针对以径流相对亏缺状态表征的水文干旱事件,采用3种常用的概率分布函数Log-normal、Gamma和Normal,拟合了赣江外洲站实测年径流资料;利用外洲站年径流的统计特征,推导了干旱历时和干旱强度2种典型干旱特征变量的理论概率分布函数的参数;计算了期望间隔时间定义下干旱历时内累积干旱强度达到某一阈值的干旱事件的重现期,并采用Monte Carlo随机模拟技术进行了验证。结果表明:根据年径流统计信息解析推求水文干旱事件的重现期,具有较强的统计基础和可信的精度,能够一定程度克服依靠有限观测水文序列进行干旱事件重现期推断的样本偏差,可为定量评估流域干旱风险,支撑流域水安全保障提供新的思路。
Abstract
To address the challenge of estimating the return level of hydrological drought events due to the limited sample size of drought events that can be obtained from measured streamflow data, we applied three commonly utilized annual runoff probability distribution functions,namely, Log-normal, Gamma, and Normal,to measured runoff data obtained from the Waizhou station on the Ganjiang River. Theoretical probability distribution functions (PDFs) for drought characteristics, including duration and severity, were derived using statistical properties of annual runoff. The return period, defined as the mean interarrival time of drought events surpassing a certain severity threshold, was computed and validated through Monte Carlo simulation. Results demonstrate that deriving return periods of hydrological drought events using PDFs of drought duration and severity establishes a robust statistical basis with credible accuracy. The proposed method partially mitigates sample bias in estimating drought return periods based on limited observed hydrological series, offering a novel approach to assessing future drought risk.
关键词
水文干旱 /
重现期 /
计算方法 /
年径流 /
赣江流域
Key words
hydrological drought /
return period /
calculation method /
annual runoff /
Ganjiang River basin
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基金
国家自然科学基金项目(51809243);水资源与水电工程科学国家重点实验室(武汉大学)开放研究基金项目(2018SWG03);江西省鄱阳湖水资源与环境重点实验室开放研究基金项目(2020GPSYS06)
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