An integrated process-based framework for flood phase segmentation and assessment

Abstract

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (I_F) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R² = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation.

Keywords:

• Flood process
• phase segmentation
• flood assessment
• ontology
• multisource data

Author contributions

Shuang Yao: Writing—original draft preparation, Methodology, Software. Wenying Du: Writing—review and editing, Formal analysis. Nengcheng Chen: Conceptualization, Supervision, Project administration. Chao Wang: Writing—review and editing, Validation. Zeqiang Chen: Writing—review and editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The sample data and code of this study is available at https://doi.org/10.6084/m9.figshare.20166323.v1.

Additional information

Funding

This work was supported by grants from the National Key Research and Development Program of China [No. 2021YFF0704400], the National Nature Science Foundation of China Program [Nos. 41890822, 42201438], Special Fund of Hubei Luojia Laboratory [No. 220100034], the State Scholarship Fund and the Open Fund of National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430074, China [Grant No. 2022KFJJ07]. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

Notes on contributors

Shuang Yao

Shuang Yao is a PhD candidate in the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) at Wuhan University. She is interested in flooding theory and application, prediction and simulation, and spatiotemporal analysis.

Wenying Du

Wenying Du is an associate professor in the National Engineering Research Center of Geographic Information System at China University of Geosciences. Her research interests focus on flood monitoring methods and knowledge graph construction.

Nengcheng Chen

Nengcheng Chen is a professor in the National Engineering Research Center of Geographic Information System at China University of Geosciences. His research interests focus on observation sensor network, spatiotemporal big data, geo-simulation decision and smart city.

Chao Wang

Chao Wang is an associate professor in the LIESMARS. His main research interests are smart city, land use change and its ecological and environmental effects.

Zeqiang Chen

Zeqiang Chen is a professor in the National Engineering Research Center of Geographic Information System at China University of Geosciences. His research interests include sensor networks, spatiotemporal big data intelligence and its application in smart watersheds.