Probabilistic drought characterization in the categorical form using ordinal regression Journal of Hydrology

Drought is an insidious natural hazard that may cause tremendous losses to different sectors, including agriculture and ecosystems. Reliable drought monitoring and early warning are of critical importance for drought preparedness planning and mitigation to reduce potential impacts. Traditional drought monitoring is generally based on drought indices, such as Standardized Precipitation Index (SPI), that are computed from hydro-climatic variables. The U.S. Drought Monitor (USDM) classifies drought conditions into different drought categories to provide composite drought information by integrating multiple drought indices, which has been commonly used to aid decision making at the federal, state, and local levels. Characterizing drought in categories similar to USDM would be important for decision making for both research and operational purposes. However, drought monitoring, based on a variety of drought indices, is challenged by the classification of drought into categories used by USDM. In this study, an ordinal regression model is proposed to characterize droughts in USDM drought categories based on several drought indices, in which the probability of each drought category can be estimated. The proposed method is assessed by comparing with USDM in Texas and a satisfactory performance for estimating drought categories is revealed.

干旱是一种潜伏隐匿的自然灾害，可对农业、生态系统等诸多领域造成巨大损失。可靠的干旱监测与预警，对于开展干旱防范规划与减灾工作以降低潜在影响而言至关重要。传统干旱监测通常基于各类干旱指数，例如标准化降水指数（Standardized Precipitation Index, SPI），这类指数由水文气候变量计算得到。美国干旱监测系统（U.S. Drought Monitor, USDM）通过整合多类干旱指数，将干旱状况划分为不同等级以提供综合干旱信息，该系统已被广泛用于辅助联邦、州及地方层面的决策制定。以类似USDM的等级体系表征干旱状况，对于科研与业务场景下的决策工作均具有重要意义。然而，基于多类干旱指数开展的干旱监测，在适配USDM的干旱等级划分标准时面临诸多挑战。本研究提出一种序数回归模型（ordinal regression model），基于多类干旱指数对USDM的干旱等级体系进行干旱状况表征，该模型可估算各干旱等级对应的发生概率。本研究以美国得克萨斯州的USDM数据为参照对所提方法进行评估，结果显示该方法在干旱等级估算任务中表现出色。