As the demand for accurate hydrological models continues to grow, the need for efficient calibration processes becomes paramount. The Lisflood OS offers a comprehensive solution for calibrating and validating the Lisflood hydrological model against streamflow observations. In this article, we will explore how the Lisflood OS streamlines the calibration process, providing both automation and accuracy.
Understanding the Lisflood OS
The Lisflood OS is a collection of scripts designed to automate the calibration process for the Lisflood model. Developed by Hylke Beck in 2014, the calibration tool has since been modified by Feyera Aga Hirpa, Valerio Lorini, and Alfieri Lorenzo to enhance its functionality and usability. The calibration procedure consists of several Python scripts that run sequentially, ensuring the correct outcomes for each step of the process.
The Calibration Process
The calibration process begins with the CAL_1_CAL_VAL_PERIODS.py script, which computes the calibration and validation periods based on the available streamflow data. Catchments with streamflow records shorter than the specified length will not be processed. The CAL_2_PREP_MISC_REMOVE_SMALL_CATCH.py script further refines the calibration list by eliminating stations within a certain percentage of the upstream area. This step helps avoid clusters of stations that may skew the calibration results.
In the CAL_3_PREP_MISC.py script, maps with station locations, interstation regions, sampling frequency, and inflow locations are generated. The script also produces a CSV file with direct station linkages and a CSV file listing the catchment area and identifier.
The workload is then spread across multiple lists using the CAL_4_SPREAD_WORKLOAD.py script, allowing for parallel processing of the calibration. Each list can be run on a separate PC or node, facilitating faster execution of the calibration process.
The CAL_5_PREP_FORCING.py script prepares the forcing data by creating directories for each station and producing subsetted mask and station maps for the interstation regions. The subsequent CAL_6_CUTMAPS_LAUNCH.py script further subsets the maps to the interstation regions of the catchments.
The actual calibration process begins with the CAL_7_PERFORM_CAL.py script, which runs the calibration for each station using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multi-objective genetic algorithm. The script generates objective function scores, statistics, and Pareto optimal solutions for each catchment.
The CAL_8_CLEAN_SUBCATCH_DIRS.py script removes unnecessary files created during the calibration runs, optimizing disk space and streamlining the post-calibration process.
Finally, the CAL_9_PARAMETER_MAPS.py script produces maps with calibrated parameter values assigned to the interstation regions, ensuring accurate representation of the model.
Visualizing the Results
The CAL_10_COMPUTE_STATS_AND_FIGURES_TIME_SERIES.py script allows for the visualization of the calibration and validation results. Figures depicting the calibration and validation outcomes can be generated, providing a clear representation of the model’s performance.
Tips & Tricks for Optimal Performance
To ensure the Lisflood OS runs efficiently, it is recommended to check the meteorological forcing NetCDF files for chunking, uncompressed formats, and non-unlimited time dimensions. These optimizations can significantly improve the speed of the Lisflood runs. Additionally, storing the forcing data on local disks rather than over the network can further enhance performance.
Conclusion
The Lisflood OS streamlines the calibration process for the Lisflood hydrological model, providing automated calibration and validation against streamflow observations. By automating the calibration process and optimizing performance, the Lisflood OS allows users to obtain accurate hydrological model results in a time-efficient manner. Whether you are a hydrologist, researcher, or water resource manager, the Lisflood OS is a valuable tool that can enhance the accuracy and efficiency of your hydrological modeling projects.
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