The emergence of large-sample hydrology datasets has opened many new opportunities to derive more robust and more generalizable conclusions about hydrological processes and models. Here I showcase several examples of how large-sample hydrology can help unveil unknown hydrological behaviors, test and refine existing hypotheses, and challenge current modeling practices. Such advancements can include generating a better understanding of how climate, landscapes, and humans shape the diversity of hydrological conditions we encounter worldwide but can also focus on general emergent behaviors that are surprisingly similar between places. I also reflect on how large-sample hydrology datasets could evolve to become an even more productive playground for hydrology to advance.  

How to cite: Berghuijs, W.: Large-sample hydrology lessons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3944, https://doi.org/10.5194/egusphere-egu24-3944, 2024.

Risks to the planet's freshwater systems are currently subjected to soaring concern worldwide. We applied the coupled Social-Ecological Systems approach to study and systematize the risks due to ten major water related stressors (variability, overuse, groundwater, floods, droughts, organic pollution, salinity, eutrophication, drinking water, sanitation). We used gridded socioeconomic indicator data for the analysis of human exposure and its vulnerability (adaptive capacity) to these stressors in 540 river basin units covering the whole world. Among the stressors, lack of appropriate sanitation scored highest, followed by droughts and eutrophication. The large and densely populated Asian basins, Ganges-Brahmaputra-Meghna, Indus, and Yangtze, topped, followed with the largest African basins (the Nile, Niger, and Congo/Zaire). The other top-ten basins were Rajasthan Inner Basins, Huang, Hai, and Myanmar South Coastal Basins. The ranking changed when weighting the stressor data (on physical entities) with socioeconomic vulnerability data (on societal ones). Each included basin unit manifested a specific risk profile. For the basin units, we developed a typology using principal component and cluster analyses. This allowed us identification of the roles of vulnerability and population exposure in worldwide river basin risk framework and revealed distinctive basin clusters associable with the following characterizations: (1) too little water – high salinity – high variability – overexploited, (2) high organic pollution, eutrophic, flood prone – highly populated, (3) water abundant, (4) lacking infrastructure – low socioeconomic development. These clusters largely form a sequence as for instance there are basins that fall at the edge of (1), with many similarities already to (2), etc. The analysis provides a new perspective to comparison of world’s river basins and looking for novel learning opportunities for river basin management and risk reduction policies, especially in a multihazard-multirisk setting allowing the identification of the basin-specific risk profile and the roles of vulnerability and exposure.

How to cite: Varis, O.: Typology of world’s river basins regarding socio-ecological resilience to ten major water related risks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3507, https://doi.org/10.5194/egusphere-egu24-3507, 2024.

Low-flows and floods cannot be viewed as purely natural phenomena since their
occurrence and characteristics are influenced by water storage and regulation.
Reservoir-regulation has strong impacts on flow seasonality and can intensify
or attenuate hydrological extremes and change their duration. It is yet hardly
quantified how reservoir regulation affects low- and high flows in the Alps, where
most reservoirs are operated for hydropower production. We need a better un-
derstanding of the effect of reservoir-regulation on hydrological extreme events
in order to assess the readiness of current regulation schemes for the future.
However, the analysis of river flow and estimation of hydrological extremes is
challenging in regulated catchments, particularly in large-samples studies, where
detailed information about reservoir-regulation is missing.
In this study, we analyse how reservoir-regulation has changed the magni-
tude and frequency of hydrological extreme events in the European Alps. To do
so, we have compiled a dataset of discharge stations and reservoirs which in-
cludes reservoir characteristics such as the first year of operation or the storage
capacity. With this information, we distinguish between discharge time series
before and after reservoir construction for about 70 catchments in the European
Alps and calculate a normalized reservoir storage capacity for each catchment.
Then, we calculate flood return periods based on annual maxima discharges
and a generalized extreme value distribution and the minimum 7 day moving
average runoff (MAM7) for each time series. We compare flood and low-flow
characteristics before and after reservoir construction for each catchment to as-
sess the influence of reservoir-regulation on hydrologic extremes. Furthermore,
we analyse changes in the seasonality of hydrological extremes and evaluate how
it is affected by seasonal reservoir-regulation schemes.
Our preliminary results show that reservoirs affect both, low-flows and floods.
Annual low-flows have mostly increased since reservoir-construction, while their
variability has decreased. Annual maximum flows with low return periods (be-
low 10-years) have mostly decreased after reservoir-construction with catch-
ments with a larger normalized storage capacity showing a stronger effect of
reducing extreme flows. Consequently, we conclude that reservoirs operated for
hydropower production mostly have an alleviating effect on both low-flows and
floods.

How to cite: Götte, J., Zappa, M., and Brunner, M.: How does reservoir-regulation impact hydrological extremes in the Alps?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12781, https://doi.org/10.5194/egusphere-egu24-12781, 2024.

Urbanisation is a critical driver of changes in streamflow. These changes are not uniform across catchments due to the diverse changes to water sources, storage, and pathways in urban river systems from impervious areas, abstractions, sewage networks, and sewage treatment plans. While land cover data are typically used to explain urbanisation, water management practices are poorly quantified. Consequently, urbanisation impacts are often difficult to detect and quantify, and the relative impact of these factors is currently poorly understood.

Here, we assess urban impacts on streamflow dynamics for a large-sample of catchments across England and Wales using data characterising water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales and have different impacts on streamflow dynamics. Wastewater discharges increase low flows and reduce flashiness in urban catchments, while urban land cover increases flashiness and frequency of medium and high flow events. We demonstrate the need to move beyond land cover metrics and include other features of urban river systems in large-sample hydrological analyses to quantify current and future drivers of urban streamflow.

How to cite: Coxon, G., McMillan, H., Bloomfield, J. P., Bolotin, L., Dean, J. F., Kelleher, C., Slater, L., and Zheng, Y.: Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5850, https://doi.org/10.5194/egusphere-egu24-5850, 2024.

Catchments are important in hydrology, and a catchment classification framework helps to understand the catchment hydrological behaviors, explain the differences between catchments, and predict in ungauged catchments. However, no research has yet established a global catchment classification framework.

We selected a group of hydrological signatures to represent catchment hydrological behaviors, and used the fuzzy clustering method to classify natural catchments. To explain the classification rules and the catchment attributes dominating the classification, we used decision tree and random forests, respectively. The results show that: the global natural catchments are divided into six classes by the fuzzy clustering method, most of the classes are extreme in at least one hydrological behavior, and the selected hydrological signatures can distinguish the catchment groups; The decision tree gives explicit classification rules, with an accuracy rate of over 93%, which reasonably explains the fuzzy clustering results and facilitates the judgment of catchment classes; The precipitation characteristics, aridity index and the lowest altitude of catchments are considered to be the dominant catchment attributes for catchment classification, among which the average daily precipitation is the most important; Compared with physiography, land cover, soil and geological factors, the relative importance of climate factors in catchment classification exceeds 50%; The global catchment classification pattern output by random forests is a comprehensive reflection of hydrological signatures and can better reflect the hierarchical differences in hydrological behavior among catchments in contrast to climate classification.

The validity of the proposed global classification pattern is supported by its consistency with regional studies conducted in Europe, the United States, and Australia. Furthermore, about 64.1% classification accuracy of catchment class and 62.0% simultaneous hit rates of eight hydrological signatures can be achieved by the random forests model, demonstrating the ability of proposed catchment classification in estimating the hydrological behavior of ungauged catchments. As the first step towards global catchment classification, this study developed a natural catchment classification method based on hydrological similarity using data-driven approaches, obtained a global distribution map, and laid the foundation for establishing a generally accepted global catchment classification framework.

How to cite: Xu, H., Wang, H., Liu, P., Liu, W., and Zhou, C.: Global natural catchment classification based on hydrological similarity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4626, https://doi.org/10.5194/egusphere-egu24-4626, 2024.

Quantification of precipitation partitioning into evaporation and runoff is crucial for predicting future water availability. Over longer time scales, the widely used Budyko Framework, which is a curvilinear relationship between evaporative index (i.e., actual evaporation over precipitation) and aridity index (i.e., potential evaporation over precipitation), robustly quantifies precipitation partitioning under prevailing climatic conditions. Global long-term records indicate that catchments generally follow Budyko curves; however, a narrow scatter around these curves have been demonstrated in various studies, raising questions about the framework's applicability. To address this, we quantified (based on historical long-term water balance data of over 2000 river catchments world-wide) the global, regional and local distributions of deviations from parametric Budyko curves, between multiple 20-year periods over the last century. This process resulted in four 20-year distributions of deviation for each catchment. On average, it was observed that in 73% of the catchments, the long-term median deviation values across these distributions were not significantly different from zero suggesting minimal to no median deviations. Furthermore, it is found that for majority of the catchments (78%) the four 20-year distributions of deviations are not significantly different to each other implying consistency in deviations among different 20-year periods. Our analysis revealed that, for 80% of these catchments, the long-term median deviations, for the last century, fall within the range of ±0.02 with a very narrow spread in Interquartile Range values. These findings demonstrate that while catchments do not precisely follow the expected Budyko trajectories, the deviations are small and quantifiable. Consequently, by taking into account these deviations, the Budyko Framework remains a valuable tool for predicting future evaporation and runoff under changing climatic conditions, within quantifiable margins of error.

How to cite: Ibrahim, M., Coenders, M., Hrachowitz, M., and van der Ent, R.: Catchments deviate less from their own Budyko curves over time than previously thought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5544, https://doi.org/10.5194/egusphere-egu24-5544, 2024.

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. S_r is an essential characteristic of hydrological systems as it controls the partitioning of precipitation into evaporation and runoff. Understanding the influence of climatic and landscape characteristics on S_r is essential for predicting how different ecosystems will respond to disturbances such as human activities and climate change. While the magnitude of S_r on ecosystem scale is partly influenced by landscape characteristics such slopes, bedrock properties and soil characteristics, there is widespread consensus that it is primarily controlled by climate conditions (i.e., the temporal dynamics of water and energy availability) as vegetation optimizes its root system to sustain atmospheric water demand.

Several studies have identified the influence of various climatic variables on Sr, but for different regions conflicting influences of these variables on Sr appeared. So far, it remains unclear what aspects of the climate are most important controls on Sr on global scale. This research aims to bridge this gap by exploring how different climatic and landscape characteristics influence the magnitude of S_r globally. Based on discharge measurements in a large sample of catchments worldwide (~4000), we estimated the actual S_r using the memory method as in Van Oorschot et al. (2021, 2023). With a random forest model we were able to adequately predict S_r using various climatic and landscape characteristics. Analysis of the driving variables of the random forest model show that the precipitation inter-storm duration is the most dominant control on S_r, and positively influences S_r in all regions. On the other hand, the influence of mean precipitation on S_r is conflicting in different regions. We found that in water limited regions, increased mean precipitation leads to increased S_r, while in energy limited regions, increased mean precipitation leads to decreased in S_r. Furthermore, the developed model is used to extrapolate the catchment S_r estimates to a global gridded map of S_r ensuring coverage of data-scarce regions. This extrapolated map can be used for more adequate modelling of subsurface vegetation water availability in large scale hydrological and land surface models.

van Oorschot, F., van der Ent, R. J., Hrachowitz, M., and Alessandri, A.: Climate-controlled root zone parameters show potential to improve water flux simulations by land surface models, Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, 2021.

van Oorschot, F., van der Ent, R. J., Alessandri, A., and Hrachowitz, M.: Influence of irrigation on root zone storage capacity estimation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2622, 2023.

How to cite: van Oorschot, F., van der Ent, R., Viering, T., Alessandri, A., and Hrachowitz, M.: How catchment ecosystems globally manage root water access under different (climate) conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3984, https://doi.org/10.5194/egusphere-egu24-3984, 2024.

River flow is affected by change in climate and land use. Today, we see many different magnitudes and directions of trends of change across European rivers with respect to streamflow, which affects water supply and hydrological extremes such as floods and droughts. Moreover, the drivers of change in streamflow and its temporal trends vary on multiple scales from local to regional to continental. 

In this study, we identify changes, trends and possible patterns of change in the hydrological response across the whole of Europe, as well as its underlying drivers. We do this by using multi-decadal streamflow data that was collected from more than 15000 European stream flow gauging stations in 39 European countries. This large-sample dataset, named EStreams and set to be published in 2024, provides valuable new perspectives on the hydrological response in Europe.

In the analysis, similar catchments across Europe were clustered into groups, based on their hydrological response, as characterised by a wide range of hydrological signatures. This allowed to identify the different controls of hydrological response between the groups, such as climate, landscape and seasonal water balance.

Furthermore, the high-resolution streamflow dataset used allowed for the opportunity to zoom in further and gave a meaningful look at the differences within the clustered groups. This ensured that it was possible to investigate the differences in hydrological responses that were primarily dictated by landscape characteristics, as within cluster catchments are assumed to have limited climate variability. 

Altogether, mapping out the different hydrological responses across Europe and the differences in hydrological response within nested sub-catchments gave a comprehensive identification and quantitative description of dominant landscape characteristics shaping the hydrological response within clusters of hydro-climatically distinct European regions.

How to cite: Rudlang, J., Hrachowitz, M., V. M. do Nasciamento, T., van der Ent, R., and Fenicia, F.: River Flow Dynamics across Europe: Insights from continental to regional scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7429, https://doi.org/10.5194/egusphere-egu24-7429, 2024.

Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. The Global Runoff Data Centre (GRDC) is an international data centre operating under the auspices of the World Meteorological Organization (WMO) at the German Federal Institute of Hydrology (BfG). Established in 1988, it holds the most substantive collection of quality assured river discharge data worldwide. Primary providers of river discharge data and associated metadata are the National Meteorological and Hydrological  Services of WMO Member States.

As the awareness for open data and reproducibility has increased in recent years, GRDC is working to simplify data provision to its users and to comply with the FAIR (findable, accessible, interoperable, reusable) principles. GRDC data and products are accessible online for non-commercial use (https://grdc.bafg.de). However, there are still hurdles on the way to a completely open and free exchange of data such as restrictive data policies and a lack of data standardisation.

Caravan is a community initiative to create a large-sample hydrology dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world (Kratzert et al. 2023). Compared to existing large-sample hydrology datasets, the focus on Caravan is to use globally consistent forcing and attribute data to facilitate global studies. Additionally, Caravan provides the code to derive community extension on Earth Engine with as little as catchment boundaries and streamflow data required. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.      

This dataset is the 6th extension to the original Caravan data set. It is based on a subset of hydrological discharge data and station-based watersheds from GRDC, which are covered by an open data policy (Attribution 4.0 International; CC BY 4.0). The dataset covers stations from 5357 catchments and 25 countries, spans 1950 – 2023, and is already publicly available on Zenodo: https://zenodo.org/records/10074416

Reference:

Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). https://doi.org/10.1038/s41597-023-01975-w

How to cite: Färber, C., Plessow, H., Mischel, S., Kratzert, F., Addor, N., Shalev, G., and Looser, U.: GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14974, https://doi.org/10.5194/egusphere-egu24-14974, 2024.

High-quality datasets are essential to support hydrological science and modeling. Several datasets exist for specific countries or regions (e.g. the various CAMELS datasets). However, these datasets lack standardization, which makes global studies difficult. Additionally, creating large-sample datasets is a time and resource consuming task, often preventing the release of data that would otherwise be open.

About a year ago, we released the Caravan (as in “a series of camels”) dataset, a community initiative that consists of 

• a large-sample hydrology dataset which is derived from globally consistent data sources, and
• open source code that facilitates the creation of Caravan extensions to new regions by leveraging cloud computing on Earth Engine.

On release, the Caravan dataset included 6830 gauges from 14 different countries with daily streamflow records (median record length ~30 years), 9 meteorological variables (from 1981 - 2020) in different daily aggregations, 4 hydrological reference states, and a total of 221 catchment attributes.

Since then, the dataset has been extended with several thousands of gauges in various, previously uncovered regions by different community members. Importantly, GRDC has joined the Caravan community effort and released a Caravan extension for 5357 watersheds (covering the period from 1950-2022) from the GRDC station catalog from 25 different countries. 

At this point, and with all extensions combined, the Caravan dataset now consists of 22494 gauge stations from 35 countries and contains a total of 660,382 years of streamflow records (median still at ~30 years).

With this submission, we want to reflect in more detail on the current state of the Caravan community efforts and share our thoughts and ideas for the future of Caravan. Additionally, we welcome interactions with owners of hydrological datasets interested in contributing to Caravan and discussions with users of large-sample datasets to understand the needs and desires for datasets and inform our future efforts. All information on Caravan can be found at https://github.com/kratzert/Caravan/

How to cite: Kratzert, F., Addor, N., Shalev, G., and Gilon, O.: Living among Artiodactyls - Current status and future plans of the Caravan dataset, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12051, https://doi.org/10.5194/egusphere-egu24-12051, 2024.

High-quality observational data is critical for driving, evaluating, and calibrating geo-environmental models, particularly data-driven models. The quality and availability of such data greatly influence the output of these models. Over recent decades, numerous national, regional, and global observational datasets have been developed for catchment-scale hydrological modeling. However, the regional and national datasets often differ widely in data sources, formats, and variables, making their use challenging and time-consuming. Furthermore, existing global datasets do not include all available data sources and thus have limited coverage. In this presentation, we introduce a harmonized, comprehensive database that amalgamates existing national, regional, and global datasets into a unified, user-friendly resource. Our database consists of daily streamflow observations, daily time series of 10 meteorological variables, climatic and physiographic attributes, and catchment boundaries for over 28,000 catchments worldwide. These catchments range in size from 2~km$^2 to 1300~km$^2 (mean 150~km$^2$) and the number of daily streamflow observations per catchment ranges from 200 to 18,000 (mean 400). The meteorological data covers precipitation, temperature, humidity, radiation, and wind speed for each catchment. We included precipitation estimates from 17 state-of-the-art products such as CHIRPS, ERA5, GSMaP, IMERG, MSWEP, and SM2RAIN. To explore the database and retrieve data, we have developed a straightforward Python-based Application Programming Interface (API). All related code will be open sourced and accompanied by extensive documentation and usage examples. We anticipate this database will be an invaluable resource for various hydrological studies, including model calibration, evaluation, inter-model comparisons, and the assessment of different forcing datasets.

How to cite: Abbas, A. and Beck, H.: A large sample harmonized database of daily streamflow, meteorological data, and catchment attributes for over 28,000 global catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19406, https://doi.org/10.5194/egusphere-egu24-19406, 2024.

References

1. Addor, N., Newman, A. J., Mizukami, N. & Clark, M. P. The CAMELS data set: Catchment attributes and meteorology for large-sample studies. Hydrol Earth Syst Sci 21, 5293–5313 (2017).

2. Coxon, G. et al. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth Syst Sci Data 12, 2459–2483 (2020).

3. Höge, M. et al. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland. Earth Syst Sci Data 15, 5755–5784 (2023).

4. Klingler, C., Schulz, K. & Herrnegger, M. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe. Earth Syst Sci Data 13, 4529–4565 (2021).

5. Kratzert, F. et al. Caravan - A global community dataset for large-sample hydrology. Scientific Data 2023 10:1 10, 1–11 (2023).

6. Färber, C. et al. GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre (0.1) [Data set]. Zenodo https://zenodo.org/records/8425587 (2023) doi:10.5281/ZENODO.8425587.

7. Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM)-Part 1: The production of a daily streamflow archive and metadata. Earth Syst Sci Data 10, 765–785 (2018).

8. Gudmundsson, L., Do, H. X., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM)-Part 2: Quality control, time-series indices and homogeneity assessment. Earth Syst Sci Data 10, 787–804 (2018).

9. Chen, X., Jiang, L., Luo, Y. & Liu, J. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022). Earth Syst Sci Data 15, 4463–4479 (2023).

How to cite: M. do Nascimento, T. V., Rudlang, J., Höge, M., van der Ent, R., Seibert, J., Hrachowitz, M., and Fenicia, F.: EStreams: Building an integrated dataset of streamflow, hydro-climatic variables and landscape attributes for catchments in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6641, https://doi.org/10.5194/egusphere-egu24-6641, 2024.

The field of hydroclimatology is witnessing a transformative era with the convergence of various technologies and methodologies aimed at enhancing research reproducibility and collaboration. Within this context, hydroclimatic datasets have emerged as fundamental tools for unraveling the interplay between climate and hydrology, resonating across geographical boundaries. Particularly, the exploration of large-scale datasets can shed light on hydrological differences and similarities across diverse catchments, serving both scientific and educational purposes. Efforts to enhance the availability of such datasets are ongoing globally, with the introduction of initiatives like CAMELS (catchment attributes and meteorology for large-sample studies). Despite this collective global effort to unravel hydroclimatic complexities, and the abundance of online hydrologic databases, valuable information remains fragmented and scattered across different platforms. Much local data is still presented and documented in languages other than English, impeding the transfer of knowledge between local and international communities. For example, a considerable portion of open hydrologic data provided by Swedish governmental authorities is solely accessible in Swedish, hindering its integration into pan-European or global research.

Therefore, we here introduce the community-accessible CAMELS-SE dataset, which covers 50 catchments in Sweden spanning a wide range of hydroclimatic, topographic and environmental catchment properties. The dataset includes daily hydroclimatic variables (precipitation, temperature, and streamflow) over a 60-year period (1961-2020), and information on geographical location, landcover, soil classes, hydrologic signatures, and regulation for each catchment. Data was collected from various sources, such as the Swedish Meteorological and Hydrological Institute (SMHI), the Swedish Geological Survey (SGU) and several Copernicus products provided by the European Environment Agency (EEA). The compiled, spatially-matched, and processed data is publicly available online through the Swedish National Data Service (https://snd.gu.se/en). CAMELS-SE adds a new region to the list of existing CAMELS datasets, offering a valuable resource for studying hydrological processes, climate dynamics, environmental impacts and sustainable water management strategies in Nordic regions.

How to cite: Teutschbein, C.: Introducing CAMELS-SE: Connecting 60 Years of Hydroclimatic Observations with Catchment Attributes for 50 Sites in Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15238, https://doi.org/10.5194/egusphere-egu24-15238, 2024.

Hydrologic observatories have been a cornerstone of hydrologic science for many decades, advancing hydrologic process understanding with focused field observations and targeted experiments. Observatories present our key opportunity for achieving great depth of hydrologic investigation, most often at the headwater catchment scale. We address two main aspects concerning hydrologic observatories in this contribution: (1) While reviews of individual hydrologic observatories and observatory networks exist, no study has investigated the diversity of observatories to understand whether common aspects increase the likelihood of scientific success. We synthesise information from 80 hydrologic observatories and conduct 25 interviews with observatory leads to fill this gap. We find that scientific outcomes are most enhanced by involving scientific and stakeholder communities throughout observatory inception, design, and operation; by enabling infrastructure to be adjustable to changing ideas and conditions; and by facilitating widespread data use for analysis. (2) While observatories are key for advancing local hypotheses, the transferability of knowledge gained locally to other places or scales has often been difficult or even remained elusive. Headwater catchments in particular show a wide range of process controls often only understood if viewed in a wider regional context of climatic, topographic, or other gradients. We therefore must place observatories into the wider tapestry of hydrologic variability, for example through comparison with large samples of catchments, even though significantly less information is available to characterise these diverse systems. We provide some thoughts on how this connection could be improved through digital infrastructure, mobile observational infrastructure and a renewed focus on gradients and contrasts of controlling processes. We believe that there is a significant opportunity to enhance transferrable knowledge creation in hydrology.

How to cite: Wagener, T., Coxon, G., Bloomfield, J. P., Buytaert, W., Fry, M., Hannah, D. M., Old, G., and Stein, L.: The value of hydrologic observatories for large sample hydrology and vice versa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8643, https://doi.org/10.5194/egusphere-egu24-8643, 2024.

CAMELS datasets are recognized in the hydrological community as consistent and comprehensive benchmark datasets for hydrological and meteorological analyses. CAMELS stands for "Catchment Attributes and MEteorology for Large-sample Studies”. CAMELS datasets link landscape and catchment attributes (e.g. land use, geology, soil properties), hydrological time series (e.g. water level, discharge) and meteorological time series (e.g. precipitation, air temperature) in a large number of catchment areas. They clearly indicate the uncertainties and processing of individual variables and thus enable the comparison of models and data in different landscapes, but also contribute to the general understanding of hydrological processes across landscapes. This is crucial for assessing the consequences of the climate crisis and improves the basis for water resource management decisions. Although CAMELS datasets are intensively used in other countries, such a dataset is still lacking for Germany.

This contribution highlights the crucial importance of consistent and easily accessible benchmark datasets for hydrological research and education. We discuss both the challenges faced so far in compiling the dataset and the future ambitions of the project. In addition, an overview is given of the scope of the first version of the CAMELS-DE data set, which will include around 2,000 measuring stations with daily time series of discharge and water level with an average length of nearly 50 years in mainly small and medium-sized catchments. Also included are the landscape and catchment attributes as well as meteorological time series. A key focus is on the easy availability and straightforward import of data into programming environments. We discuss how such benchmark datasets not only increase efficiency in the use of environmental data, but also play a key role in ensuring the reproducibility of research results. Especially in the age of machine learning learning, they form an indispensable basis for modern, data-driven hydrology. By integrating CAMELS-DE into the research landscape, we want to emphasize that data publications and benchmark datasets are much more than a by-product of a doctoral thesis, but rather the basis and key to modern environmental science.

How to cite: Dolich, A., Ebeling, P., Stölzle, M., Kiesel, J., Götte, J., Guse, B., Haßler, S., Mälicke, M., Tarasova, L., Heidbüchel, I., Hauffe, C., Müller-Thomy, H., and Loritz, R.: CAMELS-DE: Benchmark dataset for hydrology – significance, current status and outlook, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17667, https://doi.org/10.5194/egusphere-egu24-17667, 2024.

Hydrological methods based on the analysis of data from a large sample of catchments with different characteristics (large-sample hydrology; comparative hydrology) allow a comprehensive analysis of the hydrological regime and thus a description of hydrological variability and change in the components of the water balance. These methods provide insight into hydrological processes shaped by environmental and climatic factors and allow more general conclusions to be drawn. However, besides climate and runoff data, catchment attributes, such as geology, soils, topography and vegetation, are essential for effective hydrological behaviour analysis. For these reasons, the global hydrological community has recently developed a number of freely available large-scale datasets known as CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), which provide catchment attributes, as well as hydrological and meteorological time series, in a comparable structure at national scales. The aim of this contribution is to present the current state of preparation of the CAMELS database for Czechia (CAMELS-CZ) as a reference data platform for analysis and modelling, using a large sample of catchments.

The database contains 389 catchments in Czechia maintained by the Czech Hydrometeorological Institute (CHMI) for which daily runoff data are available for at least 30 years. Catchments cover a variety of elevations (200–1600 m a.s.l) and runoff regimes (from pluvial to nival). Climate attributes were calculated from newly created daily climate grids (mean daily precipitation, mean daily air temperature) available in spatial resolution 1 km. Vegetation attributes are calculated based on Landsat data and the Corine Land Cover database. Soil texture database, hydraulic soil characteristics and geology maps are used for soil and geology attributes calculation. The subset of the catchments included in the upcoming CAMELS-CZ database has already been used for several purposes, mostly in mountain areas to analyse changes in snow cover and their influence on both low and high flows. For this subset, simulations of the conceptual hydrological model have been performed and used. The future goal is to prepare runoff simulations for all catchments included in the CAMELS-CZ database which will be publicly available for use among the hydrological community.

How to cite: Jenicek, M., Tyl, R., Nedelcev, O., Ledvinka, O., Šercl, P., Bernsteinová, J., and Langhammer, J.: CAMELS-CZ: A catchment attribute database for hydrological and climatological studies using a large sample of catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3872, https://doi.org/10.5194/egusphere-egu24-3872, 2024.

Large-Sample Hydrology (LSH) plays a crucial role in understanding different hydrological processes, using large basin datasets as fundamental resources that allow researchers to explore multiple facets of hydrology (Addor et al. 2020). In recent years, multiple LSH datasets adapted to the national scale have been developed. We present BULL, a novel basin dataset for large-sample hydrological studies in Spain. BULL includes data from 503 watersheds, providing daily hydrometeorological time series (streamflow and climatic variables) and attributes related to basin characteristics. To collect these attributes, the recommendations included in the CARAVAN (Kratzert et al. 2023) initiative for the generation of a truly open global hydrological dataset have been followed. BULL covers the entire territory of Peninsular Spain, which is characterized by its wide climatic and hydrological variability, including catchments ranging from 100 km² to 2000 km². One of the main novelties of BULL to other national-scale datasets is the analysis of the hydrological alteration of the basins included in this dataset. The hydrological alteration is calculated by statistical comparison of the monthly flow values measured in the gauges and the flow values obtained from the Integrated System for Rainfall-Runoff Model (SIMPA) (Estrela and Quintas, 1996) developed by the Center for Hydrographic Studies (CEDEX), for the entire Spanish territory. This aspect is especially important in countries such as Spain, which is characterized as one of the countries in the world where rivers suffer from the highest levels of anthropization. The BULL dataset is made freely available to scientific users via the open-access repository Zenodo.

References:

Addor, N., Do, H.X., Alvarez-Garreton, C. et al. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges. Hydrological Sciences Journal 65, 712–725 (2020). https://doi.org/10.1080/02626667.2019.1683182

Estrela, T., Quintas, L., 1996. A distributed hydrological model for water resources assessment in large basins. Proceedings of 1st International Conference on Rivertech. Vol. 96, pp. 861–868.

Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). https://doi.org/10.1038/s41597-023-01975-w

How to cite: Senent-Aparicio, J., Castellanos-Osorio, G., Segura-Méndez, F., López-Ballesteros, A., Jimeno-Sáez, P., and Pérez-Sánchez, J.: Introducing the BULL Database – Spanish Basin attributes for Unraveling Learning in Large-sample hydrology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8081, https://doi.org/10.5194/egusphere-egu24-8081, 2024.

Large-sample hydrology aims to identify common patterns in the hydrologic behaviour of numerous catchments at the regional, continental and global scales. Large-sample datasets play a fundamental role in the context of large-sample hydrology as they collect data from multiple catchments and hydroclimatic variables.

This study focuses on the characterization of the water balance for 189 Spanish headwater catchments. The different ratios derived from the water balance equation will be calculated using multiple hydroclimatic datasets available for the Spanish domain for two consecutive and equally long periods: 1990-2005 and 2006-2020. Precipitation data will be extracted from a gridded dataset at 0.05º resolution from the Spanish Meteorological Agency (AEMET). Streamflow time series will be provided by the Spanish Center for Public Work and Experimentation (CEDEX). Evaporation data will be gathered from the Global Land Evaporation Amsterdam Model (GLEAM) versions 3.7a and 3.7b.

The results of this work will highlight the potential of using large-sample datasets to characterize the water balance for the Spanish catchments and will reveal key changes in their hydrologic behaviour during the last three decades.

ACKNOWLEDGMENTS: This study has been funded by a Humboldt Research Fellowship from the Alexander von Humboldt Foundation.

How to cite: Yeste, P. and Bronstert, A.: Large-sample evaluation of the water balance for the Spanish catchments using multiple hydroclimatic datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8309, https://doi.org/10.5194/egusphere-egu24-8309, 2024.

In recent years, various national databases of geomorphoclimatic watershed attributes have been released. Relevant examples are the CAMELS datasets for countries such as the United States, Australia, Chile, Brazil, Switzerland, France, Germany, and the United Kingdom (now integrated into Caravan), and LamaH-CE. 

This work introduces FOCA (Italian FlOod and Catchment Atlas), a national-scale collection of 631 Italian basins that we fully characterized by providing more than 100 attributes related to geomorphology, soil, land cover, NDVI, climate, and extreme precipitation. The basins reported in FOCA are derived from a national-scale inventory of peak floods and annual maximum daily floods named "Catalogo delle Piene dei Corsi d'acqua Italiani", realized thanks to a data rescue initiative performed by merging recent data, already available in digital format, with historical information available on printed documents.

The selection of descriptors that we included in FOCA followed three main criteria: a) national spatial coverage; b) absence of regional or local distortions; c) adequate spatial resolution. Preference was given to local sources, resorting to global data only in specific cases. The inclusion of basin boundaries will allow users to assess additional descriptors using their models or datasets.

FOCA stands out from other national datasets due to its robust collection of geomorphological descriptors, computed using the r.basin algorithm of GRASS GIS and subjected to thorough quality controls. Another distinctive feature is the incorporation of extreme rainfall characteristics, evaluated using station data instead of reanalysis data — deviating from the approach often seen in the development of CAMELS datasets. For this purpose, the Improved Italian - Rainfall Extreme Dataset (I²-RED) has been used. I²-RED is a national collection of rainfall extremes measured by more than 5000 rain gauges from 1916 up to the present that was developed as the outcome of a data rescue project.

With this nationwide data collection, a wide range of environmental applications, with particular reference to flood studies, can now be undertaken on the Italian territory.

How to cite: Claps, P., Evangelista, G., Ganora, D., Mazzoglio, P., and Monforte, I.: FOCA: a new quality-controlled collection of floods and catchment attributes in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12459, https://doi.org/10.5194/egusphere-egu24-12459, 2024.

Catchments descriptors are widely used in hydrological science to infer dominant hydrological processes, identify, and transfer information across catchments and scales. However, persistent use of descriptors aggregated as spatially-lumped values (i.e., catchment averages), without considering their spatial variability within catchments might hamper the efficiency of these tasks. In this study, we use interpretable machine learning to investigate the value of topographically enhanced catchment descriptors (i.e., weighting them using distance to outlet, distance and height to the nearest drainage and stream order) belonging to seven distinct categories (i.e., climate, topography, land use, geology, hydrogeology, soil physical properties, and soil water properties) for predicting mean values, variability and seasonality characteristics of hydrological droughts and runoff events occurred in 401 German catchments in the period 1979-2002.

We found that the spatially-differentiated catchment descriptors aggregated with topographical enhancing are able to predict droughts and runoff events characteristics more accurately than the lumped descriptors. The improvement is particularly promising for prediction of runoff event characteristics. Particularly, descriptors aggregated using height above the nearest drainage and stream order are essential for accurate prediction of variability of runoff events characteristics, while the proximity to the stream and to the outlet are more relevant for predicting their seasonality. In case of droughts, the descriptors weighted by the proximity to the stream improve the predictions of the variability and seasonality of duration and severity (i.e., deficit volume) of hydrological droughts. Moreover, we show that spatially-differentiated aggregation has the potential to identify the importance of descriptors that appeared irrelevant when aggregated in lumped way, particularly shading a light on the role of mean annual potential evapotranspiration and forest land cover descriptors for the prediction of mean values and seasonality of time scale of runoff events, and the role of groundwater yield and wetland land cover to predict the variability of time rise of runoff events. Our study highlights that development of the methods for spatially-differentiated aggregation has potential to disentangle the effects of different physio-geographical controls on event response in different catchments and to improve its predictability in ungauged locations.

How to cite: Ziani, C., Ribbe, L., Aala, S., and Tarasova, L.: The value of spatially-differentiated  catchments descriptors for predicting characteristics of hydrological events in German catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8259, https://doi.org/10.5194/egusphere-egu24-8259, 2024.

Traditionally, hydrological models are applied to one or a few catchments because preparation of the input and calibration data for a more extensive set of catchments is challenging. The availability of data sets with hydrometeorological time series for large numbers of catchments has been a game changer in hydrological catchment modeling in recent years. One example are the CAMELS data sets with the basic data to run hydrological models for several hundreds of catchments in various countries. In several recent studies, we have used these data sets for bucket-type modeling of a large number of catchments in different regions. In this presentation, I will discuss some of our main findings:

• Variability of results: Simulation results vary considerably between catchments, making it pertinent to apply a model to a large number of catchments for robust results.
• Uncalibrated model performance: Simple bucket-type models can provide surprisingly good results for some catchments even when not calibrated. This needs to be considered when we assess model performances.
• Prediction in ungauged catchments: It can be challenging to improve simulations for ungauged catchments by regionalization as it is not obvious how to choose the most suitable donor catchments. Thanks to data sets with a vast number of potential donor catchments, we found that almost perfect donor catchments seem to exist in most cases. However, the challenge remains to identify them.
• Model structure: For some catchments, a simplified soil routine with only one free parameter (instead of three) outperformed the standard model version.
• Value of data: Large samples of catchments allow us to evaluate the value of different data types: a limited number of streamflow gaugings and other data types, such as stream level, stream width or water level class data, can be informative for streamflow simulations.

How to cite: Seibert, J., van Meerveld, I., Vis, M., Clerc-Schwarzenbach, F., and Pool, S.: A few hundred catchments later – lessons learned from modeling large catchment samples, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14923, https://doi.org/10.5194/egusphere-egu24-14923, 2024.

Previous studies have shown that information on the discharge dynamics (e.g., variation in the water level) is valuable to constrain the parameters of a lumped hydrological model for some catchments, and that information on the discharge volume further improves model performance for most catchments. It has been suggested that for some catchments an estimate of the mean discharge already leads to a good model fit but so far, there have not been any systematic studies to test this. Therefore, it remains unclear for which catchments (i.e., for which regions or for catchments with specific characteristics) information on the discharge dynamics are most valuable for model calibration, for which catchments an estimate of the mean annual discharge is already sufficient, and for which catchments both data sources are needed for model calibration. Therefore, we used a subset of the Caravan large-sample dataset and assessed the value of water level measurements, estimates of the mean discharge, and both data sources together for the calibration of a simple bucket-type hydrological model. Preliminary results suggest that mainly climatic characteristics determine the relative value of the different data types for hydrological model calibration. This type of assessment of the value of data for a wide range of catchments allows for more optimal allocation of resources when it comes to obtaining limited data for the calibration of hydrological models for ungauged catchments.

How to cite: Clerc-Schwarzenbach, F., van Meerveld, I., Vis, M., and Seibert, J.: What is more important for model calibration: information on the discharge dynamics or information on the discharge volume?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-164, https://doi.org/10.5194/egusphere-egu24-164, 2024.

The question of what makes two catchments hydrologically similar is of fundamental importance for the understanding of catchment hydrology and for transferring hydrological information from gauged to ungauged catchments. In the regionalisation of rainfall-runoff model parameters, the definition of a similarity measure for identifying the donors basins as a function of catchment characteristics is an essential step for the most consolidated techniques. As demonstrated by a very large number of studies in the literature, conducted all around the world, the main controls of catchment similarity may change subtsantially across different hydroclimatic regions.

The recent availability of large-sample catchment dataset for rainfall-runoff studies in different hydroclimatic regions across the globe allows scientists to conduct comparative experiments for enhancing our knowledge about the factors that shape hydrological processes, including catchment similarity and regionalisation.

The aim of this study is to test how hydroclimatic characteristics in different regions of the world influence the main factors that control catchment similarity when regionalising rainfall-runoff model parameters, using a homogenised modeling protocol.

Two conceptually different bucket-type rainfall-runoff models are calibrated on more CAMELS-type large samples of catchments all around the world, characterised by different hydroclimates and data availability (i.e. streamgauge density). For each regional sample and for each model, one of the most consolidated parameter regionalisation approaches, based on the choice of a set of “most similar” donor catchments and on the transfer of the entire sets of model parameters from the donors to the target catchment, is applied in jack-knife cross-validation. Naturally, in such approach the choice of the donors (and therefore the regionalised model parameters) strictly depends on the catchment descriptors used to define the similarity measure between target and gauged basins.

Assuming that the higher is the similarity of the donors to the target catchment, the better is the model performance, the idea of the work is to assess which catchment features better represent similarity for the transfer of model parameters in each of the regional samples. In particular, it is interesting to analyse if and how such features change across different hydroclimates. In order to reach such goal, the regionalisation technique is implemented by including different typologies and combinations of climatic and/or morphological characteristics when defining similarity, therefore obtaining different donors and different regionalised model performances. The findings achieved in the different large samples are compared, mainly focusing on how the set of basin descriptors bringing to the best model performances varies across the different hydroclimatic regions.

How to cite: Neri, M. and Toth, E.: Exploring the controls of catchment similarity for the transfer of rainfall-runoff model parameters: a comparative study in different large-sample datasets around the globe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12411, https://doi.org/10.5194/egusphere-egu24-12411, 2024.

Choosing a suitable model and determining the best calibration method are complex processes. These can be simplified by comparing uncalibrated models and analyzing modeling results based on catchment characteristics. The amalgamation of these two stages forms "informing water systems analysis." This study examines the application of the Water Systems Integrated Modelling framework (WSIMOD), which is a comprehensive water systems model applied previously for catchments in the UK, and the Soil and Water Assessment Tool (SWAT), a commonly used hydrological model in India. The comparison is conducted using a catchment classification scheme based on physiography. This approach establishes a connection between the catchment characteristics and the model performances, providing valuable insights for the analysis of water systems. WSIMOD demonstrates superior performance compared to SWAT in its out-of-the-box configuration, particularly when simulating average flows. WSIMOD necessitates a greater amount of data preparation compared to SWAT, but it involves a less complex calibration process. The performance of SWAT is highly dependent on the characteristics of each catchment, necessitating the use of multi-site calibration. WSIMOD's performance is not significantly influenced by catchment characteristics, enabling regions within the same agro-ecological zone to share identical parameter values. The catchment classification analysis indicated that to enhance the accuracy of the SWAT model, it is necessary to select topography, precipitation, and soil parameters for calibration. Additionally, the infiltration rate and residence times of water should be further refined to improve the WSIMOD model. This proposed methodology facilitates and simplifies the processes of model selection and calibration.

How to cite: Srivastava, S., Liu, L., Wadhwa, A., Reghunath, G., Budamala, V., Dobson, B., Kumar Dasika, N., and Mijic, A.: Catchment Classification-Based Comparison of Hydrological Models to Inform Water Systems Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3850, https://doi.org/10.5194/egusphere-egu24-3850, 2024.

Accurate meteorological forcings are a fundamental component for reliable hydrological modelling. Gridded meteorological products offer spatially distributed information facilitating hydrological model applications. In addition, they are often available at large scale (e.g. regional or continental scale), easing the application on large samples of basins, and generally enhancing the replicability of the experiments. Nevertheless, the accuracy of these products varies, and it must be rigorously assessed to ensure the validity of model simulations.

This study aims to evaluate the accuracy of four gridded meteorological products: three based on ground observations (E-OBS, SCIA, and ARCIS) and one reanalysis (ERA5-Land), across a large sample of over 150 catchments in three administrative regions of Northern Italy. To assess their reliability, we adopt an indirect evaluation method. This involves assessing the performance of a conceptual hydrological model, which is forced with each of the four gridded meteorological products, over the selected catchments.

The E-OBS dataset, developed by the ECA&D project, offers climatic variables at a 0.1° x 0.1° (~11 x 11 km) resolution from 1950 onwards across Europe. ERA5-Land is a global scale reanalysis dataset from ECMWF which provides data at a 9 x 9 km resolution from 1950. Finally, ARCIS (Pavan et al., 2019) and SCIA (Desiato et al., 2007) datasets are Italian meteorological products, respectively at 5 x 5 and 10 x 10 km spatial resolution, starting from 1961.

For the study catchments, four distinct meteorological forcings, including the daily time series of areal mean precipitation, temperature, and potential evapotranspiration, were estimated using each of the four gridded products. Daily streamflow data were collected from three different regional agencies managing hydroclimatic data and were manually validated.

The rainfall-runoff model used for the indirect validation is the CemaNeige-GR6J (Coron et al., 2023), a daily lumped and continuously simulating model. We investigate the performances of the model in simulating streamflow, in order to get insights on the reliability of the gridded products across the region and along the years.

Model performances are also analysed against catchment features (such as orography and presence of upstream reservoirs) and data set characteristics (such as gauge network density) to investigate whether certain conditions influence the representativeness of the gridded products and the corresponding streamflow simulations, enhancing our understanding of their applicability and limitations. 

References

Coron, L., Delaigue, O., Thirel, G., Dorchies, D., Perrin, C. and Michel, C. (2023). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.7.4, doi: 10.15454/EX11NA, URL: https://CRAN.R-project.org/package=airGR.

Desiato, F., Lena, F., & Toreti, A. (2007). SCIA: a system for a better knowledge of the Italian climate. Bollettino di Geofisica Teorica ed Applicata, 48(3), 351-358.

Pavan, V., Antolini, G., Barbiero, R., Berni, N., Brunier, F., Cacciamani, C., ... & Torrigiani Malaspina, T. (2019). High resolution climate precipitation analysis for north-central Italy, 1961–2015. Climate Dynamics, 52, 3435-3453.

How to cite: Sarigil, G., Neri, M., and Toth, E.: An Indirect Validation of National and International Gridded Precipitation Products in Northern Italy through Rainfall-Runoff Model Application, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6800, https://doi.org/10.5194/egusphere-egu24-6800, 2024.