| 03 Feb 2026
Integrating coupled surface–subsurface modeling and field measurements: insights for rewetting a degraded fen peatland
Abstract. Peatlands play a crucial role in regional water balance and carbon dynamics but are often degraded due to drainage and agricultural use. In Germany, many drained peatlands have shifted from carbon sinks to CO₂ sources. Rewetting these ecosystems is therefore essential to restore their ecological functions and mitigate greenhouse gas emissions. However, effective rewetting requires a detailed understanding of peatland hydrology and its response to climatic and management conditions. To address this need, this study employs a fully coupled surface–subsurface hydrological model (HydroGeoSphere) to analyze the complex hydrological functioning of a typical degraded fen peatland site (11.6 ha) in Brandenburg, Germany. The model-based quantification of hydrological fluxes is basis for assessing peatland vulnerability to climate variability and land use while informing potential rewetting strategies aimed at reducing CO₂ emissions. The studied peatland is connected to a regional aquifer and intensively drained by a system of ditches. Simulations used daily meteorological inputs and detailed field measurements from 2015 to 2023. Evapotranspiration (ET) was parameterized using field-measured vegetation dynamics (seasonal leaf area index and management schedules), while measured ditch water levels served as hydraulic boundary conditions. The site was spatially divided into different management units with distinct vegetation parameters. The peat profile was represented by two layers (a 0.3 m highly degraded surface peat overlying a 0.7 m less degraded layer) overlying sand (aquifer) and till (aquifer base). The model was evaluated from different angles against eddy covariance ET and groundwater table dynamics during a calibration period (2016–2020) and a validation period (2021–2023) using a multi-metric approach. The model successfully reproduced seasonal water-table fluctuations and ditch–peatland interactions, including ET-driven hydraulic gradient dynamics between summer and winter. Simulated ET closely matched eddy covariance measurements, with RMSE values of 64 mm yr⁻¹, 10.2 mm month⁻¹, and 1.01 mm d⁻¹, and showed only minor biases during dry conditions, while over the year seasonal dynamics of ET were also well captured by the model. The model reproduced groundwater variations with sufficient accuracy, achieving KGE values of 0.80–0.85, NSE of 0.83–0.86, and RMSE of 0.15 m during calibration and validation. The analysis of seasonal and interannual water-storage changes showed pronounced shifts between hydrological surplus and deficit, demonstrating that drained fens are highly sensitive to evapotranspiration demand and prolonged drought. The modeling approach captured key hydrological processes with high robustness. The model’s water balance analysis provides an initial assessment of potential management measures, under the given climatic and hydrological conditions, that could enable effective rewetting of the peatlands. These findings support ongoing peatland restoration initiatives on drained peatlands in Europe.
The manuscript studies hydrological controls on water-table dynamics in a drained fen peatland. The authors combine long-term field observations with a coupled surface–subsurface hydrological model (HydroGeoSphere). The model is used to quantify the peatland water balance and analyze groundwater dynamics. Results show that strong evapotranspiration in summer lowers groundwater levels and reverses the hydraulic gradient between the peatland and drainage ditches. Peat stratigraphy also plays an important role in controlling water storage and water-table variability. These processes are key to understanding hydrological functioning and challenges for peatland rewetting. The manuscript is generally well written and presents an interesting integration of field observations and modeling. However, several aspects of the methodology and presentation could be improved to strengthen the reproducibility of the model setup and the practical implications for peatland restoration.
Main comments:
1. Although the paper discusses implications for peatland rewetting, the model simulations represent only the current drained conditions. Explicit rewetting scenarios were not tested, which limits the ability to evaluate restoration strategies directly. I suggest including a set of simple scenario simulations (e.g., raising ditch water levels by different amounts or reducing ditch drainage conductivity) to allow the model to directly assess potential rewetting strategies and strengthen the study's practical relevance.
2. The evapotranspiration parameterization assumes that ET reaches potential evapotranspiration at LAI ≈2.5, even though observed LAI at the site reaches about 7. This assumption effectively removes most vegetation variability from the hydrological response and may underestimate the influence of canopy dynamics on transpiration and groundwater drawdown. The model could be improved by testing alternative ET formulations that account for higher LAI values.
3. The paper reports good model performance metrics. Still, it does not clearly describe the calibration procedure or identify which parameters were calibrated, making it difficult to assess parameter sensitivity or reproduce the model setup.
Minor comments:
1. Figure 8 attempts to show seasonal and interannual variability using a heatmap, but the color gradients make the evapotranspiration patterns difficult to interpret. A line plot showing monthly values across years would likely communicate the seasonal cycle and year-to-year differences more clearly.
2. Figure 10 uses a 3D perspective to illustrate surface flooding, but the visualization makes it difficult to interpret actual water depths and spatial patterns. A top-down map or cross-sectional slices would likely provide clearer quantitative information about flooding dynamics.
3. Why was NSE not reported for the evapotranspiration comparison in Fig. 11b while it was included for groundwater in Fig. 11c? Including the same performance metrics (e.g., NSE, KGE, RMSE) for both variables, or explaining why different metrics were used, would improve consistency and help readers evaluate model performance.
4. The tables mainly summarize model parameters, but they are rarely referenced explicitly in the text, which makes it harder for readers to connect the discussion to the parameter values used in the simulations. Explicitly referring to the tables in the text (e.g., “soil hydraulic parameters are summarized in Table X”) would help guide readers and improve the clarity of the model description.