons, resulting in smaller changes in wetland area at the annual timescale. However, the SSP585 projections yield a significantly larger reduction of wetland area in summer and fall, with wetlands at the 25th percentile and median sizes decreasing by up to ~25% by the end of this century (Fig. 2d, e). Consequently, the SSP585 projections exhibit a significant reduction of wetland area at the annual timescale. The projected changes in wetland areas remain relatively stable from mid-century to end-century under SSP126, suggesting that wetlands can be conserved if the global warming level is constrained. However, under the high emissions scenario, the projected reduction in wetland areas intensifies by the end of the century. For example, the median wetland areas in future summer and fall seasons are even smaller than the 5th percentile of the historical period (Fig. 2d, e), implying that drier wetland environments will be much more common during growing seasons under the SSP585 scenario.

Regionally, the shift of wetland area seasonality varies (Fig. 3). The northeastern and western US are projected to have a higher wetland area earlier in the year which is consistent with previous studies that attribute that to earlier onset of snowmelt induced by the warmer climate49. However, the midwestern and southern US will experience a delayed wetland area seasonality, potentially associated with springtime soil wetness changes49. The direction of seasonal shift in wetland dynamics is consistent between the two SSP scenarios, but the shift is more pronounced in the higher emissions scenario. Such seasonality changes can present challenges for water management and agriculture and have consequences for ecosystem diversity based on plant adaptive potential50.

Changes in wetland dynamics are more pronounced at the basin scale, with a strong spatial pattern featuring divergent trends (Fig. 4). For example, the multi-model ensemble simulations project a pattern of change that is spatially consistent for both SSP126 and SSP585, with an increase in wetland area in the western mountainous regions, Midwest, Northeast, and Florida and decreasing wetland area in Southwest, Southern Great Plains, the southern Appalachian Mountains, and southeastern Canada. The spatial contrast intensifies with the warming level, such that 15% and 35% of the continent show at least a 25% absolute change in wetland area by the end of the century under SSP126 and SSP585, respectively. Increasing wetland areas are mainly located in snow-dominated regions during winter (Fig. 2b), and attributed to earlier snowmelt and higher fraction of liquid precipitation with warming enhance winter runoff49. However, most regions are projected to have less wetlands during growing seasons (Supplementary Fig. 10), except for Florida and Rocky Mountain regions, where wetland area is projected to increase throughout the year.

Attributing the changes in wetland dynamics
Changes in both temperature and precipitation can influence wetland area by perturbing the surface water balance. The projected changes in wetland area are strongly correlated with the concurrent precipitation and temperature changes, with an averaged correlation coefficient of 0.76 and 0.86, respectively, based on multiple linear regressions across all the basins under SSP126 and SSP585 (Supplementary Fig. 11a, b). Notably, wetland area is only sensitive to one climatic factor in certain regions, such as precipitation changes in the southeastern US under SSP126 (Supplementary Fig. 11c, e) and temperature changes in the coastal mountains of the western US under SSP585 (Supplementary Fig. 11d, f). As the changes in temperature and precipitation are highly correlated across the models (Supplementary Fig. 12), the changes in wetland area are statistically strongly correlated with both temperature and precipitation for some regions. However, through different impacts of temperature and precipitation on the generation mechanism of wetlands, either temperature or precipitation changes can dominate the wetland changes in different regions. For example, the decreasing wetland trend in southeastern Canada results from perched water table (i.e., water table underlain by soil ice) drop associated with increasing temperature rather than precipitation increases. Note that the soil ice in southeastern Canada is not related to permafrost, but rather due to the freezing of soil moisture in the subsurface when the temperature falls below the freezing point. Therefore, a detailed attribution analysis is required to further uncover the mechanisms that drive the wetland changes (see details in Methods).

Overall, precipitation changes dominate wetland changes under SSP126, while warming is the dominant factor under SSP585. For the SSP126 scenario, 55% of all the basins are controlled by precipitation changes (Fig. 5a), while 72% are controlled by temperature changes for SSP585 (Fig. 5b). Previous studies identified precipitation to be the major driver of wetland variabilities based on satellite observations20,22, consistent with the larger role of precipitation in the wetland dynamics under the lower emissions scenario with global mean warming below 2oC throughout this century. However, the dominant driver shifts from precipitation to temperature (or both) for the higher greenhouse gas emissions scenario, with global warming exceeding 4oC by 2100 relative to 2000, highlighting the higher evaporation is increasingly negatively affecting wetland area. Higher temperatures likely reduce wetland area due to evapotranspiration (ET) that grows because of enhanced air vapor pressure deficit. This can also be due to an increase in infiltration losses in the colder season because of the increased soil hydraulic conductivity (e.g., ice in the soil thaws) and the higher fraction of liquid precipitation.

Broadly, wetland dynamics are controlled by the net precipitation (difference between precipitation and ET). The shift of the dominant wetland driver from precipitation to temperature is caused by the different sensitivities of precipitation and ET to temperature increases. Specifically, warming does not strongly constrain regional precipitation changes (Fig. 5c), which are also influenced by the changes in atmospheric circulation, but higher temperatures have a significant control on ET (Fig. 5d), as the ratio of surface latent to sensible heat fluxes increases with temperature51,52. Therefore, under SSP126 with limited warming, the increase of precipitation can be larger than the increase of ET (i.e., the blue diamonds are below the 1:1 line in Fig. 5e), dominating the wetland changes. However, under SSP585 with higher warming level, ET increases substantially and always surpasses the precipitation increase (i.e., the magenta circles are above the 1:1 line in Fig. 5e), which results in unidirectional drying trend in wetland dynamics. As a result, temperature increase becomes the dominant driver for wetland changes under the higher emission scenario. Despite the shift in the dominant driver from precipitation in SSP126 to temperature in SSP585, the spatial pattern of the wetland area change directions are similar between the two scenarios (Fig. 4). This is because both precipitation and ET increase with warmer temperature (Fig. 5c, d), albeit at different rates for precipitation under the two scenarios, and the patterns of precipitation change are similar between the two scenarios. With ET increasing monotonically with warming and precipitation change showing more variable behavior with increasing temperature, there are larger reductions per 1°C warming in the SSP585 scenarios, as compared to SSP126 (Supplementary Fig. 13).

Unlike in most other regions, temperature significantly dominates the wetland dynamics in southeastern Canada and the southwestern US for both the lower and higher emissions scenarios. In cold regions (e.g., southeastern Canada), surface water accumulates in wetlands when the soil is frozen, thereby constraining infiltration (e.g., ice reduces hydraulic conductivity), or partially frozen, when the surface soil can be easily saturated since water percolation is inhibited (e.g., with shallower perched water table). By ignoring these processes, the commonly used diagnostic wetland scheme projects expanding wetland areas over the cold regions33 due to the rising groundwater level (Fig. 6a) caused by increased precipitation (Fig. 6b) and higher temperatures projected in the future53. However, our physically based wetland scheme projected shrinking wetlands since the higher temperatures thaw the soil ice, which increases infiltration from wetlands (Fig. 6a) and leads to deepening of the perched water table (Fig. 6c). Although lower hydraulic conductivity due to frozen soil is the major generation mechanism of wetlands in southeastern Canada, we note the surface water can stay in wetlands during warm periods due to soil saturation. The southwestern US is a hot and dry region, where wetland area dynamics are mainly controlled by evaporative losses. Increased evaporation with warming reduces wetland area in that region year-round under both scenarios. Although wetlands are not common for dry regions, they cover significant areas in northern high latitudes42. With a high sensitivity of the wetland water cycle to temperature changes in cold regions (e.g., annual temperature around or below 0°C, Supplementary Fig. 14), global warming may induce a significant loss of wetlands in these regions – an unexpected result supported by physical considerations of thermal and water-related processes.

Impacts of climate change on major wetland habitats
Wetland habitats will significantly shrink in 36% and 41% of the study basins (p < 0.05 in ANOVA test) under SSP126 and SSP585, respectively (Fig. 7a, b). The major wetland habitats (encircling in green in Fig. 7a) show larger impacts caused by the climate change. By the end of this century, wetland habitats near Salt Lake, upper Mississippi, and Southeast Canada will shrink by 27% (18%), 18% (10%), and 45% (30%) under the higher (lower) emissions scenario according to multi-model ensemble mean of the projections (Fig. 7c–e). Our projected reduced wetland habitats are consistent with previous studies assessing the sensitivity of wetlands to climate change in the Great Salt Lake region54 and Prairie Pothole Region55. The lower Mississippi region is projected to have a significant increase in wetlands habitats by about 10% (Fig. 7f), which can be caused by the increased streamflow56. Notably, the Everglades wetland region in Florida exhibits different sensitivities under the two emission scenarios with a 30% increase in wetland habitats under SSP126 and a 14% decrease in wetland habitats under SSP585 (Fig. 7g). A similar nonlinear response of the Everglades wetland to different climate change scenarios was reported in a previous study57. Such uncertainty may be caused by the change of the dominant driving factor from precipitation control (under milder warming) to temperature control (under larger warming, Fig. 5), or changes in maximum precipitation dynamics (Supplementary Fig. 15), resulting in changes in available water. Furthermore, the projected wetland habitats exhibit greater temporal variability in the future compared to historical period (Fig. 5c–g). Consequently, the major wetland habitats can experience more significant reduction during drier years than normal years, especially under the higher emissions scenario.

Importantly, we find that the change of functional wetland habitats will not always follow the change of inland wetland area under climate change, which is contradictory to the assumption used in previous studies33. For example, wetland area is projected to increase over upper Mississippi and decrease over lower Mississippi (Fig. 4), but the changes of wetland habitats exhibit an opposite direction (Fig. 7). The difference between the projected changes in wetland area (Fig. 4c, d) and wetland habitats (Fig. 7a, b) can be attributed to the changes in inundation seasonality and increase in precipitation variability with warming58. Specifically, the maximum inundation during the growing season can decrease as the peak inundation shift to winter months, while the annual averaged inundation increases compared to historical period. As the proper inundation during the growing season is needed for the function of wetland habitats but projected to decrease substantially in the future warming conditions (Supplementary Fig. 10), wetland habitats can be more sensitive to global warming than wetland area. It emphasizes the significance of considering inundation seasonality to better understand wetland evolution in the future.

Methods
Wetland definitions
In this study, we define ‘wetland area’ as a region within a computational cell inundated due to either pluvial or fluvial processes, excluding rivers, lakes, and reservoirs, while lakes can be classified as wetland in other study76. Therefore, wetland area represents the area of a grid cell that covered by both non-permanent and permanent inundated water. Such a definition of wetland area is consistent with the surface water dynamics observed by satellites with the rivers, lakes, and reservoirs removed.

The definition of “wetland habitat” is based on expert understanding that although wetland environments (e.g., emergent wetlands) do not need to be covered by water permanently, they have to be inundated for at least 1 month during the growing season to develop suitable biotic characteristics. Therefore, we define wetland habitat as the maximum wetland area during the growing season resolved on a monthly basis (growing season represents months with surface temperature higher than 5°C). Such a definition of wetland habitat is consistent with the fact that the computational cell becomes inundated only during the months outside of the growing season period. We note our definition of wetland habitat (i.e., maximum wetland area for the months with surface temperature higher than 5°C) may further introduce uncertainties to our analysis.

Model description
E3SM, a state-of-the-science ESM, is used to simulate wetland dynamics and project their future changes. E3SM is a fully coupled ESM with the atmosphere, land, ocean, land ice, sea ice, and river components. E3SM version 2 is used here, and detailed model description and validation cases are provided in Ref. 43 In this study, the wetland area dynamics in E3SM are simulated as the sum of pluvial and fluvial inundation mechanisms in the land and river components briefly described below.

The E3SM land model (ELM) was developed based on the Community Land Model 4.5 (CLM4.5)77. Surface water storage component was introduced to simulate the pluvial inundation process and to store excess rainfall, runoff, and snowmelt (See Supporting Information). The simulated inundation in ELM is controlled by surface-subsurface interactions (i.e., infiltration and water excess). Due to the typically coarse resolution of ESM simulations, a sub-grid scale scheme is implemented to include topographic impacts on different processes (Supplementary Text 1). While a fraction of a grid cell can be covered by snow and/or water, the infiltration rate is assumed to be constant across the entire grid cell (Supplementary Fig. 7). The infiltration capacity (q infl,max) is formulated as:

q infl,max = 1-f sat Θ ice k sat, (1)

where Θ ice represents the ice impedance factor to include the presence of ice in the soil, k sat is the saturation hydraulic conductivity, and f sat is the saturated area fraction, which is determined by the topographic characteristic and water table depth:

f sat=f max exp(-0.5×f over×Z wt ), (2)

where f max is the maximum saturated fraction, Z wt is water table depth [m], and f over is a decay factor [m−1] determining how water table depth controls area saturation fraction. In the default ELM configuration, f over is set to be 0.5 [m−1] for all grid cells. The underlying assumption is there is no infiltration for the saturated area fraction.

The original infiltration scheme can result in unrealistic small surface water inundation when the soil temperature is above freezing and infiltration in the surface water storage is overestimated (Supplementary Fig. 7). The overestimation of infiltration in surface water storage occurs because a uniform infiltration rate is applied over the whole grid cell despite different surface conditions (snow, surface water, floodplain, open soil) being present. However, infiltration below the surface water should be much smaller than for soil that is not covered by water78. Therefore, a sub-grid infiltration scheme has been developed to improve the realism of modeling wetland inundation in ELM. To constrain the infiltration from surface water storage, the saturation fraction is assumed to overlap with the surface water fraction rather than being uniformly distributed in different fractions (Supplementary Fig. 7). Specifically, the infiltration capacity of the surface water fraction (q infl,max,f h2osfc) and other areas (q infl,max) is estimated according to the following two potential situations (Supplementary Fig. 7):

q infl,max,f h2osfc = Θ ice k sat,f h2osfc > f sat (3)

q infl,max = (1-f sat - f h2osfc) Θ ice k sat,f h2osfc ≤ f sat (4)

where f h2osfc is the ELM-simulated inundation. If the infiltration rate is larger than the available capacity in the soil, the excess infiltrated water will be discharged to surface water storage and becomes standing surface water. We note f h2osfc can capture both periodic (i.e., non-permanent) and permanent inundation, as the sink terms of the surface water storage (e.g., evaporation, infiltration, and outflow) may not always be larger than the source terms (e.g., rainfall, surface runoff, and subsurface discharge).

Model for Scale Adaptive River Transport (MOSART)79 is the river component of E3SM. It routes freshwater from the land to the ocean through river networks. Specifically, MOSART has a subgrid structure for routing flows over hillslopes, tributaries, and main channels. It uses kinematic or diffusive wave equations. Ref. 35 implemented a macro floodplain inundation scheme with MOSART to simulate inundation dynamics on floodplains when streamflow exceeds the channel capacity. The macro scale floodplain inundation scheme uses the relationship between the flood water volume and inundated area to simulate the riverine inundation dynamics. This volume-area relationship is described by the surface elevation distribution (e.g., at spatial resolution of 90m) within the computational unit, assuming that riverine inundation propagates from lower elevations to higher elevations34. For example, one can estimate the floodplain inundation fraction (f fp) given the excess volume (V excess, the total river channel volume minus river channel capacity) in the main channel:

f fp=F(V excess ), (5)

where F() represents the volume-area relationship of the floodplain (e.g., elevation profile), derived from the Cumulative Density Function (CDF) of finer resolution sub-grid elevations.

Model configuration
We ran simulations using E3SM version 2 with active ELM and MOSART modules over the North American Land Data Assimilation System (NLDAS) domain at a spatial resolution of 0.125°×0.125° (i.e., ~12.5 km × 12.5 km), including continental United States (CONUS), southern Canada, and northern Mexico (i.e., 25°–53° North). The hourly meteorological forcing of NLDAS phase 2 (NLDAS-2) is used to drive ELM from 1979 to 2020 to obtain model outputs for calibration (see below a description of the calibration procedure). The time steps for ELM and MOSART are 30 min and 60 min, respectively, with a coupling frequency of 180 min between the two models. The default 0.125°×0.125° ELM surface parameters80 for the NLDAS domain was used. The topographic parameters (i.e., flow direction, river length, slope, etc.) of MOSART were generated by the Dominant River Tracing algorithm;81 and the spatiotemporally varying Manning’s roughness coefficients of hillslope, subnetwork, and main channel are estimated online based on land cover and water depth at each time step37. Further, the relationship of Eq. (4) was derived from the 90m-resolution Digital Elevation Model (DEM) from Hydrological Data and Maps Based on SHuttle Elevation Derivatives at multiple Scales (SHED)82. and downscaled CMIP6 climate forcings70 (i.e., precipitation, temperature, humidity, radiation, etc.) that archived in the Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b) at 0.5°×0.5° (i.e., ~50 km × 50 km) and daily scale. As ELM requires sub-daily inputs, we followed the procedure of https://vic.readthedocs.io/en/vic.4.2.d/Documentation/ForcingData/ to disaggregate the daily forcing to a sub-daily scale, and used the method from Ref. 83 to determine the timing of daily maximum and minimum temperatures. Atmospheric forcings from five climate models for two greenhouse gas emissions scenarios, SSP126, and SSP585, were used for drive ELM-MOSART coupled simulation to project the future wetland changes: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. We prescribed the land use land cover changes in the future simulation using the projections are from the Land-Use Harmonization LUH284.

Dataset of surface water dynamics
We used the global surface water dynamics dataset from GLAD15 as observational benchmark dataset in this study. GLAD provides global monthly, annual, and seasonal (i.e., monthly averaged) surface water and permanent water data derived from Landsat images taken during 1999–2020. As there are many data gaps in observations at the monthly scale, seasonal surface water data were used for calibration and annual surface water data were used to validate the model. The original spatial resolution of 30m×30m was upscaled to a model resolution of ~12.5 km × 12.5 km by averaging the values of the finer resolution grid cells within the coarse model resolution grid cell for comparison with the model simulations. The coarse grid cell was assigned to “No data” if over 20% of the finer grid cells within the coarse grid cell contained gaps. Although rivers, lakes, and reservoirs are permanent surface water bodies, they represent different ecosystems than wetlands. We removed permanent water from the upscaled seasonal and annual GLAD surface water to exclude rivers, lakes, and reservoirs. However, permanent wetlands (e.g., surface water ponds and swamp, etc.) can be unintentionally removed as well, which may result in underestimation in the upscaled GLAD surface water dynamics. In addition, GLAD dataset cannot capture the wetlands formed due to soil saturation, which are abundant for some regions (e.g., Supplementary Fig. 17).

Calibration procedure
Fluvial inundation (simulated in MOSART) process is relatively well represented in ESMs since 90m resolution DEM is used to capture the floodplain storage effects34,35. However, the pluvial inundation (simulated in ELM) process adopts constant values for some parameters without justification12,77. Calibration is necessary to constrain the parametric uncertainty since sub-grid parameterization are needed to compensate for the typical coarse resolution of ESMs. However, the satellite observations used to validate wetland dynamics cannot differentiate between fluvial and pluvial inundation. Therefore, model simulations that estimate the sum of the two inundations are used to compare and/or calibrate the model against observations.

The following two ELM parameters that control the pluvial inundation process were selected for model calibration: (1) f c determines the potential maximum inundated area in ELM and (2) f over affects f sat and constrains the infiltration rate under the inundated area. We ran coupled ELM-MOSART simulations with 100 parameter values randomly sampled from a uniform distribution of f c ~U[0.001 0.4] and f over ~U[0.15], while the default values of other parameters were used. At each grid cell, the parameter set that maximized NSE85 was identified as the best parameter. The NSE is given as

NSE=1- [Σ (yGLAD i - yPluvial i + yFluvial i)2] / [Σ (yGLAD i - yGLAD)2], (6)

where yGLAD i represents the seasonal surface water fraction from the GLAD dataset, yPluvial i is the ELM-simulated surface water inundation, yFluvial i is the MOSART-simulated floodplain inundation, and i is the month index. Both the GLAD benchmark and model simulated inundation in the above equation are monthly averaged values from 1999 to 2020. Other satellite datasets14 can be used as benchmark for calibration as well, though only GLAD was used in this study.

The calibrated parameters can be found in Supplementary Fig. 18. The annual GLAD surface water dynamics are further used to evaluate the calibrated simulation in terms of annual variability and changing trends.

Attribution analysis
Surface water (Sw), temperature (T), and precipitation (P) are aggregated to the watershed scale with the Hydrologic Unit Codes 8 (HUC 8)86 within the contiguous US and the Canadian National Hydrographic Network Index in Canada (https://www.nrcan.gc.ca/science-and-data/science-and-research/earth-sciences/geography/topographic-information/geobase-surface-water-program-geeau/watershed-boundaries/20973). The dominant driver for the changes in the simulated surface water (ΔSw) in the future is determined to be either the change of temperature (ΔT), change of precipitation (ΔP), or both factors. Due to the strong correlation between ΔT and ΔP, we implemented the following procedure to identify the dominant factor for a given location:

1. For each simulation driven by the atmospheric forcing of a climate model, the annual time series of ΔSw, ΔT, and ΔP were derived by subtracting the averaged Sw, T, and P during the control period (1971–2000) from the future time series (2015-2100).

2. For each simulation of ΔSw, ΔT, and ΔP, their 10-year moving average series were calculated and the multi-model means of ΔSw, ΔT, and ΔP with equal weights for the climate models were then computed.

3. Two linear least-squares regression models using the multi-model ensemble means of ΔSw, ΔT, and ΔP were developed: ΔSw=β0 +β1 ×ΔT, ΔSw=β0 +β1 ×ΔP. The corresponding correlation coefficients: ρ (ΔSw vs: ΔT), and ρ0 (ΔSw vs: ΔP) were determined.

4. If ρ>ρ threshold, ρ0>ρ threshold, β1 <0, and β01 >0, then ΔSw is controlled by both ΔT and ΔP.

5. If step 4 is false, the location with ρ>ρ threshold and β1 <0 is temperature controlled. If ρ0>ρ threshold and β01 >0, then the location is precipitation controlled.

6. If both conditions in step 5 are false, a multilinear regression model was developed: ΔSw=β00 +β01 ×ΔT+β02 ×ΔP+β03 ×ΔT×ΔP, and the correlation coefficient, ρ'' (ΔSw vs: ΔT×ΔP), is determined. If ρ00>ρ threshold, then ΔSw is controlled by the interactions between ΔT and ΔP. Otherwise, ΔSw cannot be explained by ΔT or ΔP.

In this study, we select ρ threshold=0.5. We note the correlation coefficients were calculated for each basin with annual time series of ΔSw, ΔT, and ΔP. Such spatial (basin) and temporal (annual) averaging significantly reduces the spatial and temporal autocorrelation in the samples being analyzed.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_2": "This study anlyzes the impact of projected climate scenarios on wetlands areas in North America during the 21st century. Depending on the magnitude of the change in each scenario, the shrinking of wetland areas will range betewwn 5 and 25% of their current areas. The main driver (temperature or rainfall) is identified for each scenario depending on the location of the wetland. As the drying is expected to occur during summer, consequences on wetland habitats were also estimated.\nIn my previous review, I mentionned the need for the model used here to simulate wetland extent to be validated. The authors successfully achieved this valdation.\nI think this work is important to better understand the consequences of climate change on wetlands that provide many ecosystem services.", "reviewer_4": "Xu et al.\nClimate change and inland wetlands\nXu and coauthors explore inland wetland hydrological dynamics using an Earth systems model and an ensemble approach to climate change through 2100. There’s a lot in this paper, and useful information for subsequent analyses. At this juncture, I found myself asking more questions than I found answered by the paper. There are not intractable problems, it is more that the clarity I found necessary for Nature Coms was often missing, leading to some conclusions that might not be completely supported (without caveats). For instance, inland wetlands in this study are limited to inundated systems – but not permanently inundated systems. That’s fine for an operational CONUS-scale study, but I’d argue that the repeated use of the word “wetland” or even “inland wetland” is not appropriate. The study is on non-permanently flooded inland surface waters, including wetlands. Further, with that condition on wetlands I wasn’t able to discern later if their model runs addressed those currently permanently flooded waters that might become semi-permanently flooded (e.g., dry at least one month but flooded at least one month during the growing season). The use of the word wetland and inland wetland has many implications yet I feel the authors need to provide additional clarity to the readers as to what’s meant throughout the entirety of the paper (e.g., did they contrast the GIEMS data with their wetlands after removing the permanently inundated waters?). Further, the study does not address “North America” but rather the conterminous US plus a smidge of Mexico and an area of southeastern Canada that – to me – appears to misbehave. That is, the authors take pains to explain how ‘soil ice’ affects infiltration in that area of Canada…yet the Canadian government explicitly excludes that area from modern maps (2022) of permafrost (which, I can only assume, is what’s meant by soil ice). Another major issue that I found was in describing for Nature Com readers the granularity of the study. Data from sources that are 30 m (GLAD) as well as 90 m were used, then interposed and coarse-grained to 0.125 x 0.125 arc degrees – which my hamfisted analyses suggest is around 120 km2. The point: wetlands, especially those that are pluvial in nature are generally pretty small. And models that are coarse, like this one, will miss a lot of the dynamics of those systems. Sure, explorations at CONUS scale require coarse-graining. But explorations of surface-water inundation dynamics in seasonally flooded wetlands require finer grained analyses – or at least readily digestible information for the reader to understand some of the grain-related limitations of the study. (I note that zooming into demonstrate the utility of their approach, at scales reasonable for readers to discern individual wetlands and/or complexes, would be useful and possible in the SI.) In closing, there’s much the authors should be proud of in their analyses. So that others can replicate their approaches – including their conclusions – assumptions and limitations of the study to me should be much more clearly articulated and incorporated into the analyses so that the conclusions are contextualized in a more nuanced way (as befits a model analysis of this scale, scope, and conclusion).\nL39 Would be remiss to not include Nahlik and Fennessy here.\nNahlik, A. M. and M. S. Fennessy (2016). \"Carbon storage in US wetlands.\" Nature Communications 7: 13835.\nL46 Good place to reference Royal Gardner & Nature piece (relevant to US, anyway)\nGardner, R. C. (2023). \"What the US Supreme Court decision means for wetlands.\" Nature 618: 215.\nL48 periodically to permanently inundated…\nL50 …though we note that not all wetlands express surface water (e.g., saturated soils are sufficient to create wetland ecosystems).\nL59 maybe drop the intrinsic, hidden for the simplicity of “deriving the relationships”.\nL60 should wetlands be plural? Should it be possessive if so?\nL67 Consider adding Park et al. 2022. Also, the GLAD data (Figure S1) show that most of CONUS is covered between April and November 2010 (i.e., it has data, though those values may be low). If the point is to show that surface water fractionation is low during that period, then GLAD may not be the correct source. For instance, the Global Surface Water product (Pekel et al. 2016, already in references) shows the availability of water in a given Landsat pixel over the period of record (~30 years now). Or, the DSWE does much the same (Jones 2019; https://www.usgs.gov/landsat-missions/landsat-collection-1-level-3-dynamic-surface-water-extent-science-product). I strongly recommend zooming in to an area of interest w the data (be it GLAD or other) to allow readers to see what 30 m resolution data is providing. Figure S2 is otherwise not particularly useful.\nAnd regarding the seasonality, Borja et al. (2020) used the Pekel et al. GSW to identify the global extent of ephemeral waters…which is exactly what this study is analyzing as well. Why not use these data? At the least, the presence of these data (Borja et al. 2020) should likely have come to the attention of the authors and needs to be referenced.\nBorja, S., et al. (2020). \"Global Wetting by Seasonal Surface Water Over the Last Decades.\" Earth's Future 8(3): e2019EF001449.\nJones, J.W. (2019). Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests. Remote Sens., 11, 374\nhttps://doi.org/10.3390/rs11040374.\nJones, J. W. (2015). Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network. Remote Sensing, 7(9), 12503-12538.\nhttp://dx.doi.org/10.3390/rs70912503.\nPark, J. et al. (2022). Seasonality of inundation in geographically isolated wetlands across the United States. Environmental Research Letters 17: 054005\nL68 Maybe add a line to answer this question, “What are Earth system models?” other than tools. Are they process-based models? Statistical models? GIS-based prediction tools? Etc.\nL83 Fan et al. (2013) developed a global database of depth to groundwater that allows for applications such as this to explore how groundwater supports the presence of wetlands.\nFan, Y., H. Li and G. Miguez-Macho (2013). \"Global Patterns of Groundwater Table Depth.\" Science 339(6122): 940-943.\nL86 is awkwardly phrased. Maybe missing a word (“Accurately represent…”)\nL86 I’ve not seen “diagnostic scheme” used to describe what is essentially a model or approach to mapping wetlands.\nL88 What are high latitudes? Clarify for the reader.\nL99 I’d argue that the calibration was done using a 30-m resolution dataset, not that the ESM data were calibrated against the high-resolution data. Two problems with that statement are that the ESM data are relatively coarse-grained, much coarser than the GLAD data and the calibration occurred after the GLAD data were modified to match the grain of the ESM. Secondly, unless I’m mistaken, the GLAD data are 30 m. That’s medium resolution at best. In fact, it’s probably best to simply state the resolution (in meters) and don’t add the modifier regarding low, medium, or high resolution.\nL101 Readers might want to know what other datasets are available. For instance, why not use the Pekel et al. (2016) dataset? Or some of the land cover products from ESA? Not saying that using GLAD is wrong by any stretch, but the readers will want to know (and should be told) that you’re aware of other data sets and chose to use GLAD because…why? (GLAD is comprehensive – more comprehensive than other dataset how? And GLAD was ‘carefully validated’ – what does that mean, and though I appreciate careful validation, I’m also interested in highly accurate validation and a robust end product. Please include something along those lines that describe why GLAD was chosen over other data.)\nL116 Though discussed further in the methods, the resolution of the model needs to be in here. GLAD is identified as a “high resolution” dataset with the implication then that the ESM is similarly of high resolution. But my understanding is that the ESM is at roughly 120 km2. Readers need to know that (and I’m acknowledging that many readers won’t go to the full methods section to discern that, and I point out further that the mixing of arc degrees and SI units further confuses the reader and should be avoided; use SI.)\nL118 Figure S4 needs clarification. The scale has 0 for GLAD surface water fraction, but the caption states that “white area represents no data.” It’s currently both 0 and No Data, then. I recommend changing no data to gray. (And, as before, zooming in allows readers to visually discern the model, which can be reassuring.)\nL120 The positive trend in wetland evolution should be simplified, as the authors mean, I think, “The creation or expansion of more non-permanently flooded waters”. Two issues with this phrase include the use of evolution (what is evolving here?) and the lack of support for the conclusion. Where are the data that support the claim that more wetlands have been created between 2000 and 2020? For instance, the Fish and Wildlife Service publishes the Status and Trends series on wetlands, with the last one analyzing trends through 2009. This document (p. 16, here) states that “Overall, freshwater wetlands realized a slight increase in area between 2004 and 2009.” There are other analyses, such as the varied works by the National Land Cover Database that could be cited as well. Zou et al. (2018) is another one to consider citing.\nZou, Z., X. Xiao, J. Dong, Y. Qin, R. B. Doughty, M. A. Menarguez, G. Zhang and J. Wang (2018). \"Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016.\" Proceedings of the National Academy of Sciences 115(15): 3810.\nL122 GIEMS requires a reference. Perhaps one of these…\nFluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F., and Hamilton, S. K.: Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data, Remote Sens. Environ., 158, 348–361, https://doi.org/10.1016/j.rse.2014.10.015, 2015.\nPrigent, C., C. Jimenez and P. Bousquet (2020). \"Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2).\" Journal of Geophysical Research: Atmospheres 125(3): e2019JD030711.\nL124 Is the relationship with GIEMS associated w the wetland area, or the inundation? That is, the authors take pains to explain that their analyses exclude permanently inundated waters. But did they exclude permanently inundated pixels (waters) from the GIEMS data? There needs to be clarity throughout the manuscript on the specifics of the system analyzed and contrasted.\nL134 Were the permanently flooded areas removed from (b), the simulated surface waters? And at this scale it is very difficult to discern how well the model performed at the meaningful local or large scale. I’d recommend adding (in the SI, as this is crowded already) a zoomed-in, high scale image that demonstrates the performance of the model such that individual pixels can be visually discerned.\nL150 How were the basins selected (or delineated)?\nL151 I only see the CONUS analyses here, nothing about the entirety (or even majority) of Canada or Mexico. Thus, the “continental scale” statement cannot be supported.\nL154 I see more than just the MS valley. What about the California Central Valley? Colorado River? Okefenokee and Okeechobee swamp and lake, respectively? The Basin and Range area of Nevada? The Atlantic Coastal plain watersheds?\nL154-156 Why are these wetland areas removed from pervious studies? Were the wetland areas removed or the surface water pixels removed?\nL188 Historical is misspelled in the figure 2a.\nL253 I’m assuming that ‘soil ice’ could also be called permafrost. The map from the Canadian government shows zero permafrost in the southern (southeastern) section of the country. Please clarify the statement and conclusion.\nhttps://ftp.maps.canada.ca/pub/nrcan_rncan/raster/atlas_5_ed/eng/environment/land/mcr4177.jpg\nL259 Why not spell out Temperature and Precipitation in the colorful line-bar instead of using Ta and Pr, which are not as clear and require reading the caption?\nL264 Why was this particular example watershed selected?\nL276 “…warming amplified water sinks in the wetland hydrological cycle…” could this be simplified to express how higher evap is increasingly negatively affecting wetland area? The current phrasing is overly complicated.\nL280 see L253\nL318 The conclusion that wetlands hydraulic conductivity will increase because of decreased soil ice (read: permafrost?) seems spurious. The authors have not provided literature that supports their notion that Canadian wetlands (or Prairie Pothole wetlands, if they want to analyze that more central region shared by the US and Canada) are wetlands because of “soil ice” that is assumed to be frozen and hence impermeable. Sure, the water in wetlands and ponds freezes during the winter…but the creation of a perched wetland due to soil ice that’s maintained year round (or not, according to their models) is not supported.\nL324 I am unsure of what is a cold region and a hot region. Perhaps a map that defines this would be useful. Or, perhaps using the Köppen-Geiger map to interpret their results would be helpful. Source:\nBeck, H. E., N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg and E. F. Wood (2018). \"Present and future Köppen-Geiger climate classification maps at 1-km resolution.\" Scientific Data 5(1): 180214.\nL331 Rather than call this the Upper Mississippi River, it is probably more informative to call it the Prairie Pothole Region. Further, the results should be couched relative to other studies – do the authors’ results hold up? Here are some PPR studies, at the least, to consider. And I would expect the authors to further couch their results in other analyses of the Everglades, the Mississippi Alluvial Valley, and the Great Salt Lake – all of which have been analyzed by others for climate change effects (search, and you will find…).\nGanming, L. and S. F. W. (2012). \"Climate‐driven variability in lake and wetland distribution across the Prairie Pothole Region: From modern observations to long‐term reconstructions with space-for-time substitution.\" Water Resources Research 48(8).\nMcKenna, O. P., D. M. Mushet, D. O. Rosenberry and J. W. LaBaugh (2017). \"Evidence for a climate-induced ecohydrological state shift in wetland ecosystems of the southern Prairie Pothole Region.\" Climatic Change 145(3): 273-287.\nMillett, B., W. C. Johnson and G. Guntenspergen (2009). \"Climate trends of the North American prairie pothole region 1906–2000.\" Climatic Change 93(1-2): 243-267.\nRenton, D. A., D.M. Mushet, E.S. DeKeyser (2015). Climate Change and Prairie Pothole Wetlands—Mitigating Water-Level and Hydroperiod Effects Through Upland Management, Scientific Investigations Report 2015-5004. U.S. Department of the Interior, U.S. Geological Survey.\nL373 One of the most confusing parts of this study is the use of non-permanent wetlands by design, but then the use of the term “wetlands” throughout (which, again, includes permanent waters to most…). For instance, the previous pages discussed how some areas would have an increase in wetlands, and some a decrease. Were the increases de novo wetlands, pixels that were previously ‘dry’ that had a >1 month (see 28-31 day comment, below) modeled “water present” during the growing season? What about the wetlands that were ‘wet’ for just a month but under the modeled climate changes ceased to be wet for that month? I assume they were losses. What about wetlands that were wet for, hypothetically, 10 months but then decreased to just one month (during the growing season)? Was that captured by the model and reported? And what about permanent waters (wetlands, to most) that ceased to be permanently (surface water) inundated during the growing season? Where they at all included – were they considered “new wetlands” since, but this study’s definition, waters that are dry for a month or more are considered wetlands (though permanently flooded waters are not)?\nL373 Climate change is most certainly not a natural driver but caused by human alterations of the climate (e.g., IPCC for a quick-n-dirty reference). Remove this parenthetical.\nL378 How was wetland size discerned? Were the neighboring pixels region-grouped? And how were these statistical analyses done to explore how sizes increase or decrease over time? This should, IMO, be done via a multiple comparison test and reported…\nL378 When “continental scale” is used, I see “CONUS” instead. I don’t see any Mexican nor substantive Canadian analyses to support “continental scale” here or anywhere in the paper.\nL382 Wetland hydrology seasonality? Or seasonality of non-permanent wetland hydrology?\nL397 I think conclusions addressing the entirety of the “Northern Latitude high regions” based on the relatively temperate zones to the east of the Great Lakes are a bit overblown. Especially in light of the lack of supporting data associated with “soil ice” in this analysis.\nL400 Studies, perhaps?\nL400 Consider additional supporting docs here, FWIW…\nhttps://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EF001858\nhttps://www.nature.com/articles/ngeo1160\nL416 sequent?\nL420 constraining?\nL442 Bottomland hardwoods of the Mississippi Alluvial Valley are flooded forested areas…and they are often able to be discerned through satellite mixed-pixel analyses. Furthermore, the NLCD identifies forested wetlands as one of their classes, and the NWI has palustrine forested and palustrine shrub scrub as woody wetlands. It is incorrect to state that most forested wetlands are tropical. (Not to mention mangrove systems, but that’s another story…)\nL447 – I did not see any discussion material on how the decision to limit wetlands in this analysis to those systems with a) inundation (versus saturation) discerned by satellites thereby missing those with emergent vegetation, such as all the NLCD herbaceous wetlands or NWI emergent palustrine wetlands, or b) that these inundated wetlands were further limited to those that were not permanently flooded, thereby missing a great deal of waters (see, e.g., Figure 4 here)\nVanderhoof, M. K., et al. (2023). \"High-frequency time series comparison of Sentinel-1 and Sentinel-2 satellites for mapping open and vegetated water across the United States (2017–2021).\" Remote Sensing of Environment 288: 113498.\nMaterials and Methods\nL454 Regarding permanently flooded waters: It is an important distinction that permanently flooded wetlands are NOT included in this study. One might argue this is sufficient to retitle this paper as it focuses on “seasonally inundated inland surface waters” rather than on what most would articulate as inland wetlands (which include the gradient from permanently flooded to ephemeral systems). For instance, Lane and D’Amico (2016, Figure 4), identify high abundances of “permanently flooded” National Wetlands Inventory water regimes from non-floodplain-based palustrine wetlands across CONUS, in particular in the southeast and Atlantic coastal areas. These abundant wetlands would be missed by this study…which ultimately is fine, as long as the caveat is noted more explicitly than it is currently in this paper.\nL457 Regarding the use of the word “inundated” and its meaning regarding standing water as a required definition of a wetland. The origin of this expert understanding should be cited.\n“The definition of ‘wetland habitat’ is based on expert understanding that although wetland environments (e.g., emergent wetlands) do not need to be covered by water permanently, they have to be inundated for at least one month during the growing season to develop suitable biotic habitats.”\nIt’s okay to have an operational definition of wetlands – but that should be contextualized and acknowledged more explicitly. For instance, this operational definition of wetlands excludes all wetlands that are saturated but not inundated. Inundation is ‘nice’ because satellites can relatively easily capture the signature of water. But that is not the case with saturated soils. Yet saturated soils are critical to wetlands (see, e.g., Fan et al. 2013, in particular their Figure S17, wherein the water table depth of within 25 cm of the surface is plotted with wetland area, with an R2 of 0.89). Tootchi et al. (2018) utilized the Fan et al. approach and derived a global wetland map with (relatively) high accuracy to comparable datasets, further supporting the use of saturated soils as maintaining wetlands globally.\nThe conceptual definition of wetlands used here by the authors, wetlands are supported by fluvial and pluvial processes that support wetland (hydrologic) characteristics, is operationalized to this: Surface water present for [at least 30 days] during the growing season, but not permanently inundated.” This should be more clearly stated in the main text as well as in the methods section.\nThat’s because most experts (in North America, or at least the US) would most likely refer to both the Cowardin classification system (Coward et al. 1979) or the 1987 Army Corps of Engineers manual (ref) to define wetlands – and both of these documents do not require standing water to define wetlands. Coward et al., for instance, classify wetlands thusly (with my highlights for emphasis, Status and Trends doc):\n“Wetlands are lands transitional between terrestrial and aquatic systems where the water table is usually at or near the surface or the land is covered by shallow water. For purposes of this classification wetlands must have one or more of the following three attributes: (1) at least periodically, the land supports predominantly hydrophytes31, (2) the substrate is predominantly undrained hydric soil, and (3) the substrate is non-soil and is saturated with water or covered by shallow water at some time during the growing season of each year.”\nThe Corps defines wetlands for jurisdictional purposes as areas that \"...are inundated or saturated by surface or ground water at a frequency and duration to support, and that under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated soil conditions” (my emphasis). Further, the Corps uses approximately 12 inches to define ‘near the soil surface’. So if an area has saturated soils near the surface and other wetland-defining characteristics, the Corps would consider it a wetland – despite not having water above the soil surface.\nI’ll be looking to see if the authors acknowledge the limitations of their approach. Though an example of emergent marsh is given, I would argue their approach likely misses many emergent marsh because of not only the lack of saturated soils as a defining characteristic, but also because emergent vegetation can block the spectral signature of water.\nSide note: I’d argue for changing the “…inundated for one month…” to days, since months can be 28, 29, 30, or 31 days in length. And, for what it is worth, the 5 C value is typically reserved for below the soil surface (as that affects microbial activity). The short-hand for at the surface is frost-free days. Here’s some relevant info from the 1987 Corps Manualv:\nGrowing season. The portion of the year when soil temperatures at 19.7 in. below the soil surface are higher than biologic zero (5o C) (U.S. Department of Agriculture & Soil Conservation Service 1985). For ease of determination this period can be approximated by the number of frost-free days (U.S Department of the Interior 1970).\nL457-458 I believe this should be changed to “suitable biotic characteristics” at the end, not “habitats”.\nL460 does the journal require a period after the Celsius abbreviation?\nL481 Where is the data to show where ice in the soil would be expected to affect infiltration?\nL484 Where is the data to show the expected depth to groundwater? The reference provided by Fan et al. (2016) would be a useful, citable map for the authors to contrast their assumptions.\nL493 A reference supporting this supposition is needed.\nL503 What is the input stream and river map used to be flooded? NHD? Something else? This is important to know how far up the stream network is mapped and modeled. (As well as replication.)\nL508 90 m is generally not considered high resolution esp in the days of sub-meter resolution satellite data. 30 m is generally considered ‘medium resolution’; 90 m is fairly coarse.\nL519 This is roughly 120 km2, correct? The readers of Nature Com should be provided that approximate cell size (at the equator, etc.) rather than having to do the math. I strongly recommend using SI throughout the manuscript for clarity (and definitely not jumping back and forth w arc degrees and meters). The ‘size matters’ wrt wetlands, so providing easy to read understanding of the granularity of this model is important.\nL527 This implies that Eq 4 solves at finer resolutions than 90 m, when it seems to be in fact a 90-m product that’s used. (This also goes to the point of mixing arc degrees and metric units, L519 comment; I recommend sticking with metric throughout.)\nL535 I’m pretty sure that journal doesn’t reference websites in this many; please refine.\nL551 highly recommend keeping the data to meter-scale pixels instead of degrees. It’s intuitive to the reader to go from 30 m to 120 km2 by doing this or that, but not so much to go from 0.00025°×0.00025° to 0.125°×0.125°.\nL554 Why not use other products that might be higher resolution (and independent?) to remove rivers, lakes, and reservoirs? Examples include the Global Lakes and Wetlands Database (1km, Lerner and Doll 2004), GlobeLand30 product (Chen et al., 2015), European Space Agency (ESA) WorldCover 2020 (Zananga et al., 2021), and the Dynamic World dataset (Brown et al., 2022); the Pekel data could work here, too at 30-m resolution…\nChen, J., J. Chen, A. Liao, X. Cao, L. Chen, X. Chen, C. He, G. Han, S. Peng, M. Lu, W. Zhang, X. Tong and J. Mills (2015). \"Global land cover mapping at 30m resolution: A POK-based operational approach.\" ISPRS Journal of Photogrammetry and Remote Sensing 103: 7-27.\nLehner, B. and P. Doll (2004). \"Development and validation of a global database of lakes, reservoirs and wetlands.\" Journal of Hydrology 296(1-4): 1-22.\nPekel, J.-F., A. Cottam, N. Gorelick and A. S. Belward (2016). \"High-resolution mapping of global surface water and its long-term changes.\" Nature 540(7633): 418-422.\nZanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O. (2021). ESA WorldCover 10 m 2020 v100.\nL549 SI is a good place to show that there were data gaps. And if there are many data gaps in monthly GLAD data, why wouldn’t we expect there to be many data gaps in seasonal (i.e., 3-month periods)? And if the model was calibrated seasonally but validated annually, why should we trust any of the results presented in Figure 2 b-e (the seasonal analyses)?\nL555 also removed permanently flooded ponds and wetlands, it should be noted\nL559 What is a finer-resolution DEM? 10-m? 90-m? 250 m? Clarify.\nL582 HUC08, noted.\nSupplemental Comments\nEq 5: What supports the use of the default configurations for Fc and u in this equation? How would the model perform if a different suite of parameters were selected? Are there parameters appropriate for a CONUS-scale model? Can that be supported by literature?\nFigure S1: How did the authors handle the apparent errors in GLAD that affected the distribution of water across the CONUS? For instance, it’s apparent that March, May, October, and November panels have generally vertical areas of No Data (perhaps due to the scan-line correct failure; I’m unsure). How did those errors affect your analyses? (And why not cross-walk the GLAD data with NLCD data to see how well it captured the wetlands and open waters?)\nFigure S3 What are the demarcations of the CONUS (here and throughout)? Were the permanent waters removed from the comparison?\nFigure S4 and throughout: Were the Bahamas included in these analyses? If not, I recommend removing them from the mapped study area. Same with the islands off the southern coastal of CA (and perhaps the area around Cape Cod, too). Note that the legend includes white as zero values whereas the caption says white is no data. Recommend using gray for no data here and throughout.\nFigure S5 Clarify in the caption that these were analyzed at what pixel size? Was this the 120 km2 pixel contrast, after the GLAD data had been coarse-scaled to 120 km2? And were the surface waters here not including those permanently inundated systems?\nFigure S6c Why is there such a great discrepancy between the GIEMS and modeled surface water in 2005? Add to caption or text. And, here and elsewhere, does the modeled surface water include those permanent bodies? Does GIEMS include ‘permanently inundated’ data?\nFigure S7 I wonder if the scale should be red to blue? The white to blue makes it hard to discern if there are any data in the low inundation maps. To wit, should readers assume that the white is not inundated? (I’m not colorblind that I know of but very light blue and white are hard to discern…)\nFigure F8 square scatters? There’s probably a better term. Doublecheck – PDFs can have y-axis values greater than 1.0?\nFigure S9 The striking difference between southeastern Canada and the northeastern United States is eye-catching. I recognize that there are different data available depending on the country. Is that striking difference because of underlying geological and climatological forcings, or a function of the data source? That’s important to articulate in the manuscript…it certainly looks to be an artifact…\nFigure S12 Why are there pretty substantive areas of Mexico in this plot, but not in any of the above HUC plots? Where were the HUCs derived for Mexico?\nFS13 Add linear regression equation and R2 to the plot. Note that the linear nature is fairly evident w SSP585 but the SSP126 relationship does not appear to be linear. Why should you force a linear relationship on it?"}, "author_response": "Reviewer #4 (Remarks to the Author):\nXu et al. Revision to “Climate change will reduce inland wetland areas and disrupt their seasonal regimes in North America”\nThe authors have done well in responding to the multiple pages of comments that were made to an earlier version of this dense manuscript. I have tried to reread the paper with my original perspective and not find “new things” on which to comment. Most of the comments, therefore, are stylistic as my original comments were addressed by the authors. I did have a few areas that I think could use some additional touch, however, and note them below.\nResponse: We appreciate the thoughtful comments from the reviewer. This comment shows that the definitions used in the previous manuscript version required additional work, which is what has been done in the revised version (Line 627 – Line 649). For completeness of the response, we provide unambiguous definitions below.\nFirst, within the range of permanently inundated systems, we consider rivers, lakes, and reservoirs as water bodies representing different types of ecosystems from those present in permanently inundated wetlands. Thus, we excluded rivers, lakes, and reservoirs from the definition of wetlands. This is explicitly described in the main text (Line 167, Line 599, Line 629) and was also stated in the previous manuscript version.\nSecond, by design, our model simulates both permanent and non-permanent inundated waters. As described in the manuscript (Line 658 - 707), the model simulates fluvial and pluvial inundation process in the river floodplain and land components, respectively. For fluvial inundation, as correctly understood by the reviewer, the simulated inundated waters are not permanent, since the river flow does not always exceed the channel capacity (e.g., during drier seasons). However, for pluvial inundation, whether the inundation waters persist year-round or does not depend on the simulated water storage. The surface water storage in the model is determined by the balance between precipitation, evaporation, surface runoff, infiltration, and outflow (e.g., the inundated water in the wetland overflows when the water volume is larger than the wetland effective storage capacity). For some grid cells, due to very low evaporation and infiltration, the surface water storage can be above zero the whole year. As a result, permanently inundated areas can persist in some grid cells due to pluvial inundation. This can be confirmed by Figure R1a which shows the seasonal variation of the total simulated inundated area in an exemplary watershed (USGS 08020401 Lower Arkansas watershed). The inundated area of the watershed is always larger than 0 in both historical and future periods. In other words, the model simulates both permanently inundated waters and a seasonal expansion of wetland areas.\nThird, the model does simulate possible transitions of permanently inundated areas into seasonally inundated regions (as well as transitions in the opposite direction). Using the same example in Fig. R1, one can appreciate that the minimum inundation area simulated in the future period is mostly smaller than that in the historical period (Figure R1b). This is testament to the above statement that a fraction of the permanent inundation in this watershed is projected to transition to seasonal inundation in the future.\nTo expand on that point at the scale of the entire study domain, we analyze the change of annual minimum inundation between the future and historical periods for the study domain. Figure R2a and b show that many watersheds have annual minimum inundation larger than zero in both the historical and future periods. In addition, Figure R2c demonstrates that our model can simulate the transitions of both permanent inundation to seasonal inundation (i.e., minimum inundation decreases from historical to future periods), and vice versa.\nTo be consistent with how the model is applied (i.e., we excluded rivers, lakes, and reservoirs in our definition of wetland, see above), we removed permanent surface water bodies identified by Global Land Analysis & Discovery (GLAD; Pickens et al., 2020) dataset from GLAD seasonal surface water dynamics before this dataset was used for model calibration. Although the permanently flooded ponds and other permanent wetlands should be included in the benchmark dataset, they could be unintentionally excluded from the upscaled GLAD in above procedure. Additionally, it is still challenging to separate permanent wetlands from other permanent water bodies from GLAD. Therefore, the use of the upscaled GLAD dataset for calibration may result in an underestimation of the total inundation areas (i.e., seasonal + permanent inundation), and this bias can propagate to our model simulation through the calibration process.\nAlthough a different dataset, Global Lakes and Wetlands Database (GLWD; Lehner and Döll, 2004), provides a map for lakes, rivers, and reservoirs separated from wetlands, the summed areas of lakes, rivers, and reservoirs (e.g., ~510,000 𝑘𝑚!) are much larger than the permanent surface water captured in GLAD (e.g., ~280,000 𝑘𝑚!). Therefore, removing lakes, rivers, and reservoirs based on the GLWD map from the original GLAD dataset can result in smaller total inundation areas, than removing the permanent surface water bodies (i.e., rivers, lakes, reservoirs, ponds, etc.) identified in the GLAD product. It is also a matter of consistency that we “prepare” the model calibration data using the same dataset and not a hybrid one (i.e., a mix of GLAD and GLWD), which will introduce additional significant uncertainties.\nAs a result of the apparent confusion with model capabilities and setup of the experimental design, in the revised manuscript, we stressed that our model can capture both seasonal and permanent inundated waters at Line 687 – Line 690 and highlighted the relevant uncertainties of removing permanent water bodies at Line 595– Line 611.\n2071 2074 2077 2080 2083 2086 2089 2092 2095 2098\n2\n(a) Monthly simulated inundation [km ]\n220000\n115500\n110000\n5500\n00\n1971 1974 1977 1980 1983 1986 1989 1992 1995 1998\n2075 2080 2085 2090 2095 2100\n2\n6600 (b) Annual minimum simulated inundation [km]\n5500\n4400\n3300\n2200\n1100\n00\n1975 1980 1985 1990 1995 2000\nFigure R1. (a) Simulated total inundation areas of a watershed at monthly scales from historical period (1971-2000 on the bottom X-Axis) and future period under SSP585 scenario (2071-2100 on the top X-Axis). Subplot (b) shows the annual minimum total inundation areas of an exemplary watershed for both historical period (1971-2000 on the bottom X-Axis) and future periods (under the SSP585 scenario, 2017-2100 on the top X-Axis). The selected watershed is USGS 08020401 Lower Arkansas watershed located at Lon: -91.6587°, Lat: 34.2740°.\nFigure R2. (a) Averaged annual minimum total inundation fraction during (a) the historical period (1971 – 2000) and (b) the future period (2071-2100) under the SSP585 scenario. Subplot (c) shows the change of the total inundation fraction between the future and historical periods.\nThe use of the word wetland and inland wetland has many implications yet I feel the authors need to provide additional clarity to the readers as to what’s meant throughout the entirety of the paper (e.g., did they contrast the GIEMS data with their wetlands after removing the permanently inundated waters?).\nResponse: We removed rivers, lakes, and reservoirs (which we do not define as wetlands, per above) from the GLAD dataset to obtain a calibration set. As explained above, our simulated inundation does include permanently inundated waters. It is difficult to compare our results with the GIEMS dataset directly because the latter includes permanent surface water bodies (e.g., rivers, lakes, reservoirs, etc.) that we have removed from GLAD for calibration. Thus, we normalized our simulation and GIEMS dataset (e.g., remove the mean and divide by the standard deviation) to focus on the validation of monthly temporal variation and interannual variation. \nFurther, the study does not address “North America” but rather the conterminous US plus a smidge of Mexico and an area of southeastern Canada that – to me – appears to misbehave.\nResponse: We used exactly the same domain as used by the North American Land Data Assimilation System (NLDAS), which provides parameters and data inputs for modeling at the model spatial resolution (i.e., ~12.5𝑘𝑚×12.5𝑘𝑚). To further clarify the extent of our study area, we specified our domain covers the continental United States, southern Canada, and northern Mexico (25°−53° North) in the abstract and at Line 129 and Line 712 – Line 713 in the main text.\nThat is, the authors take pains to explain how ‘soil ice’ affects infiltration in that area of Canada…yet the Canadian government explicitly excludes that area from modern maps (2022) of permafrost (which, I can only assume, is what’s meant by soil ice).\nResponse: We thank the reviewer for bringing the new information about permafrost. First of all, we want to clarify that the model also doesn’t simulate permafrost. ‘Soil ice’ mentioned in the manuscript is not permafrost. Instead, ‘soil ice’ refers to seasonal freeze-up of near-surface soil moisture due to temperature falling below the freezing point. Please let us know if there is a better term should be used. In the manuscript, our conclusion is that the reduced content of soil ice due to global warming increases the infiltration rate.\nAnother major issue that I found was in describing for Nature Com readers the granularity of the study. Data from sources that are 30 m (GLAD) as well as 90 m were used, then interposed and coarse-grained to 0.125 x 0.125 arc degrees – which my hamfisted analyses suggest is around 120 km2. The point: wetlands, especially those that are pluvial in nature are generally pretty small. And models that are coarse, like this one, will miss a lot of the dynamics of those systems. Sure, explorations at CONUS scale require coarse-graining. But explorations of surface-water inundation dynamics in seasonally flooded wetlands require finer grained analyses – or at least readily digestible information for the reader to understand some of the grain-related limitations of the study. (I note that zooming into demonstrate the utility of their approach, at scales reasonable for readers to discern individual wetlands and/or complexes, would be useful and possible in the SI.)\nResponse: We agree with the reviewer that high resolutions (e.g., less than 30m) and possibly a different type of physics formulation (e.g., 2D overland flow model coupled with non-isothermal models of energy exchange above and belowground) can potentially yield additional details on the space-time variation of pluvial inundation process. Whether this would add anything substantive to the broad patterns of process interplay in the projected wetland changes in the 21st century elucidated by this study is unclear. We do not infer a clear reason from on why such an approach would be critically important or, rather, why the “zooming-in” exercise on individual wetlands and/or complexes would bring anything of substantial value to the novelty of results our research already demonstrates. We contend that it is out of scope for this research.\nBeyond the philosophical point of questionable added value to the broad patterns of change we already discover, we note that it is not currently computationally feasible to implement a high-resolution model at the continental (or even regional) scales, especially for the long-term climate-scale simulations carried out in this research. For example, it took us about 1.3 million CPU hours for the calibration and future ensemble simulations at our model resolution (e.g., ~12.5𝑘𝑚−12.5𝑘𝑚). We note that many impactful papers assessing climate change impacts on land-surface dynamics rarely used resolutions finer than 0.5°: clearly, there are persisting limits to what can actually be done given the state-of-science tools and computational facilities (e.g., Avis et al., 2011; Jung et al., 2010; Zhang et al., 2022; Zhang et al., 2023; Zhou et al., 2023). To our best knowledge, current large-scale wetland projections relied on over-simplified algorithms (e.g., deriving wetland based on ground water table) and even coarser spatial resolutions (e.g., 0.25°~1°) (Avis et al., 2011; Ekici et al., 2019; Xi et al., 2021), as compared to our study. The clear distinction of our work from these studies is that it aims to improve the representation of wetland dynamics in an Earth system model by (1) running the model at ~12.5𝑘𝑚×12.5𝑘𝑚, a relatively high spatial resolution (in the context of Earth system modeling); (2) using a physically-based inundation scheme (i.e., the simulated inundation can interact with other land-surface processes); and (3) calibrating uncertain parameters against satellite observations. We highlight our advancement in the introduction section (Line 138 – Line 164).\nSince we implement our model at ~12.5𝑘𝑚×12.5𝑘𝑚 resolution, we cannot show the simulated inundation dynamics below this resolution or at the resolution of satellite pixels. We only simulate the averaged inundation area for each ~12.5𝑘𝑚×12.5𝑘𝑚 grid cell. At a broader level in our attempt to capture the observed wetland dynamics, our model performance is very good - to - excellent, as demonstrated in Figure 1 (correlation = 0.84 and NSE = 0.7) and Figure S8 (validation against an independent dataset that was not involved in the calibration). This gives us confidence that the discovery of process interplay in the projected wetland changes is correct and the broad patterns of change are valid, even though the projection details at higher resolutions remain obscure.\nIn closing, there’s much the authors should be proud of in their analyses. So that others can replicate their approaches – including their conclusions – assumptions and limitations of the study to me should be much more clearly articulated and incorporated into the analyses so that the conclusions are contextualized in a more nuanced way (as befits a model analysis of this scale, scope, and conclusion).\nResponse: We appreciate the reviewer’s efforts of reviewing our revised manuscript. We have carefully addressed the comments in the revised manuscript. Please find our point-by-point response in the following.\nFor instance, throughout the figures I see that the model inundation maximum is 0.1 (e.g., Fig 1b). Is that indeed the case? For instance, the Everglades is an exceptionally wet area, nearly year-round. Are there not wetted areas that are inundated >0.1 of the time within the cell resolution? Or were the cells downscaled for visual display purposes? It would be useful to clarify for readers what the maximum inundation just may be within the area shown.\nResponse: The maximum inundation can be larger than 0.1 for some regions, for example, the Everglades as the reviewer suggested. We set the maximum value in the colorbar to be 0.1 for better visualization because most regions have maximum inundation less than 0.1. In the revised manuscript, we modified the label in the colorbar to be >=0.1.\nS2 comes after S3\nResponse: We change Fig.S2 to Fig.S7 in the revised manuscript to make sure the figures from the supplementary material are discussed in the correct order in the main text.\nL131 the average performance (NSE, R2) perhaps should be given in the main text so that readers can understand how well the model is performing without requiring a trip to the Supplemental Material section\nResponse: Thank you for the suggestion. In the revised manuscript, we added the performance metric at Line 131.\nWe lost S7 in the main text (so going from S6 to S8 will confuse readers); might as well do away w S7 by itself and have it as an inset in Figure S6.\nResponse: Thank you for the suggestion. Adding Figure S7 as an inset in Figure S6 will make the figure very busy. In the revised manuscript, we moved Figure S7 to the end of the supplementary materials as Figure S19. Then the supplementary materials figures in the main text are discussed in the correct order.\nL141 should perhaps add, “…(Fig. S9) when there is likely significant over-estimation of surface water extent.” I added that as S9 more-or-less follows expected snow abundance in the mountainous areas of the country (and Pennsylvania, fwiw). Those areas with high wetlands in the winter are areas with a great deal of snow (noted by the authors, but that it’s an over-estimation indicator should assuage some readers who will otherwise see it as tracking mountain snow).\nResponse: Thank you for the suggestion. We added this at Line 140 – Line 141 in the revised manuscript.\nL353-354 adding this information into the abstract, if there is room, my increase the interest across readership\nResponse: The impacts of climate change on the major wetland habitats were mentioned in the abstract at Line 24 – Line 26. However, due to the word limit in the abstract, we cannot specify the specific values of the relative changes.\nL407 change “significantly” to “substantially” as the implication of the stat-loaded word is that the ~5% decrease is not significant whereas the ~10% decrease is (i.e., it begs the question of significant when compared to what, through what statistical test, and with what reported p-value and assumptions)\nResponse: We changed “significantly” to “substantially” as the reviewer suggested.\nL430 “Other study also projected” needs to be rephrased\nResponse: We modified it to “Other study also suggested a potential loss of wetland…”.\nL434 increasing infiltration is a fine rationale, but I caution the authors to include a caveat – what happens to your increased infiltration when the available soil-water holding capacity is exceeded? There’s a finite amount of space for water to go, and those areas are typically pretty water-logged to begin with. If that caveat isn’t incorporated into this discussion (rationalizing the findings of this study versus others) it will leave your conclusions open to a sharp retort.\nResponse: In E3SM land model (ELM), the infiltration rate is constrained when soil-water approaches the soil-water holding capacity. For example, the infiltration rate is zero when the soil is saturated (See Eq (3) in the Methods section). In addition, if the vertical groundwater flow is larger than the lower soil layer capacity, the additional water is discharged to the surface water storage and become standing surface water. Therefore, the situation mentioned by the reviewer is well considered in our model process. We clarified this at Line 464 – Line 466 in the revised manuscript.\nL444 Considering that the maximum inundation in the figures presented is 0.1 (e.g., Figure 1(b)), there’s not much room for the wetland organisms to operate within (as 0.0 to 0.1 is rather small relative to 0.1 to 1.0). So in essence there’s not much of a threshold in the authors’ model to ‘allow’ for wetlands.\nResponse: The maximum inundation fraction of a grid cell can be much larger than 0.1 over some regions. We set the range of the colorbar to be from 0 to 0.1 for better visualization. Given our simulations are at spatial resolution of 0.125°×0.125° (i.e., ~12.5𝑘𝑚×12.5𝑘𝑚), an inundation fraction of 0.1 represents substantial inundation area, for example, 0.1×12.5𝑘𝑚×12.5𝑘𝑚 = 15.625 𝑘𝑚#.\nL478 This is also due to human issues associated with naming conventions. For instance, the Ramsar classification of wetlands includes lakes (e.g., https://www.dcceew.gov.au/water/wetlands/ramsar/wetland-type-classification and Richardson, D. C., M. A. Holgerson, M. J. Farragher, K. K. Hoffman, K. B. S. King, M. B. Alfonso, M. R. Andersen, K. S. Cheruveil, K. A. Coleman, M. J. Farruggia, R. L. Fernandez, K. L. Hondula, G. A. López Moreira Mazacotte, K. Paul, B. L. Peierls, J. S. Rabaey, S. Sadro, M. L. Sánchez, R. L. Smyth and J. N. Sweetman (2022). \"A functional definition to distinguish ponds from lakes and wetlands.\" Scientific Reports 12(1): 10472.) It would be remiss to not acknowledge that this stems from definitional issues – likely moreso than by other “science-based” issues.\nResponse: We added this reference in the revised manuscript at Line 411 to acknowledge lakes are counted as wetland in other classification systems.\nL520 fix ref^43 (same w L558, and L591, elsewhere to ensure citations comport to journal standards\nResponse: We changed it to ref. 43 after we checked the published paper from Nature Communications.\nL614 It would be interesting to contrast a small number of focal areas to see how many wetlands were included in the GLAD data versus wetlands in the National Land Cover Dataset (NLCD, classes 90 and 95, I believe). Reporting those data (around L614) would not invalidate any findings of this study but would provide context associated with scale and resolution (and expectations for subsequent researchers). That's to note NLCD can be used as the benchmark by which the GLAD wetland abundance can be contrasted so readers will know whether GLAD wetlands hit or miss the mark - and NLCD at 30m will be a high bar, but useful (and more useful than things like the GLWD, which is coarse and dated).\nResponse: Thank you for your comment. We compared GLAD and NLCD for two 1°×1° regions from Mississippi river valley and Prairie Pothole Region, respectively, in Figure R1. We included this figure in the supplementary materials Fig. S17 and referred it in the revised manuscript at Line 529 – Line 530. This comparison suggests the GLAD dataset may underestimate woody wetlands defined in NLCD. However, it is not clear to us if NLCD captures the wetlands accurately since NLCD was derived from Landsat images as well and uncertainty is inevitable."}]

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