Toward a better understanding of curve number and initial abstraction ratio values from a large-dataset of watersheds perspective

The code within this repository is founded on the analysis of the Natural Resources Conservation Service Curve Number (NRCS-CN) method and its parameters. The analyses make use of the [CARAVAN dataset and your extensios] (https://zenodo.org/records/6578598). The separation of streamflow into baseflow and runoff is based on the methodology established by Xie et al. (2020) (https://doi.org/10.1016/j.jhydrol.2020.124628). Contained within this repository are 5 Jupyter Notebooks, each focused on the application of the NRCS-CN method using distinct data analysis approaches: Principal Components Analysis Random Forest NRCS-NEH NRCS-ASYMPTOTIC NRCS-LEAST-SQUARES Additionally, the results of the metrics from applying these methods can be found in the files Metrics_runoff_G1 and Metrics_runoff_G2. The folder Times_series contains the time series of simulated runoff using the NRCS methods. Channel Log: Version v2 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. Version 1.0 - In this version, the Asymptotic method was considered with natural data本仓库内的代码基于自然资源保护服务曲线数值法（Natural Resources Conservation Service Curve Number, NRCS-CN）及其参数的分析构建。本次分析使用了[CARAVAN数据集及其扩展集](https://zenodo.org/records/6578598)。将径流划分为基流与地表径流的方法，参考了谢等人（2020）提出的研究框架（https://doi.org/10.1016/j.jhydrol.2020.124628）。 本仓库包含5个Jupyter笔记本（Jupyter Notebooks），每个笔记本均围绕NRCS-CN法的应用展开，采用了不同的数据分析方法： - 主成分分析（Principal Components Analysis） - 随机森林（Random Forest） - NRCS-NEH - NRCS-ASYMPTOTIC - NRCS-LEAST-SQUARES 此外，上述各方法的评价指标结果可在Metrics_runoff_G1与Metrics_runoff_G2文件中获取。Times_series文件夹内存储了采用NRCS系列方法模拟得到的径流时间序列数据。 版本日志说明： - 版本v2：官方论文正式发布版本，未对数据集内容做出修改，仅补充了论文配套代码的静态副本。 - 版本1.0：该版本中渐近方法采用自然实测数据开展计算。