SUMMA Simulations using CAMELS Datasets on CyberGIS-Jupyter for Water

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-month simulation to demonstrate the capabilities of the notebooks. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

本资源专为在连接的JupyterHub计算平台上执行而配置，旨在帮助模型构建者复现并在此基础上扩展Van Beusekom等人（2021年）论文中的研究结论。为此，本资源中包含三个不同的Jupyter笔记本，它们针对一个示例CAMELS站点及预选的18个月模拟周期，对论文目标进行探索，以展示笔记本的功能。第一个笔记本负责处理CAMELS数据集的原始输入数据，以便作为SUMMA模型的输入。第二个笔记本使用来自第一个笔记本的输入数据，执行SUMMA模型，并使用原始和修改后的强迫场，具体细节在笔记本中进一步描述。最后，第三个笔记本利用第二个笔记本的输出，采用Kling-Gupta效率（KGE）来可视化SUMMA模型输出的敏感性。关于每个Jupyter笔记本的更多信息以及如何运行笔记本的逐步指导，可以在本资源包含的Readme.md文件中找到。通过这三个笔记本，模型构建者可以将上述方法应用于（一个或所有）671个CAMELS流域及其选择的任何模拟周期。