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We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis , a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applicationsmore »and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.« less

The Smoothed Particle Hydrodynamics (SPH) method is a Lagrangian approach that has been widely used to eliminate numerical dispersion for solving advection‐dispersion equation (ADE) of groundwater solute transport under advection‐dominated situations. It has been found that accuracy of SPH results is severely deteriorated, when particles are irregularly distributed in a model domain with heterogeneous hydraulic conductivity. To resolve this problem, we developed a new approach called Interactively Corrected SPH (IC‐SPH), which is an improved version of the Corrected SPH (C‐SPH) method. IC‐SPH uses an interactively corrected kernel gradient to construct concentration gradients used to solve ADE. This correction is made for each particle by using not only the particle's neighbor particles within the particle's support domain but also the particles within each neighbor particle's support domain. We evaluated IC‐SPH performance in two numerical studies. One considers diffusive transport with an analytical solution, and the other considers advection‐dispersion transport in a heterogeneous field of hydraulic conductivity. For each numerical study, several numerical experiments were conducted using multiple sets of irregularly distributed particles with different levels of particle irregularity. The numerical experiments indicate that, while IC‐SPH is more computationally expensive than SPH and C‐SPH, IC‐SPH produces more accurate ADE solutions, andmore »converges faster to the analytical solution. IC‐SPH is mathematically general, and can be applied to a wide range of problems that require solving ADE.

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Oxygen and hydrogen isotopes were used in this study to detect a hydraulic connection between a sinkhole lake and a karst spring. In karst areas, surface water that flows to a lake can drain through sinkholes in the lakebed to the underlying aquifer, and then flows in karst conduits and through aquifer matrix. At the study site located in northwest Florida, USA, Lake Miccosukee immediately drains into two sinkholes. Results from a dye tracing experiment indicate that lake water discharges at Natural Bridge Spring, a first‐magnitude spring 32 km downgradient from the lake. By collecting weekly water samples from the lake, the spring, and a groundwater well 10 m away from the lake during the dry period between October 2019 and January 2020, it was found that, when rainfall effects on isotopic signature in spring water are removed, increased isotope ratios of spring water can be explained by mixing of heavy‐isotope‐enriched lake water into groundwater, indicating hydraulic connection between the lake and the spring. Such a detection of hydraulic connection at the scale of tens of kilometers and for a first‐magnitude spring has not been previously reported in the literature. Based on the isotope ratio data, it was estimated that, duringmore »the study period, about 8.5% the spring discharge was the lake water that drained into the lake sinkholes.

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