This repository supports the EGU 2024 study titled "Progressive assessment of multivariate parameter estimation in distributed hydrological modelling using spatial patterns of remote sensing data." The research highlights challenges in traditional hydrological model calibration, which typically relies on observed flow records. Despite efforts, these estimations often show significant inconsistencies, even when modeled flows closely match observed data. The study employs the mesoscale hydrologic model (mHM) and integrates spatial patterns from remote sensing data to address these calibration challenges.
The mesoscale hydrologic model (mHM) and the SPAtial EFficiency metric (SPAEF) are central to our simulations, testing various scenarios where remote sensing data patterns are integrated into the calibration process. This method allows for a nuanced understanding of hydrological processes across different spatial scales.
We utilized the OSTRICH tool to enhance the mHM model by integrating created objective functions for calibration process.
Due to storage limitations, the entire model can be downloaded from https://www.dropbox.com/scl/fi/w5lqxaepx35in3z5smg7z/mHM_Ostrich.zip?rlkey=tqc9whjp2o3vtukwwbfggx45g&dl=0
Simply run OSTRICH.bat.
For additional calibrations, update the ostIn file.
Matott, LS. 2017. OSTRICH: an Optimization Software Tool, Documentation and User's Guide, Version 17.12.19. University at Buffalo Center for Computational Research. Link.
Koch, J., Demirel, M. C., Stisen, S. 2018. "The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models." Geoscientific Model Development, 11(5), 1873–1886. Link, DOI: 10.5194/gmd-11-1873-2018.
Moreira, V., Silva, F., and Welerson, C.: Progressive assessment of multivariate parameter estimation in distributed hydrological modelling using spatial patterns of remote sensing data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-790, https://doi.org/10.5194/egusphere-egu24-790, 2024.
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