Abstract Drought monitoring and forecasting play a vital role in making drought mitigation policies. In previous research, several drought monitoring tools based on the probabilistic models have been developed for precise and accurate inferences of drought severity and its effects. However, the risk of inaccurate determination of drought classes always exists in probabilistic models. The aim of this paper is to reconnaissance the advantage of the weighted Markov chain (WMC) model to accommodate the erroneous drought classes in the monthly classifications of drought. It was assumed that to increase the precision in drought prediction, the role of standardised self-correlation coefficients as weight may incorporate to establish and restructure the accurate probabilities of risk for incoming expected drought classes in the WMC framework. Consequently, the current research is based on the experimental findings of seventeen meteorological stations located in the Northern Areas of Pakistan. In this study, the standardised precipitation evapotranspiration index (SPEI) at a 1-month time scale based drought monitoring approach is applied to quantify the historical classification of drought conditions. The exploratory analysis shows that the proportion of each drought class varies from zone to zone. However, a high proportion of near-normal drought classes has been observed in all the stations. For the prediction of future drought classes, transition probability matrices have been computed using R statistical software. Our findings show that the probability of occurrences of near-normal is very high. Overall, the results associated with this study show that the WMC method for drought forecasting is sufficiently flexible to incorporate the change of drought conditions; it may change both the transition probability matrix and the autocorrelation structure.

中文翻译：

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测量和重构预测干旱等级的风险：基于加权马尔可夫链的模型在一个月时间尺度上标准化降水蒸散指数 (SPEI) 的应用

摘要 干旱监测和预报在制定干旱减缓政策中起着至关重要的作用。在先前的研究中，已经开发了几种基于概率模型的干旱监测工具，用于精确和准确地推断干旱严重程度及其影响。然而，在概率模型中始终存在不准确确定干旱等级的风险。本文的目的是考察加权马尔可夫链（WMC）模型的优势，以适应干旱月度分类中错误的干旱等级。假设为了提高干旱预测的精度，可以将标准化自相关系数作为权重的作用结合起来，以建立和重组 WMC 框架中即将到来的预期干旱类别的准确风险概率。最后，目前的研究基于位于巴基斯坦北部地区的 17 个气象站的实验结果。在这项研究中，基于 1 个月时间尺度的干旱监测方法的标准化降水蒸散指数 (SPEI) 被应用于量化干旱条件的历史分类。探索性分析表明，每个干旱等级的比例因地区而异。然而，在所有站点中都观察到了高比例的接近正常的干旱等级。为了预测未来的干旱等级，已经使用 R 统计软件计算了转移概率矩阵。我们的研究结果表明，发生接近正常的概率非常高。全面的，与本研究相关的结果表明，用于干旱预测的 WMC 方法具有足够的灵活性，可以纳入干旱条件的变化；它可能会改变转移概率矩阵和自相关结构。