Evaluating hydrometric networks for prediction in ungauged basins: a new methodology and its application to England and Wales

Hannaford, Jamie; Holmes, M. G. R.; Laizé, Cédric; Marsh, Terry; Young, Andrew

doi:10.2166/nh.2012.115

Cited by 18 publications

(8 citation statements)

References 25 publications

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“…The mean totals for abstractions and discharges are a very broad guide that point to the possible influence of abstractions but do not quantify the net influence of these impacts on the actual flow regime. Other (less widely available) metrics have been applied in the UK which use modelling approaches to assess the net impact of abstractions/discharges across the whole flow regime (for example the LowFlows Enterprise methodology; see also Hannaford et al, 2013).…”

Section: Abstraction and Dischargesmentioning

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

127

119

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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: Abstraction and Dischargesmentioning

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

127

119

View full text Add to dashboard Cite

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“…BFIHOST was preferred over the BFI calculated directly from the streamflow record, which would lead to circularity as the latter could itself be altered by human influences present in the record. The BFI-HOST has been widely used in the UK to define catchment similarity, in a host of regionalisation methods ( for examples see Hannaford et al, 2013, and references therein). Information about human influences is pivotal to this kind of study.…”

Section: Study Area and Datamentioning

confidence: 99%

Human influences on streamflow drought characteristics in England and Wales

Tijdeman

Hannaford

Stahl

2018

Hydrol. Earth Syst. Sci.

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Abstract. Human influences can affect streamflow drought characteristics and propagation. The question is where, when and why? To answer these questions, the impact of different human influences on streamflow droughts were assessed in England and Wales, across a broad range of climate and catchments conditions. We used a dataset consisting of catchments with near-natural flow as well as catchments for which different human influences have been indicated in the metadata ("Factors Affecting Runoff") of the UK National River Flow Archive (NRFA). A screening approach was applied on the streamflow records to identify human-influenced records with drought characteristics that deviated from those found for catchments with near-natural flow. Three different deviations were considered, specifically deviations in (1) the relationship between streamflow drought duration and the base flow index, BFI (specifically, BFIHOST, the BFI predicted from the hydrological properties of soils), (2) the correlation between streamflow and precipitation and (3) the temporal occurrence of streamflow droughts compared to precipitation droughts, i.e. an increase or decrease in streamflow drought months relative to precipitation drought months over the period of record. The identified deviations were then related to the indicated human influences. Results showed that the majority of catchments for which human influences were indicated did not show streamflow drought characteristics that deviated from those expected under near-natural conditions. For the catchments that did show deviating streamflow drought characteristics, prolonged streamflow drought durations were found in some of the catchments affected by groundwater abstractions. Weaker correlations between streamflow and precipitation were found for some of the catchments with reservoirs, water transfers or groundwater augmentation schemes. An increase in streamflow drought occurrence towards the end of their records was found for some of the catchments affected by groundwater abstractions and a decrease in streamflow drought occurrence for some of the catchments with either reservoirs or groundwater abstractions. In conclusion, the proposed screening approaches were sometimes successful in identifying streamflow records with deviating drought characteristics that are likely related to different human influences. However, a quantitative attribution of the impact of human influences on streamflow drought characteristics requires more detailed case-by-case information about the type and degree of all different human influences. Given that, in many countries, such information is often not readily accessible, the approaches adopted here could provide useful in targeting future efforts. In England and Wales specifically, the catchments with deviating streamflow drought characteristics identified in this study could serve as the starting point of detailed case study research.

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“…That said, there is a long history of using quantitative analysis of environmental data to provide information that might enable improved sampling system design, including correlation, cluster, principal component, information theoretic (entropic), geostatistical, and other types of analysis (e.g., Bras and Rodríguez-Iturbe, 1976;Caselton and Husain, 1980;Flatman and Yfantis, 1984;Burn and Goulter, 1991;Yang and Burn, 1994;Norberg and Rosén, 2006;Fleming, 2007;Pires et al, 2008;Mishra and Coulibaly, 2010;Archfield and Kiang, 2011;Neuman et al, 2012;Putthividhya and Tanaka, 2012;Mishra and Coulibaly, 2014). A review specifically of streamflow monitoring system design applications of such methods is provided by Mishra and Coulibaly (2009), and for a recent example of continued innovation in this field, see Hannaford et al (2013). The network theoretic approach implemented here adds to this rich heritage.…”

Section: Application To Hydrometric Networkmentioning

confidence: 99%

Complex network theory, streamflow, and hydrometric monitoring system design

Halverson

Fleming

2015

Hydrol. Earth Syst. Sci.

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Abstract. Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.

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Evaluating hydrometric networks for prediction in ungauged basins: a new methodology and its application to England and Wales

Cited by 18 publications

References 25 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

Human influences on streamflow drought characteristics in England and Wales

Complex network theory, streamflow, and hydrometric monitoring system design

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