Estimation of the prevalence of streamflow intermittence in the Yellow River basin
Creators: name: Zhang, Linyan
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 370704
subject: Surface water hydrology
classificationCode: 370799
subject: Hydrology not elsewhere classified
classificationCode: 370703
subject: Groundwater hydrology
classificationCode: 370999
subject: Physical geography and environmental geoscience not elsewhere classified

description: 1. Overview and background2. Repository content3. Data format and projection4. Citations and acknowledgements<b>1. Overview and background</b>This documentation describes the data used for the research article: Zhang, L. Y., Abbasi, M., Yang, X. L., Hosseini-Mogharic, S. M., Ren, L. L., Döll, P. (2024). Estimation of the prevalence of non-perennial rivers and streams in anthropogenically altered river basins by Random Forest modeling: A case study for the Yellow River basin. In this study, we first include human impact variables and develop four high-resolution Random Forest model variants with different combinations of predictor variables to evaluate the spatial prevalence of non-perennial reaches in the Yellow River basin where the streamflow is strongly influenced by human water abstractions and artificial reservoirs. Model validation was done in the cross-validation by using daily time series of observed streamflow at 224 gauging stations. Predictor importance and partial dependence plots were derived to understand the relevance between the predictor variables and intermittence status. The data repository includes the following three datasets:1. RF: the files include the RF code and the predictors and target for four RF model variants2. Data: the files include the observed daily streamflow and location of gauging stations and reaches3. Results_shp: the files for generating the figures<b>2</b><b>. Repository content</b>The data repository has the following structure:<b>-- RF.zip</b><b>|</b><b>----- RF_tuning_hyperparameters.py:</b> <i>code for selecting the optimal hyperparameter combinations for four RF model variants.</i><b>|----- RF_retrained.py:</b> <i>code for applying the four RF model variants with their optimal hyperparameter combinations to each reaches.</i><b>|----- Predictors:</b> <i>RF model variants predictors</i><b>|-----------RF_all_variant.xlsx:</b> <i>a table of 224 gauging stations data for all 21 predictors and target</i><b>|-----------RF_all_variant_reaches.xlsx:</b> <i>a table of 38871 reaches data for all 21 predictors </i><i>and target</i>To run the files serialized as ‘.py’ files requires first installing the "<i>spyder" </i>a scientific environment written in Python (https://www.spyder-ide.org/).<b>-- Data.zip</b><b>|----- observed_daily_streamflow.xlsx:</b> <i>a table of the time series of observed daily streamflow at the 224 gauging stations during 2007-2017</i><b>|----- shp:</b> <i>the shapefiles of the spatial reaches and gauging stations</i><b>|----------- gauging_stations:</b> <i>points of 224 Yellow River basin gauging stations for producing figure 1</i><b>|----------- reaches:</b> <i>lines of 33871 Yellow River basin reaches for producing the background river reaches of </i><i>figures 3, 6 and 7</i><b>-- Results_shp.zip</b><b>|----- predictive_accuracy_results:</b> <i>points shapefile for producing figure 3a-d in the study.</i><b>|----- probability_streamflow_intermittence:</b> <i>lines shapefile of the probability of streamflow intermottence for four RF model variants for producing figure 6 in the study.</i><b>|----- spatial_distribution:</b> <i>lines shapefile of all 21 predictors for producing figures S1 in the study.</i><b>3. Data format and projection</b>The geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file shapefile formats. Each shapefile consists of three main files (.dbf, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format.These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).<b>4. License and citations</b>4.1 License agreementThis documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and acknowledgements.Citations and acknowledgements of this dataset should be made as follows: Zhang, L. Y., Abbasi, M., Yang, X. L., Hosseini-Mogharic, S. M., Ren, L. L., Döll, P. (2024). Estimation of the prevalence of non-perennial rivers and streams in anthropogenically altered river basins by Random Forest modeling: A case study for the Yellow River basin.<i>Journal of hydrology (Submitted).</i> We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.25683612) should also be provided.