Brust Colin, Kimball John S, Maneta Marco P, Jencso Kelsey, Reichle Rolf H
Numerical Terradynamic Simulation Group, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States.
Regional Hydrology Lab, Geosciences Department, University of Montana, Missoula, MT, United States.
Front Big Data. 2021 Dec 21;4:773478. doi: 10.3389/fdata.2021.773478. eCollection 2021.
Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.
干旱是影响美国的最具生态和经济破坏力的自然现象之一，给美国经济造成数十亿美元的损失，并导致生态系统健康状况普遍恶化。人们采用了许多干旱指数来监测当前干旱的程度和状况，以便农民和地方政府等利益相关者能够做出适当反应。提前数周至数月预测干旱状况的方法不太常见，但能提供更有效的早期预警系统，以加强干旱应对、缓解和适应规划。为解决这一问题，我们引入了DroughtCast，这是一个用于预测美国干旱监测（USDM）的机器学习框架。DroughtCast基于这样的认识运行：近期水文和气象异常会推动未来干旱状况的变化。我们将模拟气象和卫星观测的土壤湿度作为循环神经网络的输入，以准确预测未来1至12周的美国干旱监测数据。我们的分析表明，降水、土壤湿度和温度是预测未来干旱状况时最重要的输入变量。此外，对2017年北部平原快速干旱的案例研究表明，DroughtCast能够在一场极其严重的干旱事件发生前12周就做出预测。鉴于该模型良好的预测能力，DroughtCast可能为土地管理者和地方政府应对干旱影响并做好准备提供一个很有前景的工具。
