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.

本资源适配联网JupyterHub计算平台运行，可帮助建模人员复现并拓展Van Beusekom等人2021年发表论文的研究成果。为此，本资源开发并集成了三款不同的Jupyter Notebook，针对一个CAMELS站点示例与预先选定的18个月模拟周期开展论文研究目标相关的探索，以此展示这些Notebook的功能。第一款Notebook用于处理CAMELS数据集的原始输入数据，将其作为SUMMA模型的输入；第二款Notebook则基于第一款Notebook生成的输入数据，按照Notebook中进一步说明的方式，使用原始及修正后的强迫数据运行SUMMA模型；第三款Notebook则调取第二款Notebook的输出结果，借助Kling-Gupta效率系数（Kling-Gupta Efficiency, KGE）可视化展示SUMMA模型输出的敏感性。本资源附带的Readme.md文件中，包含了各Jupyter Notebook的详细说明以及运行这些Notebook的分步操作指南。借助这三款Notebook，建模人员可将上述方法应用于671个CAMELS流域的任意（单个至全部）流域及自选的模拟周期中。