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

2020 ◽  
Vol 12 (4) ◽  
pp. 2459-2483 ◽  
Author(s):  
Gemma Coxon ◽  
Nans Addor ◽  
John P. Bloomfield ◽  
Jim Freer ◽  
Matt Fry ◽  
...  
Keyword(s):  
Time Series ◽  
Great Britain ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Large Sample ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Human Management ◽  
Temperature Radiation ◽  
The Uk

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.

scholarly journals CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

2020 ◽  
Author(s):  
Gemma Coxon ◽  
Nans Addor ◽  
John P. Bloomfield ◽  
Jim Freer ◽  
Matt Fry ◽  
...  
Keyword(s):  
Great Britain ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Large Sample ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Human Management ◽  
Temperature Radiation ◽  
Hydrological Extreme ◽  
The Uk

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 timeseries 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 timeseries covering 1970–2015 (a period including several hydrological extreme episodes) 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 are 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 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.

scholarly journals Future Flows Climate: an ensemble of 1-km climate change projections for hydrological application in Great Britain

2012 ◽  
Vol 4 (1) ◽  
pp. 143-148 ◽  
Author(s):  
C. Prudhomme ◽  
S. Dadson ◽  
D. Morris ◽  
J. Williamson ◽  
G. Goodsell ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Great Britain ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Time Step ◽  
Climate Change Projections ◽  
Groundwater Levels ◽  
Temperature And Precipitation ◽  
Climate Change Uncertainty

Abstract. The dataset Future Flows Climate was developed as part of the project ''Future Flows and Groundwater Levels'' to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications, and to enable climate change uncertainty and climate variability to be accounted for in the assessment of their possible impacts on the environment. Future Flows Climate is derived from the Hadley Centre's ensemble projection HadRM3-PPE that is part of the basis of UKCP09 and includes projections in available precipitation (water available to hydrological processes after snow and ice storages have been accounted for) and potential evapotranspiration. It corresponds to an 11-member ensemble of transient projections from January 1950 to December 2098, each a single realisation from a different variant of HadRM3. Data are provided on a 1-km grid over the HadRM3 land areas at a daily (available precipitation) and monthly (PE) time step as netCDF files. Because systematic biases in temperature and precipitation were found between HadRM3-PPE and gridded temperature and precipitation observations for the 1962–1991 period, a monthly bias correction procedure was undertaken, based on a linear correction for temperature and a quantile-mapping correction (using the gamma distribution) for precipitation followed by a spatial downscaling. Available precipitation was derived from the bias-corrected precipitation and temperature time series using a simple elevation-dependant snow-melt model. Potential evapotranspiration time series were calculated for each month using the FAO-56 Penman-Monteith equations and bias-corrected temperature, cloud cover, relative humidity and wind speed from HadRM3-PPE along with latitude of the grid and the day of the year. Future Flows Climate is freely available for non-commercial use under certain licensing conditions. It is the dataset used to generate Future Flows Hydrology, an ensemble of transient projections of daily river flow and monthly groundwater time series for representative river basins and boreholes in Great Britain. doi:10.5285/bad1514f-119e-44a4-8e1e-442735bb9797.

scholarly journals Future Flows Climate: an ensemble of 1-km climate change projections for hydrological application in Great Britain

2012 ◽  
Vol 5 (1) ◽  
pp. 475-490 ◽  
Author(s):  
C. Prudhomme ◽  
S. Dadson ◽  
D. Morris ◽  
J. Williamson ◽  
G. Goodsell ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Great Britain ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Time Step ◽  
Climate Change Projections ◽  
Groundwater Levels ◽  
Temperature And Precipitation ◽  
Climate Change Uncertainty

Abstract. 1. The dataset Future Flows Climate was developed as part of the project "Future Flows and Groundwater Levels" to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications, and to enable for climate change uncertainty and climate variability to be accounted for in the assessment of their possible impacts on the environment. 2. Future Flows Climate is derived from the Hadley Centre's ensemble Projection HadRM3-PPE that is part of the basis of UKCP09 and includes projections in available precipitation (water available to hydrological processes after snow and ice storages have been accounted for) and potential evapotranspiration. It corresponds to an 11-member ensemble of transient projections from January 1950 to December 2098, each a single realisation from a different variant of HadRM3. Data are provided on a 1-km grid over the HadRM3 land areas at a daily (available precipitation) and monthly (PE) time step as NetCDF files. 3. Because systematic biases in temperature and precipitation were found between HadRM3-PPE and gridded temperature and precipitation observations for the 1962–1991 period, a monthly bias correction procedure was undertaken, based on a linear correction for temperature and a quantile-mapping correction (using the gamma distribution) for precipitation followed by a spatial downscaling. Available precipitation was derived from the bias-corrected precipitation and temperature time series using a simple elevation-dependant snow-melt model. Potential evapotranspiration time series were calculated for each month using the FAO-56 Penman Montieth equations and bias-corrected temperature, cloud cover, relative humidity and wind speed from HadRM3-PPE along with latitude of the grid and the day of the year. 4. Future Flows Climate is freely available for non commercial use under certain licensing conditions. It is the dataset used to generate Future Flows Hydrology, an ensemble of transient projections of daily river flow and monthly groundwater time series for representative river basins and boreholes in Great Britain. 5. doi:10.5285/bad1514f-119e-44a4-8e1e-442735bb9797

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

2018 ◽  
Vol 22 (11) ◽  
pp. 5817-5846 ◽  
Author(s):  
Camila Alvarez-Garreton ◽  
Pablo A. Mendoza ◽  
Juan Pablo Boisier ◽  
Nans Addor ◽  
Mauricio Galleguillos ◽  
...  
Keyword(s):  
Time Series ◽  
Arid Regions ◽  
Snow Water Equivalent ◽  
Potential Evapotranspiration ◽  
Data Sources ◽  
Large Sample ◽  
Daily Streamflow ◽  
Multiple Data ◽  
Diverse Data ◽  
High Elevations

Abstract. 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.

scholarly journals Carl Nielsen in the United Kingdom

2012 ◽  
Vol 5 ◽  
Author(s):  
Paolo Muntoni
Keyword(s):  
United Kingdom ◽  
Great Britain ◽  
The Public ◽  
The United Kingdom ◽  
Wide Range ◽  
Long Time ◽  
The Times ◽  
The Uk ◽  
The Guardian ◽  
British Research

The United Kingdom has always been receptive to the Danish composer Carl Nielsen. For a long time Great Britain was the only country outside Scandinavia to show interest in his works, which met both the favour of the public and the appreciation of critics. No other country has produced such a comprehensive list of articles, studies and reviews about Nielsen’s music. An overview of the commentaries on Nielsen’s most performed works, namely the Fourth and Fifth Symphony, published on two major British newspapers – The Times and The Guardian – documents how the opinion on his music constantly changed. Critiques range from an initial enthusiastic acclaim to a half-hearted appreciation, and later to revaluation and revival. An analysis of a selected work, the Sixth Symphony, sheds light on the breadth and variety of what can be now considered a well-established research tradition. Robert Simpson pioneered such research in the 1950’s, but it was during the last decade of the 20 th century that the most interesting developments unfolded. Despite the wide range of interpretations, it is possible to track within British research on Carl Nielsen some underlying features that, in interplay with other factors, can help to explain the composer’s popularity in the UK.

Coastal domain analysis for geo-coastal assessment in Great Britain

2020 ◽  
Author(s):  
Kathryn Lee ◽  
Rowan Vernon ◽  
Chris Williams ◽  
Andres Payo Garcia ◽  
Jonathan Lee
Keyword(s):  
Great Britain ◽  
Coastal Management ◽  
Rock Properties ◽  
Expert Assessment ◽  
Coastal Vulnerability ◽  
Partitional Clustering ◽  
Wide Range ◽  
Modelling Environment ◽  
Clustering Data ◽  
The Uk

&lt;p&gt;Coastal erosion and flooding are an increasing issue in Great Britain and pose a significant threat to people living and working in coastal environments, as well as the associated threats to infrastructure and assets. Recent storms, including Storm Callum in 2018, Storm Frank in 2014 and the east coast tidal surge in 2013, have highlighted these issues and caused widespread flooding, power outages and travel disruption. Repairs to homes, buildings, infrastructure and coastal defences cost tens of millions of pounds and took several months to complete with disruption to life, livelihoods and the national economy continuing long after the events. &amp;#160;&lt;/p&gt;&lt;p&gt;The geomorphological variability of Great Britain&amp;#8217;s ca. 11,000 mile long coastline, from steep, hard cliffs to weak, easily erodible cliffs and wide flat estuaries, is challenging to represent and therefore consider in a modelling environment. Consequently, the variability, particularly in cliff geology, lithology and rock properties, is often under-represented in coastal modelling and coastal management planning. This results in potentially critical factors such as cliff complexity (e.g. multiple lithologies, jointing and bedding structures, permeability), cliff morphology, and the coastal buffer, being overlooked, all of which can influence the way coastal landforms respond to changing climatic drivers. Finding an accessible, objective and multi-scaled way of communicating this variability to a wide range of coastal practitioners is important in helping to address coastal vulnerability and increase resilience regionally and nationally.&lt;/p&gt;&lt;p&gt;Using a novel partitional clustering approach, we have developed a new classification system for the coastline of Great Britain, which divides the coastline into specific domains based on a range of physical variables. This method combines data available from the existing BGS Coastal Vulnerability Dataset which includes geology type, cliff strength, foreshore environment and inundation potential. In addition, open source datasets, including wave power and height, tide height and tidal current speed, have been incorporated. The datasets have been attributed to ca. 4 million transects at 5 m intervals along the coastline. Effective multivariate clustering data driven techniques, with expert assessment, have been used to cluster the dataset in an iterative way. This approach enables the capture of the thoughts and processes that we as geomorphologists consider when comparing one coastal area with another and will provide a tool for communicating variability in the coast and its resilience to erosion and flooding.&lt;/p&gt;&lt;p&gt;This is the first time such a method has been applied nationally in Great Britain and will provide a potential new benchmark for describing the GB coastline and the changes that it may be subject to. The resulting coastal domains dataset will soon be made available to practitioners in the UK and will assist in making more informed decisions when considering coastal management.&lt;/p&gt;

Large-sample hydrology to foster open and collaborative research: a review of recent progress and grand challenges

2020 ◽  
Author(s):  
Gemma Coxon ◽  
Nans Addor ◽  
Camila Alvarez-Garreton ◽  
Hong X. Do ◽  
Keirnan Fowler ◽  
...  
Keyword(s):  
Human Impacts ◽  
Individual Case ◽  
Large Sample ◽  
Large Sets ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Diverse Data

&lt;p&gt;Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models and provide the foundation for improved understanding of the link between catchment characteristics, climate and hydrological responses. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities for open hydrology, yet they are also limited by their lack of comparability, accessibility, uncertainty estimates and characterization of human impacts.&lt;/p&gt;&lt;p&gt;Here, we underscore the key role of LSH datasets in open hydrologic science and highlight their potential to enhance the transparency and reproducibility of hydrological studies.&amp;#160; We provide a review of current LSH datasets and identify their limitations, including the current difficulties of inter-dataset comparison and limited accessibility of hydrological observations. To overcome these limitations, we propose simple guidelines alongside long-term coordinated actions for the community, which aim to standardize and automatize the creation of LSH datasets worldwide. This presentation will highlight how, by producing and using common LSH datasets, the community can increase the comparability and reproducibility of hydrological research.&lt;/p&gt;&lt;p&gt;This research was performed as part of the Panta Rhei Working Group on large-sample hydrology and is based on https://doi.org/10.1080/02626667.2019.1683182.&lt;/p&gt;

scholarly journals Potential impacts of climate change on water quality and ecology in six UK rivers

2009 ◽  
Vol 40 (2-3) ◽  
pp. 113-122 ◽  
Author(s):  
P. G. Whitehead ◽  
A. J. Wade ◽  
D. Butterfield
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Time Series ◽  
Soluble Reactive Phosphorus ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Climate Change Scenarios ◽  
Wide Range ◽  
The Uk ◽  
Impacts Of Climate Change

A modelling study has been undertaken to assess the likely impacts of climate change on water quality across the UK. A range of climate change scenarios have been used to generate future precipitation, evaporation and temperature time series at a range of catchments across the UK. These time series have then been used to drive the Integrated Catchment (INCA) suite of flow, water quality and ecological models to simulate flow, nitrate, ammonia, total and soluble reactive phosphorus, sediments, macrophytes and epiphytes in the Rivers Tamar, Lugg, Tame, Kennet, Tweed and Lambourn. A wide range of responses have been obtained with impacts varying depending on river character, catchment location, flow regime, type of scenario and the time into the future. Essentially upland reaches of river will respond differently to lowland reaches of river, and the responses will vary depending on the water quality parameter of interest.

scholarly journals CAMELS-AUS: Hydrometeorological time series and landscape attributes for 222 catchments in Australia

2021 ◽  
Author(s):  
Keirnan Fowler ◽  
Suwash Chandra Acharya ◽  
Nans Addor ◽  
Chihchung Chou ◽  
Murray Peel
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Arid Zone ◽  
Anthropogenic Influence ◽  
Rating Curve ◽  
Large Sample ◽  
Large Samples ◽  
Uncertainty Estimates ◽  
The Usa ◽  
Landscape Attributes

&lt;p&gt;Large samples of catchments are becoming increasingly important to gain generalisable insights from hydrological research.&amp;#160; Such insights are facilitated by freely available large sample hydrology datasets, with one example being the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) series of datasets.&amp;#160; Here we present CAMELS-AUS, the Australian edition of CAMELS. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85&amp;#8201;% have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75&amp;#8201;%) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset, the first of its kind in Australia, allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from and the corresponding paper is available at https://essd.copernicus.org/preprints/essd-2020-228/.&lt;/p&gt;

scholarly journals A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction

2019 ◽  
Vol 23 (8) ◽  
pp. 3247-3268 ◽  
Author(s):  
Katie A. Smith ◽  
Lucy J. Barker ◽  
Maliko Tanguy ◽  
Simon Parry ◽  
Shaun Harrigan ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
River Flow ◽  
Calibration Method ◽  
Drought Event ◽  
Flow Data ◽  
Model Simulations ◽  
Multi Objective ◽  
Hydrological Variability ◽  
The Uk

Abstract. Hydrological models can provide estimates of streamflow pre- and post-observations, which enable greater understanding of past hydrological behaviour, and potential futures. In this paper, a new multi-objective calibration method was derived and tested for 303 catchments in the UK, and the calibrations were used to reconstruct river flows back to 1891, in order to provide a much longer view of past hydrological variability, given the brevity of most UK river flow records which began post-1960. A Latin hypercube sample of 500 000 parameterisations for the GR4J model for each catchment were evaluated against six evaluation metrics covering all aspects of the flow regime from high, median, and low flows. The results of the top ranking model parameterisation (LHS1), and also the top 500 (LHS500), for each catchment were used to provide a deterministic result whilst also accounting for parameter uncertainty. The calibrations are generally good at capturing observed flows, with some exceptions in heavily groundwater-dominated catchments, and snowmelt and artificially influenced catchments across the country. Reconstructed flows were appraised over 30-year moving windows and were shown to provide good simulations of flow in the early parts of the record, in cases where observations were available. To consider the utility of the reconstructions for drought simulation, flow data for the 1975–1976 drought event were explored in detail in nine case study catchments. The model's performance in reproducing the drought events was found to vary by catchment, as did the level of uncertainty in the LHS500. The Standardised Streamflow Index (SSI) was used to assess the model simulations' ability to simulate extreme events. The peaks and troughs of the SSI time series were well represented despite slight over- or underestimations of past drought event magnitudes, while the accumulated deficits of the drought events extracted from the SSI time series verified that the model simulations were overall very good at simulating drought events. This paper provides three key contributions: (1) a robust multi-objective model calibration framework for calibrating catchment models for use in both general and extreme hydrology; (2) model calibrations for the 303 UK catchments that could be used in further research, and operational applications such as hydrological forecasting; and (3) ∼ 125 years of spatially and temporally consistent reconstructed flow data that will allow comprehensive quantitative assessments of past UK drought events, as well as long-term analyses of hydrological variability that have not been previously possible, thus enabling water resource managers to better plan for extreme events and build more resilient systems for the future.

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