An investigation into remote sensing techniques and field observations to model hydraulic roughness from riparian vegetation
An investigation into remote sensing techniques and field observations to model hydraulic roughness from riparian vegetation
复制标题
对遥感技术和实地观测模拟河岸植被水力粗糙度的研究
DOI：
10.1002/rra.4053
复制
发表时间：
2022
影响因子：
2.2
通讯作者：
Zhang, Su
中科院分区：
文献类型：
--
作者：
Chaulagain, Smriti;Stone, Mark C.;Dombroski, Daniel;Gillihan, Tyler;Chen, Li;Zhang, Su
河岸植被在河流和洪泛区系统中提供了许多值得注意的功能，包括其对水动力过程的影响。传统的方法来预测水动力特性的存在下，植被涉及应用静态曼宁的粗糙度，这并不直接考虑植被的特性，并忽略了由于当地水深和流速的粗糙度的变化。本研究的目的是：（1）在二维（2D）水动力学模型中实现模拟植被引起的水力粗糙度的数值程序;（2）评估两种植被粗糙度方法的性能;（3）比较基于实地和遥感采集方法的植被参数和水动力学模型结果。两个粗糙度算法耦合到现有的二维水力求解器，这需要植被参数来计算空间分布的粗糙度系数。通过野外调查和机载激光雷达（LiDAR）数据确定了美国加州圣华金河的植被参数。使用基于植被的粗糙度方法建模的水面高程产生了可接受的整体性能，但结果对植被参数化方法（基于场与LiDAR）敏感。根据植被种类和基于植被的方法的排放量，观察到粗糙度和水力条件（水深和流速）的空间变化。该方法考虑了物理环境的复杂性，而不是依赖于传统的粗糙度作为模型输入。因此，本文提出的方法有利于描述具有植被空间变化的区域的水力条件（例如，密度和密度）。然而，需要进行更多的研究，以量化模型在空间分布的水深和流速以及植被特征参数化方面的性能。
Riparian vegetation provides many noteworthy functions in river and floodplain systems, including its influence on hydrodynamic processes. Traditional methods for predicting hydrodynamic characteristics in the presence of vegetation involve the application of static Manning's roughness, which does not directly account for vegetation characteristics and neglects changes in roughness due to local water depth and velocity. The objectives of this study were to (1) implement numerical routines for simulating vegetation‐induced hydraulic roughness in a two‐dimensional (2D) hydrodynamic model; (2) evaluate the performance of two vegetation roughness approaches; and (3) compare vegetation parameters and hydrodynamic model results based on field‐based and remote sensing acquisition methods. Two roughness algorithms were coupled to an existing 2D hydraulic solver, which requires vegetation parameters to calculate spatially distributed roughness coefficients. Vegetation parameters were determined by field survey and using airborne light detection and ranging (LiDAR) data for San Joaquin River, California, USA. Water surface elevations modeled using vegetation‐based roughness approaches produced an acceptable overall performance, but the results were sensitive to the vegetation parameterization method (field based vs. LiDAR). Spatial variations in roughness and hydraulic conditions (water depth and velocity) were observed based on vegetation species and discharges for vegetation‐based approaches. The proposed approach accounts for the complexities of the physical environment instead of relying on traditional roughness as model inputs. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the area having spatial variation of vegetation (e.g., species and density). However, additional research is needed to quantify model performance with respect to spatially distributed water depth and velocity and parameterization of vegetation characteristics.
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DOI：
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发表时间：
2005
期刊：
影响因子：
--
作者：
C. Wilson;T. Stoesser;P. Bates
通讯作者：
P. Bates
DOI：
--
发表时间：
2013
期刊：
影响因子：
--
作者：
Tyler Gillihan
通讯作者：
Tyler Gillihan
影响因子：
3.7
作者：
Kamoske, Aaron G.;Dahlin, Kyla M.;Serbin, Shawn P.
通讯作者：
Serbin, Shawn P.
改变河道-漫滩连通性对水动力洪水波过程影响的数值研究的支持数据
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
C. Byrne
通讯作者：
C. Byrne
评估 HEC-RAS 中集成的河岸植被粗糙度计算方法
影响因子：
2.4
作者：
Junna Wang;Zhonglong Zhang
通讯作者：
Zhonglong Zhang