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Reconstructed monthly river flows for Irish catchments 1766-2016
Paul O'Connor1, Conor Murphy1, Tom Matthews2
1Irish Climate Analysis and Research Units Department of Geography Maynooth University Maynooth Co. Kildare Ireland.
Geoscience Data Journal
|July 5, 2021
Summary
A 250-year river flow archive for Ireland was created using hydrological modeling and artificial neural networks. This valuable dataset aids in understanding river flow variations and climate impacts on catchments.
Area of Science:
- Hydrology
- Climate Science
- Data Science
Background:
- Long-term river flow data is crucial for water resource management and climate change impact studies.
- Historical meteorological data rescue and reconstruction are essential for extending hydrological records.
Purpose of the Study:
- To develop a 250-year (1766-2016) monthly river flow reconstruction for 51 Irish catchments.
- To quantify uncertainties in river flow reconstructions and evaluate their accuracy.
Main Methods:
- Utilized meteorological data rescue, gridded precipitation, and temperature reconstructions.
- Employed the GR2M hydrological model and an Artificial Neural Network for flow modeling.
- Quantified uncertainties using hydrological model structure and parameter variations.
Main Results:
- Achieved good skill in reconstructions, with Mean Absolute Error (MAE) of 9.3 mm/month (13.3%) and Root Mean Square Error (RMSE) of 12.6 mm/month (18.0%).
- Identified notable flow extremes: highest annual flows in 1877, lowest in 1855, highest winter flows in 2015/16, lowest summer flows in 1826, and low autumn flows in 1933.
- Validated reconstructions against quality-assured precipitation data from 1850-2000.
Conclusions:
- The reconstructed river flow database provides a robust tool for analyzing catchment-specific responses to climate variability and extremes.
- Enables detailed assessment of hydrological changes on annual, seasonal, and monthly timescales across Ireland.
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