Employing K-means clustering to reconstruct missing water surface elevations in LiDAR digital elevation models for hydrodynamic simulation

IF 12.8 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI:10.1016/j.watres.2026.125409

Liming Liu , Takahiro Koshiba , Keiko Wada , Xi Chen , Chiachih Chang , Haruki Matsui , Shunsei Ono , Kenji Kawaike

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引用次数: 0

Abstract

Accurate Digital Elevation Models (DEM) data are essential for reliable hydrodynamic modeling of runoff processes, particularly for estimating runoff depth. However, DEM data inherently contain uncertainties over river and lake surfaces and along riverbanks. To address this issue, this study proposes a reconstruction approach in which river and riverbank sites are identified using a k-means clustering algorithm, riverbank elevations are reconstructed with inverse distance weighting, and river and lake elevations within a specified range are replaced with the minimum riverbank elevation to estimate water surface elevations. Validation indicates that estimated water surface elevations closely match observed values (R² = 0.98) and correspond to historical normal levels, although slight overestimations occur at a few locations relative to historical peak water levels. Comparing hydrodynamic simulation results before and after DEM reconstruction, river storage was overestimated due to negative uncorrected river surface elevations, leading to underestimated runoff depths near rivers. Overall, this study provides a method for estimating water surface elevations based on DEM data, offering a reference for more accurate runoff simulations and urban flood risk prediction in area with data scarcity or uncertainty in the future.

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利用k均值聚类重构LiDAR数字高程模型中缺失的水面高程

精确的数字高程模型（DEM）数据对于径流过程的可靠水动力学建模至关重要，特别是对于估算径流深度。然而，DEM数据本身包含河流和湖泊表面以及河岸的不确定性。针对这一问题，本研究提出了一种基于k-means聚类算法识别河流和河岸站点，利用逆距离加权重建河岸高程，并将指定范围内的河流和湖泊高程替换为最小河岸高程来估算水面高程的重建方法。验证表明，估算的水面高度与观测值密切匹配（R2 = 0.98），并与历史正常水平相对应，尽管相对于历史峰值水位，在少数地点出现了轻微的高估。对比DEM重建前后的水动力模拟结果，由于未校正的负河面高程，高估了河流蓄水量，从而低估了河流附近的径流深度。总体而言，本研究提供了一种基于DEM数据估算水面高程的方法，为未来数据稀缺或不确定地区更准确的径流模拟和城市洪涝风险预测提供了参考。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.