Do we really need a dataset for Western Patagonia?
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.
PatagoniaMet v1.0 (Pmet from here on) is a compilation of hydrometeorological observed data (Pmet-obs), and a monthly gridded product of precipitation and temperature (Pmet-sim). Pmet-obs was developed considering a 4-step quality control process applied in 344 hydro-climatic time series obtained from from nine institutions in Chile and Argentina (precipitation, air temperature, streamflow and lake level stations). Based on this database and currently available uncorrected gridded products, Pmet-sim (0.05º, 1990-2019) was developed using statistical bias correction processes, machine learning (random forest) and hydrological methods (Budyko framework).