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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-23
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Feb 2018

Research article | 16 Feb 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

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

Camila Alvarez-Garreton1,2, Pablo A. Mendoza3, Juan Pablo Boisier1,4, Nans Addor5, Mauricio Galleguillos1,6, Mauricio Zambrano-Bigiarini1,7, Antonio Lara1,2, Cristóbal Puelma1,6, Gonzalo Cortes8, Rene Garreaud1,4, James McPhee3, and Alvaro Ayala9,10 Camila Alvarez-Garreton et al.
  • 1Center for Climate and Resilience Research (CR2), Santiago, Chile
  • 2Instituto de Conservación, Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia, Chile
  • 3Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile
  • 4Department of Geophysics, Universidad de Chile, Santiago, Chile
  • 5Climatic Research Unit, School of Environmental Sciences, University of East Anglia, UK
  • 6Faculty of Agronomic Sciences, Universidad de Chile, Santiago, Chile
  • 7Department of Civil Engineering, Faculty of Engineering and Sciences, Universidad de La Frontera, Temuco, Chile
  • 8Department of Civil and Environmental Engineering, University of California, Los Angeles, California, USA
  • 9Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 10Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland

Abstract. We introduce the first catchment data set for large sample studies in Chile (South America). The data set includes 516 catchments and provides catchment boundaries, daily streamflow records and basin-averaged time series of the following hydrometeorological variables: 1) daily precipitation retrieved from four gridded sources; 2) daily maximum, minimum and mean temperature; 3) daily potential evapotranspiration (PET); 4) 8-day accumulated PET; and 5) daily snow water equivalent. In addition to the hydro-meteorological time series, we use diverse data sets to extract key landscape attributes characterizing climatic, hydrological, topographic, geological and land cover features. We also describe the degree of anthropic intervention within the catchments by relying on publicly available water rights data for the country. The information is synthetized in 64 catchment attributes describing the landscape and water use characteristics of each catchment. To facilitate the use of the dataset presented here and promote common standards in large-sample studies, we computed most catchment attributes introduced by Addor et al., (2017) in their Catchment Attributes and MEteorology for Large-sample Studies dataset (CAMELS dataset) created for the United States, and proposed several others. Following this nomenclature, we named our dataset CAMELS-CL, which stands for CAMELS dataset in Chile. Based on the constructed dataset, we analysed the main spatial patterns of catchment attributes and the relationships between them. In general, the topographic attributes were explained by the Andes Cordillera; climatic attributes revealed the basic features of Chilean climate; and hydrological signatures revealed the leading patterns of catchment hydrologic responses, resulting from complex, non-linear process interactions across a range of spatiotemporal scales, enhanced by heterogeneities in topography, soils, vegetation, geology and other landscape properties. Further, we analysed human influence in catchment behaviour by relating hydrological signatures with a novel human intervention attribute. Our findings reveal that larger human intervention results in decreased annual flows, runoff ratios, decreased elasticity of runoff with respect to precipitation, and decreased flashiness of runoff, especially in drier catchments. CAMELS-CL provides unprecedented information in South America, a continent largely underrepresented in large-sample studies. The proximity of the Andes means that this dataset includes high-elevation catchments, which are generally poorly represented world-wide due to data-scarcity. The CAMELS-CL dataset can be used to address a myriad of applications, including catchment classification and regionalization studies, the modelling of water availability under different management scenarios, the characterisation of drought history and projections, and the exploration of climate change impacts on hydrological processes. This effort is part of an international initiative to create a multi-national large sample data sets freely available for the community.

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CAMELS-CL provides a catchment data set in South America. We provide catchment boundaries and the hydro-meteorological variables required to study water balance. We computed catchment attributes to quantify the anthropic intervention within the 516 catchments, and their main climatic, hydrological, topographic, geological and land cover features. We analysed the spatial patterns of catchment attributes and demostrate that human intervention significantly affects hydrologic response.
CAMELS-CL provides a catchment data set in South America. We provide catchment boundaries and...
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