African inland wetland area on the rise during the 21st century

Domain: chemistry
Published: 2026-03-06
Editorial outcome: major_revision

Abstract
Wetlands face threats from climate change and human activities worldwide, yet the status of African wetlands remains unknown. This study mapped African wetlands and assessed area loss, drivers, and future trends under climate change using 270,000 sampling points, 810,000 Landsat images, and soil moisture data from 14 CMIP6 models. The results reveal no large-scale loss of wetlands in Africa from 1984 to 2021 (0.51% net loss), with the loss concentrated in coastal areas (9.64% net loss), while inland wetlands show a slight increase in area (0.50 % net increase). A comparison of the time series of wetland area and related drivers showed that the change of inland wetland area is closely related to climate change, and human activities have exacerbated the loss of coastal wetlands. TOPMODEL projections suggest an upward trend in inland wetland area by 2100, but uncertainty persists and inland wetlands remain at risk of loss in the future.

Introduction
Wetlands are critical to achieving global commitments related to biodiversity, climate change, and sustainable development1. Since the signing of the Ramsar Convention in 1971, protecting wetland ecosystems has been officially recognized as an international priority2,3. The Aichi target set by the Convention on Biological Diversity in 20104 and Sustainable Development Goal target 6.6.1 of the 2030 Sustainable Development Agenda5, along with the Paris Climate Agreement, are closely linked to wetlands6. Despite the widespread distribution of wetlands around the world, some evidence indicates that wetland areas are rapidly decreasing7-9. According to the latest research, global wetlands have decreased by 21% since 1700. This wetland loss has been concentrated in Europe, the United States, and China and has expanded rapidly during the mid-twentieth century2. Africa remains the least developed region in the world, and its wetland status and loss remain a matter of debate10. Whether large-scale losses of African wetlands have occurred in the past, what drives them and how they will change in the future have always been topics of interest. Understanding the value of wetlands and the ongoing changes they are experiencing is essential to ensure their protection and rational use11,12. Accordingly, we conducted the remote sensing mapping of African wetlands using an approach that combined high spatial resolution and long-term series over the past 38 years (1984-2021). From the data collected, we systematically analyzed and studied three aspects of African wetlands: historical loss, change drivers, and wetland change under future climate change.

Following the definition of wetlands presented in the Ramsar Convention on Wetlands3, this study conducted a consistent analysis of the distribution of and changes to African wetland ecosystems in eight subcategories of two major categories—inland wetlands and coastal wetlands (Supplementary Fig. 1 and Table 1) 10. We began by using remote sensing methods to classify and map African wetlands from 1984 to 202113,14. We extracted African wetlands as systematically and accurately as possible by evenly arranging as many as 270,000 sampling points (Supplementary Fig. 2) at intervals of 0.1° (latitude) × 0.1° (longitude) throughout Africa. We then confirmed the wetland type of each individual sampling point through visual interpretation. Based on these sampling points, we used the random forest classifier to classify and extract African wetlands in 9 periods from 1984 to 2021 on the Google Earth Engine (GEE) platform. After completing this high-resolution mapping of African wetlands in the historical period (1984-2021), we counted the time series of African wetland area and used them for a comparative analysis with the time series of climate change (including temperature, precipitation, drought, and soil moisture)15-18 and human activities19,20 to assess the impact of different drivers on wetland changes21. To further investigate the loss of wetlands in Africa under future global climate change, we used CMIP6 climate data22,23 and the TOPMODEL-based diagnostic model24 to predict and analyze changes in inland African wetlands by 2100 based on the classification results of historic periods (Supplementary Fig. 3). The TOPMODEL method, the most popular model for wetland simulation, has been widely applied to downscale mean water table depth in a catchment or grid scale to wetland area based on local topography distribution25-27. The present study used the SM data produced by 14 models of the CMIP6 project (Supplementary Table 2 ) as the input for its discussion of the changes in the area of inland wetlands in Africa under the four shared socioeconomic pathway (SSP126, SSP245, SSP370, and SSP585) scenarios by the end of the current century (more details in the Methods section).

Results
Wetland variation in Africa from 1984 to 2021

Our results reveal that no large-scale loss of African wetlands occurred on the whole from 1984 to 2021. The possibility of a huge loss of more than 50% of global wetlands28 has been met with skepticism in light of the fact that the phenomenon occurs only in certain wetland categories in some specific regions29, such as North America, Europe, and Asia, and cannot be extrapolated to the broader geographic regions or other wetland categories9,30. Africa represents an important part of the world’s wetland ecosystem, leading to intense scholarly interest in the possibility of large-scale losses over the past 38 years31. However, our study found that from 1984 up to date, although Africa had lost about 138,500 km2 of wetlands, these losses were offset by a gain of 132,400 km2 (Supplementary Fig. 4), indicating that Africa’s wetlands, even though slightly reduced in overall area, have not experienced loss on a large scale. According to our statistics, the total area of African wetlands in 1984 was 1,200,800 km2 (excluding shallow marine water) and net loss area was about 6,100 km2 over 38 years, accounting for only 0.51% of the wetland area in 1984 (Supplementary Table 3).

The loss of wetlands in Africa varies with their wetland category and zonality (Fig. 1). Among all eight wetland categories, only inland surface water, inland salt pan, and shallow marine water areas increased, demonstrating gains of 3.82%, 5.80%, and 12.28%, respectively (Here and the subsequent wetland area change were net change). In contrast, all vegetation-covered wetland categories experienced area loss over the same time span. The two categories with the largest area loss over the 38-year period were inland swamps and coastal marshes, which showed a net loss of 6684.40 and 4406.23 km2, respectively. Am ong the wetland categories where loss occurred, we noted a much higher rate of loss in coastal wetlands compared to inland wetlands in Africa: specifically, tidal flats were decreased by 12.2%, coastal swamps reduced by 9.50%, and coastal marshes declined by 8.65%. As for inland wetlands, the areas of inland swamps and inland marshes decreased by 1.60% and 0.22%, respectively. We also found an interesting phenomenon when reviewing the statistics at the country level in that the countries featuring increased wetland area were mainly concentrated in north of the equator, while the wetland area of southern African countries decreased to some extent (Supplementary Fig. 5). Among all countries, the Democratic Republic of the Congo exhibited the most serious wetland loss, a decrease of about 12,000 km2 from 1984 to 2021, which could be mainly attributed to the loss of forest wetlands in the Congo Basin21,32. Additionally, the entire southern part of Africa (south of 10°S latitude) demonstrated a considerable wetland loss and little wetland growth compared to the other parts of Africa.

The 38-year time span allowed us to study African wetlands in terms of time series and trends. Accordingly, we discovered different trends when comparing changes in the areas of coastal wetlands and inland wetlands in Africa from 1984 to 2021 (Fig. 2; Supplementary Fig. 6). The coastal wetland (excluding shallow marine water) lost 11,500 km2 in area over 38 years, comprising nearly 9.64% of the whole, and its area change time series revealed a completely linear decline trajectory29. This observation indicates that the coastal wetland ecosystem, which is at the same time one of the most productive and highly threatened systems in the world, has been severely damaged in Africa, as in the other areas of the globe, which is basically consistent with the conclusions of other studies13,14,33. Compared with the large-scale loss of coastal wetlands, the time series of inland wetlands displayed a slight upward trend, with a net increase of 0.50% in area. On the whole, from 1984 to 2021, African wetlands did not increase or decrease linearly but followed a strange trajectory that can be characterized as slightly decreasing overall, followed by a significant decline in a specific year (the middle year 2005, 2017) and then recovery to some extent. We believe that the large-scale loss of coastal wetlands within the 38-year period has offset the increase in the area of inland wetlands, resulting in a slight decline in the total area of wetlands in Africa as a whole34. It should be noted that due to missing remote sensing imagery and cloud contamination, the classification results of this study differ from existing datasets such as GIEMS, WAD2M, and GWL-FCS30. Consequently, current research conclusions—particularly for periods before 2000—remain subject to uncertainty. A detailed comparison between our results and these datasets, along with explanations of the differences, is provided in the Supplementary Information.

Driving forces for African wetland variation

Global coastal wetlands are highly threatened by human activities, and Africa is no exception 14,29. This study used the Human Footprint dataset (HFP) to assess the intensity of human disturbance in the coastal and inland wetland regions of Africa from 2001 to 202020,35. Based on our analytical results, within those two decades, the average HFP in the coastal wetland area of Africa was 13.46, while that in the inland area was 6.79, indicating that the degree of human disturbance in the coastal wetland area was approximately twice as much as that in the inland wetland on average (Supplementary Fig. 7). It is worth noting that HFP in both coastal and inland regions grew at a high rate. Compared with 2000, HFP in inland and coastal regions of Africa increased by 11.02% and 11.91%, respectively, by 2020 (Supplementary Fig. 8). Population growth and increasing economic development have caused a certain degree of damage to coastal wetlands around the world (including Africa). The primary direct driver for the loss of coastal wetlands has been converted to other land uses (aquaculture, agriculture, coastal developments, etc.), which is the general consensus of the international community29,33,36. Although human interference contributed less to changes in inland wetlands in Africa, the signs of human activities are increasing year by year, demanding increased public attention. Of course, in addition to the interference posed by human activities, sea-level rise, storm erosion, and other unique drivers in coastal areas have caused some damage to wetlands, comprising one of the reasons why coastal wetland losses are higher than those of their inland counterparts37-40.

Climate change also has had a certain impact on African inland wetlands. In this study, we compared the correlation between climate drivers (temperature, precipitation, drought, and soil moisture) and wetland area changes in Africa over the past 38 years from the perspective of time series (Fig. 3)41. As a key indicator of climate change in Africa42, the temperature increased almost linearly by about 1°from 1984 to 2021, but this change in temperature did not bring about a corresponding linear increase or decrease in wetland areas in Africa. Interannual precipitation in Africa exhibits substantial uncertainty across different data products43, and the spatiotemporal patterns of precipitation from 1984 to 2021 are not consistent at the continental scale. Notably, decadal-scale variability plays an important role in explaining precipitation trends and the episodic (phase-like) changes in wetland extent: the episodic increases and decreases of wetland area in some regions can be partly attributed to internal climate oscillations on decadal or longer timescales rather than to sustained linear trends. Although some regions show moderate correlations between wetland area and precipitation, this relationship is highly spatially heterogeneous and temporally non-stationary (see Supplementary Information). Moreover, hydrological processes such as evapotranspiration, surface runoff, and groundwater recharge also modulate the effect of precipitation on local wetlands44-46; therefore, integrated, region-specific analyses are required. As one of the extreme climate phenomena caused by climate change, drought had little impact on African wetlands as a whole yet showed a high degree of consistency with wetland changes locally18,47. The forest wetlands in the Congo Basin have been on a downward trend in recent years, which is connected to the continuous drought in the region (Supplementary Fig. 9)21. Soil moisture, as a response factor to the drivers of climate change such as temperature, precipitation and evapotranspiration, reflects the state of wetlands to some extent, and is the most relevant climate factor to wetland changes24. The loss and subsequent recovery of African wetlands before 2005 and the increase in area after 2017 were consistent with the changes in soil moisture.

The above analysis uncovers a strong correlation between inland wetlands and climate drivers. Of course, analyzing the impact of climate change on wetlands only from the perspective of time series was rather one-sided, not only because of the complex relationship and feedback between climate (precipitation, temperature, extreme climate, etc.) and wetlands but also because of the possible synergistic effect of climate drivers and human factors37. Our work demonstrates that continuous monitoring, experiments, and simulations are still needed to understand the drivers of wetland changes to fundamentally solve the problem of wetland loss.

Inland wetland trends under future climate trajectories

In the 21st century, the annual average temperature in some regions of Africa may rise at about 1.5 times the rate of global temperature increase23,48, while the frequency and intensity of precipitation remain more uncertain49-51. Global climate change is expected to exacerbate the loss and degradation of many wetlands. However, predictions of this loss have not been quantified29. In this study, the CMIP6 SM data and the TOPMODEL-based diagnostic model were used to simulate and analyze changes in African inland wetlands under the influence of climate change in the future (to 2100)23,24 (more details in the Methods section). According to our simulation results, the area of African inland wetlands shows an upward trend over the next 70 years under all of the SSP scenarios, whether SSP126, SSP245, SSP370, or SSP585 (Fig. 4). Even though the soil moisture data of the 14 models vary greatly, and with the extension of the simulation time, the uncertainty of the simulation results was also increasing. However, most of the models showed that the impact of climate change on inland wetlands tended to be positive, and the average wetland area based on the 14 models increased steadily under the four SSP scenarios (Supplementary Fig. 10).

Similar to the changes observed in historical periods, African inland wetlands will also exhibit different degrees of loss in area over the next 70 years, but the losses will be offset by higher gains (Supplementary Fig. 11). Based on the average of the results of 14 simulations, it is found that the inland wetland area will have a net increase of more than 10 % under the SSP126/245/370/585 scenarios from 2023 to 2100. The future growth areas of inland wetlands are mainly concentrated in northern Africa, especially in the sub-Saharan Sahel region. In the SSP126 scenario, which features more moderate climate change than the others, 17% of Africa’s regions will experience wetland area growth; in comparison, the SSP585 scenario predicts that 35% of the regions will experience wetland area expansion (Fig. 5).

The results of the 14 models show a positive trend, but uncertainty still persists and a risk of future wetland loss remains (Fig. 6). In particular, even under the SSP126 scenario of green sustainable roads, the simulation results point to a net loss of about 63,000 km2 of wetlands in Africa. These losses can be severe, permanent and with negative implications of specific wetland ecosystem types that cannot be “offset” by gains elsewhere. From the perspective of spatial distribution, the future areas of wetland loss in Africa are located mainly in the Congo Basin and western Africa; comparatively, the loss of wetlands in North Africa (Mediterranean coast) is also serious, although the region does not have much wetland distribution. It is worth noting that the loss of forest wetlands in the Congo Basin is at risk of further aggravation. The simulation of the four SSP scenarios illustrates a downward trend in Congo Basin, which is basically consistent with the trend of the classification results in the historical period and is also consistent with previous research findings 21,32. In the SSP370 and SSP585 scenarios featuring more severe levels of global climate change, the Okavango Delta, Etosha National Park, and Zambia National Park in southern Africa are also at risk of wetland loss.

Human activities, climate change, and other factors jointly drive future changes in African wetlands; conversely, wetland changes will have both direct and indirect impacts on human well-being. In recent years, the COVID-19 pandemic has fundamentally people’s perceptions of health and the environment. It has been realized that unsustainable use and inappropriate management of wetlands can extend beyond the loss of ecosystem services to include direct risks, including diseases1. As a vital component of the world’s wetland ecosystems, the relatively well-preserved wetland ecosystems in Africa (compared to countries or regions such as Europe, China, and the United States2) have made critical contributions to global biodiversity and climate change in the past and will play an increasingly important role in the future. In addition, as the most easily reclaimed and cultivated land type, wetlands have played a vital role in the livelihood of African people for thousands of years52. It is important to note that Africa's wetlands have not experienced large-scale overall loss, but there has been substantial reduction in certain regions (such as the Congo Basin, Southern Africa) and in coastal wetlands. The quality of Africa's wetlands still needs to be assessed, as stable or expanding wetland areas do not necessarily indicate a favorable condition. The degradation of the ecological functions of existing wetlands continues to pose serious threats to local communities, such as the shrinking of Lake Chad's surface area. The future expansion of Africa’s wetland area offers the potential to provide food and water for more people and alleviate poverty in remote rural areas. However, it must be realized that the simulation of this study is only an assessment of the future trend of African inland wetlands. The impact of extreme weather (floods, droughts, La Niña phenomenon, etc.) and the direct destruction of wetlands by human activities make future wetland changes highly uncertain. Therefore, we suggest that African government departments should remain sensitive and vigilant and consider these findings in future planning, including disaster management planning and risk assessment, so that African wetland ecosystems can play a key role in responding to the decisive challenges of our era.

Using high spatial resolution maps of African wetlands over the past 38 years, this study analyzed the drivers of wetland loss and change in Africa. Based on the analytical results, future changes in African inland wetlands were further predicted under different SSP scenarios. Our findings support the following conclusions: (1) There was no large-scale loss of African wetlands from 1984 to 2021. The losses were concentrated in the coastal areas, the Congo Basin and southern Africa. (2) The primary drivers of changes in coastal and inland wetlands in Africa were different. Climate change was closely related to the change of inland wetlands, while human activities had a greater impact on coastal wetlands. (3) There was a trend toward the further expansion of inland wetlands over the next 70 years, but uncertainties still exist and a risk of future wetland loss remains.

An in-depth understanding of wetlands is fundamental to their conservation and utilization. Even in the context of rising wetland area, irrational human activities (e.g., damming, reclamation, and irrigation) can still bring humanitarian disasters to residents, and the focused conservation of Africa’s wetlands remains urgent and necessary. Importantly, governance should pursue integrated strategies that protect wetland functions while enabling sustainable economic growth and improved livelihoods; our findings are not intended to discourage development, but to inform development pathways that are compatible with wetland conservation and community resilience. We hope that our work will contribute to improving the management and conservation of African ecosystems and, in the process, help support those who depend on these ecosystems.

Methods
Wetland classification system

Africa, the world 's second largest continent, accounts for about one-fifth of the total land area of the earth53. It was estimated that the global wetland area was nearly 9.2 million square kilometers, of which 1.3 million square kilometers were located in Africa12,54,55. Due to the different definitions and mapping methods of wetlands, existing studies have different estimates of the spatial extent of wetlands around the world, including Africa56.

This study’s investigation was based on the definition of wetlands proposed by the Ramsar Convention on Wetlands, as “areas of marsh, fen, peatland, or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed 6 m.” Accordingly, we proposed a wetland remote sensing classification system in line with African realities. African wetlands are divided into two categories, inland wetlands and coastal wetlands. Specifically, inland wetlands include the sub-categories inland surface water, inland marsh, inland swamp, and inland salt pan. Coastal wetlands include the sub-categories coastal marsh, coastal swamp, tidal flat, and shallow marine water (Supplementary Table 1)10. This study only distinguishes natural wetlands since paddy fields, for example, are less widely distributed in Africa and are more suitable for classification as farmland57.

The scope of this study is the entire African continent and its affiliated islands, including 61 countries or regions, with an area of about 30 million km2. The specific boundaries used in the study were determined based on the boundaries of African countries in the large-scale international boundary (LSIB) dataset. Based on this boundary, the coastal area boundary was extended to a depth of 6 m. The water depth data were drawn from the global relief model of Earth’s surface (ETOPO1)58 (Supplementary Fig. 1).

Remote sensing classification method

In this study, the wetland remote sensing classification and mapping work in the historical period of Africa were mainly divided into four parts: image preprocessing, wetland sampling, feature construction, and classification mapping (more details in the Supplementary Information).

Remote-sensing image preprocessing included standard cloud-and-shadow masking and noise removal. We applied robust annual compositing (e.g., multi-temporal median composites) to suppress short-term cloud contamination. To evaluate the effectiveness of these procedures, we compared our outputs against independent, cloud-insensitive reference products (for example, GIEMS). These comparisons indicate that, owing to the substantial improvement in Landsat data coverage and revisit frequency after 2000 and to our compositing plus multi-dataset validation strategy, the wetland time series constructed for the period after 2000 show no evidence of a systematic bias attributable to cloud contamination. By contrast, data before 2000 are affected by larger image gaps and persistent cloud cover, resulting in reduced sample size and representativeness and therefore greater uncertainty in quantitative trend estimates. Relevant comparison figures and statistical summaries are provided in the Supplementary Information.

The random forest method in GEE was used to classify wetlands59. This algorithm has various advantages, including high accuracy, the ability to effectively run on large datasets, and the capability to evaluate the importance of each feature in classification problems; in addition, the algorithm does not over-fit the training samples60. Before performing formal classification, several experimental areas were selected in Africa, such as the Congo Basin, the Sudd Wetland in South Sudan, and the Okavango Delta, using different supervised classification classifiers and a variety of wetland classification feature combinations for classification experiments. Finally, random forest classification was identified as meeting our research requirements with its fast and accurate characteristics. In the process of classification, the sampling points obtained by visual interpretation were randomly assigned in a ratio of 7:3, meaning that 70% of the sampling points were used to train the classifier, while the remaining 30% were used for accuracy verification.

Since the study area covered an area of about 30 million km2 across Africa, coupled with a large number of sampling points and training datasets, it was impossible to classify and extract wetlands throughout Africa at one time, even in GEE. Therefore, the entire African land mass was divided into 22 sub-regions, and each region was classified separately, after which the results were spliced to obtain a complete African wetland classification result.

Area statistics and time series

The wetland area involved in this study was based on the wetland classification map. A total of 9 wetland maps were generated from the classification of African wetlands in historical periods (more details in the Supplementary Information). The area of all wetland pixels was accumulated in each of the wetland maps with a spatial resolution of 30 m in the GEE platform to obtain the wetland area of each of the 9 periods.

The study’s time series of drivers, such as climate and human activities, also entailed calculating according to the same time and spatial resolution as the classification. The data regarding temperature, precipitation, PDSI, SM, and HFP were uniformly resampled to a resolution of 30 m, whereupon the mean values of the main distribution areas of wetlands in 9 periods were calculated. Ultimately, the time series consistent with the spatial and temporal resolution of wetland area was obtained, which we then compared with the wetland time series to analyze the correlation and consistency. The trend lines in the figures of the paper were obtained via one-dimensional linear fitting based on the time series data, and the R2 value was given.

Human activities and climate data

The study used the Human Footprint to assess the overall impact of human activities on wetlands. The HFP is a composite index calculated as a weighted sum of eight classes of human-pressure maps, including maps of population density, infrastructure, human accessibility, and energy supply. The higher the value, the greater the human pressure on the environment19.HFP data comprise the most widely used index to measure human impact in the world and show the gradient of human impact. The HFP map space used in this study has a total of 21 maps, with a resolution of 1 km, from 2000 to 202061.

In order to evaluate the climate drivers affecting wetland changes, we used climate data, such as temperature, precipitation, drought index (PDSI), and SM, for our analysis. The temperature and precipitation data were from the ERA5 monthly comprehensive data provided free of charge in GEE, meaning the average temperature at 2m height (monthly average) and total precipitation (monthly sum). ERA5 refers to the fifth-generation atmospheric reanalysis data of the European Centre for Medium-Range Weather Forecasts (ECMWF) for global climate. The drought index was derived from the Palmer Drought Severity Index (PDSI) data in TerraClimate data15. TerraClimate is a global dataset featuring monthly land surface climate and climate water balance data with a resolution of 4,638.3 m. PDSI values range from -4317 to 3418; the smaller the value, the higher the degree of drought. SM data include both actual observed data and simulated data from CMIP6 (more details in the Supplementary Information).

Future Wetland Simulation

The simulation process of African inland wetlands in the future period was mainly divided into three stages (Supplementary Fig. 3). The first stage was the remote sensing classification and mapping for wetland of the historical period (1984-2021). The 10-period wetland maps produced in this stage, and the subsequent analysis of change drivers, showed that the inland wetlands in Africa were closely related to the climate, and climate data could be used to predict future changes in inland wetlands. The classification results of the historical period were also used to determine some parameters (such as M) in the TOPMODEL-based diagnostic model and to verify the subsequent simulation results.

The second stage was the inland wetland simulation of historical periods (1984-2021), that is, TOPMODEL-based diagnostic model with soil moisture from the FLDAS dataset. The main purpose of this simulation was to determine the specific values of some parameters in the simulation process and verify the accuracy of the simulation method. Different from soil moisture data simulated in CMIP6, soil moisture data obtained from FLDAS was used as the real value of soil moisture in this study. The WTD was first calculated based on FLDAS soil moisture data (Equations (4)), and then calculated the average WTD and CTI of more than 40,000 basins in Africa. Since the range of M is set to 1-15, the CTI threshold from 15 values of M is generated (Equations (2)), and then 15 corresponding African simulated wetland areas are obtained. According to the classification results of historical periods, the RMSE corresponding to 15 M values was calculated, and then determined the M value of 40,000 watersheds based on the minimum RMSE. After calculating the CTI threshold using the best fitted M value, the TOPMODEL simulated wetland area based on FLDAS soil moisture for 9 periods was finally obtained.

The third stage was the inland wetland simulation of the future periods (2015-2100), that is, TOPMODEL-based diagnostic model with soil moisture from the CMIP6 dataset. Similar to the previous simulation process, the WTD was calculated under four future scenarios based on the soil moisture data of 14 CMIP6 models, and then the inland wetland area from 2015 to 2100 in four SSP scenarios was obtained. In addition, in order to better demonstrate the long-term sequence changes of African wetlands, the inland wetland area changes were also simulated during the period 1984-2014 based on the CMIP6 soil moisture data under the ‘historical’ scenario, and compared the differences between the historical period simulation based on CMIP6 data and the actual observation results (remote sensing classification) (Supplementary Fig. 12). More details of the product evaluation are available in the supplementary information.

Wetland classification accuracy assessment

In order to make the extraction of wetlands as accurate and detailed as possible, about 270,000 visual interpretation sampling points were arranged throughout Africa, of which 30 % of the sampling points were not used for the training of random forest classifiers, but for the accuracy verification after classification as independent verification points, that is, the calculation of confusion matrix. The results of the remote sensing error matrix showed that the wetland maps of 9 periods from 1984 to 2021 had high accuracy. Moreover, with the passage of time, the quality and quantity of Landsat images have improved, the classification accuracy has also increased slightly (Supplementary Fig. 13 and Table 4). On the whole, the average producer accuracy of wetlands in 9 periods was 80.76 %, and the average user accuracy was 91.77 %. Even in the poor image quality of 1984-1990, the accuracy of wetland producers and users can reach 77.97 % and 91.52 %. For the subdivided wetland categories, surface water, coastal swamp, and tidal flat were wetland categories with high classification accuracy due to their obvious and unique remote sensing characteristics, with an average accuracy of more than 85 %. The classification accuracy of herbaceous marshes and inland salt marshes was relatively low, about 70 %. More details of the product evaluation are available in the supplementary information.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_2": "• The overall observation of a rise in wetland coverage in the future and the downplaying of wetland loss in the past goes against a lot of independent reports and academic articles. It is well-documented that population explosions and the increasingly drying of the African continent will have dire consequences on the environment and wetlands. The authors need to ensure rigorous error analysis (i.e. error propagation and how it influences the rate of change over the time series before future projection; and the error linked to the TOPMODEL etc. in the case of future projection) to ensure that the report change is actual change and not propagated model error. This point is the major sticking point in this article and would need to be addressed by the authors in a detailed analytical way. Such resources which conflict with the outcomes of this study are available below (to name a few):\n1) TOWARDS THE SUSTAINABLE USE OF AFRICAN WETLANDS – John Pascal Simaika, Anne Alje Van Dam and Albert Chakona (eds). Frontiers in Environmental Science; https://www.frontiersin.org/research-topics/9466/towards-the-sustainable-use-of-african-wetlands\n2) https://gga.org/protecting-africas-wetlands-a-critical-component-of-environmental-governance/\n3) https://www.wetlands.org/new-report-degradation-wetlands-sahel-wrecking-lives-driving-migration/\n4) https://link.springer.com/article/10.1007/s00027-012-0259-2\n5) https://www.tandfonline.com/doi/full/10.1080/00207233.2018.1494931?casa_token=ZOeqNdTl_d0AAAAA%3A3oj_snqm_Yg52Wc8dIbTC63YDl-V1vZKUFcxGlwe2d8qfzkyPM7ZBOV3YrUf3FR0-EvdBBVxJqROOQ\n• The introduction is missing a section on the details of threats to wetlands which could result in a wetland loss as well as the other side of the coin concerning wetland gain (what conditions/factors increase wetland distribution?)\n• The HHI index needs to be expanded upon – what parameters drive it and what remote sensing variables were used to create the index? What does the scale mean when interpreting the index? Not clear\n• Pg8; lines 5-7: Has population increased along the coast more so than internally (to warrant wetland loss)? You need to additional contextual support from the literature here.\n• Future wetland trajectory: it would be great to breakdown the influencing parameter from these climate models to support this trend (e.g. is an increase in rainfall/SM/Temperature expected?). Also how did this section of analysis take into account the anthropogenic aspect?\n• Pg12; lines 9-11: Is an increase in temperature and increase in CO2 really support an increase in wetland coverage? What about future precipitation and SM trends? Greater digging and literature support are required here.\n• Pg 17: Were palustrine or vegetated wetlands (with no surface water present) included in the analysis?\n• Pg18: What was your source of an independent test dataset? How was the model calibration and validation dataset collected and from what source? Later in the methods section, you mention ‘visual inspection’ – how do you maintain impartiality and reduce sampling bias?\n(Remarks on code availability)\nThe code is clear and easy to replicate in the Google Earth Engine interface. The incorporation of an independent validation dataset from a variety of local sources would have helped improve the validity of this scripted outputs immensely.", "reviewer_3": "Review on “African inland wetland area on the rise 1 during the 21st century”, submitted to Nature Communications, by Li et al\nAfrican wetlands form an integral part of the continent’s ecology, so this study is making a valuable contribution to the understanding of the present and future dynamics of African wetlands. However, since climate change is a consideration here, more of the current literature for Pan African needs to be included. The work presented supports the conclusions and claims, but there may be additional important work required to justify the data interpretation and conclusions (significance of trends). Notwithstanding, enough detail is provided on the methods in order to enable reproduction of the work.\nMajor concerns\n1. Pan African climate change work needs to be included:\na. Engelbrecht F.A., Adegoke J., Bopape M-J., Naidoo M., Garland R., Thatcher M., McGregor J., Katzfey J., Werner M., Ichoku C. and Gatebe C. (2015). Projections of rapidly rising surface temperatures over Africa under low mitigation. Env. Res. Letters. 10 085004. https://doi.org/10.1088/1748-9326/10/8/085004.\nb. Dosio A., Jury M.W., Almazroui M., Ashfaq M., Diallo I., Engelbrecht F.A., Klutse N.A.B., Lennard C., Pinto I., Sylla M.B. and Tamoffo A.T. (2021). Projected future daily characteristics of African precipitation based on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX‑CORE) climate models. Climate Dynamics 57 3135–3158. https://doi.org/10.1007/s00382-021-05859-w.\nc. Dosio A, Lennard C, Spinoni J. Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations (2022). Climatic Change 70 13.\n2. Page 4, line 3: Yes, but for some areas their small-scale losses may have huge ecological impacts. These impacts may be more visible if the study area is made smaller, e.g., only looking at the southern African region as opposed to the whole Africa.\n3. Page 6, line 12: The decrease seems small over southern Africa, but is the ~2% loss from a Pan African perspective or from a southern African one?\n4. A number of figures in the main text and also the supplementary material indicates linear trends, but without showing what the statistical significance levels of these trends are, the trend results are irrelevant."}, "author_response": "1) Response: We would like to express our gratitude to the reviewer for his/her suggested comments. The feedback and critiques not only helped us identify shortcomings in our manuscript but also provided a clear guidance for an further improvement in our research work.\nThe reviewer mentioned that the conclusions of our writing contradict many independent reports and academic articles, so we would like to clarify this point further. The statement in our manuscript that \"there was no large-scale reduction in African wetlands from 1984 to 2021\" may have been somewhat misleading. We acknowledge this miscommunication in our wording, and we have made some revisions to the conclusion section of the main text to address this issue. The revised details can be found on [Page 17, lines 03-05]:\nOur findings support the following conclusions: (1) There was no large-scale loss of African wetlands 1984 to 2021. The losses were concentrated in the coastal areas, the Congo Basin and southern Africa.\n[Page 17, lines 12-14]:\nAn in-depth understanding of wetlands is fundamental to their conservation and utilization. Even in the context of rising wetland area, irrational human activities (e.g., damming, reclamation, and irrigation) can still bring humanitarian disasters to residents, and the focused conservation of Africa's wetlands remain urgent and necessary. We hope that our work will contribute to improving the management and conservation of African ecosystems and, in the process, help support those who depend on these ecosystems.\n[Page 16, lines 10-16]:\nIt is important to note that although Africa's wetlands have not experienced significant overall loss, there has been substantial reduction in certain regions (e.g., the Congo Basin, Southern Africa) and in coastal wetlands. The quality of Africa's wetlands still needs to be assessed, as stable or expanding wetland areas do not necessarily indicate a favorable condition. The degradation of the ecological functions of existing wetlands continues to pose serious threats to local communities, for example, the shrinking of Lake Chad's surface area.\nIn this study, we classified African wetlands into two major categories—inland wetlands and coastal wetlands. The trend and trajectory of coastal wetlands showed a significant decline, a conclusion that aligns with the mainstream research on coastal wetlands, including the Nature paper \"The global distribution and trajectory of tidal flats\" and the Science paper \"High-resolution mapping of losses and gains of Earth’s tidal wetlands. \"\nRegarding African inland wetlands, our research results indicate a trajectory of initial decline followed by a subsequent increase. Specifically, from 1984 to around 2000, the area of African inland wetlands exhibited a downward trend. However, after 2000, the area began to increase, which is reflected in our statement that \"African inland wetland area on the rise during the 21st century.\"\nWe are very grateful to the reviewer for providing numerous references and news reports related to the loss and degradation of African wetlands. However, it is important to note that most of the existing independent reports and academic articles on Africa are limited to localized field surveys and lack comprehensive studies on the African continent as a whole. While our results do reflect the wetland losses mentioned in some of these reports, there is no fundamental conflict with their conclusions or statements. Notably, many of the wetland losses identified in these field reports occurred before 2000, which is consistent with our study's findings.\nWe addressed the reports and papers cited by the reviewer as examples.\n1) Editorial: Towards the Sustainable Use of African Wetlands\nThis article underscored the importance and necessity of sustainable development of African wetlands, and we fully agreed with the viewpoints expressed. The article began by highlighting a characteristic of African wetland research, namely the \"limited availability of data.\" Davidson's paper particularly emphasized the \"need to enhance awareness of wetland changes globally, especially in regions like Africa,\" and noted that \"the evidence of wetland area changes published was incomplete and limited, particularly for Africa, Neotropics, and Oceania.\"\nThe descriptions of wetland area loss in this editorial mainly stemmed from assessments using the WET Index1. However, this made the paper's conclusions about losses in Africa still unreliable. The WET Index was an indicator of wetland area changes based on incomplete and heterogeneous data (i.e., time series records of wetland extents from published literature), and it was not a direct observation of African wetlands. While the WET Index could reflect global wetland range change trends to some extent, it also had considerable limitations. This was particularly true for Africa, where the construction of the WET relied heavily on unevenly distributed wetland records concentrated in a few countries such as Uganda, Kenya, and in North African nations like Algeria and Libya, as shown in the figure. It should have been acknowledged that the accuracy of this index in predicting wetland changes in Africa was significantly lower than in regions like Europe, the USA, and China. We believed that estimates of wetland change trends in Africa based on the WET Index should have been treated with considerable uncertainty and were likely less reliable than our results obtained from remote sensing classification.\n[Figure Redacted]\nFigure 1. Time series data used to construct the WET Index (from the paper \"Improvements to the Wetland Extent Trends (WET) index as a tool for monitoring natural and human-made wetlands\").\n2) Protecting Africa's wetlands: critical to environmental governance\nThis article highlighted that wetlands are crucial resources, and protecting African wetlands was vital for environmental governance. The article discussed the state of African wetlands, particularly focusing on the degradation and disappearance of wetlands in South Africa and Zimbabwe. We fully agreed with the issues identified in the article, as our research also yielded similar results. As depicted in Figure 1b of the main text, not only in South Africa and Zimbabwe but across the entire southern part of Africa (regions south of 10°South latitude), a significant degree of wetland loss was evident.\nSince the wetland area in this region does not constitute a large percentage of the continent's total wetland area, we did not focus on this area in our paper, which was a significant oversight in our wetland research. We have already made certain additions to the main text to address this. The revised details can be found on [Page 06, lines 15-17]:\nIn addition, the entire southern part of Africa (south of 10°S latitude) showed considerable wetland loss and little wetland growth compared to other parts of Africa.\nFigure 2. Wetlands in Southern Africa have experienced area loss.\n3) New report: Degradation of wetlands in the Sahel is wrecking lives, driving migration\nThis article discussed a report titled \"Water Shock: Wetlands and Human Migration in the Sahel Region,\" which explored how poor management of water resources led to ecosystem degradation and triggered human migration, including relocation to Europe.\nThe report highlighted Lake Chad as one of the most well-known examples of wetland degradation in Africa. The article stated that \"due to water extraction for irrigation projects, the Lake Chad basin had lost 95% of its surface water area,\" a statement we acknowledged as accurate. However, it was essential to note that the degradation of Lake Chad referred to in the report was solely the lake surface, that is, a reduction in the surface water area. Observations of the current state of Lake Chad revealed that the degraded water surface had not been completely exposed as dry land but was covered by herbaceous vegetation. The region's soil maintained waterlogged conditions or seasonal water accumulation, considered a transitional zone between aquatic and terrestrial ecosystems. In our research definition, it was still considered part of the wetlands.\n[Figure Redacted]\nFigure 3. Degradation of the Lake Chad wetlands, showing the shrinkage of the lake surface.\nThe phenomenon of wetland degradation should have indeed raised concern. However, this did not conflict with our research findings. In our study, such flooded grasslands were also considered part of herbaceous marshes. Therefore, when calculating the area of wetlands in this region, we believed it was only appropriate to emphasize the degradation of the water surface, rather than a reduction or disappearance of the entire wetland area. Of course, we also added and emphasized this issue in the main text, noting that the stability or expansion of wetland area did not necessarily indicate the health of the wetlands. The ongoing degradation of existing wetlands could still pose a severe crisis to local residents (e.g., Lake Chad).\nThe additional content can be found on [Page 16, lines 10-16]:\nIt is important to note that although Africa's wetlands have not experienced significant overall loss, there has been substantial reduction in certain regions (e.g., the Congo Basin, Southern Africa) and in coastal wetlands. The quality of Africa's wetlands still needs to be assessed, as stable or expanding wetland areas do not necessarily indicate a favorable condition. The degradation of the ecological functions of existing wetlands continues to pose serious threats to local communities, for example, the shrinking of Lake Chad's surface area.\n4) The status of wetlands, threats and the predicted effect of global climate change: the situation in Sub-Saharan Africa\nThis paper reviewed the major threats faced by wetlands in Sub-Saharan Africa through case studies, including typical wetland areas such as the Niger River, Lake Chad, the Sudd Wetlands, and the Congo Basin. The paper discussed various human activities (such as water storage, irrigation, hydroelectric power generation) and climate change threats to African wetlands.\nLosses in wetlands like the Niger Delta, Lake Chad, and the Congo Basin, mentioned in the paper, were all reflected in Figure 1b of our paper. Our advantage was that we conducted a comprehensive analysis from a remote sensing perspective, which included not only typical regions like the Niger River, Lake Chad, or the Congo Basin but also various small lake wetlands. This comprehensive analysis led to the conclusion that African wetland areas, on the whole, were increasing. Additionally, the paper heavily cited data from field surveys and analyzed the impacts of population growth and changes in local laws and policies on the wetlands of the region, including impacts on fish, migratory birds, and other wildlife. These survey data were mostly concentrated before the year 2000 (mid-20th century), which is consistent with our research findings that the area of African wetlands showed a declining trend from 1984 to around 2000.\n[Figure Redacted]\nFigure 4. Loss of Wetlands in Africa.\nRegarding climate change predictions, the paper was published in 2013 and primarily based its climate change assessment on the IPCC Fourth Assessment Report (2007). However, the latest CMIP6 simulations now offer different forecast results. Research shows that, apart from Southern Africa and Madagascar, most regions of Africa are expected to experience a fivefold increase in extremely wet years, particularly in the Sahel region, where the annual average precipitation is projected to increase2,3. For a detailed discussion, refer to the supplementary material—Climate Change in Africa.\n5) A spatial and temporal assessment of wetland loss to development projects the case of the Kampala–Mukono Corridor wetlands in Uganda\nThe paper reported on the assessment of spatial and temporal losses of wetlands in the Kampala-Mukono Corridor (KMC) due to development projects (DP). The discussion in this paper is similar to previous reports, and we acknowledge the wetland losses caused by human activities (in this case, project development). However, since the focus of our study was not on some specific areas in Africa with severe losses, these details were not mentioned in our manuscript.\n2) Response: We fully understood the reviewer's concerns about distortions in results caused by errors. These concerns mainly include two aspects: the rate of change in the time series of wetland area during historical periods and the impact of uncertainties in historical time series on the future prediction. We have made targeted modifications, additions, and explanations to address these issues.\nThe reviewer pointed out the lack of a rigorous error analysis. In the manuscript , we validated the effectiveness of our classification results using remote sensing classification confusion matrices, comparisons with the other wetland maps, and comparisons with TOPMODEL simulation results. The error range for the classification of African wetlands was not mentioned in the original paper, but has been addressed in this revision.\nFollowing the methods used in the Science journal article \"High-resolution mapping of losses and gains of Earth’s tidal wetlands,\" we employed the bootstrap resampling method to calculate the 95% confidence intervals for wetland classification accuracy and area across ten historical periods (For specific steps, see Supplementary Discussion 5 - Uncertainty of African Wetland Classification).\nThe mean of the sampling distribution is used as the estimate of accuracy for the study, and the 0.025 and 0.975 percentiles of the sampling distribution are used as the 95% confidence intervals. The accuracy and area results are presented in the following table:\nTable 1. Confidence Intervals for Wetland Classification Accuracy\nYear OA OA_Lower OA_Upper UA UA_Lower UA_Upper PA PA_Lower PA_Upper\n1987 0.957 0.945 0.967 0.941 0.903 0.975 0.753 0.689 0.810\n1993 0.959 0.948 0.969 0.904 0.860 0.943 0.771 0.708 0.827\n1998 0.964 0.954 0.973 0.932 0.895 0.963 0.827 0.776 0.874\n2002 0.970 0.961 0.978 0.920 0.882 0.953 0.876 0.833 0.915\n2005 0.959 0.949 0.969 0.877 0.827 0.925 0.761 0.697 0.826\n2008 0.970 0.961 0.979 0.948 0.917 0.975 0.855 0.811 0.900\n2011 0.967 0.957 0.976 0.923 0.882 0.958 0.843 0.789 0.889\n2014 0.969 0.960 0.977 0.902 0.862 0.941 0.854 0.805 0.901\n2017 0.968 0.959 0.977 0.868 0.819 0.913 0.862 0.807 0.909\n2020 0.967 0.958 0.976 0.926 0.885 0.961 0.837 0.787 0.883\nOA：Overall Accuracy; UA：User Accuracy; PA：Producer Accuracy\nTable 2. Confidence Intervals for Wetland Classification Area (km2)\nYear AW AW_Lower AW_Upper CW CW _Lower CW _Upper IW IW _Lower IW _Upper\n1987 1200819.68 1500699.641 1100116.463 119543.48 141726.8062 109275.9418 1081276.2 1376995.219 983871.3555\n1998 1184975.43 1458040.776 1081358.445 113697.99 134029.2562 106336.2496 1071277.44 1336335.776 966274.3558\n2002 1173041.54 1447062.656 1065721.009 109747.14 128857.3385 99161.59913 1063294.4 1339030.695 960445.7763\n2005 1163539.39 1437120.484 1039141.793 112893.96 131814.9177 103377.7366 1050645.43 1326521.879 923863.2727\n2008 1185585.18 1444905.975 1077920.931 113547.1 144072.8619 103611.7288 1072038.08 1307338.387 966963.8107\n2011 1188910.32 1430237.024 1076535.806 109208.78 131866.0773 102589.1519 1079701.54 1309841.039 960819.6479\n2014 1193304.48 1394771.47 1075370.338 107757.58 122707.4735 102300.3441 1085546.9 1290954.941 958681.4366\n2017 1180854.93 1356023.484 1068016.764 107551.51 125476.7617 102804.8331 1073303.42 1237423.243 953582.0155\n2020 1194688.76 1381294.592 1092780.026 108015.11 127957.2642 101347.5106 1086673.65 1261103.118 981261.077\nAW: African Wetlands; CW: Coastal Wetlands; IW: Inland Wetlands\nRegarding the impact of uncertainties in historical time series data on future wetland trend predictions, we consider it to be negligible.\nOur research utilized the TOPMODEL approach, which is mainly based on the Nature Climate Change journal paper “Future impacts of climate change on inland Ramsar wetlands.”This paper highlighted that uncertainties in simulation results primarily arise from three sources: the input soil water data, the choice of wetland calibration data and methods, and the inherent uncertainties of the TOPMODEL itself.\nThe reviewer's main concern pertains to the second source of uncertainty, specifically the use of historical period classification results (which include uncertain wetland area time series) as calibration data, potentially impacting the reliability of future wetland predictions.\nIt's important to clarify that the use of wetland data for calibration in TOPMODEL is solely for adjusting the simulated wetland area size and does not involve correcting interannual changes or trends. The parameter that requires calibration in the model's formula is M, which is an adjustable parameter describing the exponential decrease in soil permeability with depth. The M value varies across different basins, and its range is set between 1 and 15. The M value derived from wetland classification results is a fixed value for a basin and does not change with future simulation time. Therefore, we believe that the impact of historical period time series uncertainties on future predictions is limited to the relative size of wetland areas, and in principle, it does not affect interannual changes and trends.\nThe uncertainty in the input soil water data is a major factor affecting future wetland simulation results. In this study, we used soil moisture data from 14 CMIP6 models as input, with uncertainties mainly reflected in the significant differences between these model data. These differences are far greater than the uncertainties introduced by model error propagation. We have provided different time series curves (Figure 4 in the main text) for these input data and analyzed them. Additionally, the inherent uncertainty of the TOPMODEL itself is demonstrated in the supplementary material. For specific information, please refer to Supplementary discussions 6—Effectiveness and Uncertainty of Inland Wetland Simulation.\n3) Response: Regarding the statement that \"population explosion and increasing aridity across the African continent will have severe consequences for the environment and wetlands,\" we can only agree in part. Indeed, the dramatic increase in Africa's population has exerted significant pressure and impact on local environments and ecosystems, including wetland ecosystems. In our manuscript, we conducted a preliminary assessment and analysis using the Human Influence Index. However, regarding the future climate change conditions on the African continent, there is no consensus among existing studies. The assertion that the African continent is becoming increasingly arid is not reliable.\nAccording to the latest CMIP6 simulation results, except for Southern Africa and Madagascar, most regions of Africa are expected to experience a fivefold increase in extremely wet years, particularly in the Sahel region, where the annual average precipitation is projected to increase2,3. The paper published in Nature Ecology & Evolution, \"Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa,\" mentions that due to increases in carbon dioxide and precipitation, even arid lands in Africa are showing a greening trend. In the Sahel region, future precipitation is not predicted to decrease. Forecasts indicate that the average rainfall in the area will remain relatively stable, but the frequency of droughts and extreme rainfall events may increase4.\n[Figure Redacted]\nFigure 5. Future Precipitation Changes in the Sahel Region (from the paper \"A Global Perspective on African Climate\").\nAdditionally, multiple studies have indicated that the global wetland area is expected to increase over the next century (21st century). For instance:\n1. Zhang et al. used the LPJwsl land surface process model (incorporating TOPMODEL for simulating wetlands) to simulate the dynamics of global inland wetlands in the 21st century under four climate warming scenarios of the Coupled Model Intercomparison Project Phase 5 (CMIP5). They found that by the end of the 21st century, the area of global inland wetlands is projected to increase by 5%, 8%, 12%, and 20% under the four scenarios, respectively. The increase in wetland area is mainly concentrated in the high latitudes of the Northern Hemisphere, while tropical wetland areas slightly decreased5.\n2. Comyn-Platt et al., utilizing output from a medium-complexity climate model to drive the land surface process model JULES (which uses TOPMODEL for simulating wetlands), discovered that wetland areas in permafrost regions are expected to increase by 0.26 million km² and 0.28 million km² under future warming scenarios of 1.5°C and 2°C, respectively6.\n3. Kleinen and others used the MPI-ESM Earth system model to simulate changes in global wetlands from 1860 to 3000. They predict that tropical wetlands will expand due to increased precipitation over the next 1000 years, while high-latitude wetlands will expand due to a reduction in ice cover7.\nIn summary, the results of our study do not conflict with existing mainstream research on African wetlands. Our research focused on the overall trend of changes in African wetlands. Although we also observed localized losses of wetlands, we did not emphasize these findings, thereby overlooking the pressures and challenges that localized wetland degradation poses to local residents in Africa. This oversight is a limitation of our paper.\nPrompted by the reviewer's comments, we have recognized that emphasizing the overall increase in African wetlands could potentially mislead policy making and conservation efforts. Consequently, we have added content to our paper to further stress that, even against a backdrop of increasing wetland areas, unsustainable human activities (e.g., damming, water storage, and irrigation) can still lead to humanitarian disasters for local communities. Thus, the focused conservation of African wetlands remains urgent and necessary.\n1.Comment: The introduction is missing a section on the details of threats to wetlands which could result in a wetland loss as well as the other side of the coin concerning wetland gain (what conditions/factors increase wetland distribution?)\nResponse: This study primarily utilized historical wetland classification and future period simulation methods to reveal the overall trend of wetland area changes in Africa during the 21st century. The manuscript does not delve deeply into the driving factors behind these changes, whether they involve loss or increase of wetlands.\nWe recognized that due to Africa's vast geographical and cultural diversity, the factors contributing to wetland loss and gain vary significantly across different regions. Our research has merely analyzed the overall changes in African wetlands. A more detailed and in-depth study, such as examining the specific impacts of human activities (population growth, water usage, agricultural expansion, etc.) on wetlands, will be our next research focus.\nIndeed, incorporating details about threats to wetlands in studies of spatial and temporal changes in African wetlands is essential, as this can draw attention to the need for conservation of African wetlands. Therefore, we have added the following content at the end of the paper. The revised details can be found on [Page 17, lines 12-14]:\nAn in-depth understanding of wetlands is fundamental to their conservation and utilization. Even in the context of rising wetland area, irrational human activities (e.g., damming, reclamation, and irrigation) can still bring humanitarian disasters to local residents, and focused conservation of Africa's wetlands remains urgent and necessary. We hope that our work will contribute to improving the management and conservation of African ecosystems and, in the process, help support those who depend on these ecosystems.\n2.Comment: The HHI index needs to be expanded upon – what parameters drive it and what remote sensing variables were used to create the index? What does the scale mean when interpreting the index? Not clear\nResponse: We apologize for any confusion caused by the inappropriate wording in our manuscript. In this study, we utilized the Human Influence Index (HII) to assess the impact of human activities on African coastal and inland wetlands. The HII is a composite indicator that quantifies human pressure on the environment. This pressure, often referred to as a threat to biodiversity, arises from human actions that may harm nature.\nThe HII is composed of nine global data layers, encompassing human population pressure (population density), human land use and infrastructure (built areas, night-time lights, land use/land cover), and human access routes (coastlines, roads, railways, navigable rivers).\nThis means that a higher HII value for a region indicates greater impact from factors such as population growth, land development, or various transportation routes. In our research, we used the HII as a proxy for the direct and indirect impacts of various human activities (such as water storage, irrigation, hydroelectric power) on African wetlands. The advantage of using HII is that it allows for a consistent temporal analysis with HII maps, providing a comprehensive overview of the various human pressures on African wetlands, especially those low-intensity pressures that are difficult to detect via remote sensing.\n3.Comment: Pg8; lines 5-7: Has population increased along the coast more so than internally (to warrant wetland loss)? You need to additional contextual support from the literature here\nResponse: Thank you very much for this helpful suggestion.\nIn the text, we stated, \"Population growth and increasing economic development have caused some degree of destruction to coastal wetlands around the world.\" This conclusion is derived from the Millennium Ecosystem Assessment report \"Ecosystems and Human Well-being: Wetlands and Water,\" which identifies population growth and increasing economic development as major indirect drivers of degradation and loss of both inland and coastal wetlands.\nThe reviewer may have misunderstood the intent of this statement. We did not mean to suggest that population growth in African coastal areas necessarily exceeds that in inland areas. As previously mentioned, our analysis of drivers did not delve into any specific human activity factors (for example, population growth, land reclamation, and construction of water management facilities) but instead used the HII as a composite index to assess human pressures on African wetlands.\nBased on our findings, the HII values in coastal areas are more than twice those in inland areas, indicating that coastal wetlands face significantly higher human pressures than inland wetlands, which substantiates the rationale behind coastal wetland loss.\nRegarding the differences between coastal and inland areas in terms of population and urban development and their impact on African wetlands, we plan to conduct a more thorough discussion using case study methods in future research.\n4.Comment: Future wetland trajectory: it would be great to breakdown the influencing parameter from these climate models to support this trend (e.g. is an increase in rainfall/SM/Temperature expected?). Also how did this section of analysis take into account the anthropogenic aspect?\nResponse: Thank you very much for this reviewer's suggestion. We have added to this content, specifically in Supplementary Discussion 2—Climate change in Africa.\nSupplementary Figure. 14 | The spatial distribution of temperature and precipitation changes by 2100 under the SSP126 scenario.\nSupplementary Figure. 15 | The spatial distribution of soil moisture changes by 2100 under the SSP126 scenario.\nHere, we have also added spatial changes and time series graphs of temperature, precipitation, and soil moisture under four SSP scenarios, to further aid to understand the reviewer's comments.\nFigure 6. Spatial distribution of future temperature changes in Africa from 2015 to 2100. Panels a-d correspond to SSP scenarios 126, 245, 370, and 585 respectively.\nFigure 7. Spatial distribution of future precipitation changes in Africa from 2015 to 2100. Panels a-d correspond to SSP scenarios 126, 245, 370, and 585 respectively.\nFigure 8. Spatial distribution of future soil moisture changes in Africa from 2015 to 2100. Panels a-d correspond to SSP scenarios 126, 245, 370, and 585 respectively.\nFigure 9. Time series of temperature changes.\nFigure 10. Time series of precipitation changes.\nFigure 11. Time series of soil moisture changes.\nFor this part of the analysis, we did not directly incorporate human factors.\nHowever, the CMIP6 climate simulation data that we used factored in socioeconomic development scenarios (different from SSP scenarios) during their production.\nTherefore, it can be argued that human factors were indirectly considered to some extent in our analysis.\nThe Shared Socioeconomic Pathways (SSPs) are a set of new emissions scenarios driven by different socioeconomic assumptions designed to simulate changes in societal factors throughout the 21st century, including population, economic growth, education, urbanization, and the rate of technological development. For example, SSP5-8.5, characterized as a high emissions pathway, envisions a future with high fossil fuel consumption where the global economy grows rapidly, and the population increases substantially. Therefore, the CMIP6 climate data consider certain human factors during simulations. However, we have not yet conducted an in-depth study on the direct impacts of human factors on African wetlands up to the year 2100. The primary reason is that compared to climatic factors, human factors are significantly influenced by local government policies, laws, and economic development levels, which may introduce considerable uncertainty. Currently, there are no reliable data on future human factors available for wetland simulation and analysis.\nAlthough current results show that the area of inland wetlands in Africa has been increasing since the year 2000 and climate factor simulations also predict further expansion of inland wetlands in the future, human factors will undoubtedly have negative impacts on African wetlands going forward. This represents one source of uncertainty in future wetland simulations. We have addressed this in the section \"Uncertainty of African Wetland Classification\" in the supplementary materials.\n5.Comment: Pg12; lines 9-11: Is an increase in temperature and increase in CO2 really support an increase in wetland coverage? What about future precipitation and SM trends? Greater digging and literature support are required here.\nResponse: Thank you to the reviewer for pointing out the areas of our manuscript that were not clearly articulated.\n“It is found that the inland wetland area will have a net increase of more than 10 % under the SSP126/245/370/585 scenarios from 2023 to 2100, and the more intense the SSP scenario is, featuring higher global temperature rise and more carbon dioxide emissions, the higher the degree of wetland area expansion can be anticipated.”\nRegarding the sentence in our paper, we intended to convey that the extent of future wetland expansion increases progressively from SSP126 to SSP585.\nTemperature and CO2 were mentioned merely as representations of different SSP scenarios. We apologize for any ambiguity caused by our wording, and we have made the following modifications to the text. The revised details can be found on [Page 12, lines 09-11]:\nBased on the average of the results of 14 simulations, it is found that the inland wetland area will have a net increase of more than 10 % under the SSP126/245/370/585 scenarios from 2023 to 2100.\nFor the future trends in precipitation and soil moisture in Africa, we discussed and supplemented in the previous question.\n6.Comment: Pg 17: Were palustrine or vegetated wetlands (with no surface water present) included in the analysis?\nResponse: This study was divided into a spatiotemporal change analysis for the historical period (1984-2021) and a wetland simulation analysis for the future period (up to 2100).\nFor the analysis of wetlands during the historical period, we examined the loss and gain across eight categories of wetlands, including those with vegetation. In our future wetland simulations, we also included inland wetlands or vegetated wetlands that lack surface water. However, large inland lakes such as Lake Victoria, Lake Tanganyika, and Lake Malawi, as well as coastal wetlands, were not included in the analysis.\n7.Comment: Pg18: What was your source of an independent test dataset? How was the model calibration and validation dataset collected and from what source? Later in the methods section, you mention ‘visual inspection’–how do you maintain impartiality and reduce sampling bias?\n1) Response: For the validation of wetland classification results, independent test datasets primarily came from two sources:\n1. Approximately 270,000 visually interpreted sampling points were deployed across various locations in Africa. Of these, 30% of the sampling points were not used for training the random forest classifier but were instead used as independent validation points for post-classification accuracy verification, such as calculating confusion matrices.\n2. A certain number of wetland validation points were randomly selected from the global Regularly Flooded Wetlands (RFW), the Congo Basin forest wetlands dataset (CARPE wetlands map), and the World Mangrove Atlas, to assess the consistency of this wetland classification product.\n2) Response: The calibration data for the TOPMODEL consisted of ten maps showing the distribution of African wetlands from 1984 to 2021, obtained through historical period classification.\nThis study did not use other wetland datasets from those such as the Global Inundation Estimate from Multiple Satellites 2 (GIEMS-2), the Global Wetland Area and Dynamics for Methane Modeling (WAD2M), or the global Regularly Flooded Wetlands (RFW). This decision was primarily because these datasets have a spatial resolution of 0.25°(at an equatorial spatial resolution of approximately 28 kilometers), which significantly reduces the accuracy of wetland area extraction compared to the results of our wetland classification, which has a 30-meter spatial resolution.\nSince TOPMODEL simulates wetland distribution up to the year 2100, there are no available wetland datasets to validate the effectiveness of the simulation. In this study, we primarily validated the accuracy of historical period wetland simulations to indirectly demonstrate the effectiveness of the TOPMODEL approach. For a detailed explanation, see Supplementary Discussion 6 - Effectiveness and Uncertainty of Inland Wetland Simulation.\n3) Response: Visual interpretation for obtaining training data is a common method in the field of remote sensing classification. Compared to field surveys, it is the most efficient and cost-effective method.\nThe training data for this study were obtained by employing three experienced remote sensing professionals, along with the author. They used high-resolution Google Earth Engine’s imagery, wet and dry season Landsat images, Digital Elevation Model (DEM) imagery, and vegetation index imagery to classify wetland categories at 270,000 uniformly distributed sample points across Africa. For areas of contention, we also referred to existing wetland maps, such as the Global Regularly Flooded Wetlands (RFW) and the Congo Basin forest wetlands dataset for additional guidance in our judgments.\nThere is no robust method to validate the training dataset obtained through visual interpretation. Generally, validation is performed by assessing the accuracy of the final classification results.\nConcerning sampling bias, we believe it is not a significant issue. To extract wetland information as accurately and comprehensively as possible, we used a systematic uniform sampling approach based on a 0.1° x 0.1° latitude-longitude grid. The distribution and density of the sampling points were designed to closely approximate the actual state of the wetlands (see Supplementary Figure 2).\n1.Comment: Pan African climate change work needs to be included\nResponse: Thank you to the reviewer for providing literature related to climate change in Africa, which has helped address the gaps in our study regarding climatic aspects.\nWe have taken the reviewer's advice to \"incorporate more literature related to pan-African climate change.\" Consequently, we have added relevant content to the main text and the supplementary material in the discussion section, including the literature provided by the reviewer. The revised details can be found on [Page 11, lines 04-06]:\nIn the 21st century, the annual average temperature in some regions of Africa may rise at about 1.5 times the rate of global temperature increase2,8, while the frequency and intensity of precipitation remain more uncertain3,9,10.\nSupplementary Discussion 2—Climate Change in Africa:\nClimate change referred to the long-term transformation or alteration of climate at a specific location, region, or globally, resulting from either natural variations or human activities. Africa is the second most populous continent in the world, significantly impacted by climate change while having low adaptive capacity, making it one of regions the most vulnerable to climate change……\n2.Comment: Page 4, line 3: Yes, but for some areas their small-scale losses may have huge ecological impacts. These impacts may be more visible if the study area is made smaller, e.g., only looking at the southern African region as opposed to the whole Africa.\nResponse: We fully agree with this point by the reviewer's perspective that the loss of wetlands in specific areas remains stark and cannot be overlooked. It's important to recognize that while there is an overall increase in Africa's wetlands, it does not mask the severity of local losses. In response to this issue highlighted by the reviewer, we have made additions to the main text to address this concern. The revised details can be found on [Page 16, lines 10-16]:\nIt is important to note that although Africa's wetlands have not experienced significant overall loss, there has been substantial reduction in certain regions (e.g., the Congo Basin, Southern Africa) and in coastal wetlands. The quality of Africa's wetlands still needs to be assessed, as stable or expanding wetland areas do not necessarily indicate a favorable condition. The degradation of the ecological functions of existing wetlands continues to pose serious threats to local communities, for example, the shrinking of Lake Chad's surface area.\n3.Comment: Page 6, line 12: The decrease seems small over southern Africa, but is the ~2% loss from a Pan African perspective or from a southern African one?\nResponse: The reviewer seems to be puzzled by the approximately 2% loss ratio shown in Supplementary Figure 5, which depicts the wetland loss from 1984 to 2021 in southern African countries (such as South Africa, Botswana, Malawi, etc.).\nHere, we will detail the calculation steps for this figure. We first gathered data on the changes in wetland areas for African countries between 1984 and 2021. If a country's wetland area decreased during this period, it was marked as a wetland loss country. The decrease in wetland area (loss area) for these countries was then summed to obtain the total wetland loss area for all of Africa. The loss ratio for each country was calculated by dividing its specific decrease in wetland area by the total loss area for the continent. The same method was applied to calculate increases for countries where wetlands grew.\nIt is important to note that the magnitude of this ratio is directly related to the total wetland area in each country. Those countries like South Africa and Botswana have much smaller wetland areas compared to countries like the Congo or South Sudan. Therefore, regardless of the intensity of wetland area changes in these countries with smaller wetlands, their loss or gain ratios will not appear very high.\nIn summary, this approximately 2% ratio is viewed from a Pan-African perspective.\n4.Comment: A number of figures in the main text and also the supplementary material indicates linear trends, but without showing what the statistical significance levels of these trends are, the trend results are irrelevant.\nResponse: Thank you very much to the reviewer for highlighting the areas of imprecision in our study. Following the reviewer's suggestion, we have revised and supplemented the figures in the main text and supplementary materials. The revised details can be found on [Page 07, lines 09-14]:\nFig. 2 | Changes and trends in the area of inland and coastal wetlands in Africa during the historical period. The black dotted line is the inland wetland change trend, and the red dotted line is the coastal wetland change trend. The trend line is obtained by one-dimensional linear fitting in MATLAB, and P-value and Z was calculated. The horizontal coordinates were the middle years of 10 classification periods.\nSupplementary Figure. 6 | Time Series and trends of area changes in African wetlands from 1984 to 2021. The linear trend was calculated since 2000. P-value and Z value were obtained by Mann-Kendall method.\nSupplementary Figure. 8 | Comparison of HII between coastal wetlands area and inland wetlands area in Africa after 2000. HII was averaged over the same time span as the classification. The horizontal coordinates are the middle years of the seven periods after 2000, and the vertical coordinates are the HII values. P-value was obtained by the Mann-Kendall method."}, {"round": 2, "reviewer_comments": {"reviewer_4": "This is an interesting and ambitious paper that seeks to map trends in wetland extent over the period 1984-2021, and extrapolate forward to assess how climate change will impact wetlands in the future. The use of Landsat data over such a large region is impressive and the results are of general interest to a wider audience, making it potentially publishable in Nature Communications. However I have a number of reservations about aspects of the work which in my opinion need to be addressed for the work to be published here.\nMajor comments\n1. Evaluation of interannual variability against independent datasets\nFigure 1b shows the change in African wetlands from 1984 to 2021. I am somewhat familiar with the Sudd wetland and was surprised to see areas of loss and gain in close proximity to each other. Hydrologically this seems rather curious, and in contrast to a very impressive study focused on this area by Hardy et al (2023). They document interannual variability in inundation over this period also using Landsat data. They demonstrate a remarkable expansion of the Sudd, particularly since 2019, and evaluate it against independent datasets. The Sudd is one of the largest wetlands in Africa, and the apparent misdiagnosis of the long-term trend here (with areas of loss and gain) raises serious concerns for me about the quality of the mapping. I would be reassured if the authors looked carefully at that study and evaluated their own results against that benchmark.\nIn a similar vein, I would like to see a comparison of the interannual variability presented in this study with the GIEMS dataset of wetlands (Bernard et al, 2024) across Africa. That dataset is at much coarser spatial resolution, bringing with it its own issues (e.g. missing small wetlands), but its interannual variability at the scale of such major wetlands should be credible. As it covers the period 1992-2020, it offers an independent dataset to evaluate the current methodology.\n2. Climate change, climate variability and the statistics of the rainfall-wetland relationship\nThe authors appear to have a view of African climate variability and change that emphasises the latter over the former. For example (P7 L1), they describe the time series of inland wetlands as “a strange trajectory”. In the context of strong interannual variability in climate (rainfall), particularly at the scale of large river catchments (e.g. Niger, Congo, Nile), I don’t see this variability as strange at all. I would expect strong interannual variability in wetland extent dominating over any continental-scale rainfall trends. Instead (P1 L23) they state that “change of wetland area [is] closely related to climate change.”\nThe authors state (P10 L7-8) that “from 1984-2006… precipitation decreased significantly and continuously”, based on 4 time points. What accumulation period do these 4 time points actually represent? To justify their statement about “significant and continuous”, they should show interannual rainfall variability and test the rainfall trend over the period 1984-2006. The authors should also note the high degree of uncertainty in African precipitation trends across datasets (Maidment et al, 2015). The precipitation in the dataset that they use (ERA5) is not considered particularly reliable. In this context, a much better choice would be the ERA5-Land dataset (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5-land ), where they correct biases in the raw ERA5 precipitation.\nRelated to this, the authors state (P10 L5-9) that “the change in precipitation explains the violent fluctuation of wetland time series”, based on Figure 3b. The authors need to quantify how much of the variability the precipitation time series actually explains with a linear regression (and associated P-value). Given the lack of time points in their dataset, I would strongly recommend that they also perform a series of correlation analyses at sub-continental scale. Interannual rainfall variability is not spatially coherent at the continental scale, and consistent relationships between smaller-scale droughts and pluvials and wetland extent over effectively independent regions would greatly strengthen their arguments. Of course, it is reasonable to expect strong interannual rainfall variability to drive wetland extent, but Figure 3b does a poor job of evidencing that relationship.\n3. Cloud contamination\nApart from the brief mention in SI (P4) about the omission of the 1991-1995 period, there is no discussion of the problem of estimating wetland extent using Landsat in cloud-dominated regions (most notably the Congo Basin). At the very least, I would expect the paucity of cloud-free images in such areas would enhance the uncertainty in the resulting wetland mapping there. Is that not the case? I would like to see some evidence of that.\n4. Representation of fluvially-driven inundation\nClarification is needed for how TOPMODEL represents wetlands which are primarily driven by non-local rainfall. For example, interannual fluctuations in the Sudd and Niger Inland Delta are dominated by rain that fell hundreds of kilometres away and that spills out on the floodplain. I found it hard to infer how large a region contributes to the precipitation used in these areas from the description in SI section 8, which mentions over 40,000 watershed units. Would a map showing (at least the larger) watershed units help, or are they all too small to be represented on an African map? If the latter is the case, what does it mean to assume that TOPMODEL can simulate wetlands such as the Sudd based only on local rainfall? I assume that assimilation of remotely-sensed soil moisture data in FLDAS implicitly captures fluvial-driven variability in such cases, but that source of information is not available in the climate change simulations, which do not depict the key hydrological processes.\nMinor points\nP3 L2 and throughout: The authors refer to “10 periods from 1984 to 2021” though only 9 points are shown in Figures 2 and 3. In SI (P4) they note that the period 1991-5 suffered from lack of data and presumably this explains its absence in those figures. For consistency in the main text, it would make sense to talk about analysis of 9 periods, not 10.\nThe authors overstate the case with respect to the historical trend in inland wetland (P6 L19) “completely opposite trends… in coastal and inland wetlands”. Whilst the downward trend in coastal wetland is statistically robust, the inland trend (P=0.175) cannot be distinguished from zero at even the 10% level.\nP8 L10 “has been conversion to…”\nI may have missed it, but what soil moisture levels (in FLDAS and the CMIP6 models) were actually used? On a related note, the y-axis range in SI Fig. 20 (~650 m3/m3) is clearly wrong.\nSI Figure 19 shows that TOPMODEL forced by FLDAS soil moisture does a good job at representing interannual wetland variability. Can the authors clarify how the calibration procedure (to determine the value of the M parameter) has influenced this simulation? In other words, was there data held back from the calibration step to evaluate the model simulations?\nIn Figure 5, what range of values does a “constant” wetland area correspond to in the bar charts? In the maps, it would be better to depict that “constant” orange over a range at equal distance either side of zero (e.g. -3 to +3)."}, "author_response": "Response: We are most grateful to the editor for inviting a new expert to reassess our manuscript. Undoubtedly, the reviewer possesses deep familiarity and professional expertise in the field of African wetlands. The novel perspectives on wetlands they have introduced—such as methane emissions and GRACE‐derived terrestrial water storage—have further enriched this study, and we express our sincere appreciation.\nReviewer Comment 1 raises two principal issues, which we address in turn.\n1. The reviewer questions the apparent loss of Sudd wetland extent in our analysis and notes that the areas of loss and gain are in close proximity—a result that appears to conflict with Hardy et al. (2023).\n2. The reviewer suggests that our study be compared with the GIEMS dataset to analyze interannual variations in African wetland area over the period 1992–2020.\nResponse to Issue 1\nWe have carefully studied Hardy et al. (2023) and the TropWet methodology they employed. We fully endorse both the conclusions and the methodological rigor of their work. We believe that the discrepancy between their findings and ours arises primarily from two factors:\n1. Definition of wetland. TropWet extracts four principal wetland classes within the Sudd inundation zone: (1) inundated vegetation, (2) saturated soils, (4) open water, and (7) aquatic vegetation. The “inundated vegetation” category corresponds to vegetation flooded during the October–December inundation period, and thus encompasses areas that many studies would classify as grassland. In contrast, our study adopts a classification oriented toward stable wetland ecosystems or landscape patterns. We do not include grassland vegetation subject only to short‐term flooding within our “herbaceous marsh” category. Consequently, although our extracted wetland extent is closely linked to hydrological dynamics, the development of a self-sustaining wetland ecosystem exhibits a temporal lag and does not coincide directly with inundation events. Moreover, our final temporal reference—2020, covering the 2019–2021 interval—coincides with the onset of Sudd flood expansion; we thus hypothesize that only multi-year inundation would yield a detectable increase in herbaceous marsh extent under our definition.\n2. Classification methodology. TropWet maximizes the influence of rainy-season hydrology (flooding and precipitation) on floodplain wetlands and adjacent grasslands, yielding strong correlations between their wetland extraction and GRACE‐derived terrestrial water storage as well as Lake Victoria water level. By contrast, our approach seeks to discriminate wetlands from other upland cover types (grassland, forest). To achieve this, we incorporated a “flow distance” parameter as a simulated hydrological feature, thereby excluding most inundated grasslands (see red‐boxed areas in Figure 2). Detailed procedures are available in our previous publication on African wetland classification.\nFigure 1. Sude Wetland in 2020 (this study). Landsat imagery spanning 2019–2021; green areas denote inland marsh and yellow areas denote moist soil.\nFigure 2. Inundated extent of Sude Wetland based on TropWet data, October–December 2020.\nIn addition, to ensure classification reliability, we employed multi-year Landsat composites—using seven-year spans for the 1980s and three-year spans after 2000—rather than relying on just a few months of imagery from a single year as in the TropWet approach. While this strategy inevitably dampens our ability to capture intra-annual seasonal variations (for example, during the October–December (OND) short rains), and thus prevents us from precisely mapping wetland expansion within that specific season as TropWet does, our aim is to analyze long-term changes in the entire African wetland ecosystem. This trade-off was therefore an intentional design choice. In our results, the Sudd wetland shows a net loss between 1984 and 2021, which we attribute to the degradation and shrinkage of smaller riverine wetlands around its margins. Although extensive short-term flooding occurred after 2019, the newly inundated areas lie mostly in the Sudd’s core. The temporary submergence of surrounding grasslands does not meet our criteria for wetland expansion.\nIn summary, we agree with Hardy et al. (2023) that the inundated extent of the Sudd wetland expanded substantially during the short rainy seasons after 2019. However, based on our methodological framework and research focus, the extent of herbaceous marsh—defined as stable wetland vegetation—has remained relatively unchanged.\nReferences：\nLi A, Song K, Chen S, et al. Mapping African wetlands for 2020 using multiple spectral, geo-ecological features and Google Earth Engine[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 193: 252-268.\nResponse to Issue 2\nWe downloaded and processed the GIEMS‐MC dataset and also gathered the WAD2M dataset developed by Zhang et al. (2021) and the GWL‐FCS30 wetland product released by Zhang et al. (2024) for comparative analysis.\nFigure 3. Scatter charts comparing the current results against the African wetland flood extent from GIEMS-MC.\nFigure 4. Scatter charts comparing the current results against the African wetland flood extent from GWL_FCS30.\nFigure 5. Scatter charts comparing the current results against the African wetland flood extent from WAD2M.\nThe GIEMS-MC dataset covers the period from 1992 to 2020 and thus partially validates our pre-2000 findings. As illustrated, the interannual trajectory of African wetland area in GIEMS-MC closely mirrors our results: an initial decline followed by a subsequent increase. The greatest discrepancies occur in the 1980s, during which GIEMS-MC indicates only a slight decrease, whereas our analysis shows a more pronounced reduction. We attribute this divergence to the limited availability of Landsat and other remote-sensing data in the 1980s; accordingly, both our study and prior work retain a degree of uncertainty regarding wetland extent during that decade, rendering the observed difference justifiable. In contrast, post-2000 comparisons yield more robust conclusions. Both GIEMS-MC and the 30 m-resolution GWL-FCS30 dataset clearly demonstrate an upward trend in African wetland area on interannual time scales. GWL-FCS30—a newly released global, 30 m time-series wetland dynamics product covering 2000–2022—exhibits interannual variations in African wetlands that align closely with our findings. By comparison, the WAD2M dataset does not display a consistent upward or downward trajectory and diverges significantly from the other two products.\nReferences：\nZhang Z, Fluet-Chouinard E, Jensen K, et al. Development of the global dataset of wetland area and dynamics for methane modeling (WAD2M) [J]. Earth System Science Data, 2021, 13(5): 2001-2023.\nZhang X, Liu L, Zhao T, et al. Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022[J]. Scientific Data, 2024, 11(1): 310.\nConclusion\nFinally, we contend that the differences between our study and the reviewer’s referenced work arise primarily from divergent research foci and emphases. The wetland extent extracted in our study is neither more temporally responsive than the TropWet method nor more sensitive to flood inundation. However, with respect to more precisely delineating the boundary between wetlands and uplands and analyzing the gradual evolution of African wetland ecosystems, we are confident in the robustness of our results.\nInterestingly, we observed that the reviewer’s cited paper reports the global annual methane growth rate, whose interannual variability aligns more closely with the overall change in African wetland area documented in our study (and not solely with the Sudd wetland). Although our research did not explore the relationship between wetland area fluctuations and methane emissions directly, our findings may prompt researchers in related fields to pay greater attention to changes in African wetlands.\n[Figure Redacted]\nFigure 2 in Hardy et al. (2023) illustrates: (A) the NOAA global annual methane growth rate (ppb); and (B) the OND (October–December) flood inundation extent computed by the TropWet model, expressed as a percentage anomaly relative to the 1984–2022 mean extent.\nFigure 6. Comparison of the trend in African wetland area with the global annual methane growth rate.\n2.Climate change, climate variability and the statistics of the rainfall-wetland relationship\nResponse: We sincerely appreciate the reviewers for pointing out the shortcomings in our manuscript. We have humbly accepted the reviewers' suggestions and have provided explanations and corresponding revisions. In this set of comments, the reviewers primarily raised three key concerns:\n1. The paper overlooks the high uncertainty in precipitation trends across different datasets. The reviewers requested evidence supporting the statement that \"precipitation showed a persistent and significant decrease during 1984–2006...\" and suggested validation using the ERA5-Land dataset.\n2. The paper needs to quantitatively examine the relationship between precipitation and wetland fluctuations by employing linear regression (including p-values) to measure the explanatory power of the precipitation series. Additionally, the reviewers recommended supplementing the analysis with subcontinental-scale correlation assessments.\n3. The paper neglects interannual climate variability and overattributes wetland changes to climate change.\nResponse to Issue 1\nWe sincerely thank the reviewers for providing the references. We have carefully reviewed the suggested literature and reintroduced multi-source precipitation data for further analysis. Regarding the reliability of the ECMWF_ERA5 dataset used in our study, the reviewers recommended validation using the ERA5-Land dataset. Accordingly, we processed the ECMWF_ERA5_LAND dataset and compared the results with those derived from ECMWF_ERA5. As shown in the figure, the two datasets exhibit no significant numerical differences. ECMWF_ERA5 employs the total_precipitation variable, while ERA5_LAND uses total_precipitation_sum.\nFigure 7. Comparison of results between ECMWF_ERA5_MONTHLY and ECMWF_ERA5_LAND_MONTHLY. Monthly accumulated precipitation (m) in the major wetland distribution areas of Africa.\nRegarding the comment that \"precipitation trends exhibit high uncertainty across different datasets,\" we fully acknowledge the reviewers' concern and have expanded our analysis by incorporating 44 additional precipitation datasets, including:\n(1) ERA5-Land\n(2) GPCP (Global Precipitation Climatology Project)\n(3) TerraClimate\n(4) FLDAS\nThis multi-dataset approach ensures a more robust evaluation of precipitation trends and helps mitigate uncertainties associated with relying on a single data source.\nFigure 8. Comparison of time series of four precipitation datasets. Monthly accumulated precipitation (mm) in the major wetland distribution areas of Africa.\nThe results indicate that the precipitation trends in the major wetland distribution areas of Africa did not exhibit a significant decline, except for ERA5_LAND (MK test: P < 0.05, Z = -5.76). This was an oversight in our study, and we have supplemented the main text accordingly. The revised details can be found on [Page 10, lines 05-15]:\nBased on FLDAS-derived precipitation, the interannual variability of rainfall appears to account for the pronounced fluctuations observed in Africa’s wetland extent time series. From 1984 to 2006, a sustained decline in precipitation coincided with a marked contraction of wetland area, whereas the subsequent rebound in rainfall was accompanied by wetland recovery. However, substantial uncertainties exist in Africa’s interannual precipitation estimates across different datasets, and these uncertainties are spatially heterogeneous at the continental scale. We believe that although the amount of precipitation can directly affect the area of wetlands, evapotranspiration, runoff, and groundwater supply will also have an impact on local wetlands; therefore, the impact of precipitation on wetlands requires specific analysis of particular areas (more details in the Supplementary Information).\nReference:\nMaidment, R. I., Allan, R. P., & Black, E. (2015). Recent observed and simulated changes in precipitation over Africa. Geophysical Research Letters, 42(19), 8155-8164. https://doi.org/10.1002/2015gl065765\nData availability\nERA5-Land is available from https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR.\nECMWF_ERA5_MONTHLY is available from https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY.\nGPCP is available from https://www.ncei.noaa.gov/products/global-precipitation-climatology-project.\nTerraClimate is available from https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE.\nFLDAS is available from https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001.\nResponse to Issue 2\nAs the reviewers pointed out, we had underestimated the spatial complexity of interannual precipitation variability. Precipitation data over Africa still carry uncertainties, and the changes in African precipitation from 1980 to 2020 do not exhibit continental-scale spatial homogeneity. In response, we have supplemented our analysis by incorporating a region-specific assessment of the relationship between precipitation and wetlands at the subcontinental scale.\nWe primarily supplemented data from seven representative African wetlands (Fig. 9), examining the response relationship between wetland area and interannual precipitation variability:\n(1) Congo Basin\n(2) Inner Niger Delta\n(3) Lake Chad region\n(4) Sudd Wetland and surrounding areas\n(5) Zambezi River Basin\n(6) Lake Bangweulu\n(7) Okavango Delta\nFigure 9. Classification results of representative African wetlands (2020)\nThe significance of interannual trends in wetland area (with missing data supplemented by linear interpolation) and precipitation in these regions from 1980 to 2020 was examined (Table 1).\nTable 1. Trend analysis of wetland area changes\nRegion S-statistic Z-score p-value Sen's Slope Significance\n1 -542 -6.076479 1.23E-09 -663.4078 Significant\n2 688 7.716341 1.2E-14 107.6894 Significant\n3 566 6.346045 2.21E-10 167.5459 Significant\n4 -584 -6.54822 5.82E-11 -270.0125 Significant\n5 -80 -0.887323 0.374905 -3.077 Not significant\n6 336 3.762699 0.000168 19.80628 Significant\n7 -164 -1.830806 0.06713 -16.11553 Not significant\nTable 2. Trend analysis of precipitation changes\nRegion S-statistic Z-score p-value Sen's Slope Significance\n1 -488 -5.469953865 4.5E-08 -0.613137 Significant\n2 300 3.358349499 0.000784 0.229096 Significant\n3 256 2.864144221 0.004181 0.245298 Significant\n4 -248 -2.774288716 0.005532 -0.240429 Significant\n5 202 2.257619563 0.023969 0.377895 Significant\n6 130 1.448920018 0.14736 0.167962 Not significant\n7 160 1.785878161 0.074119 0.259604 Not significant\nIt can be observed that, at the subcontinental scale, several representative wetland regions in Africa exhibit a predominantly significant monotonic trend in precipitation. Notably, the Congo Basin in Central Africa shows a significant downward trend, while the Inland Niger Delta in the Sahel region demonstrates a significant upward trend. Correspondingly, wetland areas in these regions—such as the forested wetlands of the Congo Basin and the herbaceous marshes of the Niger River—also exhibit significant decreasing and increasing trends, respectively, in response.\nTo further quantitatively assess the correlation between wetland area and precipitation (monthly accumulated precipitation, in millimeters), the Spearman correlation coefficients and associated p-values were calculated. The results show that, among the seven subcontinental-scale wetlands selected for analysis, five exhibit statistically significant correlations between wetland area and precipitation, with moderate correlation strength (0.3 ≤ |ρ| < 0.7). The wetlands of the Okavango Delta and Lake Bangweulu regions do not show significant correlations with precipitation variability. Moreover, none of the seven regions exhibit a strong correlation (|ρ| ≥ 0.7) between wetland area change and precipitation.\nTable 3. Spearman's rank correlation tests\nRegion Wetland Precipitation Spearman's p-value Significance\nArea (km²) (mm) ρ\n1 296311.3479 145.1321746 0.680139373 2.15E-06 Significant\n2 7708.180048 36.42429107 0.555052265 0.000211 Significant\n3 15492.05352 38.61809293 0.425958188 0.005866 Significant\n4 86741.74304 79.58088573 0.342160279 0.02909 Significant\n5 5117.687515 75.65805395 0.330313589 0.035449 Significant\n6 12513.2459 99.07811464 0.294076655 0.062359 Not significant\n7 5318.688789 42.87566381 0.113240418 0.4795 Not significant\n*Wetland area and precipitation values represent means across nine temporal periods\nResponse to Issue 3\nWe understand the reviewers’ concerns regarding whether the current results are sufficient to attribute wetland changes to climate change, particularly in relation to precipitation.\nClimate change refers to long-term shifts or alterations in climate at a specific location, region, or globally, resulting from either natural variability or human activities. Africa, the second most populous continent, is one of the regions most vulnerable to climate change due to its high exposure and low adaptive capacity (Sutton et al., 2011; Barros et al., 2014). According to the World Meteorological Organization's State of the Climate in Africa 2023 report, the rate of warming in Africa slightly exceeds the global average increase (https://wmo.int/publication-series/state-of-climate-africa-2023). Although precipitation across Africa does not show a statistically significant overall increase or decrease, clear trends are evident in certain regions. As noted earlier in our subcontinental-scale correlation analysis between wetland area and precipitation across seven wetlands, precipitation significantly increased in three regions and significantly decreased in two. Furthermore, based on the Spearman correlation coefficients, changes in wetland areas—such as those in the Congo Basin (declining area), the Inland Niger Delta, and Lake Chad—are significantly positively correlated with precipitation.\nIn this study, we acknowledge that we may have overemphasized the analysis of climate factors (e.g., temperature, precipitation) at the continental scale in relation to wetland extent changes, while overlooking the complexity and regional variability of climate change across different parts of Africa. We concur with several of the reviewers’ points, yet we also maintain that increases in precipitation can indeed contribute to the expansion of wetland areas. Accordingly, we have revised the manuscript to avoid broadly attributing wetland changes to overall precipitation trends and have instead focused on subcontinental-scale regional analyses. We sincerely appreciate the reviewers’ professional and valuable suggestions.\nReference:\nSutton MA et al (2011) Summary for policy makers. Eur Nitrogen Assess. https://doi.org/10.1017/cbo9780511976988.002\nBarros VR et al (2014) Climate change 2014 impacts, adaptation, and vulnerability Part B: regional aspects: working group II contribution to the fifth assessment report of the intergovernmental panel on climate change. https://doi.org/10.1017/CBO97 81107415386\nSupplementary Fig. Comparative analysis of wetland area and precipitation trends in selected African wetlands\n(1) Congo Basin\n(2) Inner Niger Delta\n(3) Lake Chad region\n(4) Sudd Wetland and surrounding areas\n(5) Zambezi River Basin\n(6) Lake Bangweulu\n(7) Okavango Delta\n3. Cloud contamination\nResponse: Due to cloud cover, the reduction in usable Landsat imagery poses a significant challenge for wetland classification, particularly in equatorial regions of Africa. This issue was indeed encountered in our study. Below, we describe our solutions in detail:\nThe first strategy involved extending the temporal span of imagery. Initially, we attempted to extract wetland areas in the Congo Basin using Landsat imagery composited over a single year. However, we found that this approach was insufficient for achieving complete spatial coverage in the region. This limitation was especially pronounced in pre-1990 Landsat imagery, where post-cloud-masking procedures resulted in severe data loss. To address this, we extended the temporal window (see Table 1). For the 1980s, we utilized imagery from 1984 to 1990—covering a total of seven years. During this period, we generated composite feature images by calculating spectral statistics (including maximum, minimum, and median values of key spectral bands, vegetation indices, and water indices), following the method of Murray et al. (2019). In subsequent periods, as the availability of Landsat imagery improved, the temporal window was progressively shortened to five years and eventually to three years.\nTable 4. Number of Usable Landsat Images Across Ten Time Periods\nTime Period Temporal Number of\nSpan (Years) Images\n1987 7 57951\n1993 5 52944\n1998 5 69605\n2002 3 51431\n2005 3 57024\n2008 3 60176\n2011 3 47695\n2014 3 128781\n2017 3 149039\n2020 3 136850\nCertainly, the inconsistency in the volume of imagery across different periods introduces uncertainty in classification accuracy. In 1987, the producer’s and user’s accuracies for wetland classification across Africa were only 77% and 91%, respectively. By 2020, these figures had improved to 85% and 93%. We consider cloud cover an uncontrollable factor in wetland classification. However, the measures implemented in this study have substantially increased the availability of usable Landsat imagery, and the resulting classification outputs can reasonably reflect the spatial distribution of wetlands in the 1980s to a certain extent.\nA second method involved incorporating the “flow distance” feature—a hydrologically relevant wetland indicator derived from the D8 single-flow direction algorithm and hydrological process modeling. This approach follows the method proposed by Bwangoy et al. (2010) and significantly improves the accuracy and reliability of detecting forested wetlands (woody swamps) in the Congo Basin.\nThere is a strong spatial correlation between wetland distribution and river proximity. Wetlands are significantly distinguishable from upland ecosystems (e.g., forests, grasslands, and croplands) based on their nearness to rivers; the vertical distance from most wetland types to the nearest river is typically less than 10 meters. This pattern can be attributed to the mechanisms of wetland formation: areas within this buffer zone are generally flat and poorly drained, subject to periodic river inundation and groundwater recharge, creating stable wetland environments.\nAs a static terrain-hydrological feature invariant over time, we first applied a pit-filling process to eliminate minor topographic depressions in the surface raster, ensuring the smoothness of the terrain data. Following a comprehensive comparison of three flow-direction algorithms (D8, MFD, and DINF), and considering the hydrological characteristics required for arid-region wetlands, we ultimately selected the D8 single-flow-direction algorithm to construct the terrain flow-direction raster. This approach precisely determines the optimal flow path from each pixel to its downslope adjacent cell. Upon completing the flow-direction modeling, we generated progressively accumulating flow-accumulation rasters using the flow-accumulation algorithm, accurately characterizing the spatial expansion patterns of surface runoff. Subsequently, a dual-threshold method was employed for stream network extraction, with particular focus on evaluating the differences in channel network density between two flow-accumulation thresholds: 1,000 and 20,000. The lower threshold (1,000) emphasizes the delineation of fine-scale tributary networks, while the higher threshold (20,000) targets the extraction of major channel structures. Through comparative analysis, this method effectively reveals the hierarchical characteristics of wetland drainage systems.\nCloud contamination is an unavoidable challenge in remote sensing–based wetland studies. We have applied the two methods described above to mitigate its impact as much as possible. Our results indicate that data from post-2000 are essentially unaffected by clouds, whereas for the pre-2000 period, the remaining uncertainty stems primarily from the limited availability of Landsat imagery.\nReference:\nMurray, N.J., Phinn, S.R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M.B., Clinton, N., Thau, D., Fuller, R.A., 2019. The global distribution and trajectory of tidal flats. Nature, 565, 222–225.\nBwangoy J-R B, Hansen M C, Roy D P, et al. Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices[J]. Remote Sensing of Environment, 2010, 114(1): 73-86.\n4. Representation of fluvially-driven inundation\nResponse: Thank you to the reviewer for your valuable comments regarding the use of TOPMODEL in our study to simulate wetlands primarily influenced by non-local precipitation sources, such as the Sudd Wetland and the Niger Inland Delta. Below is a detailed response to your concerns:\nThe methodological framework for wetland simulation in this study primarily follows the work of Yi Xi et al. (2021), published in Nature Climate Change, titled \"Future impacts of climate change on inland Ramsar wetlands.\" The approach is outlined as follows:\n[Figure Redacted]\nFigure 10. Introduction to the TOPMODEL-based diagnostic model, adapted from Yi Xi et al. (2021)\n[Figure Redacted]\nFigure 11. Calculation steps for water table depth (WTD), adapted from Yi Xi et al. (2021)\nThe classical TOPMODEL is a semi-distributed rainfall–runoff model based on the Topographic Wetness Index (TWI), originally proposed by Beven and Kirkby (1979). It was developed to simulate soil saturation and surface runoff processes under precipitation recharge. The model uses time series inputs of precipitation (along with other meteorological variables such as evapotranspiration and temperature) and estimates water table depth (WTD) and runoff by calculating infiltration and saturation-excess overland flow, thereby enabling dynamic simulation of wetland extent in response to hydrological changes.\nIn the TOPMODEL-based diagnostic model employed in Xi et al. (2022) and in this study, precipitation time series is not directly used as the driving input. Instead, multiple reanalysis soil moisture (SM) datasets are used (in place of precipitation), together with soil temperature and freeze–thaw status, to calculate the average water table depth (WTD) for each grid cell. Subsequently, based on the analytical relationship between the Compound Topographic Index (CTI), which reflects topographic control, and the WTD, the inundation threshold CTI* is derived. This allows for the diagnosis of the proportion of inundated/saturated pixels (on a monthly basis) within each grid cell, yielding the wetland fraction fₓ, and thus enabling simulation of the spatiotemporal dynamics of wetlands.\nIn other words, this diagnostic algorithm \"decouples\" the explicit simulation of precipitation recharge in the original TOPMODEL by implicitly incorporating precipitation forcing into soil moisture reanalysis data, rather than directly treating precipitation as an independent driver. This approach is feasible for two main reasons:\n(1) Land Data Assimilation Systems (LDAS) such as FLDAS indirectly account for the effects of runoff and river inflow on wetland water levels by assimilating remotely sensed soil moisture observations. As a result, the reanalyzed Water Table Depth (WTD) reflects variability driven by non-local precipitation.\n(2) Although transient inundation driven by precipitation (e.g., flooded grasslands after heavy rainfall) falls under the broad definition of wetlands, its signal tends to be \"averaged out\" by short-term fluctuations in multi-decadal time series analysis. The model instead aims to capture long-term saturated zones that are more ecologically significant in shaping ecosystem patterns. Thus, using soil moisture reanalysis instead of direct precipitation inputs not only simplifies computation but also emphasizes persistent wetland dynamics.\nUpon revisiting the manuscript, we realized that we had erroneously referred to the simulation method as \"TOPMODEL,\" when in fact it should be described as a \" TOPMODEL-based diagnostic model.\" This oversight stemmed from an insufficiently nuanced understanding of the TOPMODEL methodology, and we sincerely appreciate the reviewer for bringing this to our attention. We have corrected this in both the main text and supplementary materials.\nFurthermore, we acknowledge that while the TOPMODEL-based diagnostic model efficiently utilizes the compound topographic index (CTI) and reanalyzed soil moisture (SM) to estimate long-term saturation and inundation fractions at the grid scale, it does omit explicit simulations of river routing and floodplain processes, nor does it directly incorporate precipitation time series as input. Additionally, the model cannot resolve microtopography and small-scale wetland dynamics, nor does it account for anthropogenic regulation (e.g., dams, irrigation). Consequently, it exhibits notable limitations in simulating floodplains primarily driven by riverine inputs or extreme precipitation (e.g., the Sudd Wetland). Future improvements should involve coupling with river routing models, employing higher-resolution DEMs, and integrating direct precipitation forcing.\nReference\nStocker B D, Spahni R, Joos F. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands[J]. Geoscientific Model Development, 2014, 7(6): 3089-3110.\nXi Y, Peng S, Ciais P, et al. Future impacts of climate change on inland Ramsar wetlands[J]. Nature Climate Change, 2021, 11(1): 45-51.\nMinor points\nMinor Point 1\nP3 L2 and throughout: The authors refer to “10 periods from 1984 to 2021” though only 9 points are shown in Figures 2 and 3. In SI (P4) they note that the period 1991-5 suffered from lack of data and presumably this explains its absence in those figures. For consistency in the main text, it would make sense to talk about analysis of 9 periods, not 10.\nResponse: We have carefully addressed the reviewer's comments and made the following revisions to the manuscript.\n[Page 03, line 02]:\nBased on these sampling points, we used the random forest classifier to classify and extract African wetlands in 9 periods from 1984 to 2021 on the Google Earth Engine (GEE) platform.\n[Page 05, line 04]:\nFig. 1 | Area distribution and changes of African wetlands during the historical period. a, The distribution of wetland areas in Africa from 1984 to 2021 (The average wetland area in 9 periods).\n[Page 07, line 14]:\nThe horizontal coordinates were the middle years of 9 classification periods.\n[Page 18, line 25]:\nA total of 9 wetland maps were generated from the classification of African wetlands in historical periods (more details in the Supplementary Information).\n[Page 19, line 07]:\nThe data regarding temperature, precipitation, PDSI, SM, and HII were uniformly resampled to a resolution of 30 m, whereupon the mean values of the main distribution areas of wetlands in 9 periods were calculated.\n……\nThey are not listed here individually.\nMinor Point 2\nThe authors overstate the case with respect to the historical trend in inland wetland (P6 L19) “completely opposite trends… in coastal and inland wetlands”. Whilst the downward trend in coastal wetland is statistically robust, the inland trend (P=0.175) cannot be distinguished from zero at even the 10% level.\nResponse: We have carefully addressed the reviewer's comments and made the following revisions to the manuscript. The revised details can be found on [Page 06, line 19]:\nThe 38-year time span allowed us to study African wetlands in terms of time series and trends. Accordingly, we discovered different trends when comparing changes in the areas of coastal wetlands and inland wetlands in Africa from 1984 to 2021 (Fig. 2; Supplementary Fig. 6).\nMinor Point 3\nP8 L10 “has been conversion to…”\nResponse: We sincerely thank the reviewer for the detailed and careful review. We have corrected this oversight accordingly. The revised details can be found on [Page 08, line 10]:\nThe primary direct driver for the loss of coastal wetlands has been converted to other land uses (aquaculture, agriculture, coastal developments, etc.), which is the general consensus of the international community29,33,36.\nMinor Point 4\nI may have missed it, but what soil moisture levels (in FLDAS and the CMIP6 models) were actually used? On a related note, the y-axis range in SI Fig. 20 (~650 m3/m3) is clearly wrong.\nResponse: Thank you to the reviewer for identifying this issue. The supplementary figure SI Fig. 20 indeed reflects an oversight on our part, as we did not clearly explain the methodology.\nThe FLDAS soil moisture data are provided in four layers and expressed in volumetric units (volume fraction). In our analysis, we first converted these volumetric data into units of kg/m². We then summed the soil moisture ( kg/m²) across the four depth intervals 0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm to obtain the total mass of water in the 0–200 cm soil profile at each location. This value represents the soil water storage, i.e., the total amount of water contained per unit area within the soil profile. The same processing method was applied to the CMIP6 model outputs.\nFigure 12. Calculation of Total Soil Moisture in GEE\nThe error in the original figure lies in the unit label, which was incorrectly written as m³/m³ instead of the correct unit, kg/m². We have revised both the figure and the corresponding textual descriptions accordingly. The revised details can be found in the Supplementary Information [Page 42, lines 02-04]:\nSupplementary Figure. 20 | Comparison of CMIP6 soil moisture time series and FLDAS soil moisture change trend. The FLDAS soil moisture represents the sum of soil moisture (kg/m²) across four layers: 0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm, reflecting the total amount of water contained in the soil per unit area. The CMIP6 soil moisture time series is derived by averaging the soil moisture data from 14 models.\nAdditionally, the slightly elevated values observed in the figure are due to the exclusion of arid regions—such as North Africa, the Sahara Desert, and southern Africa—where wetlands are sparse. The statistics were calculated based solely on the major wetland distribution regions in Africa (see Supplementary Figure 1), which explains the relatively higher total soil moisture values.\nTable 5. FLDAS Layered Soil Moisture Data.\nName Units Description\nSoilMoi00_10cm_tavg Volume fraction Soil moisture (0 - 10 cm underground)\nSoilMoi10_40cm_tavg Volume fraction Soil moisture (10 - 40 cm underground)\nSoilMoi100_200cm_tavg Volume fraction Soil moisture (100 - 200 cm underground)\nSoilMoi40_100cm_tavg Volume fraction Soil moisture (40 - 100 cm underground)\nFLDAS is available from https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001#bands.\nMinor Point 5\nSI Figure 19 shows that TOPMODEL forced by FLDAS soil moisture does a good job at representing interannual wetland variability. Can the authors clarify how the calibration procedure (to determine the value of the M parameter) has influenced this simulation? In other words, was there data held back from the calibration step to evaluate the model simulations?\nResponse: The reviewer raised concerns regarding the calibration process of the wetland simulation, specifically the method used to determine the parameter M. We offer the following clarification:\nAs illustrated in the figure below, the study “Future impacts of climate change on inland Ramsar wetlands” defines M as an adjustable parameter ranging from 1 to 15, used to describe the exponential decay of soil permeability with depth and to capture regional heterogeneity.\n[Figure Redacted]\nIn our study, we treated the M value in each grid cell as a fixed attribute. We systematically tested all values from 1 to 15, calculating the root mean square error (RMSE) between the observed wetland area (mean across nine historical periods) and the simulated wetland area (mean over the same nine periods). The M value that minimized RMSE was selected as the optimal parameter for that grid cell and was subsequently used for future wetland simulations.\nSupplementary Figure. 19 | Comparison of simulated wetland area time series and classified wetland area time series. The left coordinate represented the classified inland wetland area, and the right ordinate represented the simulated inland wetland area based on FLDAS soil moisture data. The vertical coordinates were the middle year of 10 periods.\nWe understand the reviewer's concern that calibrating M in this manner could potentially lead to overfitting to the classified data. However, it is important to clarify that M was calibrated only once, based on the mean wetland extent across the nine historical periods, and was not fitted separately for each period. Therefore, the chosen M value remains constant across both historical and future simulations. This fixed parameter influences only the overall magnitude of simulated wetland area and does not affect its interannual variability or temporal dynamics.\nRegarding the question of whether any data were withheld during the calibration step to independently evaluate model performance: only the simulation results using the optimal M values (post-calibration) were retained, as shown in Supplementary Figure 19. Simulations with other M values were not subjected to further analysis.\nReference in the figure:\n[25] Beven K J, Kirkby M J. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant[J]. Hydrological sciences journal, 1979, 24(1): 43-69.\nMinor Point 6\nIn Figure 5, what range of values does a “constant” wetland area correspond to in the bar charts? In the maps, it would be better to depict that “constant” orange over a range at equal distance either side of zero (e.g. -3 to +3).\nResponse: We sincerely thank the reviewer for pointing out the overly strict classification of \"stable\" wetland areas in Figure 5 and for suggesting the use of an orange (or yellow) color on the map to indicate regions with changes within a ±3 km² range.\nIn the original manuscript, the \"stable\" wetlands shown in the bar chart of Figure 5 referred only to pixels with an exact change of zero in wetland area. Clearly, this zero-threshold definition excluded minor fluctuations and resulted in an overly conservative estimate of stability, yielding too few samples in the “stable” category.\nWe have adopted the reviewer’s suggestion by redefining \"stable\" wetland areas as those with a change in area within ±3 km². These regions are now highlighted in yellow on the map. Based on this revised threshold, we recalculated the proportions of wetland area that fall into the categories of increase, decrease, and stable under the four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Corresponding updates were made to the values and color scheme in the pie charts in Figure 5.\nThe revised figure is as follows:\nFig. 5 | Inland wetland area changes in Africa by 2100 under four SSP scenarios mapped per 0.2 °grid cell. a-d are inland wetland changes under SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. The pie chart shows the proportion of areas with inland wetland area changes in Africa.\nThe corresponding textual description in the main text, under the section “Inland wetland trends under future climate trajectories,” has also been revised accordingly and highlighted in the revised manuscript. The revised details can be found on [Page 12, lines 14-15]:\nIn the SSP126 scenario, which features more moderate climate change than the others, 17% of Africa’s regions will experience wetland area growth; in comparison, the SSP585 scenario predicts that 35% of the regions will experience wetland area expansion (Fig. 5)."}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors have provided a lot of material in response to my comments! Thank you for that considerable effort. Below I flag some areas where I am still unclear about their logic. On several points, the authors do not appear to have made any change to the manuscript. I would like to see updates in the manuscript consistent with evidence presented in their response. It may be that I missed some changes in the manuscript, but lines featuring yellow highlighting in the revised manuscript are remarkably few given the 35-page response.\nGeneral response: We appreciate the reviewers’ positive recognition of our previous revision, which has strengthened our confidence in the current study.\nGiven that the last revision already included extensive additional material, the present revision focuses on addressing the specific parts of the manuscript (main text and Supplementary Information) that remained unmodified, while keeping our responses concise. Moreover, considering Nature Communications’ emphasis on logical flow and brevity in the main text, we have placed most of the detailed revisions in the Supplementary Information file and hope the reviewers will understand this approach.\nMajor point 1\nThe authors have done a good job of comparing their mapping with the TropWet dataset of Hardy et al, and arguing why their analysis differs from that study. I also appreciate their efforts to provide additional evaluation, particularly with respect to the GIEMS dataset. However, there are several points I must query them on. In their response they state (p5) “As illustrated, the interannual trajectory of African wetland area in GIEMS-MC closely mirrors our results: an initial decline followed by a subsequent increase.” I agree that “the subsequent increase” in GIEMS is very clear in their response Figure 3. However, it is quite misleading to describe the earlier part of the GIEMS time series as “an initial decline”. I struggle to see how the comparison “partially validates our pre-2000 findings”. I believe the authors when they argue that the limited availability of Landsat and other data pre-2000 affects the quality of the mapping. I think the authors need to add a sentence in the main text. I also think that the comparison they show in the response Figure 3 should be noted in the text and included in SI, though given the strong interannual variability in the GIEMS dataset, I don’t really see the value of the polynomial fits they present. I am also somewhat surprised that the authors don’t provide a correlation metric between the two datasets as, by eye, the comparison seems to provide important independent evaluation of their findings.", "reviewer_author_response": "The authors thank Reviewer 4 for the careful reading and positive recommendation. We appreciate the confirmation regarding Major Point 4. The manuscript, Methods and Supplementary Information have been updated to reflect this clarification, as noted in our point-by-point response. No further changes are required from the authors. Thank you for your constructive evaluation."}, "author_response": ""}]

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