基于降雨量和土壤湿度监测的累西腓大都市区预警方案--案例研究

Roberto Coutinho, Bruno Morais, Rodolfo Mendes, Marcio Andrade
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摘要

在巴西,山体滑坡频繁发生,造成人员伤亡和财产损失,其发生是受自然和/或人为条件的影响。雨水是引发这一现象的主要非人为因素。因此,预测山体滑坡成为管理高风险地区的重要工具。累西腓都会区(MRR)历史悠久,多年来发生过多次大规模山体滑坡。目前,有研究指出,将土壤湿度等水文信息纳入预警系统可提高预报质量。鉴于原地测量土壤湿度的重要性,在马斯特里赫特水文站安装了由雨量计和电容式湿度传感器组成的设备网络,以综合方式监测降雨量和土壤湿度。本文的目的是了解巴雷拉斯地层上的两个高风险区域的土壤水文条件,为开发集降雨和湿度于一体的滑坡预警系统(LEWS)奠定基础。数据显示,土壤湿度的变化与降雨量有很大关系,随着每小时降雨量的增加,土壤湿度会出现突变。数据还显示,在 2022 年,受监测的土壤大约有 6 个月保持湿润,这表明在此期间的中雨有可能引发山体滑坡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Alert scenarios for the Metropolitan Region of Recife-PE based on monitoring of rainfall and soil humidity – a case study
In Brazil, landslides are frequent, causing deaths and property damage, and occur under the influence of natural and/or anthropic conditions. Rain acts as the main non-anthropic agent in triggering this phenomenon. Because of this, the prediction of landslides becomes an essential tool for managing high-risk areas. The Metropolitan Region of Recife (MRR) has a large history with numerous cases of mass movements over the years. Currently, research points out improvements in the quality of forecasts by including hydrological information, such as soil moisture, in warning systems. Given the importance of measuring soil moisture in situ, a network of equipment consisting of rain gauges and capacitive moisture sensors was installed in the MRR, to monitor rainfall and soil moisture in an integrated manner. The objective of this article is to understand the hydrological conditions of the soil in two high-risk areas of the MRR, built over the Barreiras Formation to set the foundations for the development of a Landslide Early Warning System (LEWS) that integrates rain and humidity. The data showed that the variation in soil moisture is very dependent on rainfall and presents sudden variations in moisture with increasing hourly rainfall. The data also revealed that the monitored soils remained wet for approximately six months in the year 2022, highlighting the potential for moderate rainfall during this period to trigger landslides.
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