Accurate water level forecasting in the Everglades is essential for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent time-series foundation models have shown strong performance on generic tasks (represented in their pre-training), their effectiveness in domain-specific applications remains insufficiently understood. In this work, we curate a domain-specific dataset for water-level forecasting in the Everglades and observe that the performance of current state-of-the-art models remains limited. To address this gap, we leverage a retrieval-augmented mechanism that retrieves analogous multivariate hydrological episodes from an external archive of historical observations to enrich the input context of those pre-trained models. We study two retrieval strategies, statistical similarity-based retrieval and mutual information-based retrieval, and analyze how incorporating retrieved historical contexts affects predictive performance. Extensive experiments show that retrieval augmentation consistently improves long-horizon water level forecasts and yields disproportionately larger gains during extreme events, which is particularly critical for environmental decision-making. Our study provides empirical evidence that analog-based retrieval can benefit pretrained time-series foundation models in environmental science, offering practical insights into their strengths, limitations, and failure modes when applied to hydrological forecasting in the Everglades. Although evaluated in the Everglades, the proposed framework is general and can be applied to other hydrological systems given time series data. The code and data have been made publicly available at https://github.com/rahuul2992000/WaterRAF.

翻译：水位精确预报对大沼泽地的洪水缓解、干旱管理、水资源规划及生物多样性保护至关重要。尽管近期时间序列基础模型在通用任务（反映于其预训练中）上展现出强劲性能，但其在特定领域应用中的有效性仍未被充分理解。本研究针对大沼泽地水位预测任务构建了特定领域数据集，并发现现有最先进模型的性能仍存在局限。为解决这一不足，我们采用检索增强机制，从历史观测数据的外部档案中检索相似的多变量水文事件，以丰富预训练模型的输入上下文。我们研究了两种检索策略——基于统计相似性的检索和基于互信息的检索，并分析了融入检索所得历史上下文对预测性能的影响。广泛实验表明，检索增强能持续提升长周期水位预测精度，并在极端事件发生时产生显著更大的增益，这对环境决策尤为关键。本研究为基于相似事件检索提升环境科学领域预训练时间序列基础模型提供了实证依据，揭示了该类模型在大沼泽地水文预测中的优势、局限与失败模式。尽管仅在大沼泽地进行了评估，所提框架具有通用性，可应用于其他时间序列数据可用的水文系统。代码与数据已开源至 https://github.com/rahuul2992000/WaterRAF。