School of Agricultural, Computational and Environmental Sciences, Center for Applied Climate Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield Campus, Springfield, QLD, 4300, Australia.
Departments of Environmental and Atmospheric Sciences, Pukyong National University, Yongsoro-45, Nam-Gu, Busan, 48513, Republic of Korea.
Environ Monit Assess. 2018 Jul 4;190(8):450. doi: 10.1007/s10661-018-6806-0.
A water resources index based on weight-accumulated precipitation over the passage of time in heavy rainfall events is used in this study for monitoring flood risk and peak danger, as well as to develop flood warnings. In this research, an hourly water resources index (WRI) based on rainfall accumulations over the passage of time is proposed. WRI is able to monitor flood risk by taking into account the hourly effective precipitation that accumulates the precipitation (P) of both current and antecedent hours, while the contributions from the preceding hours is subjected to a time-dependent reduction function that addresses the depletion of water volume by various hydrological processes (e.g., discharge, runoff, evapotranspiration). By converting rainfall into a water resources index (WRI), the hourly precipitation over a 24-h period is redistributed to formulate a long-term water resources index (WRI) that monitors flood status based on long-term (more than 1 year) fluctuations in P and a short-term water resources index (WRI) that considers shorter (D = 24-148 hourly) accumulations of the P data. WRI was assessed for its potential in flood monitoring at two hydrologically diverse sites: Dobong (South Korea; August 1998) and Brisbane (Australia; December 2010-January 2011), and its applicability was verified using river water level (H) measurements at hydrological stations. The power spectrum density and spectral coherence of hourly rainfall, river water level, and the corresponding WRI showed good agreements, as did the low and high frequency wavelet components using the discrete wavelet transform algorithm. Importantly, WRI computed over 24 hourly accumulation periods was able to mimic the risk of short-term (flash-style) floods caused by concentrated rainfall, whereas WRI was more useful for flood risk assessment caused by an event over a long-term period. Dynamical changes in H were closely in-phase with the patterns of change noted in the WRI over the respective temporal scale. We conclude that the proposed WRI was able to replicate the flood evolution over the passage of time and, therefore, could possibly aid in the early warning of water-related disasters, demonstrating its practicality for continuous monitoring of the flood risk when a sustained period of rainfall event is observed.
本研究使用基于时间推移的权重累积降雨量的水资源指数来监测洪水风险和洪峰危险，并制定洪水预警。本研究提出了一种基于时间推移的降雨量累积的小时水资源指数（WRI）。WRI 能够通过考虑当前和前几个小时累积的有效降雨量（P）来监测洪水风险，而前几个小时的贡献则受到随时间变化的减少函数的影响，该函数考虑了各种水文过程（例如，排放、径流、蒸散）对水量的消耗。通过将降雨量转化为水资源指数（WRI），24 小时内的每小时降雨量被重新分配，以形成一种长期水资源指数（WRI），该指数根据 P 的长期（超过 1 年）波动监测洪水状况，以及一种短期水资源指数（WRI），该指数考虑较短（D=24-148 小时）的 P 数据累积。在两个水文差异较大的地点（韩国的道峰和澳大利亚的布里斯班）评估了 WRI 在洪水监测中的潜力，并使用水文站的河流水位（H）测量验证了其适用性。小时降雨量、河流水位和相应的 WRI 的功率谱密度和谱相干性表现出良好的一致性，离散小波变换算法的低频和高频小波分量也是如此。重要的是，在 24 小时的累积期内计算的 WRI 能够模拟由集中降雨引起的短期（突发式）洪水的风险，而 WRI 更有助于评估由长期事件引起的洪水风险。H 的动态变化与各自时间尺度上 WRI 变化模式密切一致。我们得出结论，所提出的 WRI 能够复制随时间推移的洪水演变，因此可能有助于对与水有关的灾害进行早期预警，证明了当持续降雨事件发生时，它在连续监测洪水风险方面的实用性。
