A Bayesian Framework for Probabilistic Seasonal Drought Forecasting
A Bayesian Framework for Probabilistic Seasonal Drought Forecasting
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DOI：
10.1175/jhm-d-13-010.1
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发表时间：
2013-11
影响因子：
3.8
通讯作者：
Shahrbanou Madadgar;H. Moradkhani
中科院分区：
文献类型：
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作者：
Shahrbanou Madadgar;H. Moradkhani
季节性干旱预测是在多变量概率框架内提出的。标准化径流指数（SSI）是用来表征水文干旱与不同的严重程度，在科罗拉多河流域的甘尼森河流域。由于径流，随后水文干旱，是自相关的变量在时间上，本研究提出了一种多元概率方法，使用Copula函数进行干旱预测贝叶斯框架内。春季流量（4月至6月）被认为是预测变量，并发现与前一个冬季（1月至3月）和秋季（10月至12月）的相关性最高。将Copula函数引入到贝叶斯框架中，建立了两种不同的预测模型来估计上一个冬季（一阶条件模型）或上一个冬季和秋季（二阶条件模型）的春季水文干旱。条件概率密度函数（PDF）和累积分布函数（CDF）的显着概率特征的春旱。预测结果表明，春旱对冬季状况的敏感性高于秋季状况，验证了先验相关分析的结果。90%的预测边界的春季流量预测表明，该模型在估计春旱的有效性。将该模式与传统的集合径流预报模式（ESP）进行了比较，发现两者的预报结果基本一致。但是，新方法的预测不确定性比ESP方法更可靠。新的概率预测模型可以为水资源管理者和利益相关者提供见解，以促进决策和制定干旱缓解计划。
Seasonal drought forecasting is presented within a multivariate probabilistic framework. The standardized streamflow index (SSI) is used to characterize hydrologic droughts with different severities across the Gunnison River basin in the upper Colorado River basin. Since streamflow, and subsequently hydrologic droughts, are autocorrelated variables in time, this study presents a multivariate probabilistic approach using copula functions to perform drought forecasting within a Bayesian framework. The spring flow (April‐June) is considered as the forecast variable and found to have the highest correlations with the previous winter (January‐March) and fall (October‐December). Incorporating copula functions into the Bayesian framework, two different forecast models are established to estimate the hydrologic drought of spring given either the previous winter (first-order conditional model) or previous winter and fall (second-order conditional model). Conditional probability density functions (PDFs) and cumulative distribution functions (CDFs) are generated to characterize the significant probabilistic features of spring droughts. According to forecasts, the spring drought is more sensitive to the winter status than the fall status, which approves the results of prior correlation analysis. The 90% predictive bound of the spring-flow forecast indicates the efficiency of the proposed model in estimating the spring droughts. The proposed model is compared with the conventional forecast model, the ensemble streamflow prediction (ESP), and it is found that their forecasts are generally in agreement with each other. However, the forecast uncertainty of the new method is more reliable than the ESP method. The new probabilistic forecast model can provide insights to water resources managers and stakeholders to facilitate the decision making and developing drought mitigation plans.