GRASS GIS在河流动力学沉积物垂直分选分析中的应用

Annalisa Minelli, G. Parker, P. Tacconi, C. Cencetti
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引用次数: 0

摘要

GRASS GIS在不同研究领域的极端多功能性是众所周知的。在此工作中说明了河流动力学分析中沉积物垂直分选的工具。特别是,GRASS GIS python模块已经编写,实现了Blom&Parker(2006)的预测排序模型,以分析河床成分在粒度方面的深度演变。该模块以DEM和相对于床质输运组成的信息作为输入。它分为两个不同且连续的阶段:第一阶段使用GRASS功能分析选定河段的河床几何特征,第二阶段是“数值”阶段,并自行实现预测模型,然后通过matplotlib库进行统计分析和绘图。此外,在没有栅格DEM地图的情况下,实现了导入激光扫描仪点云的特定程序。目前,该模块已应用于圣安东尼瀑布实验室(Minneapolis, MN)的水槽数据,并获得了一些初步结果,但对其他水槽数据的“测试”阶段仍在进行中。此外,该模块已经在Ubuntu Linux机器上为GRASS 65编写,即使GRASS 64 (windows版本)的调试也在进行中。本工作的最终目的是将该模型应用于自然河流,但仍存在一些缺陷。首先,需要一个高分辨率的DEM作为输入,其次,输入数据的数量和类型(例如所考虑的每种尺寸的床载组成体积分数)不易获得,因此最好的解决方案是通过在仪器仪表良好的河段上测试模型,以便将来将预测方法导出到未仪器仪表的河段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A GRASS GIS application for vertical sorting of sediments analysis in River Dynamics
The extreme versatility in different research fields of GRASS GIS is well known. A tool for the vertical sorting of sediments in river dynamics analysis is illustrated in this work. In particular, a GRASS GIS python module has been written which implements a forecasting sorting model by Blom&Parker (2006) to analyze river bed composition’s evolution in depth in terms of grainsize. The module takes a DEM and information relative to the bed load transport composition as input. It works in two different and consecutive phases: the first one uses the GRASS capabilities in analyzing geometrical features of the river bed along a chosen river reach, the second phase is the "numerical" one and implements the forecasting model itself, then executes statistical analyses and draws graphs, by the means of matplotlib library. Moreover, a specific procedure for the import of a laser scanner cloud of points is implemented, in case the raster DEM map is not available. At the moment, the module has been applied using flumes data from Saint Anthony Falls Laboratory (Minneapolis, MN) and some first results have been obtained, but the "testing" phase on other flume’s data is still in progress. Moreover the module has been written for GRASS 65 on a Ubuntu Linux machine, even if the debugging of a GRASS 64, Windowsversion, is also in progress. The final aim of this work is the application of the model on natural rivers, but there are still some drawbacks. First of all the need of a high resolution DEM in input, secondly the number and type of data in input (for example the bed load composition in volume fraction per each size considered) which is not easily obtainable, so the best solution is represented by testing the model on a well instrumented river reach to export in future the forecasting method to un-instrumented reaches.
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