Hu et al. (2025) A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data

Identification

• Journal: Hydrology and earth system sciences
• Year: 2025
• Date: 2025-11-06
• Authors: Boting Hu, Liwen Chen, Yanfeng Wu, Jingxuan Sun, Y. Jun Xu, Qingsong Zhang, Guangxin Zhang
• DOI: 10.5194/hess-29-6023-2025

Research Groups

• Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, China
• University of Chinese Academy of Sciences, Beijing, China
• School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, USA

Short Summary

This study developed a novel framework, WetlandSCB, to accurately quantify wetland depression water storage capacity (WDWSC) using coarse-resolution terrain data and multi-source remote sensing, overcoming limitations of high-resolution data scarcity and biases in global Digital Elevation Models (DEMs). The framework achieved WDWSC estimation with less than 10% relative error compared to field measurements, demonstrating its applicability for large-scale wetland water storage assessment.

Objective

• To develop a novel framework (WetlandSCB) for accurately estimating wetland depression water storage capacity (WDWSC) by integrating multi-source remote sensing data and prior knowledge, specifically addressing challenges posed by coarse-resolution terrain data, biases in above-water topography, incompleteness of wetland depression identification, and absence of bathymetry.

Study Configuration

• Spatial Scale: Regional (Nenjiang River Basin, northeast China, specifically Baihe Lake and Chagan Lake national nature reserves) to global applicability.
• Temporal Scale: Long-term (water occurrence maps from JRC and GLAD, land cover data from 1990-2019), with focus on current estimation.

Methodology and Data

• Models used:
  • Priority-flood algorithm (for initial wetland depression identification)
  • Morphological operators (erosion, dilation for refining wetland depression maps)
  • Spatial prediction and modeling methods (for bathymetry estimation)
  • Monotonic cubic spline and power function (for fitting hypsometric relationships)
  • 3σ rule (for outlier identification in area-level pairs)
  • Gaussian filter (for smoothing SRTM DEM)
  • Canny edge detection algorithm (for water body boundary detection)
  • SimpleCloudScore algorithm (for cloud screening)
  • Least squares method (for slope profile calculation)
• Data sources:
  • Satellite/Remote Sensing:
    • Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM)
    • SRTM Water Body Data
    • Global Surface Water Mapping Layers (JRC) water distribution and water occurrence maps
    • Global Land Analysis and Discovery (GLAD) water occurrence map
    • Sentinel-3A/3B altimetry satellite data
    • Landsat series optical images (implied for water occurrence maps)
    • ALOS DEM, MERIT DEM (for comparison)
  • Observation/Field Measurements:
    • Field measurements of topographic and bathymetric information (elevation and depth) for Baihe Lake (248 points) and Chagan Lake (657 points) using ultrasonic echo sounder (D390, Chcnav, China) and Global Positioning System (GPS) device.
  • Ancillary Data:
    • Wetland maps (30 m resolution land cover data for 1990–2019, and 30 m resolution wetland map in 2015)
    • GLC-FCS30 and CLCD (30 m resolution land cover datasets for prior information comparison)

Main Results

• The WetlandSCB framework accurately delineates wetland depression distribution, with overall accuracy and kappa coefficient exceeding 0.95, outperforming the priority-flood algorithm alone.
• The use of a water occurrence map effectively corrects numerical biases in above-water topography, increasing the Pearson coefficient and R² by 0.33 and 0.38, respectively, compared to original results.
• Coupling spatial prediction and modeling with remote sensing techniques yielded highly accurate bathymetry estimates, with relative errors less than 3% compared to field measurements. This demonstrates superiority over traditional spatial prediction and modeling methods (which had >18% relative error).
• The WetlandSCB framework achieved estimation of WDWSC with relative errors less than 10% compared to field topographic and bathymetric measurements, meeting a good level of accuracy.
• For Baihe Lake, the estimated WDWSC was 55 × 10⁶ m³, compared to 61 × 10⁶ m³ from field measurements. For Chagan Lake, it was 521 × 10⁶ m³, compared to 526 × 10⁶ m³ from field measurements.
• The accuracy of WDWSC calculation is highly dependent on DEM data quality, with MERIT DEM providing the most accurate results among global DEMs (average 25.7% relative error), but still significantly less accurate than WetlandSCB.

Contributions

• Developed a novel, comprehensive framework (WetlandSCB) for accurate WDWSC quantification using coarse-resolution terrain data, addressing critical limitations of existing methods (e.g., high cost of lidar, biases in global DEMs, absence of bathymetry).
• Introduced a method to restore cloud-contaminated water distribution images to improve the accuracy of water occurrence maps for above-water topography correction.
• Integrated morphological operators and prior water distribution information to enhance wetland depression spatial delineation accuracy.
• Combined spatial prediction and modeling with remote sensing techniques for high-precision bathymetry estimation, demonstrating improved accuracy over previous approaches.
• Provided a transferable framework applicable to other wetland areas globally where high-resolution terrain data or field measurements are unavailable, enabling large-scale wetland water storage capacity estimation.
• Demonstrated the framework's ability to provide accurate distribution and depth-area relations of wetland depression areas, which can be incorporated into hydrologic models to improve flow and storage predictions in river basins.

Funding

• National Natural Science Foundation of China (grant no. 41877160 and U24A20570)
• National Key Research and Development Program of China (grant nos. 2017YFC0406003 and 2021YFC3200203)
• Consulting Project Proposal of the Chinese Academy of Engineering (grant no. JL2023-17)
• Strategic Priority Research Program of the Chinese Academy of Sciences, China (grant no. XDA28020501)

Citation

@article{Hu2025novel,
  author = {Hu, Boting and Chen, Liwen and Wu, Yanfeng and Sun, Jingxuan and Xu, Y. Jun and Zhang, Qingsong and Zhang, Guangxin},
  title = {A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data},
  journal = {Hydrology and earth system sciences},
  year = {2025},
  doi = {10.5194/hess-29-6023-2025},
  url = {https://doi.org/10.5194/hess-29-6023-2025}
}