Pulse Brain · Growing Health Evidence Index
Peer-reviewed

The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

Camila Álvarez-Garretón, Pablo A. Mendoza, Juan Pablo Boisier, Nans Addor, Mauricio Galleguillos, Mauricio Zambrano‐Bigiarini, Antonio Lara, Cristóbal Puelma, G. Cortés, René Garreaud, James McPhee, Álvaro Ayala

Hydrology and earth system sciences · 2018

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Summary

Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time serie

Source type
Peer-reviewed study
DOI
10.5194/hess-22-5817-2018
Catalogue ID
SNmokbvy84-jsfloj
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