Orals: Tue, 29 Apr | Room 2.17
Restoring degraded peatlands is a key strategy for climate change mitigation. This has driven increased restoration efforts, especially in northern regions with widespread degradation. Continuous spatial monitoring is critical, and remote sensing enables it by providing large-scale data. In our study, we analyzed restoration-induced changes in essential climate variables across degraded northern peatlands in Finland, Estonia, Latvia, Lithuania, the UK, Canada, and the USA. We hypothesized that, prior to restoration, degraded peatlands with different initial land cover types display more pronounced differences in essential climate variables compared to intact peatlands, but these differences diminish as restoration progresses. Using over 20 years of satellite data, we observed changes driven by restoration in vegetation cover, surface temperature, and albedo, with the latter two showing the strongest indications of peatlands gradually recovering their natural state over time.
How to cite: Burdun, I., Myllymäki, M., R.E. Artz, R., Guêné-Nanchen, M., Jarašius, L., Kull, A., A. Lilleskov, E., McCullough, K., Pakalne, M., Pu, J., Sendzikaite, J., Strazdina, L., and Rautiainen, M.: Tracking peatland recovery: insights from 20 years of satellite data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-924, https://doi.org/10.5194/egusphere-egu25-924, 2025.
It is well established that nearby unconfined aquifer systems are typically hydraulically connected to shallow lakes and wetland systems. This connection between surface waters and adjacent groundwater systems has significant implications for the effective protection and management of the high environmental values often associated with lake and wetland habitats. Understanding the behaviour and interactions of groundwater and stagnant water bodies in these systems is crucial. This study explores the seasonal dynamics and interactions between groundwater, stagnant water bodies (SWBs), and surface water in the lower Gangetic floodplain of eastern India, using isotopic mapping (δ18O values) and 222Rn values.
The δ18O values of groundwater were found to vary seasonally and spatially across the region. Groundwater near the River Ganges exhibited lower δ18O values, which increased as the distance from the river increased. The lowest δ18O value (-9.0‰) occurred in the post-monsoon season, and the highest value (-0.8‰) was observed during the monsoon. The higher δ18O values in monsoon were likely influenced by irrigation, which introduced water with higher isotopic values into the groundwater. This seasonal fluctuation reflects the impact of land use and agricultural practices on groundwater composition.
The SWBs showed different δ18O patterns, with values varying seasonally due to factors like evaporation, rainfall, groundwater seepage, and the size of the water body. The mean annual δ18O value of SWBs was -0.20‰. Larger SWBs, which are less affected by evaporation and more likely to be connected to the aquifer, exhibited lower δ18O values (from -3.0 to 0‰). In contrast, smaller SWBs showed higher δ18O values due to stronger evaporation effects. A significant correlation was observed between the δ18O values of SWBs and rainfall, with a one-month lag. This suggests that the SWBs are primarily influenced by the hydrological cycle, with the addition of rainwater during the monsoon season lowering the δ18O values.
The study also examined groundwater recharge using data from three boreholes located 15 km from the River Ganges. These boreholes tapped different aquifers at varying depths, revealing seasonal fluctuations in δ18O values. Shallow boreholes (20 meters) exhibited higher δ18O values, reflecting recent rainfall and evaporation. Deeper boreholes (30 meters and 50 meters) showed more negative δ18O values, suggesting recharge from different water sources. These variations highlight the influence of different recharge events and the seasonal patterns of groundwater interaction with surface water.
Radon (222Rn) levels were measured in the boreholes to assess groundwater-surface water interaction. The radon data suggested that lithology (rock and soil types) played a significant role in groundwater composition. Borehole-1, located in silty clay, showed higher radon levels than the other two boreholes in silty sand, reflecting differences in uranium content in the soil. In conclusion, this isotopic mapping study reveals the complex, seasonal interactions between groundwater, river water, rainwater, and SWBs in the lower Gangetic floodplain, with important implications for water resource management and environmental protection.
How to cite: Sanyal, P., Kumar, V., and Jakhar, M.: Water isotopes and 222Rn in disentangling the impact of macroscale climatic controls and microscale hydrological processes in groundwater-wetland ecosystems in the lower Gangetic plain, Indian Peninsula , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-672, https://doi.org/10.5194/egusphere-egu25-672, 2025.
Wetlands provide key habitat for many species but are threatened by climate change. However, quantitatively projecting climate change impacts on wetland hydrologic regimes is difficult due to the often remote nature of wetlands, leading to a scarcity of data on wetland inundation. In these contexts, remote sensing offers a large scale tool for periodic observations of wetland water extent. In addition, wetlands can display a diverse range of inundation regimes, driven by the different hydrological processes that contribute to inflows. For example, perennial wetlands might have ongoing groundwater contributions or high rainfall that keep water levels elevated throughout the year. But more variable systems such as floodplain wetlands might rely on overbank river flows, such that they might only be inundated every few years. This large range in hydrologic variability can make modelling wetland inundation a difficult task.
We used a timeseries dataset of wetland inundation extents, extracted from Landsat-derived water observations over 1988-2022, to classify the hydrologic regime of 34,890 wetlands in the state of Victoria, Australia. Wetlands were classified as permanent, seasonal, intermittent or episodic systems, which represent increasing variability in inundation. We then calibrated a series of conceptual hydrologic and Long Short-Term Memory (LSTM) neural network models to simulate wetland inundation, driven by climate inputs. Conceptual hydrologic models were able to reasonably simulate wetland inundation for permanent, seasonal and intermittent systems, but struggled in representing the more variable episodic wetlands. For episodic wetlands, LSTM models performed better than conceptual hydrologic models, but many that calibrated well over the historic period showed unrealistic sensitivity to changes in climate inputs.
We then applied a range of climate projections to wetlands models, to understand potential future shifts in wetland hydrologic regimes, based on a subset of 8,334 of the best performing models. Climate change projections substantially reduced the proportion of permanent and seasonal wetlands and increase the proportion of episodic wetlands in Victoria. Our results suggest the biggest risk is to permanent wetlands, where even under a moderate emissions scenario nearly two thirds of permanent wetlands could transition to seasonal or intermittent systems by 2065. “Rare” wetlands (with an average inundation frequency of less than once every 10 years) are predicted to increase eight-fold under a high emissions scenario by 2065. These results demonstrate the significant vulnerability of wetland hydrology to climate change, with potential major implications for wetland habitat for freshwater ecosystems.
How to cite: John, A., Burns, G., and Nathan, R.: Regional scale projections of future change in wetland hydrologic regimes in Australia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7826, https://doi.org/10.5194/egusphere-egu25-7826, 2025.
River water levels and their dynamics are fundamental indicators of freshwater availability, climate change impacts on the water cycle, and regional water security. Although monitoring these changes is crucial for developing adaptive management strategies that balance human needs with ecosystem sustainability, traditional in-situ observations are mostly constrained to large rivers and downstream reaches. Satellite altimetry, using radar pulses to sense surface waters, open a new era to track global river stages and assess their temporal variations. Leveraging advanced Synthetic Aperture Radar techniques and our improved waveform retracking algorithm, we successfully monitored water levels at 46,993 Sentinel-3 virtual stations (VSs) from 2016 to 2024. These VSs, located at the intersections of satellite ground tracks and river channels, encompass rivers ranging from several meters to kilometers in width across diverse topographical settings. Water level change rates unveil a pronounced global wetting-drying pattern across river basins. Our analysis identifies significant water level declines in Central North America, Central South America, and Western Siberia, contrasting with widespread increases across Africa, Oceania, and Eastern and Southern Asia. These findings demonstrate the differential impact of intensifying hydrometeorological events on regional river dynamics and highlight accelerating change rates. Our results provide critical insights into water security assessment and resilience planning, emphasizing the urgent need for targeted policy interventions to address these hydrological changes.
How to cite: Long, D. and Fang, C.: Satellite altimetry reveals a contrasting wetting-drying pattern in global rivers under climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5482, https://doi.org/10.5194/egusphere-egu25-5482, 2025.
River streamflow is fundamental to hydrological science, providing essential insights for freshwater resource management, flood and drought control, and riverine ecosystem preservation. However, conventional field measurements are predominantly limited to major rivers and downstream reaches, while hydrological models heavily depend on these observations for calibration and training. The declining availability of in-situ data significantly impedes global streamflow mapping, particularly for thousands of ungauged rivers, thereby constraining our understanding of hydrological processes.
The Surface Water and Ocean Topography (SWOT) satellite, launched in 2022, provides unprecedented observations of water surface elevation (WSE), river width, and slope for global rivers, enabling discharge estimation without traditional gauging data. It employs six well-established algorithms to optimize unobserved flow law parameters (FLPs), including friction coefficients and referenced cross-sectional areas. These FLPs effectively characterize hydraulic properties for individual river reaches, with accuracy continuously improving through prolonged SWOT monitoring periods. This critical information enables the reconstruction of river discharges using historical satellite observations and fundamental flow laws (e.g., Modified Manning’s equation) with SWOT-derived FLPs.
By applying the estimated FLPs to nadir altimeter virtual stations (e.g., Jason and Sentinel-3), our methodology enables global river discharge estimation using altimetric WSEs alone. This approach facilitates comprehensive river streamflow reconstruction dating back to early satellite operations, demonstrates gauge-independent generalization capability, and establishes a novel paradigm for tracking discharge dynamics by integrating historical observations with SWOT-based discharge. Validation by the SWOT science team indicates uncertainty levels below 30% for most river reaches. Our framework establishes a foundation for analyzing global river responses to climate change and hydrometeorological extremes, offering significant potential for enhancing resilience to hydrological variations in a changing climate.
How to cite: Fang, C. and Long, D.: SWOT-Driven Global River Discharge Reconstruction: A Novel Framework for Streamflow Analysis under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4938, https://doi.org/10.5194/egusphere-egu25-4938, 2025.
Wetlands deliver critical ecosystem services, including serving as habitats for diverse species (some of which are vulnerable or endangered), facilitating nutrient cycling, storing and sequestering carbon, and offering recreational opportunities. However, over the past century, wetlands have experienced significant loss, degradation, and stress due to anthropogenic influences such as water diversion, agricultural expansion, and urbanization, as well as natural processes like sea-level rise and climate change. This underscores the urgency to protect, conserve, and restore the remaining wetlands worldwide. A fundamental component of wetland conservation, management, and restoration is the monitoring of their hydrological systems, as wetland ecosystems are inherently dependent on water availability. Hydrological monitoring is commonly conducted using stage (water level) stations, which provide high temporal resolution data but suffer from limited spatial resolution, as these stations are often distributed several kilometers apart or more. Additionally, many wetlands remain ungauged or are monitored with only a sparse network of stage stations due to logistical constraints.
Space-based remote sensing technologies offer an effective alternative, providing high spatial resolution measurements of wetland water levels and their temporal changes. These techniques include Synthetic Aperture Radar (SAR), optical imagery, and radar and laser altimetry. SAR observations yield two independent observables—amplitude and phase—each sensitive to distinct hydrological parameters. Radar and laser altimetry missions deliver centimeter-level accuracy in water-level measurements along satellite tracks.
To overcome the limitations of individual monitoring methods, we developed a novel, space-based, multi-sensor approach to estimate absolute water level changes in wetlands by integrating ICESat-2 laser altimetry and Sentinel-1 InSAR data. This approach employs ICESat-2 absolute water levels to calibrate Sentinel-1 InSAR-derived relative water level changes, generating high spatial resolution (50–200 m) maps of absolute water level changes across entire wetland areas. We applied this methodology to the South Florida Everglades, a natural laboratory characterized by significant wetland variability and abundant ground-based hydrological data. The analysis utilized all ICESat-2 observations for the region, comprising 202 ground tracks collected between October 2018 and December 2023. Additionally, we processed 146 Sentinel-1 interferograms from the same period using the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline. This yielded 103 water level change maps with temporal intervals ranging from 12 to 364 days. Validation against gauge data revealed a root mean square error (RMSE) of 15.4 cm for the absolute water level change estimates. Error sources included uncertainties in ICESat-2 observations, InSAR measurements, and the EDEN interpolation scheme. To further investigate error contributions, residuals were decomposed into short- and long-wavelength components. Short-wavelength errors, primarily attributed to InSAR data, captured localized variations, while long-wavelength errors, associated with ICESat-2 data, reflected broad-scale biases. By removing long-wavelength biases, we achieved an RMSE of 7.8 cm, demonstrating the potential for high-accuracy wetland water level monitoring using the integrated multi-sensor approach.
How to cite: Wdowinski, S. and Palomino-Ángel, S.: A Multi-sensor Approach for Hydrological Monitoring of Wetlands: Altimetry-InSAR (ICESat-2/Sentinel-1) Integration Method Development over the South Florida Everglades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11865, https://doi.org/10.5194/egusphere-egu25-11865, 2025.
Peatland degradation promotes carbon emissions, biodiversity loss, water quality decline, and slope instability. Effective mitigation of these impacts depends on understanding and monitoring peatland ecohydrology across space and time. Synthetic Aperture Radar (SAR) satellites enable large-scale monitoring at moderate to high spatial resolution and, as SAR penetrates cloud, at consistent revisit times. However, exactly how SAR backscatter relates to peatland ecohydrology, in both space and time, is incompletely understood. We investigated spatio-temporal relationships between C-band SAR backscatter intensity and peatland ecohydrological characteristics at six temperate raised bogs in Ireland. The study sites range from near-intact raised bog to industrially-degraded bare peat; the near-natural sites have a well-characterized range of raised bog ecology. We assess Sentinel-1 C-band SAR backscatter intensity (radiometric terrain corrected) in both VV and VH polarization through time for the period 2015-2024. Time series of SAR backscatter intensity for all bogs show annual oscillations that are most pronounced in VV polarization. Intensity maxima occur in winter-spring; minima occur in summer-autumn. The amplitude of intensity oscillation and mean of VV intensity through time are consistently greater for bare peat than for near-intact bogs. The mean of VH intensity in time is lowest for areas of bare peat and areas of non-vascular vegetation (moss, Sphagnum), but it is highest for areas of vascular vegetation (Heather, Calluna). The annual oscillation in SAR intensity is attributed primarily to soil moisture variation, which is controlled by groundwater levels and seasonal precipitation. SAR intensity oscillation is greatest in drained bare peat because of more intense cycles of drying and wetting. The higher mean VV intensity in time of bare peat is attributed to the lack of attenuation of the SAR pulse by vegetation. The sensitivity of mean VH intensity in time to the nature of vegetation is explained by increased volumetric scattering of the radar waves in shrub-rich areas. Spatio-temporal shifts in SAR backscatter signatures can thus help identify and monitor the impacts of human activity on temperate raised bogs. For example, responses to early stages of restoration (rewetting) were detectable in the intensity time series as decreases in backscatter intensity and reduction, or loss of annual intensity oscillation. Consequently, this study provides an improved basis for incorporating SAR remote sensing into sustainable peatland management.
How to cite: Khoshlahjeh Azar, M., Hrysiewicz, A., Donohue, S., Regan, S., Flynn, R., and P. Holohan, E.: Ecohydrological Monitoring of Temperate Raised Bogs by Using Backscatter Intensity of Synthetic Aperture Radar , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12852, https://doi.org/10.5194/egusphere-egu25-12852, 2025.
Peatlands are unique ecosystems with high biodiversity and environmental services such as water filtration and retention as well as carbon storage. Interestingly, however, in contrast to other soils and ecosystems, less is known about the extent and health (natural/rewetted versus drained/ degraded) of European peatlands1. With past human-induced drainage and degradation and recent or emerging restoration, there is an even greater need to monitor the extent and health of European peatlands1. Here we present results of a novel approach to (1) distinguish between unforested peatlands and surrounding areas (forest and grassland), and (2) separate drained/degraded from natural/rewetted peatlands, based on 12 European peatlands in three Köppen-Geiger climate classes2. We compare remote sensing data (Sentinel 2, PlanetScope optical bands) with the molecular composition of surface soils to differentiate between natural and drained peatlands. The peatland vegetation and surrounding areas are seperated based on red and near infrared (NIR) bands3. Furthermore, the natural and drained peatlands are distinguished by their Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index Red (EVI), Green Normalised Vegetation Index (gNDVI) and Greenness Index (GI); known indicators of vegetation composition and health3,4. Simultaneously, two types of soil data were measured as indicators of soil health, i) peat stoichiometry (e.g., carbon to nitrogen ratio, degree of carbon oxidation), and ii) peat molecular composition using pyrolysis gas chromatography with integrated mass spectroscopy (PYGCMS), a fast and valid method to study a wide range of molecular compounds5. In particular, we analysed the relative abundance of molecules indicative of different vegetation classes and their transformation products, as well as the relative contribution of microbial input. The results showed that the red and NIR bands were useful to distinguish between grasslands and peatlands as the reflectance of grasslands is significantly high compared to peatlands. In addition, we were able to distinguish between drained and natural peatlands by the optical indices used. The molecular composition and remote sensing indicators of the sites clearly correlated and natural and drained sites could be distinguished. Therefore, remote sensing data might serve as a fast and valid method to obtain information on the extent and health status of European peatlands under different climatic conditions.
REFERENCES
1Andersen, R. et al, https://doi.org/10.1111/rec.12415.
2Kottek, M. et al, https://doi.org/10.1127/0941-2948/2006/0130.
3Burdun, I. et al., https://doi.org/10.1016/j.rse.2023.113736.
4Räsänen, A. et al., https://doi.org/10.1016/j.jag.2022.102866.
5Klein, K. et al., https://doi.org/10.1016/bs.agron.2020.09.002.
How to cite: Groß-Schmölders, M., Gupta, S., Wania, A., Grady, M., Leifeld, J., and Alewell, C.: Building a Framework to Differentiate between Natural and Drained Peatlands in Europe by comparing Molecular and Remote Sensing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15330, https://doi.org/10.5194/egusphere-egu25-15330, 2025.
Forecasting the temporal and spatial evolution of coastal wetlands is a complex and challenging endeavour, further complicated by shifting climatic conditions. From a coastal management perspective, however, it remains essential to anticipate local vulnerabilities and the potential compromise of ecological functions. This study aims to assess the skill of subpixel and pixel imagery classification algorithms in sensing the zonation of wetlands through satellite imagery by testing in wetland areas, using high spatial resolution imagery from Unmanned Aerial Vehicles (UAVs) and high and medium spatial resolution satellite imagery and identifying the related challenges. The research was conducted in one of the most important coastal lagoons of Portugal, Ria Formosa Lagoon. Hard and soft Random Forest regression algorithms were employed to estimate the zonation of marsh plants, the former applied to UAV data (centimetric pixel size) and the latter to high and medium resolution satellite imagery (WV-2 & -3 and Sentinel, respectively, metric to decametric pixel size). Employing a gradually increasing pixel size allowed to identify error propagation during the passage from pixel to sub-pixel estimators and lower resolutions. The obtained results provide important insights to the barriers and opportunities related to varying imagery sources, carrying an important message to local managers. When discussed within the context of dominant natural and human drives, the developed maps, along with methodology and monitoring, provide valuable scientific insights into vegetation succession in a mesotidal system. More importantly, they serve as essential tools for local coastal decision-makers in identifying priorities for strategic landscape conservation planning and ensuring the sustainability of ecological function as the carbon sequestration within ecosystems.
Acknowledgements: This study contributes to the projects DEVISE (https://doi.org/10.54499/2022.06615.PTDC) and C-Land (CEXC/4647/2024), both funded by the Fundação para a Ciência e a Tecnologia, and to project TraceLands (ID PP0090200), funded by the European Space Agency.
How to cite: Carneiro, I., Katerina Kombiadou, K., Yang, Z., Silvestri, S., and Carrasco, A. R.: Identification of ecological zonation in tidal wetlands from high and medium-resolution satellite imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-960, https://doi.org/10.5194/egusphere-egu25-960, 2025.
Wetlands are essential ecosystems providing critical ecological services, yet they face significant threats from human activities and climate change. Monitoring and mapping these areas accurately is fundamental to formulating effective conservation and restoration strategies. Remote sensing, combined with advanced deep learning techniques, offers a scalable and efficient solution for wetland classification and monitoring. However, the application of these technologies is often constrained by regional variations in wetland classification systems and the challenges of distinguishing ecologically similar wetland types. Notably, no study has yet leveraged deep learning for mapping wetlands within Denmark's unique wetland classification system, as defined by the Danish Nature Conservation framework.
This study presents a comprehensive benchmark analysis of three state-of-the-art deep learning models—Fully Convolutional Network (FCN), U-Net, and DeepLabV3—for wetland segmentation using high-resolution Earth observation data. We utilize the publicly available multispectral aerial imagery (RGB and NIR) and digital elevation models (DEM) to classify Denmark’s wetland areas, such as bogs, freshwater meadows, and salt marshes. By evaluating multiple input configurations, this study investigates the impact of integrating additional spectral and elevation data on the segmentation performance.
The results demonstrate that the DeepLabV3 model outperforms other architectures, achieving the highest accuracy and F-measure when leveraging the combined RGB, NIR, and DEM data. Despite these advancements, challenges remain, particularly in distinguishing ecologically similar wetland types (e.g., freshwater meadows and bogs) and addressing issues of label noise in ground truth datasets. This study highlights potential solutions, such as the inclusion of Synthetic Aperture Radar (SAR) data for temporal analysis and the adoption of noise-robust training and contrastive learning methods to enhance model robustness.
This benchmark not only establishes a foundation for improving deep learning methodologies for wetland mapping in Denmark but also contribute to global efforts aimed at developing innovative, scalable solutions for wetland conservation and restoration.
How to cite: Larsen, J. J. and Asif, M. R.: Benchmarking deep learning models for wetland mapping in Denmark using high-resolution earth observation data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4365, https://doi.org/10.5194/egusphere-egu25-4365, 2025.
Posters on site: Mon, 28 Apr, 10:45–12:30 | Hall X1
Wetlands are among the most productive ecosystems on Earth but also one of the most threatened by environmental and climate changes. Detailed spatial and temporal monitoring of water level patterns in wetlands is crucial to understanding the ecosystem status and dynamics, as it provides information about the amount of water stored and moving through a wetland at a given time. Space-based hydrologic monitoring of wetlands has successfully complemented ground-based hydrological observations by providing invaluable measurements of water levels and their changes over time in both gauged and ungauged areas. Detection of wetlands’ water level beneath vegetation cover requires active remote sensing technologies, where several technologies such as radar and laser altimetry, SAR amplitude, and Interferometric SAR have been successfully used. The increasing availability of data from new missions poses an opportunity to advance space-based hydrological applications, but it requires a consistent evaluation of the accuracy for such a purpose.
This study evaluates the new generation of satellite radar altimeters, including Sentinel-3 and SWOT-nadir observations, for water level retrieval in vegetated wetlands. The evaluation was conducted in the South Florida Everglades using the Altimetry Time Series (AlTiS) software with data acquired between 2023–2024. We used gauge data from the Everglades Depth Estimation Network (EDEN) as a reference for the analysis. Preliminary results show that both products provide accurate water level retrievals for the tested locations, with Root Mean Square Error (RMSE) of 0.02 m and R2 of 1.00 for Sentinel-3 (n = 24), and RMSE of 0.12 m and R2 of 0.99 for SWOT-nadir (n = 29). The results provide a first insight into the potential of both missions for tracking water level changes in vegetated wetlands and open new opportunities to strengthen hydrological monitoring in unmonitored areas. The next steps of the research will include performing a systematic evaluation of the products for various wetland types and exploring the potential of integrating the observations with additional datasets in multi-sensor approaches.
How to cite: Palomino-Ángel, S. and Wdowinski, S.: Leveraging the Potential of Sentinel-3 and SWOT Radar Altimeters for Hydrological Monitoring in Vegetated Wetlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12667, https://doi.org/10.5194/egusphere-egu25-12667, 2025.
The Ouémé delta, located in southern Benin, serves as the main tributary system for the Nokoué lagoon, fed by the Sô and Ouémé rivers, that cross a vast plain of wetlands. This region experiences pronounced seasonality driven by the African monsoon, leading to significant river level fluctuations that cause major flooding and pose threats to the livelihoods and safety of riverine communities.
The aim of this study is to assess the spatio-temporal variability of water levels and inundated areas extent at seasonal and interannual scales from 2015 to 2023, using in-situ and satellite data in the Ouémé Delta.
First, water level data obtained from in-situ and Sentinel 3 altimetry in the rivers and the lagoon, show significant seasonal variability, with a difference of 8 meters between the dry season (December-April) and the wet season (September-November) in the Ouémé river. Strong interannual variability was also observed in Nokoué Lagoon, particularly between 2020, which experienced a minor flood (+0.75 m) and 2022, characterized by a major flood (+1.5m).
Then, the flood extent was analyzed using Sentinel-1A radar imagery. The average inundated area is estimated to 100 km² during wet season. Monthly flood probability maps indicate that the southern part of the Sô River is the most frequently flooded. These maps further highlight the southward propagation of flooding during the wet season. Since 2018, a marked increase in flood extent has been observed. From 2015 to 2017, the average inundated areas was around 40 km². However, post-2018, this figure has more than doubled. Between 2018 and 2023, significant year-to-year variations were observed, with a difference of 90 km² inundated areas between 2020 and 2022.
These obtained results, validated with independent data (Suomi-NPP/VIIRs flood data), provide a better understanding of the hydrological dynamics in the Ouémé delta. This work is conducted as part of the SCOast-DT and TOSCA projects, funded by the CNES (French space agency).
How to cite: Noémie, F., Alexis, C., Yves, M., Alexei, K., and Victor, O.: Seasonal and interannual variability of water-covered areas in the Ouémé delta (Bénin) from 2015 to 2023., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10013, https://doi.org/10.5194/egusphere-egu25-10013, 2025.
Drained peatlands emit vast amounts of greenhouse gases (GHGs), contributing around 4 % of global GHG emissions. Rewetting peatlands, thus, has the potential to lower global emissions significantly. Restoring drained peatlands to protect the peat body, restore species-rich peat-forming plant communities, and reduce GHG emissions is a long-term process. Monitoring rewetting and restoration of peatlands to quantify (avoided) emissions typically requires either direct measurements via the eddy covariance or closed chamber methods or vegetation maps, as vegetation can be used as a proxy for water-table depths and GHG emissions of peatlands. Both monitoring approaches are expensive and time-consuming, which makes them often unsuitable to monitor large-scale and heterogeneous peatlands over many years. Therefore, new concepts for scalable and efficient monitoring of peatlands are needed.
This research is part of a project that aims to develop an efficient monitoring system for peatlands that scales to hundreds of hectares. Our aim is to identify plant species in (degraded) peatlands using high-resolution drone imagery and a machine learning framework informed by ecological principles. In Northern Germany we acquired RGB (1 cm/px) and multispectral (2 cm/px) imagery using a DJI Mavic 3M quadcopter and processed these data into orthomosaics using WebODM. Additionally, using the point cloud from the photogrammetry process, we derived raster maps of the digital surface model (DSM), standard deviation (a proxy for plant height), and skewness (a proxy for foliage height distribution). In addition to the raster inputs, we used temperature sums (instead of date) and cloud cover percentage as inputs to the model to account for plant phenology and diverse lighting conditions. Our selection of spectral bands, points-cloud derived raster maps and metadata such as temperature sums are ecological informed epistemic priors that aim to increase the model accuracy. Ground truth data (vegetation maps) were generated by mapping the vegetation with an Emlid Reach RS3 Differential Global Positioning System. Since multiple plants can occur together in the same patch, this is a multi-class and multi-label problem from a machine learning perspective. Thus, we used One Hot Encoding to create 3D labels (height × width × species ID).
Our preliminary results show that the accuracy of machine learning models can be improved by providing the models with ecologically informed priors like plant heights and temperature sums, but the ground sampling distance remains the limiting factor for classification accuracy. Next, we will fuse our automatically generated vegetation maps with hydrological information derived from water level measurements to generate high resolution maps of GHG emissions of peatlands.
How to cite: Trouillier, M., Pönisch, D. L., Husting, T. J., Rossa, H., Bergheim, M., Couwenberg, J., and Jurasinski, G.: Automatic Vegetation Mapping in Peatlands Using Drone Imagery and Ecologically Informed Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16456, https://doi.org/10.5194/egusphere-egu25-16456, 2025.
Wetlands are among the most vital ecosystems on Earth, offering numerous ecosystem services such as habitats for biodiversity, water purification, carbon sequestration, and flood mitigation. The Bakhira Wetland, located in eastern Uttar Pradesh, India, is a designated Ramsar Site and was selected for this study to assess hydro ecologic and landscape changes. Agricultural expansion in the region has led to significant loss of natural vegetation, increased landscape fragmentation, and severe threats to wetland communities and biodiversity. These changes have adversely impacted the hydrological richness that sustains water resources. In recent years, the Bakhira Wetland has also faced challenges such as invasive species encroachment, water quality deterioration, and habitat fragmentation, primarily driven by continuous agricultural development. These issues have further affected migratory bird species, falling the ecological balance of the region. This study used remote sensing data to analyze land use and land cover changes, water extent shrinkage, siltation, aquatic vegetation dynamics, urbanization, and wetland landscape fragmentation. Results revealed a significant decline in water extent during both pre-monsoon and post-monsoon periods, largely due to excessive aquatic plant growth and substantial water withdrawals. The extent of agricultural land in the Bakhira catchment expanded from 62.44 km² in 2000 to 114.93 km² in 2022, while built-up areas grew from 5.77 km² to 8.40 km² over the same period. The study emphasized substantial habitat fragmentation and reduced ecological connectivity, particularly during the dry season, due to intensified human activities. Landscape diversity and fragmentation indices indicated an increase in the number of patches and patch density, reflecting a more fragmented habitat. However, metrics such as the Number of Patches (NP) and the Large Patch Index (LPI) showed a decline, signifying smaller, less cohesive, and more isolated patches. This fragmentation disrupts ecological flows and hinders species movement, raising concerns about long-term biodiversity conservation in the wetland. To address these challenges, sustainable practices in catchment areas are essential. This includes adopting strategies for improved cropland management, water conservation, and wetland rehabilitation. The findings from this study provide valuable insights into the hydrological functioning of wetlands and can guide future efforts in wetland resource protection, sustainable utilization, and the development of infrastructure for rational surface water use. Additionally, restoring degraded agricultural lands is crucial for maintaining ecological balance and ensuring the long-term sustainability of the Bakhira Wetland.
How to cite: Rawat, M., Pandey, A., Yadav, B., and Gupta, P. K.: Assessing the Impact of Land Use and Land Cover Changes on Wetland Landscape Patterns and Hydroecology: A Case Study of the Bakhira Wetland, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15274, https://doi.org/10.5194/egusphere-egu25-15274, 2025.
Understanding the impact of anthropogenic climate variability on the surface inundation dynamics in the wetlands of drylands: A case study of Ile-Balkhash Delta, Kazakhstan.
Kanchan Mishra1*, Philip Weber1, Kathryn E. Fitzsimmons2
1Department of Geosciences, University of Tübingen, Schnarrenbergstrasse 94-96, 72076 Tübingen, Germany.
2School of Earth Atmosphere and Environment, Monash University, Clayton VIC, Australia
(*Email: kanchan.mishra@uni-tuebingen.de)
The Ile-Balkhash Delta, a Ramsar wetland of international importance in southeastern Kazakhstan, is one of the largest deltas in arid Central Asia (ACA). Like other waterbodies in dryland regions, the Ile-Balkhash delta faces degradation and desertification driven by anthropogenic climate change and human-induced alterations. These changes disrupt the structure, function, and distribution of wetlands, resulting in ecological and socio-economic impacts, including habitat loss, declining water quality and quantity, and reduced carbon sequestration. Despite their sensitivity to environmental changes, the surface water dynamics of these wetlands remain poorly understood in arid settings.
This study aims to assess the seasonal surface inundation patterns (SIP) and their spatio-temporal dynamics in the Ile-Balkhash Delta from 1992 to 2024 using remote sensing, GIS, and logistic regression analysis. Climatic and anthropogenic drivers of wetland dynamics are identified, while a new classification algorithm quantifies degradation patterns and transitions under the current regulated hydrological regime, offering insights into physical processes and conservation strategies.
The study reveals a strong seasonal variability, with persistent water coverage peaking in spring (15.4%) and declining in summer (10.4%), reflecting substantial reductions during drier months. Interannual variability shows peaks in wetland areas during years such as 2000, 2004, 2010, 2016, and 2018, likely linked to upstream discharge and snowmelt. However, a marked decline in coverage post-2018 suggests potential shifts in the hydrological conditions of the wetlands. The analysis further highlights that upstream inflows and hydrological connectivity exert a stronger influence on wetland dynamics than localized rainfall and temperature, which primarily regulate evaporation rates. Across the entire delta (27,791 km²), total lost (231.95 km²) and gained (246.04 km²) areas are nearly balanced. However, persistent water remains limited (617.28 km², 10.6%), while seasonal and temporary water has expanded, emphasizing the dominance of temporary water areas. Regionally, the coastal region (SR-1, 2,750 km²) shows a net increase in inundation, with gains (117.84 km²) far exceeding losses (7.99 km²), resulting in dynamic seasonal water coverage. In contrast, the main Central Ile River Delta (SR-2, 5,357 km²) shows a net areal decline, with losses (127.35 km²) surpassing gains (40.70 km²), despite heightened seasonal fluctuations. Similarly, the southern arid inland regions (SR-3, 1,039 km²) exhibit modest gains (11.64 km²) dominated by larger losses (37.46 km²), indicating a shift toward ephemeral water occurrences. The findings highlight the complex and dynamic nature of water variability in the Ile-Balkhash Delta, emphasizing the need for integrated water management strategies to address ongoing hydrological changes and support wetland conservation under evolving climate and human pressures.
How to cite: Mishra, K., Weber, P., and Fitzsimmons, K. E.: Understanding the impact of anthropogenic climate variability on the surface inundation dynamics in the wetlands of drylands: A case study of Ile-Balkhash Delta, Kazakhstan., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3657, https://doi.org/10.5194/egusphere-egu25-3657, 2025.
Water governance in transboundary lakes and wetlands where there are shared water bodies between countries presents significant challenges due to cultural diversity, differences in regulations, and conflicts over water use. In this paper, we will evaluate the water governance mechanisms developed in Latin America for the coordinated management of wetlands and lakes. We will show the challenges in two systems with a shared institution for lake management: the Lempa river basin between El Salvador, Honduras and Guatemala, and Lake Titicaca between Bolivia and Peru. Through the evaluation in workshops and a survey with the members of these institutions, we gather lessons learned on how the constitution of these institutions allows countries to address the coordinated management of water bodies and wetlands. Using earth information data, it is possible to objectively evaluate water dynamics in terms of quality and quantity, overcoming political will barriers.
How to cite: Santos Santos, T. F. and Ayala, G.: Water governance in transboundary lakes: Earth Observation Data for Objective decisions , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21357, https://doi.org/10.5194/egusphere-egu25-21357, 2025.
The Ganga Plain (GP) is one of the largest alluvial floodplains in the world, where river meanders and floodplain wetlands are crucial surface water resources that support millions of people. Nearly 80% of GP wetlands are geographically isolated wetlands (GIWs), meaning that precipitation provides most of their water. These GIWs shrink and develop into vegetative patches as a result of groundwater extraction in the surrounding regions. This study has utilized geostatistical approaches to analyse hydrological connectivity and rainfall patterns for three decades at the sub-basin scale, transecting different hydroclimatic regimes. Hydrological connectivity, defined as the water-mediated transfer of matter and energy within or between hydrological cycle components, was evaluated using two methods: (i) structural connectivity, which is determined by terrain slope/ruggedness, and a rainfall-NDVI-weighted C factor map normalized by the Soil Index, and (ii) functional connectivity, which combines flow accumulation data to link rainfall patterns, connectivity, and water movement. We have mostly used freely available datasets from Google Earth Engine and processed them using cloud computing.
The initial finding revealed that 44% of wetlands in the Ganga Plain (GP) are classified as unstable, declining, lost, or intermittent, with an accuracy of 84%, emphasizing their susceptibility to deterioration. Rainfall in the GP varies significantly by location, ranging from 3000 mm in the north to 500 mm in the southwest, altering hydrological connectivity even more. Disruptions in this connectivity cause an unstable water flow, affecting wetland functionality and stability. The study highlights the importance of targeted actions in preserving hydrological connectivity across climatic zones and maintaining sustainable wetlands.
Keywords: Google Earth Engine, Ganga Plain, Wetlands, Hydrological Connectivity.
How to cite: Sinha, A., Singh, M., and Sinha, R.: Spatio-temporal analysis of hydrological connectivity of floodplain wetlands in the Ganga Plains, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-790, https://doi.org/10.5194/egusphere-egu25-790, 2025.
Many people depend on inland water bodies as water resource. However, fundamental ecosystem services, like the provision of water for drinking and irrigation purposes, can be affected by eutrophication, climate change and invasive species. This challenge requires the regular observation and monitoring of freshwater bodies to take measures that preserve their valuable ecosystem services and warn the local population in the event of water-related health risks. Aquatic macrophytes and phytoplankton can be detected from space and used as a proxy to monitor the trophic state and water quality, for example by using vegetation indices such as NDVI or FAI. However, these indices fail to discriminate reliably between floating vegetation (like water hyacinth) and submerged vegetation.
The Aquatic Macrophyte Index (AMI) presented in this paper uses information from the green and shortwave infrared (SWIR) part of the electromagnetic spectrum to distinguish between aquatic macrophytes and phytoplankton. The respective plant’s water content causes a detectable absorption in the SWIR, which allows to differentiate aquatic macrophytes from phytoplankton. A fixed threshold of the AMI allows classification of aquatic macrophytes and phytoplankton, respectively, without the need to select a threshold for different study areas as it is the case for currently applied state of the art spectral indices. Satellite sensors of the Landsat and Sentinel-2 mission have the required bands for the computation of the AMI. Cloud free records of a harmonized dataset that combines imagery from both missions favor the performance of close to real time monitoring as well as timeseries analysis of the past decades.
The AMI can be applied to monitor the distribution of aquatic weed such as water hyacinth. The application of the AMI is exemplified using Lake Chivero (Zimbabwe) as case study representing the issue of hypertrophic lakes that are infested with rapidly expanding invasive species and algal blooms.
How to cite: Münch, F., Penning de Vries, M., and van der Wal, D.: Remote sensing based Water Hyacinth monitoring using the novel Aquatic Macrophyte Index (AMI), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4184, https://doi.org/10.5194/egusphere-egu25-4184, 2025.
Wetlands are critical ecosystems essential to human and environmental health, delivering diverse ecosystem services such as food security, biodiversity preservation, climate change mitigation, water filtration, aquifer feeding, and regulation. When present, wetlands play a vital role in catchment drainage systems, providing water storage, flow regulation, infiltration, and chemical processing. Despite their ecological significance, wetlands have been decreasing in number and extent globally. However, the construction of artificial wetlands has steadily increased in recent decades due to recognition of their eco-hydrological importance and benefits. Wetlands are widely acknowledged as an effective nature-based solution. The ecological status of wetlands and the performance of their ecosystem services are closely tied to the hydroperiod. A wetland's hydroperiod is determined by the interplay of water inflows and outflows, the geomorphology of the catchment, and subsurface properties. Understanding and managing the hydroperiod of artificial wetlands is vital to maintaining and increasing ecological and hydrological integrity. Hence, artificial wetlands must be assessed based on both their internal functional processes and their hydrological interactions with the surrounding environment, including water exchanges, sediment transport, and nutrient dynamics.
This study analyzes an artificial wetland in the Tuscany, Central Italy, known as Oasi di Gabbianello. Built in 2004, it has become an important ecological site, offering resources and habitat to various plant and animal species, especially migratory birds. The study objectives were to: i) characterize the hydroperiod and its key drivers; ii) develop both a conceptual and numerical hydrological model; and iii) validate the models using hydrometeorological and stable isotope data. These models are essential for enhancing the resilience of the wetland to future climatic stresses.
A monitoring program was implemented, including continuous measurements of meteorological conditions, water inflow and outflow, and wetland water levels, alongside biweekly sampling for stable isotope analysis over one year. Continuous monitoring enabled a hydroperiod characterization, revealing precipitation and stream inflow as primary water gains, while evaporation and overflow constituted major losses. Seepage was undetectable at the resolution of observation. Both conceptual and numerical models accurately represented water volume variations, while stable isotope data highlighted transitions between recharge- and evaporation-dominated periods, providing an additional dataset for model validation.
Keywords: Wetland; Hydrology; Water Isotopes; Modelling.
How to cite: Dani, A., Nigro, M., Fioletti, S., Preti, F., and Penna, D.: Characterizing an Artificial Wetland through Hydrologic and Isotopic Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12134, https://doi.org/10.5194/egusphere-egu25-12134, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot A
EGU25-885 | ECS | Posters virtual | VPS4
Wetland Health in Transition: Resilience and Ecosystem Services Amid Urbanization and Land-Use ChangeWed, 30 Apr, 14:00–15:45 (CEST) | vPA.24
The accelerated pace of urbanization, population growth, and extensive land-use changes has significantly disrupted the ecological balance and functionality of riverine wetland ecosystems, leading to substantial degradation of wetland health. This study evaluates the health and resilience of the Upper Ganga Riverine Wetland (UGRW) in India, which has experienced significant land-use transformations over the past two decades. The analysis highlights the wetland's resilience to various natural and anthropogenic stresses and its ability to sustain critical ecosystem services, including provisioning, regulating, cultural, and supporting services. The findings reveal drastic land-use and land-cover (LULC) changes, with built-up areas increasing by 245%, while forest and wetland areas decreased by 41% and 8%, respectively, between 2000 and 2020. These transformations have led to a marked decline in ecosystem resilience (23%) and a substantial reduction in ecosystem service values (ESVs), which decreased from 2138.28 million USD in 2000 to 1769.16 million USD in 2020—an overall loss of 18%. Urban expansion, deforestation, and wetland fragmentation have further exacerbated the decline in wetland health, diminishing its ecological balance and capacity to deliver vital services. This study underscores the urgent need for integrated environmental management strategies to mitigate the impact of LULC changes, conserve wetland ecosystems, and enhance their resilience. By assessing ecosystem services and their dependence on sustainable land use, this research provides critical insights for policymakers and stakeholders. It emphasizes the necessity of balancing developmental priorities with ecological preservation, offering a strategic framework to foster sustainability and resilience in one of India’s most vital riverine landscapes.
How to cite: Yadav, A., Kansal, M. L., and Singh, A.: Wetland Health in Transition: Resilience and Ecosystem Services Amid Urbanization and Land-Use Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-885, https://doi.org/10.5194/egusphere-egu25-885, 2025.
