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  1. 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 applications 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. 
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  2. null (Ed.)
    Resumo As iniciativas para a sustentabilidade das águas subterrâneas, incluindo o rendimento sustentável e as políticas de proteção das bacias hidrográficas, estão crescendo globalmente em resposta às mudanças climáticas e ao aumento da demanda humana por águas subterrâneas. Uma melhor compreensão dos impactos da gestão das bacias hidrográficas no rendimento e nos custos de gestão das águas subterrâneas—especialmente no contexto mais amplo da mudança climática e de uso da terra—é fundamental para subsidiar essas iniciativas e facilitar a gestão integrada do território e da água. Este estudo desenvolve uma nova estrutura de serviços ecossistêmicos hidrológicos de águas subterrâneas, espacialmente explícita, ligando cenários de uso da terra definidos pelas partes interessadas, uma otimização da simulação de águas subterrâneas e avaliação econômica, e a aplica ao aquífero mais utilizado do Havaí (EUA). As estimativas de rendimento sustentável e as diferenças resultantes em custos de reposição são estimadas para seis cenários de uso da terra (com níveis variáveis de desenvolvimento urbano e gestão de bacias hidrográficas) cruzados com dois cenários de demanda de água em um contexto de um clima seco futuro (via de concentração representativa [RCP] 8.5 meio-século). A dinâmica do uso da terra é considerada um fator importante, embora menos significativo, de recarga de águas subterrâneas do que a mudança climática. O grau de proteção da bacia hidrográfica, através da prevenção da disseminação de espécies vegetais invasoras não nativas de alto uso de água, é projetado para ser um sinal muito mais forte de alteração da dinâmica do uso da terra do que o desenvolvimento urbano. Especificamente, a proteção florestal completa aumenta o rendimento sustentável em 7–11% (30–45 milhões de litros por dia) e diminui substancialmente os custos de tratamento em comparação com a ausência de proteção florestal. Coletivamente, este documento demonstra o valor hidrológico e econômico da proteção de bacias hidrográficas em um contexto de um clima seco no futuro, fornecendo insights para políticas e gerenciamento integrado da terra e da água no Havaí e em outras regiões, particularmente onde as invasões de espécies ameaçam as bacias hidrográficas de origem. 
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  3. Elshall, Ahmed ; Ye, Ming (Ed.)

    Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.

     
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