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Understanding Hydrologic Variability across Europe through Catchment Classification
Anna Kuentz,Berit Arheimer,Yeshewatesfa Hundecha,Thorsten Wagener +3 more
- pp 10744
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TLDR
In this article, the authors investigated the physical controls on spatial patterns of pan-European flow signatures, taking advantage of large open datasets for catchment classification and comparative hydrology, and found that a 15 to 33% improvement in regression model skills when combined with catchment classifications versus simply using all catchments at once.Abstract:
. This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures – taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33 % (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisture-limited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables.read more
Citations
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A Ranking of Hydrological Signatures Based on Their Predictability in Space
Nans Addor,Nans Addor,Grey Nearing,Cristina Prieto,Andrew J. Newman,N. Le Vine,Martyn P. Clark +6 more
TL;DR: In this article, the authors compare and rank 15 commonly used hydrological signatures in 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large-sample Studies).
Journal ArticleDOI
A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data
TL;DR: In this article, the authors present a hydrologically informed way to quantify global climates, explicitly addressing the shortcomings in earlier climate classifications. But their classification scheme is based only on climatic information and can be evaluated with independent streamflow data.
Journal ArticleDOI
Constraining Conceptual Hydrological Models With Multiple Information Sources
Remko C. Nijzink,Susana Almeida,Susana Almeida,Ilias Pechlivanidis,René Capell,D. Gustafssons,Berit Arheimer,Juraj Parajka,Jim Freer,Dawei Han,Thorsten Wagener,R.R.P. van Nooijen,Hubert H. G. Savenije,Markus Hrachowitz +13 more
TL;DR: In this article, a parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products.
Journal ArticleDOI
CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain
Gemma Coxon,Nans Addor,Nans Addor,John P. Bloomfield,Jim Freer,Jim Freer,Matthew Fry,Jamie Hannaford,Nicholas J K Howden,Rosanna Lane,Melinda Lewis,Emma L. Robinson,Thorsten Wagener,Ross Woods +13 more
TL;DR: Coxon et al. as discussed by the authors presented the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sampleStudies).
Journal ArticleDOI
Regulation of snow-fed rivers affects flow regimes more than climate change.
TL;DR: It is concluded that in snow-fed rivers globally, the future climate change impact on flow regime is minor compared to regulation downstream of large reservoirs, and of similar magnitude over large landmasses.
References
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Journal ArticleDOI
Relationships between dynamic response characteristics and physical descriptors of catchments in England and Wales
Catherine Sefton,S.M Howarth +1 more
TL;DR: In this article, a regionalisation methodology has been applied to catchments in England and Wales enabling estimation of daily flows for any catchment in the region for which physical data and records of rainfall and temperature are available.
Journal ArticleDOI
On the need for catchment classification
Jeffrey J. McDonnell,Ross Woods +1 more
TL;DR: In this paper, the authors suggest that the underlying cause of many of our problems in catchment hydrology is the tremendous variability in space, time and process, which is present in natural hydrological systems all around the globe.
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A framework for hydrologic classification with a review of methodologies and applications in ecohydrology
TL;DR: In this paper, the authors reviewed the process of hydrologic classification, differentiating between an approach based on deductive reasoning using environmental regionalization and one based on inductive inference using streamflow classification.