Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.

与单一模型应用程序相比，模型集成有几个好处，但在湖泊建模社区中并不经常使用。尽管现有模型之间有许多相似之处（强制数据、航位图等），但设置和运行多个湖泊模型可能具有挑战性且耗时。在这里，我们展示了一个 R 包 LakeEnsemblR，它有助于运行五个不同的垂直一维水动力湖泊模型（FLake、GLM、GOTM、Simstrat、MyLake）的集合。该包需要标准化格式的输入和单个配置文件。LakeEnsemblR 将这些文件格式化为每个模型所需的输入，并提供运行和校准模型的功能。不同模型的输出被编译成单个文件，并且支持多种后处理操作。LakeEnsemblR' 工作流程标准化可以简化模型基准和不确定性量化，并改善科学家之间的合作。我们展示了 LakeEnsemblR 在两个不同湖泊中的成功应用。