气候变化领域集成服务门户(CCPortal): The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset

CCPortal

DOI	10.5194/hess-22-5817-2018
	The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset
	Alvarez-Garreton C.; Mendoza P.A.; Pablo Boisier J.; Addor N.; Galleguillos M.; Zambrano-Bigiarini M.; Lara A.; Puelma C.; Cortes G.; Garreaud R.; McPhee J.; Ayala A.
发表日期	2018
ISSN	1027-5606
起始页码	5817
结束页码	5846
卷号	22 期号:11
英文摘要	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. © 2018 Author(s).
语种	英语
scopus关键词	Arid regions; Catchments; Remote sensing; Reservoirs (water); Snow; Time series; Uncertainty analysis; Catchment water balance; Hydroclimatic conditions; In-situ measurement; Multiple data sources; Potential evapotranspiration; Precipitation products; Regional variation; Snow water equivalent; Runoff; catchment; climate conditions; data set; headwater; hydrometeorology; land cover; meteorological hazard; potential evapotranspiration; precipitation (climatology); remote sensing; runoff; streamflow; water budget; Chile; Camelidae
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159857
作者单位	Alvarez-Garreton, C., Center for Climate and Resilience Research (CR2), Santiago, Chile, Instituto de Conservación Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia, Chile; Mendoza, P.A., Department of Civil Engineering, Universidad de Chile, Santiago, Chile, Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile; Pablo Boisier, J., Center for Climate and Resilience Research (CR2), Santiago, Chile, Department of Geophysics, Universidad de Chile, Santiago, Chile; Addor, N., Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom; Galleguillos, M., Center for Climate and Resilience Research (CR2), Santiago, Chile, Faculty of Agronomic Sciences, Universidad de Chile, Santiago, Chile; Zambrano-Bigiarini, M., Center for Climate and Resilience Research (CR2), Santiago, Chile, Department of Civil Engineering, Faculty of Engineering and Sciences, Universidad de la Frontera, Temuco, Chile; Lara, A., Center for Climate and Resil...
推荐引用方式 GB/T 7714	Alvarez-Garreton C.,Mendoza P.A.,Pablo Boisier J.,et al. The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset[J],2018,22(11).
APA	Alvarez-Garreton C..,Mendoza P.A..,Pablo Boisier J..,Addor N..,Galleguillos M..,...&Ayala A..(2018).The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset.Hydrology and Earth System Sciences,22(11).
MLA	Alvarez-Garreton C.,et al."The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset".Hydrology and Earth System Sciences 22.11(2018).