Results Discharge Observation Uncertainty and Model Performance Pub

<p><br>Dataset for Publication:<br>On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance</p> <p>Authors and Affiliations:<br>- Jerom P. M. Aerts<br>&nbsp; Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Jannis M. Hoch<br>&nbsp; Department of Physical Geography, Utrecht University, Utrecht, the Netherlands<br>&nbsp; Fathom, Bristol, United Kingdom<br>- Gemma Coxon<br>&nbsp; Geographical Sciences, University of Bristol, Bristol, United Kingdom<br>- Nick C. van de Giesen<br>&nbsp; Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Rolf W. Hut<br>&nbsp; Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands</p> <p>Correspondence:<br>For inquiries about the dataset or publication, contact:<br>Jerom P. M. Aerts<br>Email: j.p.m.aerts@tudelft.nl</p> <p>---</p> <p>Overview:<br>This dataset supports the research article, "On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance."<br>It includes outputs and analyses from hydrological models and observational data across multiple flow categories and performance metrics. The dataset enables replication of the analyses and insights into the impacts of discharge observation uncertainty on model evaluation.</p> <p>---</p> <p>Dataset Structure:</p> <p>Folder: Model_Results<br>Contains results from hydrological model runs and associated analyses.</p> <p>Subfolders:</p> <p>1. MARRMoT_Models<br>&nbsp; &nbsp;- Contains results for six hydrological models implemented using the MARRMoT framework.</p> <p>2. PCR-GLOBWB<br>&nbsp; &nbsp;- Includes results for the PCR-GLOBWB hydrological model.</p> <p>3. wflow_sbm<br>&nbsp; &nbsp;- Contains results for the wflow_sbm hydrological model.</p> <p>Each Subfolder Contains:</p> <p>- Flow Categories<br>&nbsp; - Results categorized by flow regime:<br>&nbsp; &nbsp; - Low_Flow<br>&nbsp; &nbsp; - Average_Flow<br>&nbsp; &nbsp; - High_Flow</p> <p>- Gumboot Temporal Sampling Analysis<br>&nbsp; - Results of temporal sampling analyses conducted using the Gumboot method.</p> <p>- Objective Functions<br>&nbsp; - Files containing model performance metrics based on different objective functions.</p> <p>- Observations<br>&nbsp; - Observed discharge time series data used for model calibration and validation.</p> <p>- Simulation Time Series<br>&nbsp; - Simulated discharge time series generated by the models.</p> <p>---</p> <p>Data Description:</p> <p>- File Formats<br>&nbsp; - All data files are in CSV format with UTF-8 encoding.</p> <p>- Variable Descriptions<br>&nbsp; - Date: YYYY-MM-DD<br>&nbsp; - Discharge Values: Cubic meters per second (m&sup3;/s).<br>&nbsp; - Flow Categories: Indicates if data corresponds to low, average, or high flow conditions.</p> <p>- Performance Metrics:<br>&nbsp; - Includes common hydrological metrics (e.g., Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency) computed for each model and flow category.</p> <p>---</p> <p>Usage Guidelines:</p> <p>Citation:<br>If you use this dataset in your research, please cite the publication as follows:<br>&gt; Aerts, J. P. M., Hoch, J. M., Coxon, G., van de Giesen, N. C., &amp; Hut, R. W. (2024). On the importance of discharge observation uncertainty when interpreting hydrological model performance. [Journal Name, Volume, Pages]. DOI: [Insert DOI].</p> <p>Licensing:<br>This dataset is made available under the [Insert License, e.g., Creative Commons Attribution 4.0 International License (CC BY 4.0)].</p> <p>---</p> <p>Contact:<br>For further questions, feel free to contact:<br>Jerom P. M. Aerts<br>j.p.m.aerts@tudelft.nl</p>