Understanding Hydrologic Variability across Europe through Catchment Classification
01 Apr 2017-pp 10744
TL;DR: 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.
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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).
Abstract: Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration and hydrological model calibration. The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers, their sensitivity to data uncertainties, and is hence useful for signature selection. We use three complementary approaches to compare and rank 15 commonly‐used signatures, which we evaluate in 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large‐sample Studies). Firstly, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil and geology influence (or not) the signatures. Secondly, we use simulations of a conceptual hydrological model (Sacramento) to benchmark the random forest predictions. Thirdly, we take advantage of the large sample of CAMELS catchments to characterize the spatial auto‐correlation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, ii) that their relationship to catchments attributes are elusive (in particular they are not correlated to climatic indices) and iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of their drivers and better characterization of their uncertainties would increase their value in hydrological studies.
157 citations
Cites background or methods from "Understanding Hydrologic Variabilit..."
...Further, climatic indices have been shown to be good predictors of streamflow indices (Beck et al., 2015; Kuentz et al., 2017)....
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...Finally, predictions could be improved by training the random forests over smaller regions (Kuentz et al., 2017; Nearing et al., 2016), but this would be done at the expense of the generality of the statistical model....
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...…following one of these three approaches: (i) by employing a statistical model using catchment attributes as predictors (Beck et al., 2013, 2015; Kuentz et al., 2017; Lacey & Grayson, 1998; Nathan &McMahon, 1992; Yadav et al., 2007), (ii) by transferring signatures from gauged catchments…...
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...Three of these studies (Beck et al., 2015; Kuentz et al., 2017; Westerberg et al., 2016) assess the regionalization of several signatures over a large number of gauging stations (3,000 to 4,000 depending on the signature, 1,366 and 43, respectively) in different regions (global, Europe and United…...
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...This enables in particular catchment classification (Sawicz et al., 2011) and provides insights into hydrological behavior in places where little to no data are available apart from streamflow (Kuentz et al., 2017)....
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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.
Abstract: Classification is essential in the study of natural systems, yet hydrology has no formal way to structure the climatic forcing that underlies hydrologic response. Various climate classification systems can be borrowed from other disciplines but these are based on different organizing principles than a hydrological classification might need. This work presents a hydrologically informed way to quantify global climates, explicitly addressing the shortcomings in earlier climate classifications. In this work, causal factors (climate) and hydrologic response (streamflow) are separated, meaning that our classification scheme is based only on climatic information and can be evaluated with independent streamflow data. Using gridded global climate data, we calculate three dimensionless indices per grid cell, describing annual aridity, aridity seasonality, and precipitation-as-snow. We use these indices to create several climate groups and define the membership degree of 1,103 catchments to each of the climate groups, based on each catchment’s climate. Streamflow patterns within each group tend to be similar, and tend to be different between groups. Visual comparison of flow regimes and Wilcoxon two-sample statistical tests on 16 streamflow signatures show that this index-based approach is more effective than the often-used Köppen-Geiger classification for grouping hydrologically similar catchments. Climate forcing exerts a strong control on typical hydrologic response and we show that at the global scale both change gradually in space. We argue that hydrologists should consider the hydroclimate as a continuous spectrum defined by the three climate indices, on which all catchments are positioned and show examples of this in a regionalization context.
117 citations
Cites background or methods from "Understanding Hydrologic Variabilit..."
...This study uses 16 signatures that cover these five categories (Table 1), mainly following recommendations from Kuentz et al. (2016) and Addor et al. (2017)....
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...…slope, and land use, together with streamflow characteristics, often in the form of streamflow signatures such asmean flow and slope of the flow duration curve, to group similar catchments (e.g., Coopersmith et al., 2012; Kuentz et al., 2016; Sawicz et al., 2011, 2014; Yadav et al., 2007)....
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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.
Abstract: The calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. 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. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer - Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow-dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis - Satellite Application Facility and MOD16 were less effective for deriving meaningful, well-constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.
85 citations
Cites methods from "Understanding Hydrologic Variabilit..."
...…of the parameter selection procedure as described in section 2.4 were evaluated for a set of 27 hydrological signatures (Table 4), as previously defined by, among others, Shamir et al. (2005), Yilmaz et al. (2008), Euser et al. (2013), Pechlivanidis and Arheimer (2015), and Kuentz et al. (2017)....
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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).
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.
84 citations
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.
Abstract: River flow is mainly controlled by climate, physiography and regulations, but their relative importance over large landmasses is poorly understood. Here we show from computational modelling that hydropower regulation is a key driver of flow regime change in snow-dominated regions and is more important than future climate changes. This implies that climate adaptation needs to include regulation schemes. The natural river regime in snowy regions has low flow when snow is stored and a pronounced peak flow when snow is melting. Global warming and hydropower regulation change this temporal pattern similarly, causing less difference in river flow between seasons. We conclude 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. Our study not only highlights the impact of hydropower production but also that river regulation could be turned into a measure for climate adaptation to maintain biodiversity on floodplains under climate change. Global warming and hydropower regulations are major threats to future fresh-water availability and biodiversity. Here, the authors show that their impact on flow regime over a large landmass result in similar changes, but hydropower is more critical locally and may have potential for climate adaptation in floodplains.
83 citations
References
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TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
Abstract: A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.
17,405 citations
"Understanding Hydrologic Variabilit..." refers methods in this paper
...The method groups clustering objects (catchments) so that the within class variability is minimized using a combination of the k-means algorithm (Hartigan and Wong, 1979) and Ward’s minimum-variance method (Ward, 1963)....
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10,702 citations
TL;DR: The Global Lakes and Wetlands Database (GLWD) as mentioned in this paper was created by combining the best available sources for lakes and wetlands on a global scale and the application of Geographic Information System (GIS) functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands.
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TL;DR: A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre as discussed by the authors.
Abstract: A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data ar...
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TL;DR: The HydroSHEDS (Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales) dataset as mentioned in this paper provides high-quality data at a resolution and quality unachieved by previous global data sets, such as HYDRO1k.
Abstract: To study the Earth system and to better understand the implications of global environmental change, there is a growing need for large-scale hydrographic data sets that serve as prerequisites in a variety of analyses and applications, ranging from regional watershed and freshwater conservation planning to global hydrological, climate, biogeochemical, and land surface modeling. Yet while countless hydrographic maps exist for well-known river basins and individual nations, there is a lack of seamless high-quality data on large scales such as continents or the entire globe. Data for many large international basins are patchy, and remote areas are often poorly mapped.
In response to these limitations, a team of scientists has developed data and created maps of the world's rivers that provide the research community with more reliable information about where streams and watersheds occur on the Earth's surface and how water drains the landscape. The new product, known as HydroSHEDS (Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales), provides this information at a resolution and quality unachieved by previous global data sets, such as HYDRO1k [U.S. Geological Survey (USGS), 2000].
1,505 citations
"Understanding Hydrologic Variabilit..." refers background in this paper
...meanElev (T) m USGS: Hydrosheds and Hydro 1K (for latitude > 60; Lehner et al., 2008) Mean elevation...
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