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Using observational data covering 1948–2020, the environmental factors controlling the winter precipitation in California were investigated. Empirical orthogonal function (EOF) analysis was applied to identify the dominant climate regimes contributing to the precipitation. The first EOF mode described a consistent change, with 70.1% variance contribution, and the second mode exhibited a south–east dipole change, with 11.7% contribution. For EOF1, the relationship was positive between PC1(principal component) and SST (sea surface temperature) in the central Pacific Ocean, while it was negative with SST in the southeast Indian Ocean. The Pacific–North America mode, induced by the positive SST and precipitation in the central Pacific Ocean, leads to California being occupied by southwesterlies, which would transport warm and wet flow from the ocean, beneficial for precipitation. As for the negative relationship, California is controlled by biotrophically high pressure, representing part of the Rossby wave train induced by the positive SST in the Indian ocean, which is unfavorable for the precipitation. For EOF2, California is controlled by positive vorticity at the upper level, whereas at the lower level, there is positive vorticity to the south and negative vorticity to the north, the combination of which leads to the dipole mode change in the precipitation. [ABSTRACT FROM AUTHOR]

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California. [ABSTRACT FROM AUTHOR]