Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

Addor, Nans; Hong, Xiaowei; Álvarez-Garretón, Camila; Coxon, Gemma; Fowler, Keirnan; Mendoza, Pablo

doi:10.1080/02626667.2019.1683182

Cited by 112 publications

(123 citation statements)

References 116 publications

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“…In recognition of the uncertainty in PET estimates, we provide two estimates of potential evapotranspiration available from CHESS-PE. The first estimate (PET) is calculated using the Penman-Monteith equation for FAO-defined well-watered grass (Allen et al, 1998) and is used to calculate all subsequent PET catchment attributes provided in CAMELS-GB. This estimate only accounts for transpiration and does not allow for canopy interception.…”

Section: Meteorological Time Seriesmentioning

confidence: 99%

CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

Coxon

Addor

Bloomfield

et al. 2020

Earth Syst. Sci. Data

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125

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Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.

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Section: Meteorological Time Seriesmentioning

confidence: 99%

CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

Coxon

Addor

Bloomfield

et al. 2020

Earth Syst. Sci. Data

Self Cite

125

119

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“…The integrated assessment of large-sample catchment attributes is fundamental for the description and classification of landscape properties, leading to an improved understanding of similarities (or dissimilarities) between catchments. Largesample catchment hydrology is essential in terms of hydrological processes understanding (Addor et al, 2020;Beven et al, 2020). It provides an attractive venue for general inferences that would otherwise be impossible to study based on individual or small groups of catchments, aside from allowing the testing of new and existing hypotheses in hydrologic sciences (Addor et al, 2017;Gupta et al, 2014;Lyon and Troch, 2010;Wagener et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The CAMELS initiative has been widely used and other large-sample datasets have been recently developed following the CAMELS format, such as CAMELS-GB for Great Britain, covering 671 catchments and CAMELS-CL for Chile, covering 516 catchments. A list of available large-sample datasets can be found in Addor et al (2020).…”

Section: Introductionmentioning

confidence: 99%

CABra: a novel large-sample dataset for Brazilian catchments

Almagro¹,

Oliveira²,

Neto³

et al. 2020

Preprint

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Abstract. In this paper, we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land-cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model and the coordinates of the streamflow stations provided by the Brazilian Water Agency, where only the stations with 30 years (1980–2010) of data and less than 10 % of missing records were included. Catchment areas range from 9 to 4 800 000 km2 and the mean daily streamflow varies from 0.02 to 9 mm d-1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage area for each catchment, aside from more than 100 attributes within the attribute classes. The collection and processing methods are discussed along with the limitations for each of our multiple data sources. The CABra intends to improve the hydrology-related data collection in Brazil and pave the way for a better understanding of different hydrologic drivers related to climate, landscape, and hydrology, which is particularly important in Brazil, having continental-scale river basins and widely heterogeneous landscape characteristics. In addition to benefitting catchment hydrology investigations, CABra will expand the exploration of novel hydrologic hypotheses and thereby advance our understanding of Brazilian catchments' behavior. The dataset is freely available at https://doi.org/10.5281/zenodo.4070147.

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“…Further, an updated version should better characterize heterogeneities within each catchment, both for the time series and attributes. Additionally, since data uncertainties are omnipresent (Montanari, 2007;Blöschl et al, 2019b;Addor et al, 2019), they should be further explored by including additional data sources.…”

Section: Data Availabilitymentioning

confidence: 99%

CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil

Chagas¹,

Chaffe²,

Addor³

et al. 2020

Preprint

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Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3713 gauges, as well as meteorological forcing (precipitation, evapotranspiration and temperature) for 897 selected catchments. It also includes 63 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile and Great Britain. CAMELS-BR complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and in the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and to facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709338 (Chagas et al., 2020).

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Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

Cited by 112 publications

References 116 publications

CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

CABra: a novel large-sample dataset for Brazilian catchments

CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil

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