AI 中文总结
本文介绍湾评估模型（BAM），一个基于Python的开源水文模型，用于模拟佛罗里达湾54个盆地的盐度，以支持大沼泽地恢复规划中的盐度预测。
AI 中文摘要
佛罗里达湾的极端盐度事件是综合大沼泽地恢复计划（CERP）的核心关注点。20世纪80年代末，由于淡水输送减少导致的高盐条件引发了大面积海草死亡，这使得可靠的盐度预测对恢复规划至关重要。湾评估模型（BAM）是一个水文模型，模拟佛罗里达湾54个理想化盆地的盐度。除模型稳定性所需外，该模型保持质量守恒。盆地间通量根据浅滩上的水力梯度计算，每个盆地接收直接降雨和蒸散发强迫。沿近岸边界，BAM由大沼泽地深度估算网络（EDEN）的观测水位或上游恢复规划模型的输出驱动。沿海洋边缘，来自NOAA从属站调和分潮的潮汐边界条件叠加在区域平均海平面异常之上。BAM完全用Python实现且开源。在1999-2026年的延长记录中，全区域平均水位偏差为-0.024米，平均均方根误差（RMSE）为0.092米；全区域平均盐度偏差为0.00 ppt，平均RMSE为6.21 ppt。模型偏差随水文状况系统性变化，从干旱到湿润条件幅度增大，并在一个中央湾盆地符号反转，这限制了对基于异常的指标的解读。在大沼泽地近岸边界施加均匀偏移，在东北湾产生2-4 ppt的盐度响应，向海洋边缘衰减；恢复和海平面情景的更全面处理另行报告。BAM为评估大沼泽地恢复行动对佛罗里达湾盐度的影响提供了一个透明、高效且社区可访问的工具。
英文摘要
Extreme salinity events in Florida Bay are a central concern of the Comprehensive Everglades Restoration Program (CERP). A large-scale seagrass die-off in the late 1980s, driven by hypersaline conditions following reduced freshwater delivery, made reliable salinity prediction essential to restoration planning. The Bay Assessment Model (BAM) is a hydrologic model that simulates salinity across 54 idealized basins representing Florida Bay. It is mass-conservative apart from where required for model stability. Interbasin fluxes are computed from hydraulic gradients across shoals, and each basin receives direct rainfall and evapotranspiration forcing. Along the shoreward boundary BAM is driven by observed water levels from the Everglades Depth Estimation Network (EDEN), or by output from upstream restoration planning models. Along the marine margins, tidal boundary conditions from NOAA subordinate station harmonic constituents are superimposed on a regional mean sea level anomaly. BAM is implemented entirely in Python and is open source. Over the extended 1999-2026 record the domain-wide mean water level bias is -0.024 m with a mean RMSE of 0.092 m, and the domain-wide mean salinity bias is 0.00 ppt with a mean RMSE of 6.21 ppt. Model bias varies systematically with hydrologic regime, increasing in magnitude from drought to wet conditions and reversing sign at one central bay basin, constraining the interpretation of anomaly-based metrics. Applying uniform offsets at the Everglades shoreward boundary yields salinity responses of 2-4 ppt in the northeastern bay, attenuating toward the marine margins; a fuller treatment of restoration and sea level scenarios is reported separately. BAM provides a transparent, efficient, and community-accessible tool for evaluating the salinity consequences of Everglades restoration actions in Florida Bay.
Comments21 pages, 13 figures, 6 tables. Model source code, documentation, and example input data are available at https://github.com/SoftwareLiteracyFoundation/BAM
