PatagoniaMet v1.0 (PMET)

Do we really need a dataset for Western Patagonia?

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The short answer: Yes! High-quality hydrometeorological observations contributes to high-quality policies and management of natural resources. Conversely, unrepresentative, poorly collected, or erroneously archived data introduce uncertainty regarding the magnitude, rate, and direction of environmental change, undermining confidence in decision-making processes. The use of climate variables in any kind of environmental research requires quality-controlled and serially complete datasets. These datasets are used to assess most of the key aspects of climate change, such as temporal trends in mean values and variability, or extreme events. Therefore, data acquired from measurements do not always represent the real behaviour of the observed processes, and need to be corrected under homogenization schemes. Furthermore, hydrometeorological data needs to be Findable, Accessible, Interoperable and Reusable (FAIR data), requirements that are often not fulfilled.

To address these limitations, we present PatagoniaMet v1.0 (PMET), a compilation of observed hydrometeorological data (PMET-obs), and a daily gridded product of precipitation and maximum and minimum temperature (PMET-sim; 0.05º, 1980-2020). PMET-obs was developed considering a 4-step quality control process applied to 344 hydrometeorological time series (precipitation, air temperature, streamflow and lake levels) obtained from nine institutions in Chile and Argentina. Based on this dataset and atmospheric reanalysis data (ERA5), PMET-sim was developed using statistical bias correction processes (e.g., quantile mapping), spatial regression models (random forest) and hydrological methods (Budyko framework).

This dataset is part of my PhD thesis. Publication is coming soon!

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Rodrigo Aguayo
Postdoctoral researcher

My research interests include hydrology, glaciers, land-ocean interface and climate change