A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data

Abstract. Accurate quantification of wetland depression water storage capacity (WDWSC) is imperative for comprehending the wetland hydrologic regulation functions to support integrated water resources management. Considering the challenges posed by the high acquisition cost of high-resolution lidar DEM or the absence of field measurements for most wetland areas, urgent attention is required to develop an accurate estimation framework for WDWSC using open-source, low-cost, multi-source remote sensing data. In response, we developed a novel framework, WetlandSCB, utilizing coarse-resolution terrain data for accurate estimation of WDWSC. This framework overcame several technical difficulties, including biases in above-water topography, incompleteness and inaccuracy of wetland depression identification, and the absence of bathymetry. Validation and application of the framework were conducted in two national nature reserves of northeast China. The study demonstrated that, by integrating the priority-flood algorithm, morphological operators, and prior information, one can accurately delineate the wetland depression distribution, with overall accuracy and kappa coefficient both exceeding 0.95. The use of a water occurrence map can effectively correct numerical biases in above-water topography, with Pearson coefficient and R2 increasing by 0.33 and 0.38, respectively. Coupling spatial prediction and modeling with remote sensing techniques yielded highly accurate bathymetry estimates, with < 3 % relative error compared with field measurements. Overall, the WetlandSCB framework achieved estimation of WDWSC with < 10 % relative error compared with field topographic and bathymetric measurements. The framework and its concept are transferable to other wetland areas globally where field measurements and/or high-resolution terrain data are unavailable, contributing to a major technical advancement in estimating WDWSC in river basins.

Authors

Institutions

Publication Details

Journal
Hydrology and earth system sciences
Published
2025-11-06
DOI
https://doi.org/10.5194/hess-29-6023-2025
Citations
4
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
6.87
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data

Jingxuan Sun, Y. Jun Xu, Boting Hu, Guangxin Zhang et al.
4 citations
Hydrology and earth system sciences
Remote Sensing in Agriculture
6.87
article

A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data

Jingxuan Sun, Y. Jun Xu, Boting Hu, Guangxin Zhang, Yanfeng Wu, Liwen Chen, Qingsong Zhang
article en
4 citations

Abstract

Abstract. Accurate quantification of wetland depression water storage capacity (WDWSC) is imperative for comprehending the wetland hydrologic regulation functions to support integrated water resources management. Considering the challenges posed by the high acquisition cost of high-resolution lidar DEM or the absence of field measurements for most wetland areas, urgent attention is required to develop an accurate estimation framework for WDWSC using open-source, low-cost, multi-source remote sensing data. In response, we developed a novel framework, WetlandSCB, utilizing coarse-resolution terrain data for accurate estimation of WDWSC. This framework overcame several technical difficulties, including biases in above-water topography, incompleteness and inaccuracy of wetland depression identification, and the absence of bathymetry. Validation and application of the framework were conducted in two national nature reserves of northeast China. The study demonstrated that, by integrating the priority-flood algorithm, morphological operators, and prior information, one can accurately delineate the wetland depression distribution, with overall accuracy and kappa coefficient both exceeding 0.95. The use of a water occurrence map can effectively correct numerical biases in above-water topography, with Pearson coefficient and R2 increasing by 0.33 and 0.38, respectively. Coupling spatial prediction and modeling with remote sensing techniques yielded highly accurate bathymetry estimates, with < 3 % relative error compared with field measurements. Overall, the WetlandSCB framework achieved estimation of WDWSC with < 10 % relative error compared with field topographic and bathymetric measurements. The framework and its concept are transferable to other wetland areas globally where field measurements and/or high-resolution terrain data are unavailable, contributing to a major technical advancement in estimating WDWSC in river basins.

Hydrology and earth system sciencesVol. 29(21)
Louisiana State University Agricultural Center (US), Chinese Academy of Sciences (CN), Northeast Institute of Geography and Agroecology (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 3%
Remote Sensing in Agriculture
6.87
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.