Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"

This data package contains the associated data and scripts for Nagamoto et al (2025). Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022). [Manuscript in preparation]. This purpose of this study was to investigate the impact of the 21st century drought on water quantity and quality at catchments throughout the Upper Colorado River Basin (UCRB). We used stream flow, water temperature, specific conductance, air temperature, precipitation, and catchment attribute data for over 200 sites in the UCRB, collected from the National Water Information System using Basin3D (Varadharajan, 2023), GAGESII (Falcone, 2010), and the Google Earth Engine. We identified years of severe drought between 1998 and 2022 using the Standardized Precipitation Evaporation Index (SPEI), then calculated the relative change percentage of the stream flow, water temperature, and specific conductance from drought versus non-drought years. We used the attribute information from GAGESII to investigate what physical traits of catchments are associated streamflow vulnerability (greater relative change) or resilience to drought. We used land cover data from the National Land Cover Database (USGS, 2024) to assess any changes to physical attributes that may not be represented in the static attributes information in GAGESII. To increase data availability, we modeled stream temperature using methods from Willard, 2023. While the study period is water years 1998 to 2022, the raw water quantity and quality data extends to 1950 and the meteorological data extends to 1980. The data and code can be downloaded via the UCRB_drought.zip. Within the zip, the files are organized as follows: - INPUTS: Contains all input data used in UCRB_Drought_Workflow.ipynb - OUTPUTS: Contains all intermediate data created from UCRB_Drought_Workflow.ipynb as well as final products including the calculated Standardized Evapotranspiration Index (SPEI) - climatic_variables: The code used to collect meteorologic data from Google Earth Engine - feature_importance: The code used for the catchment attributes analysis - preprocessing: Code used in UCRB_Drought_Workflow.ipynb - pyeto: Code used in UCRB_Drought_Workflow.ipynb - calculations: Code used in UCRB_Drought_Workflow.ipynb - README.md - UCRB_Drought_Workflow.ipynb: The main code for the analysis - requirements_ucrb-drought.yml: The requirements file to create a virtual environment and Jupyter Lab kernel to run the code The INPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_RAW" folder contains raw data for streamflow, water temperature, and specific conductance in a ".h5" file. The "NLCD_RAW" folder contains ".csv" files with annual land cover percentages for counties within the UCRB. The "MET_RAW" folder contains a ".csv" file with monthly meteorological data (air temperature and precipitation) for the sites in the UCRB which was obtained from code in the climatic_variables folder. The "GAGESII" folder contains ".csv" files with physical catchment attribute variables for catchments across the country. The "WT_LSTM_data" folder contains ".csv" files with calculated WT (Willard, 2023) and the associated RMSEs. The "Upper_Colorado_River_Basin_Boundary" folder contains geographic data including a shapefile for plotting in the UCRB_Drought_Workflow.ipynb. The OUTPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_data" folder contains a folder "Water_year" with the associated cleaned data, metadata, and data availability information in ".csv" files, a folder "Median_Relchange" with the relative change comparing drought to non-drought years in ".csv" files, and a folder "RDC_PeakFlow_Relchange" that has ".csv" files for the relative change in peak flow. The "NLCD_data" folder contains the difference in land cover from the beginning to end of the study period and the percentage of the county that is within UCRB bounds can be found in Nagamoto et al (2025)). The "MET_data" folder contains separated monthly air temperature and precipitation data and the calculated PET in ".csv" files. The "SPEI_data" folder contains ".csv" files with calculated SPEI values (one restricted to the study period and the other with information from the entire MET data period). The "Paper_Tables" folder contains two ".csv" files containing site information and data availability and information about the GAGESII trait aggregated categories. The base directory includes the file “flmd.csv” for a list and description of all files and the file “dd.csv” for data dictionaries. Scripts for preprocessing, analysis, and figure generation are located in the associated GitHub repository found at [https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought]. UPDATE: Title and code file updated to match submitted manuscript 10-15-2025. To cite this code, please use the following BibTeX: @misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }

本数据包包含Nagamoto等（2025年）相关研究的配套数据与代码脚本，对应研究主题为《科罗拉多河上游流域（Upper Colorado River Basin, 后文简称UCRB）1998-2022年干旱驱动的径流与水质普遍退化》[手稿待投稿]。本研究旨在探究21世纪干旱对UCRB各集水区的水量与水质的影响。
研究团队依托Basin3D（Varadharajan, 2023）、GAGESII（Falcone, 2010）及谷歌地球引擎（Google Earth Engine），从美国国家水信息系统（National Water Information System）获取了UCRB内200余个监测点的径流、水温、比电导率、气温、降水及集水区属性数据。
研究采用标准化降水蒸发指数（Standardized Precipitation Evaporation Index, SPEI）识别1998-2022年间的重度干旱年份，随后计算了干旱年份与非干旱年份下径流、水温及比电导率的相对变化百分比。
研究借助GAGESII提供的属性信息，分析了集水区哪些物理特征与径流脆弱性（更高的相对变化量）或干旱恢复力相关。同时使用美国国家土地覆盖数据库（National Land Cover Database, NLCD, USGS, 2024）的土地覆盖数据，评估了GAGESII静态属性未覆盖的物理属性变化情况。
为提升数据可用性，团队参考Willard（2023）的方法构建了水温预测模型。尽管本研究的分析时段为1998-2022年水文年，但原始水量与水质数据可追溯至1950年，气象数据则可追溯至1980年。
本数据集与代码可通过UCRB_drought.zip下载。压缩包内的文件组织如下：
- INPUTS（输入数据文件夹）：包含运行UCRB_Drought_Workflow.ipynb所需的全部输入数据，其下设以下主要目录与子目录：
- RDC_WT_SC_RAW文件夹：存储以.h5格式保存的径流、水温及比电导率原始数据
- NLCD_RAW文件夹：存储以.csv格式保存的UCRB内各县级行政区年度土地覆盖占比数据
- MET_RAW文件夹：存储以.csv格式保存的UCRB各监测点月度气象数据（气温与降水），该数据通过climatic_variables文件夹内的代码获取
- GAGESII文件夹：存储以.csv格式保存的全美集水区物理属性变量数据
- WT_LSTM_data文件夹：存储以.csv格式保存的预测水温（Willard, 2023）及对应均方根误差（Root Mean Square Error, RMSE）数据
- Upper_Colorado_River_Basin_Boundary文件夹：存储地理数据，包括用于UCRB_Drought_Workflow.ipynb绘图的形状文件
- OUTPUTS（输出数据文件夹）：包含UCRB_Drought_Workflow.ipynb生成的所有中间数据与最终成果，其中包括计算得到的标准化降水蒸发指数（SPEI），其下设以下主要目录与子目录：
- RDC_WT_SC_data文件夹：包含Water_year子文件夹（存储以.csv格式保存的清理后数据、元数据及数据可用性信息）、Median_Relchange子文件夹（存储以.csv格式保存的干旱年份与非干旱年份相对对比数据）及RDC_PeakFlow_Relchange子文件夹（存储以.csv格式保存的峰值流量相对变化数据）
- NLCD_data文件夹：存储研究时段始末的土地覆盖差值数据，以及UCRB范围内各县占比信息，详见Nagamoto等（2025）
- MET_data文件夹：存储以.csv格式保存的分离后的月度气温、降水数据及计算得到的潜在蒸散发（Potential Evapotranspiration, PET）数据
- SPEI_data文件夹：存储以.csv格式保存的计算得到的SPEI值，其中一份限定于研究时段，另一份覆盖全部气象数据时段
- Paper_Tables文件夹：存储两份以.csv格式保存的文件，分别包含监测点信息、数据可用性信息，以及GAGESII属性聚合类别信息
- climatic_variables文件夹：存放用于从谷歌地球引擎收集气象数据的代码
- feature_importance文件夹：存放用于集水区属性分析的代码
- preprocessing文件夹：存放UCRB_Drought_Workflow.ipynb中使用的预处理代码
- pyeto文件夹：存放UCRB_Drought_Workflow.ipynb中使用的代码
- calculations文件夹：存放UCRB_Drought_Workflow.ipynb中使用的计算代码
- README.md：研究说明文档
- UCRB_Drought_Workflow.ipynb：本研究分析的主代码文件
- requirements_ucrb-drought.yml：用于创建运行代码所需的虚拟环境及Jupyter Lab内核的依赖配置文件
- flmd.csv：收录所有文件的列表与说明文档
- dd.csv：数据字典文件

预处理、分析及绘图生成所用完整脚本可通过关联的GitHub仓库获取，仓库地址为https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought。
更新说明：2025年10月15日，更新了研究标题与代码文件名，以匹配已投稿的手稿。
引用说明：若需引用本代码，请使用以下BibTeX条目：
@misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }