The objective of this study is to understand relations between multiple physical and environmental factors and red  tide, which is a common name for harmful algal blooms occurring along coastal regions worldwide. Large concentrations of Karenia brevis, a toxic mixotrophic dinoflagellate, make up the red tide along the West Florida Shelf (WFS) in the Gulf of Mexico. Besides being toxic, red tide causes unpleasant odor and scenery, which result in multiple environmental and socioeconomic impacts and public health issues.  Understanding the physical and biogeochemical processes that control the occurrence of red tide is important for studying the impact of climate change on red tide frequency, and accordingly assessing the future environmental and socioeconomic impacts of red tide under different mitigation techniques and climate scenarios. We use observation and reanalysis data in the WFS to train machine learning (ML) models to predict red tide, as a classification problem of large bloom or no bloom. We develop the ML model using seasonal input data of Peace River and Caloosahatchee River outflow, alongshore and offshore wind speed, and Loop Current position. The Loop Current, which is a warm ocean current that enters and loops through the Gulf of Mexico before exiting to join the Gulf Stream, can be detected from sea surface height. In addition to the observation and reanalysis data, these variables can be simulated by the Earth system models (ESMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), especially by the high-resolution models of the High Resolution Model Intercomparison Project (HighResMIP) of CMIP6. This is needed to understand the frequency and future trends of red tide under different Shared Socioeconomic Pathways (SSPs) of CMIP6. In this preliminary study, we investigate the impact of different choices regarding ML model selection and training dataset on the accuracy of red tide prediction, and the physical interpretation of the results. We also discuss the validation of ESMs data for predictive modeling, and ensemble methods for improving predictive performance. The study provides several insights that can be useful for predicting the future trends of red tide under SSPs using CMIP6 data.

The comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty involved in climate change predictions. In this study, an advanced hierarchical uncertainty analysis framework incorporated with a variance-based global sensitivity analysis, was developed to quantify different sources of uncertainty in hydrological projections under climate change. The uncertainties considered in this research are from greenhouse gas emission scenarios (GGES), global climate models (GCMs), hydrological models (Xinanjiang and variable infiltration capacity (VIC) models) and hydrological parameters, and this new methodology was implemented in a humid subtropical basin in southern China. The results indicated that the GCMs and hydrological parameters (GGESs) are the main (least) contributor of uncertainty in the discharge projections at the interannual scale. At the intra-annual scale, GCMs contribute the largest uncertainty of the discharge predictions during summer season, whereas the uncertainty due to GGESs, hydrological model and parameters is generally larger in winter. It was also found that although there is a strong temporal and spatial variability of general sources of uncertainty, this heterogeneity does not affect the importance of uncertainty sources. This study provides a better understanding of the uncertainty sources in hydrological predictions in the context of climate change. And the uncertainty analysis framework used is mathematically rigorous and can be applied to a wide range of climate and hydrological models with different uncertainty sources.