Characterizing hydrologic change through catchment classification

Sawicz, K. A.; Kelleher, Christa; Wagener, Thorsten; Troch, P. A.; Sivapalan, Murugesu; Carrillo, G.

doi:10.5194/hess-18-273-2014

Cited by 89 publications

(86 citation statements)

References 60 publications

(69 reference statements)

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“…It was calibrated using an automatic recursive partitioning based on methods described by Breiman et al (1984) and provided in R package "rpart" (see Atkinson and Therneau, 2000). CART has been used previously for understanding controls on groupings of catchments in relation to their hydrologic behavior (e.g., Sawicz et al, 2014) or of hydrologic model parameters or model input and their regional predictors (e.g., Singh et al, 2014;Deshmukh and Singh, 2016).…”

Section: Cluster Analysis For Catchment Classificationmentioning

confidence: 99%

“…The choice of the specific signatures used for classification can be guided by (i) the attempt to describe basic hydrological behavior (e.g., Ley et al, 2011;Sawicz et al, 2011;Trancoso et al, 2016); (ii) the need to relate to societally relevant issues such as floods and droughts (Wagener et al, 2008); (iii) the objective to characterize ecologically relevant characteristics of the catchment response (e.g., Olden et al, 2012); or (iv) in relation to subsequent hydrologic modeling (Euser et al, 2013;Hrachowitz et al, 2014;Donnelly et al, 2016). Studying differences and similarities in flow signatures as well as in catchment characteristics can also improve our understanding of hydrological processes under potential future conditions (Sawicz et al, 2014;Berghuijs et al, 2014;Pechlivanidis and Arheimer, 2015;Rice et al, 2015). Linking catchment descriptors (physical and climatic) and hydrological response signatures enables the inclusion of ungauged basins and provides the potential for assessing environmental change impacts across large domains.…”

Section: Introductionmentioning

confidence: 99%

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Understanding hydrologic variability across Europe through catchment classification

Kuentz

Arheimer

Hundecha

et al. 2017

Hydrol. Earth Syst. Sci.

129

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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 moisturelimited 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.

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Section: Cluster Analysis For Catchment Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding hydrologic variability across Europe through catchment classification

Kuentz

Arheimer

Hundecha

et al. 2017

Hydrol. Earth Syst. Sci.

129

View full text Add to dashboard Cite

show abstract

“…year), whereas values close to 1 indicate that all the flows occur on a single day. (Oki and Kanae, 2006;Shiklomanov et al, 2004;Vörösmarty et al, 2000). Observed time series of mean yearly or monthly streamflow has e.g.…”

Section: Design Rules For Index Calculationmentioning

confidence: 99%

“…2013; Beck et al, 2015;Olden and Poff, 2003;Sawicz et al, 2011Sawicz et al, , 2014Westerberg et al, 2016). These hydrological signatures include e.g.…”

Section: Introductionmentioning

confidence: 99%

The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment

Gudmundsson

Hong

Leonard

et al. 2018

Earth Syst. Sci. Data

136

126

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Abstract. This is Part 2 of a two-paper series presenting the Global Streamflow Indices and Metadata Archive (GSIM), which is a collection of daily streamflow observations at more than 30 000 stations around the world. While Part 1 (Do et al., 2018a) describes the data collection process as well as the generation of auxiliary catchment data (e.g. catchment boundary, land cover, mean climate), Part 2 introduces a set of quality controlled time-series indices representing (i) the water balance, (ii) the seasonal cycle, (iii) low flows and (iv) floods. To this end we first consider the quality of individual daily records using a combination of quality flags from data providers and automated screening methods. Subsequently, streamflow time-series indices are computed for yearly, seasonal and monthly resolution. The paper provides a generalized assessment of the homogeneity of all generated streamflow time-series indices, which can be used to select time series that are suitable for a specific task. The newly generated global set of streamflow time-series indices is made freely available with an digital object identifier at https://doi.pangaea.de/10.1594/PANGAEA.887470 and is expected to foster global freshwater research, by acting as a ground truth for model validation or as a basis for assessing the role of human impacts on the terrestrial water cycle. It is hoped that a renewed interest in streamflow data at the global scale will foster efforts in the systematic assessment of data quality and provide momentum to overcome administrative barriers that lead to inconsistencies in global collections of relevant hydrological observations.

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“…2013; Beck et al, 2015;Olden and Poff, 2003;Sawicz et al, 2011;Sawicz et al, 2014;Westerberg et al, 2016). These hydrological signatures include e.g.…”

Section: Introduction 20mentioning

confidence: 99%

The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality Control, Time-series Indices and Homogeneity Assessment

Gudmundsson¹,

Hong²,

Leonard³

et al. 2017

Preprint

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Abstract.This is Part 2 of a two-paper series presenting the Global Streamflow Indices and Metadata Archive (GSIM), which is a collection of daily streamflow observations at more than 30000 stations around the world. While Part 1 describes the data 10 collection process as well as the generation of auxiliary catchment data (e.g. catchment boundary, land-cover, mean climate), Part 2 introduces a set of quality controlled time series indices representing (i) the water balance, (ii) the seasonal cycle, (iii) low-flows and (iv) floods. To this end we first consider the quality of individual daily records using a combination of quality flags of the data providers and automated screening methods. Subsequently streamflow time series indices are computed for yearly, seasonal and monthly resolution. The paper closes with a generalized assessment of the homogeneity of all generated 15 streamflow time series indices, which can be used to select time series that are suitable for a specific task. The presented global set of streamflow time series indices is made freely available at https://iacweb.ethz.ch/staff/lukasgu/GSIM/GSIM_indices.zip and is expected to foster global freshwater research, by acting as a ground-truth for model validation or as a basis for assessing the role of human impacts on the terrestrial water cycle.

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Characterizing hydrologic change through catchment classification

Cited by 89 publications

References 60 publications

Understanding hydrologic variability across Europe through catchment classification

Understanding hydrologic variability across Europe through catchment classification

The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment

The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality Control, Time-series Indices and Homogeneity Assessment

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