通过增强SPI度量系统以融合多模态地面温度数据来建模和索引干旱严重程度
Modeling and indexing the drought severity by augmenting SPI metric system to incorporate multi-modal ground temperature data
AI总结：
本文通过将多模态地面温度数据融入标准化降水指数（SPI），提出一种同时考虑干旱强度和持续时间的增强型干旱严重程度评估指标，并在斯里兰卡主要水稻种植区进行校准与验证。
摘要：
干旱是由气候变化持续挑战引发的全球性威胁。基于天气变量及其模式识别干旱状况十分重要。在本研究中，我们通过整合地面温度数据来增强标准化降水指数（SPI），从而开发出一种更全面的干旱严重程度评估指标。我们的指标提供对干旱严重程度的双重评估，同时考虑干旱的强度及其持续时间。我们将该评估应用于斯里兰卡主要的水稻种植区，旨在揭示由于供水不足和长时间高温而影响水稻作物的普遍干旱状况。此外，我们通过将该指标与历史干旱记录对齐来进行校准，并随后将结果与由传统SPI指数得出的结果进行比较。
英文摘要：
Drought is a global threat caused by the persistent challenges of climate change. It is important to identify drought conditions based on the weather variables and their patterns. In this study, we enhanced the Standardized Precipitation Index (SPI) by integrating ground temperature data to develop a more comprehensive metric for evaluating drought severity. Our metric offers a dual assessment of drought severity, taking into account both the intensity of the drought and its duration. We employ this evaluation in the primary paddy cultivation region of Sri Lanka, with the aim of shedding light on the prevailing drought conditions affecting paddy crops due to insufficient water supply and prolonged periods of elevated temperatures. Additionally, we calibrate our metric by aligning it with historical drought records and subsequently compare the outcomes with those derived from the conventional SPI index.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
