EFFECT OF RUNOFF ON STORM SURGE IN PEARL RIVER ESTUARY
-
摘要: 径流-风暴潮相互作用可增大河口区风暴潮增水，增加风暴潮灾害风险。基于SCHISM模式建立了珠江河口风暴潮数值模型，以台风“山竹”为例，采用实测资料对模型计算结果进行验证，最高潮位相对误差在9%以内。设计了台风“山竹”实测径流与5年一遇洪水的对比试验，讨论了径流变化对河口风暴潮增水的影响，结果表明：河口口门站位风暴潮增水随径流量的增大而增大。径流增加对泗盛围、南沙等站位的风暴潮影响较大，在风暴潮增水达到最大值时影响最为显著。以径流动力作用为主的区域，当上游径流量增大时，对风暴潮增水起到负影响作用：如磨刀门水道，随着径流的增加，沿河道上溯的风暴潮增水逐渐减小，由灯笼山站3.22 m减小至马口站1.12 m。以潮汐动力作用为主的区域，当上游径流量增大时，对风暴潮增水起到正影响作用：如珠江干流，随着径流的增加，沿河道上溯的风暴潮增水逐渐增大，大虎站的最大增水值为3.44 m，中大站为4.24 m，从口门至后航道区域增大了0.8 m。Abstract: Runoff-storm surge interaction can increase storm surge, hence aggravating the risk of storm surge in the estuary area. In this paper, a numerical model of storm surges in the Pearl River Estuary was established based on the Semi-implicit Cross-scale Hydroscience Integrated System model. With typhoon Mangkhut as an example, observational data were used to verify model results, and the relative error of the highest tide was less than 9%. Two runoff processes were simulated and the effects of runoff changes on storm surge were discussed. The results showed that storm surge intensified with the increase in runoff volume. Runoff had a considerable influence on the storm surge at Sishengwei and Nansha stations, and the influence was the most significant when the storm surge water reached the maximum amount. In the runoff-dominated area, when the upstream runoff volume increased, a negative effect on storm surge water could be identified. For example, in the Modaomen waterway, storm surge decreased with the increase in runoff volume. It dropped from 3.22 m at Denglongshan Station to 1.12 m at Makou Station. In the tidedominated area, when the upstream runoff volume increased, a positive effect on storm surge water could be identified. For example, in the Pearl River, storm surge water increased with the growth of runoff volume. For example, it rose from 3.44 m at Dahu Station to 4.24 m at Zhongda Station.
-
Key words:
- runoff /
- storm surge water /
- Pearl River Estuary /
- typhoon Mangkhut
-
表 1 数值试验表
敏感性试验 风 潮汐 径流 控制试验 √ √ “山竹”实测径流过程 对比试验 √ √ 石角：42 158 m3/s 高要：12 480 m3/s 博罗：8 298 m3/s 
下载: 导出CSV
表 2 各站位增水变化统计表
站位 控制试验增水值/m 对比试验增水值/m 差值/m 变化率 大横琴 2.55 2.60 0.05 2.0% 灯笼山 3.22 3.25 0.03 0.9% 百顷头 2.44 2.25 -0.19 -7.8% 天河 2.52 1.83 -0.69 -27.4% 马口 2.75 1.12 -1.63 -59.3% 大虎 3.06 3.44 0.38 12.4% 泗盛围 3.02 3.59 0.57 18.9% 大盛 3.15 3.79 0.64 20.3% 黄埔 3.20 4.04 0.84 26.3% 中大 3.46 4.24 0.78 22.5%
下载: 导出CSV
-
[1] 叶荣辉, 戈军, 张文明, 等. 影响粤港澳大湾区的热带气旋统计分析[J]. 水利水电技术, 2020, 51(S1): 37-43. https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ2020S1007.htm [2] ZHENG F F, WESTRA S, SISSON S A. Quantifying the dependence between extreme rainfall and storm surge in the coastal zone[J]. Journal of Hydrology, 2013, 506(15): 172-187. [3] SAMUELS P G, BURT N. A new joint probability appraisal of flood risk[J]. Proceedings of International Civil Engineering-Water and Marine Engineering, 2002, 154(2): 109-115. [4] 黄华, 朱建荣, 吴辉. 长江口与杭州湾风暴潮三维数值模拟[J]. 华东师范大学学报: 自然科学版, 2007(4): 9-19. doi: 10.3969/j.issn.1000-5641.2007.04.002 [5] BAO Y, LIU J B, ZOU H Y. Whole simulation of storm surge for Pearl River Estuary[C]. International Conference on Mechanic Automation and Control Engineering. Wuhan, China, 2010: 4 354-4 358. [6] 端义宏, 朱建荣, 秦曾灏, 等. 一个高分辨率的长江口台风风暴潮数值预报模式及其应用[J]. 海洋学报, 2005, 27(3): 11-19. doi: 10.3321/j.issn:0253-4193.2005.03.002 [7] 孙志林, 黄森军, 焦建格, 等. 河口区径流对台风暴潮影响研究[J]. 天津大学学报(自然科学与工程技术版), 2017, 50(5): 519-526. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201705012.htm [8] 刘士诚, 陈永平, 谭亚, 等. 珠江河网1822号台风"山竹"期间风暴增水模拟及特性分析[J]. 海洋预报, 2021, 38(2): 12-20. https://www.cnki.com.cn/Article/CJFDTOTAL-HYYB202102002.htm [9] 罗志发, 黄本胜, 邱静, 等. 粤港澳大湾区风暴潮时空分布特征及影响因素[J]. 水资源保护, 2022, 38(3): 72-79, 153. https://www.cnki.com.cn/Article/CJFDTOTAL-SZYB202203010.htm [10] 罗志发, 黄本胜, 谭超, 等. 珠江河口波浪—风暴潮耦合数值模拟[J]. 广东水利水电, 2021(7): 1-6, 17. https://www.cnki.com.cn/Article/CJFDTOTAL-GDSD202107001.htm [11] ZHANG Y, BAPTISTA A M. SELFE: A semi-implicit Eulerian-Lagrangian finite-element model for cross-scale ocean circulation[J]. Ocean Modelling, 2008, 21(3-4): 71-96. doi: 10.1016/j.ocemod.2007.11.005 [12] ZHANG Y, YE F, STANEV E V, et al. Seamless cross-scale modeling with SCHISM[J]. Ocean Modelling, 2016, 102, 64-81. doi: 10.1016/j.ocemod.2016.05.002 [13] EGBERT G D, SVETLANA Y. Efficient inverse modeling of barotropic ocean tides[J]. Journal of Atmospheric and Oceanic Technology, 2002, 19(2): 183-204. [14] 罗志发, 黄本胜, 谭超, 等. 粤港澳大湾区风暴潮数值模型的建立与应用[J]. 广东水利水电, 2020(11): 58-63. [15] FUJITA T. Pressure distribution within a typhoon[J]. Geophysical Magazine, 1952, 23(4): 437-451. [16] UENO T. Numerical computations of the strom in tosa bay[J]. Journal of Oceanography, 1981, 37(2): 61-73. [17] 卢如秀, 叶锦昭. 珠江河口台风最大增水规律的研究[J]. 中山大学学报(自然科学版), 1982(2): 28-31. https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ198202002.htm -
点击查看大图
图(7) / 表(2)
计量
- 文章访问数: 16
- HTML全文浏览量: 1
- PDF下载量: 7
- 被引次数: 0
粤公网安备 4401069904700003号