The CAMELS data set: catchment attributes and meteorology for large-sample studies

Abstract

Abstract. We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.

A Ranking of Hydrological Signatures Based on Their Predictability in Space

“…To explore how the interplay of landscape attribute shapes hydrological behavior, we quantified the relative influence of the landscape attributes in the random forests (i.e., how strongly landscape attributes influence the predictions of the hydrological signatures). We leveraged the hydrometeorological times series and catchment attributes of recently released and particularly exhaustive data set covering 671 basins in the United States (the CAMELS [Catchment Attributes and MEteorology for Large‐sample Studies] data set, Addor et al, ; Newman et al, ). How well can signatures be simulated by a calibrated conceptual hydrological model? We ask whether explicitly accounting for hydrological processes (instead of adopting a purely statistical, data‐driven approach) improves the signature predictions.…”

Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks

“…A 15 possible approach could be to group the catchments by various catchment attributes (such as topography, soil properties, land cover, etc. ), recently released by Addor et al (2017a) for the catchments of the CAMELS data set. One motivation for the development of regional models was to analyze whether a model trained on multiple catchments is able to learn more general rainfall-runoff relationships.…”

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

“…A common approach for the analysis of large numbers of catchments is to explore interrelationships between catchment attributes describing landscape, climate and hydrologic behaviour. These attributes are usually calculated based on topography, soil types, geology, land cover and hydro-meteorological datasets (e.g., Oudin et al, 2008;Sawicz et al, 2011;Gupta et al, 2014;Newman et al, 2015;Addor et al, 2017). Accounting for catchments attributes in a comprehensive dataset serves various purposes.…”

“…A17 10 introduced the Catchment Attributes and MEteorology for Large-sample Studies dataset (CAMELS dataset), which uses the meteorological and streamflow data dataset collated by Newman et al, (2015) and provides quantitative estimates of a wide range of attributes for 671 catchments in the contiguous United States. The CAMELS dataset has already been used for a myriad of applications, including assessment of streamflow skill elasticity to initial conditions and climate prediction (Wood et al, 2016), snow data assimilation for seasonal streamflow prediction (Huang et al, 2017), continental-scale hydrologic 15 parameter estimation (Mizukami et al, 2017), and climate change impacts on the hydrology of the Conterminous United States (CONUS), among others. Following this nomenclature, we named our dataset CAMELS-CL, which stands for CAMELS dataset in Chile.…”

“…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.…”