State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, Shaanxi, China; NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.
NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.
Sci Total Environ. 2020 Jul 1;724:138162. doi: 10.1016/j.scitotenv.2020.138162. Epub 2020 Mar 27.
Recurring drought has caused large crop yield losses in Australia during past decades. Long-term drought forecasting is of great importance for the development of risk management strategies. Recently, large-scale climate drivers (e.g. El Niño-Southern Oscillation) have been demonstrated as useful in the application of drought forecasting. Machine learning-based models that use climate drivers as input are commonly adopted to provide drought forecasts as these models are easy to develop and require less information compared to physical-based models. However, few machine learning-based models have been developed to forecast drought conditions during growing season across all Australian cropping areas. In this study, we developed a growing season (Apr.-Nov.) meteorological drought forecasting model for each climate gauging location across the Australian wheatbelt based on multiple lagged (past) large-scale climate indices and the Random Forest (RF) algorithm. The Standardized Precipitation Index (SPI) was used as the response variable to measure the degree of meteorological drought. Results showed that the RF model could provide satisfactory drought forecasts in the eastern areas of the wheatbelt with Pearson's correlation coefficient r > 0.5 and normalized Root Mean Square Error (nRMSE) < 23%. Forecasted drought maps matched well with observed drought maps for three representative periods. We identified NINO3.4 sea surface temperature and Multivariate ENSO Index as the most influential indices dominating growing season drought conditions across the wheatbelt. In addition, lagged impacts of large-scale climate drivers on growing season drought conditions were long-lasting and the indices in previous year could also potentially affect drought conditions during current year. As large-scale climate indices are readily available and can be rapidly used to feed data driven models, we believe the proposed meteorological drought forecasting models can be easily extended to other regions to provide drought outlooks which can help mitigate adverse drought impacts.
在过去几十年中，反复发生的干旱导致澳大利亚的农作物大量减产。长期干旱预测对于制定风险管理策略非常重要。最近，大规模的气候驱动因素（例如厄尔尼诺-南方涛动）已被证明在干旱预测应用中非常有用。基于机器学习的模型，这些模型使用气候驱动因素作为输入，通常用于提供干旱预测，因为与基于物理的模型相比，这些模型易于开发并且需要较少的信息。然而，很少有基于机器学习的模型被开发出来，以预测澳大利亚所有种植区的生长季节的干旱情况。在这项研究中，我们基于多个滞后（过去）的大规模气候指数和随机森林（RF）算法，为澳大利亚小麦带的每个气候测量地点开发了一个生长季节（4 月至 11 月）气象干旱预测模型。标准化降水指数（SPI）被用作响应变量，以衡量气象干旱的程度。结果表明，RF 模型可以在小麦带东部地区提供令人满意的干旱预测，皮尔逊相关系数 r>0.5，归一化均方根误差（nRMSE）<23%。对于三个代表性时期，预测的干旱图与观测的干旱图匹配良好。我们确定 NINO3.4 海表温度和多变量厄尔尼诺指数是主导小麦带整个生长季节干旱条件的最具影响力的指数。此外，大规模气候驱动因素对生长季节干旱条件的滞后影响是持久的，前一年的指数也可能对当年的干旱条件产生影响。由于大规模气候指数易于获得并且可以快速用于为数据驱动模型提供数据，我们相信所提出的气象干旱预测模型可以很容易地扩展到其他地区，以提供干旱展望，这有助于减轻不利的干旱影响。
