A common way to characterize catchments is to use catchment descriptors that summarize important physical aspects of a given catchment, often by aggregating large geospatial datasets into a single number. Such descriptors aim at extracting information that can inform us about different aspects of catchment functioning, help us to infer dominant hydrological processes, identify similarity among different sites, and transfer information across them. In this study we analyze a large sample of research articles indexed in Scopus, that were returned from the search words “catchment characteristic”, “catchment descriptor”, “catchment attribute”, “catchment indicator” or “catchment property”, to identify current practices of catchment characterization in hydrological science and related disciplines.

We particularly focus on analyzing the variety of data sources on which catchment descriptors are usually based (e.g., digital elevation, land use, lithographic and soil texture maps), on identifying how the datasets are aggregated into catchment descriptors, and on exploring how the value of those descriptors is assessed.

Based on this large sample of studies that cover diverse research areas (e.g., water quantity, water quality, lake research, aquatic ecosystems), different types of studies (e.g., data-based analysis, hydrological and statistical modeling, field studies) and various application purposes (e.g., descriptive site comparison, catchment clustering/classification, quantitative driver/control identification, regionalization), we provide a categorized overview of practices that are currently used for catchment characterization. This overview will provide guidance for future studies by summarizing the status quo and its strengths and limitations, and by providing suggestions for future research.

How to cite: Tarasova, L., Gnann, S., Yang, S., Hartmann, A., and Wagener, T.: Current practices in catchment characterization: data sources, aggregation approaches, derived descriptors and their value, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3804, https://doi.org/10.5194/egusphere-egu22-3804, 2022.

The availability of large data samples can be useful in several research areas, including rainfall/flood frequency analysis, hydrological modelling and quantification of the hydrologic effects of catchment heterogeneities. In recent years, considerable efforts have been spent to build nation-wide databases of basin attributes, with catalogs or web repositories in USA, England, Switzerland, Austria, Canada, Australia, Brazil and Chile. We present here FaBI (Floods and attributes of Basins in Italy) i.e. the first collection of hydrologic data and gauged basin attributes encompassing the whole of Italy, that counts 631 basins and their flood records.

The collection puts together flood data and other hydrological indices on one side, and many basin geo-morpho-climatic and soil-related attributes. In terms of hydrologic data, the starting base is that of two recent databases, i.e. the Improved Italian - Rainfall Extreme Dataset (I²-RED) and the Catalogo delle Piene dei Corsi d’acqua Italiani. The latter was the main source for identification of the watersheds to consider, that are those for which extremes of daily or of peak discharges are available. On this set of 631 basins a consistent effort has produced the computation of spatially relevant attributes and indices with the condition that each variable derives from a uniform nation-wide coverage. Many attributes are related to the geomorphology of the river network, as Horton ratios, shape and amplitude factors. They were computed by processing a digital elevation model with a 30-meters spatial resolution, through the implementation of the r.basin R algorithm. On these values several quality-control procedures have been applied, starting with a check of consistency with previously published data. The raster river network extracted has been compared with a vector reference one provided by the Istituto Superiore per la Ricerca e Protezione Ambientale (ISPRA), allowing us to identify areas where it was necessary to manually force the digital elevation model. The relation between the length of the main channel and its longest path has been investigated and the Hack’s law was used to double-check the computed main channel length. Several spatial average values of climatological indices have been computed, privileging data gathered from ground stations, that are subsequently interpolated in the space. This attains average values of temperature and precipitation at different time scales, for the first time available in a unique repository. The FaBI collection provides a vast range of new opportunities to perform regional and national-scale hydrological analyses, taking advantage of the hydro-climatologic and morphologic variety of the Italian basins, that represent a vast range of transitions between Alpine and semi-arid geographic environments in a Mediterranean context.

How to cite: Claps, P., Brunetto, M., Evangelista, G., Mazzoglio, P., and Monforte, I.: FaBI: A new collection of flood data and attributes of basins in Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5563, https://doi.org/10.5194/egusphere-egu22-5563, 2022.

Environmental data are critical for understanding and managing ecosystems, including mitigation of degraded water quality. Therefore, we provide the first large-sample water quality data set of riverine water quality combined with water quantity, meteorological and nutrient forcing data, and catchment attributes for Germany in a preprocessed and structured form. The QUADICA data set (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany) covers 1386 German and transboundary catchments with a large range of hydroclimatic, topographic, geologic, land use and anthropogenic settings. The data set comprises time series of riverine macronutrient concentrations (species of nitrogen, phosphorus and organic carbon), discharge, meteorological and diffuse nitrogen forcing data (nitrogen surplus, atmospheric deposition and fixation). The time series are generally aggregated to an annual basis; however, for 140 stations with long-term water quality and quantity data (more than 20 years), we additionally provide monthly median discharge and nutrient concentrations, flow-normalized concentrations and corresponding mean fluxes as outputs from weighted regressions on time, discharge, and season (WRTDS). The catchment attributes include catchment nutrient inputs from point and diffuse sources and characteristics from topography, hydroclimate, land cover, lithology and soils. QUADICA is a comprehensive, freely available, ready-to-use data set that facilitates large-sample data-driven water quality assessments at catchment scale as well as mechanistic modeling studies. We hope to stimulate the hydrological and water quality communities to provide similar data sets to create novel research opportunities, increase our understanding of catchment functioning, and ultimately improve water quality management.

How to cite: Ebeling, P., Kumar, R., Lutz, S. R., Nguyen, T., Sarrazin, F., Weber, M., Büttner, O., Attinger, S., and Musolff, A.: QUADICA: A large-sample data set of water quality, discharge and catchment attributes for Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5918, https://doi.org/10.5194/egusphere-egu22-5918, 2022.

Over recent years, numerous open catchment datasets have been published. In 2017, the first CAMELS (catchment attributes and meteorology for large-sample studies) dataset was released for the continental US by Addor et al. (2017). It comprises data for several hundreds of catchments including dynamic time series of daily resolution over several decades for discharge, precipitation and temperature - originally compiled by Newman et al. (2015) - as well as static basin attributes such as indices on topography, soil, geology and climate. Subsequently, similar datasets for several other countries were made or will be made publicly available. Some of these also contain additional data such as attributes on glaciers or human influence like, e.g., the CAMELS datasets for Chile (Alvarez-Garreton et al., 2018) and Great Britain (Coxon et al., 2020). Such datasets build an accessible and unified basis for reproducible and complementary research.  They led to a high stimulation of hydrological research with methodologies that could not be applied before, like the joint evaluation of a large number of catchments.

We present CAMELS-CH, a new dataset for about 200 basins in Switzerland that will be released in 2022. In this collaborative project, several academic institutions and agencies work together to publish a hydro-meteorological dataset that covers both dynamic and static catchment data, and that accounts for the wide range of hydrological regimes in Switzerland, e.g., alpine environment, hydropower usage, densely populated and cultivated regions, etc. We summarize the current state of the project, remaining challenges, in particular regarding translating base data into the CAMELS format, and the final steps toward dataset publication.

References

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrology and Earth System Sciences, 21, 5293-5313, 2017

Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Cortes, G., Garreaud, R., McPhee, J., Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies-Chile dataset, Hydrology and Earth System Sciences, 22, 5817–5846, 2018

Coxon, G., Addor, N., Bloomfield, J., Freer, J., Fry, M., Hannaford, J., Howden, N., Lane, R., Lewis, M., Robinson, E., Wagener, T.,and Woods, R.: CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth System Science Data 12, 2459–2483, 2020

Newman, A., Clark, M., Sampson, K., Wood, A., Hay, L., Bock, A., Viger, R., Blodgett, D., Brekke, L., Arnold, J.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences, 19, 209-223, 2015

How to cite: Siber, R., Höge, M., Kauzlaric, M., Schönenberger, U., Horton, P., Schwanbeck, J., Viviroli, D., Zappa, M., Sikorska-Senoner, A. E., Pool, S., Floriancic, M. G., Reichert, P., Seibert, J., Addor, N., Schaefli, B., and Fenicia, F.: CAMELS-CH - Building a Common Open Database for Catchments in Switzerland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9859, https://doi.org/10.5194/egusphere-egu22-9859, 2022.

The root zone storage capacity (S_r) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. S_r is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Climate controlled root zone storage capacities can be derived from the maximum water deficit in the root zone based on water balances in gauged catchments. However, root zone parameterization in most global hydrological models does not account for a climate control on root development, being based on look-up tables that prescribe worldwide the same root zone parameters for each vegetation class. These look-up tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. Several recent studies such as Van Oorschot (2021, https://doi.org/10.5194/esd-12-725-2021) have shown that replacing tabulated S_r values with climate controlled S_r estimates results in improvements in modelling catchment river discharge.

The objective of this research is to investigate global patterns of root zone storage capacity derived from catchment water deficits of a large sample of catchments worldwide. To this aim we explore relations of catchment S_r estimates and catchment climate descriptors such as climatological potential evaporation and precipitation, and catchment vegetation characteristics. These relations at a catchment scale will be used to develop a global coverage of climate controlled of S_r to replace tabulated root zone parameters in global hydrological and climate modelling.

How to cite: van der Ent, R., van Oorschot, F., Hrachowitz, M., and Alessandri, A.: Global patterns of climate controlled root zone storage capacity based on a large sample of catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2786, https://doi.org/10.5194/egusphere-egu22-2786, 2022.

Daily streamflow data are crucial for various scientific and operational water resources applications, such as climate change impact assessment, flood forecasting, and catchment classification, among others. Streamflow is typically estimated through the implementation of hydrological models, which rely on parameters to represent hypotheses about the dominant processes in a catchment. In most cases, these parameters cannot be measured at the scales relevant for model applications and are therefore estimated through model calibration. Because most streams worldwide remain ungauged, novel parameter regionalisation techniques have been developed to predict daily streamflow over ungauged catchments. These regionalisation techniques transfer calibrated model parameters from gauged to ungauged catchments. To this end, an accurate spatio-temporal representation of crucial meteorological variables such as precipitation is essential, and therefore, most regionalisation studies have been conducted over regions with a dense network of meteorological stations. However, the characterisation of precipitation over data-scarce areas is challenging and might be subject to large uncertainties when only ground-based measurements are used. Despite that few daily regionalisation studies have used gridded precipitation products, there is no precise evaluation on how the selection of a particular precipitation product can affect the performance of the existing regionalisation techniques. Therefore, this work aims to analyse how the choice of gridded daily precipitation products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. For this purpose, we calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four precipitation products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each precipitation product. Despite differences in the spatio-temporal distribution of precipitation, all products provided good performance during calibration (median KGE's > 0.77), two independent verification periods (median KGE's > 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation (median KGE's for the best method ranging from 0.56 to 0.63). We show how model calibration can compensate, to some extent, differences between precipitation forcings by adjusting model parameters and thus the water balance components. Feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that: i) merging precipitation products and ground-based measurements does not necessarily translate into an improved hydrological model performance; ii) a precipitation product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; iii) the spatial resolution of the precipitation products does not substantially affect the regionalisation performance; and iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

How to cite: Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Mendoza, P. A., McNamara, I., Beck, H. E., Thurner, J., Nauditt, A., Ribbe, L., and Thinh, N. X.: On the selection of precipitation products for the regionalisation of hydrological model parameters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10286, https://doi.org/10.5194/egusphere-egu22-10286, 2022.

Much geomorphic research on rivers focuses on the role of frequent floods (e.g., with return periods between one and two years), which have been shown in many regions to simultaneously perform sufficient geomorphic work as well as occur often enough, so that they tend to determine the shape of the channel. As compared to the Annual Maxima (AM) method, using the Peaks-Over-Threshold (POT) method for flood frequency analysis (also known as the Partial Duration method) allows for the inclusion of a larger number of peak values from the series of past flow observations, resulting in a better estimation of the probabilistic model. As it only considers events above a threshold, POT also decreases the likelihood of incorporating smaller events, when relatively dry years occurred within the period of observations. In the AM method, such smaller events could potentially come from a different population and have an inordinate influence on the predicted floods, introducing variability. Because of all these reasons, the POT approach should result in sounder statistical analyses when predicting frequent floods, with relatively short average recurrence intervals (ARIs). However, much geomorphic research into channel-forming floods has traditionally used AM instead of POT, presumably because it is much easier to obtain annual maxima data and perform frequency analyses when there are as many data as years in the record, while there is subjectivity in choosing an adequate threshold for POT analyses.

In this work, we study the variability in peak flow estimates for frequent (return period < 3 years) events, using both AM and POT, over multiple regions in the US. The objectives are: i) to search for homogeneous hydrological regions where the relation between the two methods is similar, and ii) to study the stability of predictions obtained with the two approaches, when considering different record lengths. The former objective aims at exploring how external factors related to the geographical location and the characteristics of the basin affect discrepancies in the results achieved by the two methods, such as the climate and the size of the basin. The former affects the magnitude and average number of other flooding events, neglected by AM, that occur every year besides the annual maximum. The latter influences the extent to which different types of rainfall events, with different spatial coverages, can involve the watershed. This insight might lay some groundwork for introducing “correction coefficients” for AM-based predictions of relatively frequent floods, depending on the characteristics of the study area. The latter objective is intended to test the stability of the statistical model and check whether POT leads to less variable predictions than AM.

Special care is adopted in two crucial aspects that may introduce bias in the analysis: i) the choice of the case-study gaging stations, in order to minimize any human impact on the studied flow time series, and ii) the methodology for selecting the flood threshold in the POT method, aimed at avoiding subjective decisions.

How to cite: Dell'Aira, F., Cancelliere, A., and Meier, C. I.: Regional Variability in the Performance of Annual Maxima vs. Peaks-Over-Threshold Methods for Predicting Frequent Floods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10853, https://doi.org/10.5194/egusphere-egu22-10853, 2022.

We conduct an inner-basin evaluation of the (lateral) fluxes of the wflow_sbm model at 3km, 1km and 200m spatial resolutions the CAMELS dataset. Previous work (Aerts 2021) has shown that while the quality of streamflow predictions at basin outlets might show small differences between basins for the different model resolutions, inner basin lateral flows can differ greatly over different resolutions.

In this work we study the impact of model resolution on rainfall partitioning and subsequent impact on lateral flows. To quantify terrain characteristics, we apply the method of Gharari et al. (2011) to classify parts of each basin as wetland, hillslope, or plateau. For the different model resolutions, we calculate how much rain falls on the different classifications and study lateral flow within the basin per terrain classification type.

The results of this work will shed light on how models run at different resolutions have different internal lateral flows while still generating similar and adequate streamflow predictions. This insight will help in making informed decisions on what resolution to run a model at for a given problem to optimize both output and internal realism of the model estimations.

This study is carried out within the eWaterCycle framework; allowing for a FAIR by design research setup that is scalable in terms of case study areas and hydrological models.

How to cite: Aerts, J., Hut, R., van de Giesen, N., Drost, N., Kalvera, P., van Verseveld, W., and Weerts, A.: Inner-basin evaluation of the changes in the (lateral) fluxes of the distributed wflow_sbm hydrological model due to spatial scaling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11217, https://doi.org/10.5194/egusphere-egu22-11217, 2022.

The identification of the dominant controls for hydrological dynamics in a catchment is fundamental for the transfer of hydrological information. In particular, when the information to be transferred regards the rainfall-runoff transformation processes at fine temporal scale, as for the regionalisation of hydrological models, basin similarity should capture the sequential order and the stochastic nature of the runoff generation and propagation, considering the information content embedded in the entire streamflow hydrograph and also in its forcings.

While previous hydrological research has identified basins with similar meteorological forcings or with similar streamflow time series, a preliminary work (presented at the previous EGU General Assembly 2021, https://doi.org/10.5194/egusphere-egu21-10152) proposed, for the first time, to quantify the interaction between the entire time-series of different forcing data and streamflow observations, to be considered as novel hydrological signature and used as catchment similarity metrics. The study highlighted the potential of transfer entropy which was applied for identifying the dominant hydrological processes occurring in a catchment, measuring the transfer of information from different meteorological forcings over the basin to the corresponding streamflow time series at its outlet. The resulting transfer entropy values were then used as signatures to characterise the catchment responses, and a classification of the basins was obtained assuming that similar values of transfer entropy identify similar basins.

In the present work, the results of an improved version of the approach, applied to a large and densely gauged set of Austrian basins, are thoroughly interpreted against a set of geo-morphological and climatic catchment features and a set of typical and consolidated streamflow signatures. Then, the proposed catchment classification is compared to a benchmark clusterisation approach based on the selected streamflow signatures and the two resulting partitions are analysed in terms of internal consistency and mutual affinity.

The outcomes of the approach are promising and demonstrate the potential of transfer entropy as an additional instrument for assessing hydrological similarity and for quantifying the connection between different governing processes: the method is able to distinguish the predominant or partial role of snow melt and evapotranspiration in the region, it helps to assess differences in catchment response time and to highlight the role of high orographic precipitation in snow-dominated catchments.

Finally both clusterisations (transfer entropy-based and benchmark signature-based) are coupled to the regionalisation of a rainfall-runoff model across the study region, investigating the potential benefits in terms of model efficiency allowed by the use of the novel similarity metric in comparison to the benchmark approach. 

How to cite: Neri, M., Coulibaly, P., and Toth, E.: Catchment classification based on a measure of the interaction between streamflow and forcing time series: insights on the use of a transfer entropy signature and comparison with benchmark attributes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6313, https://doi.org/10.5194/egusphere-egu22-6313, 2022.

Reservoirs play a vital role in the supply and management of water resources around the world. In Great Britain, reservoir operations are largely determined by the local water company, and there is very little national-scale information available to quantify their impact on river flows. Consequently, large-scale hydrological modelling and data analyses often focus on ‘natural’ or ‘near-natural’ catchments. To support the long-term resilience of water supply and the environment under changing climate and demand, it is essential that we understand where, when and how reservoirs are leading to deviations from a natural flow regime. This will help inform and validate advancements in the large-scale simulation of reservoir-impacted catchments, as well as deepening our understanding of how reservoir operations influence streamflow behaviour.

Due to the age and location of reservoirs across Great Britain, there is a distinct lack of upstream or pre-construction flow timeseries. As a result, we cannot use standard approaches – such as indicators of hydrological alteration- which base analysis on the pre-and-post construction flow regimes. To fill this gap, we define a suite of hydrological signatures and compare observed streamflow timeseries from 1980- 2015 across 166 reservoir catchments and 112 near-natural catchments in Great Britain. We use signatures, characterizing the water balance, flow duration curve and low flow regime, to identify differences in streamflow between these two groups of catchments, and attribute alterations to upstream reservoir operation. We find that gauges with a reservoir upstream are more likely to induce runoff deficits exceeding total PET, and that routine reservoir releases lead to plateaus in the flow duration curve. By defining two new reservoir-based catchment descriptors, our results show that the degree of flow regulation at a gauge depends on the upstream storage capacity and the contributing area of upstream reservoirs. Such descriptors begin to identify thresholds below which the influence of reservoirs is indistinguishable, and help to characterise the extent of reservoir influence across Great Britain.

This analysis highlights groups of reservoir-impacted catchments which cannot be represented by a natural regime. It is in these locations that advancements in large-scale hydrological modelling are crucial for water resource simulation, and that the influence of reservoir operations on the flow regime must be accounted for.

How to cite: Salwey, S., Coxon, G., Pianosi, F., Hutton, C., and Singer, M.: Identifying the impact of reservoirs on the flow regime across Great Britain , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4442, https://doi.org/10.5194/egusphere-egu22-4442, 2022.