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 series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset 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 (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.

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 20 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 25 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 30 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, 35 decreased elasticity of runoff with respect to precipitation, and decreased flashiness of runoff, especially in drier catchments.Hydrol 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 5 hydrological processes. This effort is part of an international initiative to create a multi-national large sample data sets freely available for the community.. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-23 Manuscript under review for journal Hydrol. Earth Syst. Sci.

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