Anna Kuentz

Bio: Anna Kuentz is an academic researcher from Swedish Meteorological and Hydrological Institute. The author has contributed to research in topics: Baseflow. The author has an hindex of 2, co-authored 2 publications receiving 120 citations.

Understanding Hydrologic Variability across Europe through Catchment Classification

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.

Understanding hydrologic variability across Europe through catchment classification

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.

A Ranking of Hydrological Signatures Based on Their Predictability in Space

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.

A Quantitative Hydrological Climate Classification 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.

Constraining Conceptual Hydrological Models With Multiple Information Sources

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.

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

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.

Regulation of snow-fed rivers affects flow regimes more than climate change.

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.