The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset
<p>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 (inc...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/22/5817/2018/hess-22-5817-2018.pdf |
| Summary: | <p>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.</p><p>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 (<i>r</i> > 0.91) and lower
correlation (<i>r</i> < 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.</p><p>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
<a href="http://camels.cr2.cl" target="_blank">http://camels.cr2.cl</a> and downloaded from <a href="https://doi.pangaea.de/10.1594/PANGAEA.894885" target="_blank">https://doi.pangaea.de/10.1594/PANGAEA.894885</a>.</p> |
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| ISSN: | 1027-5606 1607-7938 |