地理科学进展 ›› 2020, Vol. 39 ›› Issue (4): 651-659.doi: 10.18306/dlkxjz.2020.04.012

• 雨洪专栏 • 上一篇    下一篇

确定坡面径流过程曼宁糙率系数的实验方法研究

程娅姗1,2, 王中根1,*(), 李军3, 黄振1,2, 叶翔宇1,2, 唐寅1   

  1. 1. 中国科学院地理科学与资源研究所,陆地水循环及地表过程重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中国科学院、水利部成都山地灾害与环境研究所,成都 610041
  • 收稿日期:2019-11-04 修回日期:2020-02-22 出版日期:2020-04-28 发布日期:2020-06-28
  • 通讯作者: 王中根
  • 作者简介:程娅姗(1993— ),女,河南驻马店人,博士生,研究方向为水循环模拟。E-mail: chengyashan77@163.com
  • 基金资助:
    国家重点研发计划项目(2016YFC0400902);国家自然科学基金项目(41971039);中国科学院战略性先导科技专项(XDA19030204)

Experimental study on determining Manning roughness coefficient during slope runoff process

CHENG Yashan1,2, WANG Zhonggen1,*(), LI Jun3, HUANG Zhen1,2, YE Xiangyu1,2, TANG Yin1   

  1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Institute of Mountain Hazards and Environment, CAS, Chengdu 610041, China
  • Received:2019-11-04 Revised:2020-02-22 Online:2020-04-28 Published:2020-06-28
  • Contact: WANG Zhonggen
  • Supported by:
    National Key Research and Development Program of China(2016YFC0400902);National Natural Science Foundation of China(41971039);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030204)

摘要:

曼宁糙率系数是用水动力学方法进行流速计算的关键参数。坡面流曼宁糙率系数与明渠流的不同。为确定坡面径流过程的曼宁糙率系数,自行研发了一种包括供水系统、实验水槽和数据观测记录系统的室内可变糙率坡面实验系统。通过87场预实验验证了供水系统的稳定性和准确性。以坡度、实测流量、实测水深、不同糙率板上河砂的平均直径和地表粗糙度为自变量,以曼宁糙率系数为因变量,选用均方根误差(RMSE)和决定系数(R 2)为评价指标,对166种实验场景进行了支持向量机(Support Vector Machines, SVM)训练与预测,发现:① 紊流的训练结果难以预测层流和过渡流的曼宁糙率系数,说明流态不同时,实验因素对水流的影响机制不同;② 若要较为准确地预测曼宁糙率系数,至少需要包括实测水深在内的3种因素;③ 当同时考虑4种及更多种因素时,紊流状态下均可对曼宁糙率系数进行较为准确的预测。

关键词: 曼宁糙率系数, 坡面实验, 支持向量机

Abstract:

Manning roughness coefficient is the key parameter of flow velocity calculation. Overland flow is significantly different from open channel flow. In this study, we focused on the application of Manning formula in calculating the velocity of overland flow. Compared with open channel flow, the depth of overland flow is very shallow, sometimes only a few millimeters. Thus, vegetation, soil, surface roughness, and other factors have more obvious impact on overland flow. Therefore, the existing open channel flow Manning roughness coefficient cannot be directly used in overland flow. In order to determine the Manning roughness coefficient of overland flow, in this study we developed an indoor experimental system with variable roughness on slope, which includes a water supply system, an experimental tank, and an observation and data recording system. In this system, we used uniform river sand on the flat plate to simulate different roughness of the underlying surface, and placed it in a water tank. The stability and accuracy of the water supply system were verified by 87 pre-experiments. The results show that when the water supply was stable, the discharge was consistent with the data displayed by the electronic flow meter. The 87 groups of weighing data are relatively stable and consistent with normal distribution, and the data are within the 95% confidence interval. Then we designed 166 experiment scenes through a combination of different slopes, surface roughness, and water supply flow to explore the relationship between experimental conditions and Manning roughness coefficient. Among the 166 experiment scenes, a total of six kinds of roughness were designed. The water supply flow ranged from 1 to 25 m 3/h. The slope was between 4°-25°. We used the volume method to calculate the average diameter of the river sand and the chain method to calculate the surface roughness. The experiment data were processed for Support Vector Machine (SVM) training and forecasting, which used root mean square error (RMSE) and coefficient of determination (R 2) as the evaluation indices, considered slope, measured flow, measured depth, average diameter of the river sand, and surface roughness as the independent variables, and Manning roughness coefficient as the dependent variable. The results show that no matter how many kinds of factors were considered, it was difficult to predict the Manning roughness coefficient of laminar flow and transitional flow by the training results of turbulent flow, which indicates a different influence mechanism in different flow patterns. In order to predict the Manning roughness coefficient accurately, we need three factors at least, and measured water depth must be included. When considering four or more factors at the same time, the Manning roughness coefficient could be accurately predicted in turbulent flow.

Key words: Manning roughness coefficient, slope experiment, Support Vector Machine