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

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

Using remote sensing estimates of precipitation and evapotranspiration to assess the spatial characteristics of Chilean magadrought

<p>Droughts have been traditionally monitored and analysed using ground-based data. However, developing countries usually do not have a dense network of meteorological stations to allow a reliable characterization of the spatio-temporal variability of key meteorological variables. In the last decades, remote sensing techniques have become a promising alternative to provide a spatial characterisation of drought-related variables and to quantify drought impacts.</p> <p><br> In this study we attempt to evaluate -for the fist time- the suitability of the combined use of state-of-the-art satellite-based precipitation (P, CHIRPSv2) and potential evapotranspiration (PET, MOD16) estimates to characterise the spatial distribution of the so called “Chilean megadrought”, which has affected the central-southern territory of Chile (29oS-46oS) during the last decade. Satellite data were collected for the period 2000-2014, and then the Standardized Precipitation Index (SPI) was used to analyse the impact of precipitation deficits on drought events, while the Standardized Precipitation Evapotranspiration Index (SPEI) was computed to take into account the simultaneous contribution of temperature and precipitation changes on drought characteristics. SPI and SPEI were evaluated at 12-month temporal scale, because they reflect long-term meteorological patterns and should<br> tend towards zero unless a clear trend is undergoing. Drought events are operationally described in terms of its duration, severity, maximum intensity and spatial extent, using the theory of runs with a threshold of -0.84 to identify the onset and duration of drought events.</p> <p><br> Results obtained with SPI-12 and SPEI-12, evaluated in December of each year, reveal negative values in all the study area during the megadrought (2010-2014), indicating a general deficit of precipitation and potential water availability (P-PET). The total duration of drought events increased importantly during the megadrought in comparison to previous years, reaching 40-45 months of duration (3.5 out of 5 years), with SPEI-12 identifying longer durations in some particular regions within the study area. Total severity also increased during megadrought, with values 3-4 times larger than those observed during the previous period, and with stronger severities identified with SPEI-12 in comparison to those identified with SPI-12. The maximum intensity of drought events presented<br> a "salt and pepper" spatial pattern during all the study period, with local differences between the maximum values identified with SPI-12 and SPEI-12.</p> <p><br> Findings of this work are expected to support the future implementation of an operational drought monitoring platform and to efficiently allocate economical resources devoted to mitigation in drought affected areas.</p

The CAMELS-CL dataset - links to files

CAMELS-CL 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. The dataset includes 516 catchments and 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 (doi:10.5065/D6G73C3Q), and added several others. --- CAMELS-CL can be visualised from http://camels.cr2.cl --- This research emerged from the collaboration with many colleagues at the Center for Climate and Resilience Research (CR2, CONICYT/FONDAP/15110009). Camila Alvarez-Garreton was funded by FONDECYT postdoctoral grant no. 3170428. Pablo Mendoza received additional support from FONDECYT postdoctoral grant no. 3170079. Mauricio Zambrano-Bigiarini thanks FONDECYT 11150861 for financial support. The development of CR2MET was supported by the Chilean Water Directorate (DGA), through National Water Balance Updating Project DGA-2319, and by FONDECYT grant no. 3150492. This study is a contribution to the Large-sample Hydrology working group of the Panta Rhei Research Initiative of the International Association of Hydrological Sciences (IAHS)