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. Caravan (as in “a series of camels”) is an initiative that tries to solve both of these problems by creating an open data processing environment in the cloud for the community to use.

Caravan is a globally consistent and open dataset

Caravan leverages globally available data sources that are published under an open license to derive meteorological forcings and attributes for any catchment. We use ERA5-Land for meteorological forcings and hydrological reference states (SWE and four levels of soil moisture) and HydroATLAS for the catchment attributes. Currently, Caravan consists of 6830 gauges with daily streamflow data (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.

Caravan is derived entirely in the cloud

All meteorological time series (and hydrological reference states) from ERA5-Land are processed on Google Earth Engine, which removes the burden of downloading and processing large amounts of raw gridded data. Similarly, all catchment attributes are computed on Earth Engine. The code used to derive Caravan is publicly available (https://github.com/kratzert/Caravan/) . Once you have streamflow records and the corresponding catchment polygons, deriving all other data (forcing data and attributes) is a matter of a few hours of actual work. Depending on the number of catchments, their size and spatial distribution, that are being processed at once on Earth Engine , it might take a day or two for Earth Engine to extract meteorological data and catchment attributes. 

Most importantly: Caravan is a community project

Even though the existing data in Caravan has good coverage over most climate zones, the spatial coverage is still patchy. Here is where we see Caravan as a community effort. Given the provided code, everybody with access to streamflow data and the authorisation to redistribute it can create a Caravan extension with minimal effort and share the extension with the community, thus contributing to a dynamically growing dataset. A full step-by-step tutorial is available at https://github.com/kratzert/Caravan/wiki. We envision that, with many people participating, this will result in a truly global and spatially consistent, large-sample hydrology dataset. A first Caravan extension was already published by Julian Koch (https://zenodo.org/record/7396466), which increased the number of gauges to 7138, by adding 308 gauges in Denmark.

How to cite: Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan - A global community dataset for large-sample hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5256, https://doi.org/10.5194/egusphere-egu23-5256, 2023.

The recent publication of large-sample datasets for hydrologic modeling and analysis has led to a revival of comparative hydrology. The “CAMELS” branch of these datasets currently provide catchment attributes and meteorological time series for basins located in the United States, Chile, Brazil, Australia and Great-Britain, with a dataset for France under development. A key characteristic of these datasets is that information is provided as catchment-averaged data; i.e. each catchment is treated as a lumped entity with no spatial variability. Some progress is being made to extend large-sample hydrology to include spatially distributed data, most notably by the recent LamaH dataset which covers part of Central Europe.

Here we present progress on developing a continental domain dataset for large-sample hydrology intended for spatially distributed modeling and analysis. Our domain covers the United States and Canada, expanding both geographically and climatically on the region covered by the LamaH dataset. We focus mostly on relatively undisturbed headwater catchments, because accurate data on water management policies and infrastructure can be difficult to obtain. Our aim is to provide the necessary data for process-based modeling and analysis at a sub-daily temporal resolution. 

How to cite: Knoben, W. and Clark, M.: CAMELS-spat: catchment data for spatially distributed large-sample hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2650, https://doi.org/10.5194/egusphere-egu23-2650, 2023.

Comparative hydrology has been found to deepen our understanding of hydrological processes in catchments and helps to improve the proper evaluation of hydrological models. Recently, the global hydrological community has developed a series of publicly available, large-scale  „CAMELS“-datasets that provide catchment attributes and meteorological time series of catchments on a national level. These datasets include catchment-averaged values of catchment characteristics and meteorological time series and therefore allow only lumped modeling. In this study, we introduce a new dataset "CAMELS-SAX" for large-sample studies in the region of Saxony (Germany), which has a high diversity and heterogeneity of catchment attributes, such as geology and land use. "CAMELS-SAX" consists of meteorological and hydrological time series covering 60 years of data on a daily timestep for more than 200 catchments. The dataset includes spatially distributed catchment attributes and covers an area of about 23.000 km² with undisturbed and anthropogenic-influenced catchments ranging from 1 km² up to 5.000 km², which can be used for spatially distributed modelling. We will provide the standardized dataset for the German Federal State of Saxony for studies evaluating distributed models' performance on a smaller spatial scale. In the presentation, we show an overview of catchment attributes, time series, and hydrological signatures for the subset of undisturbed catchments. In addition, we present the results of a sensitivity analysis of the hydrological behavior caused by climate change.

How to cite: Hauffe, C., Brandes, C., Lei, K., Pahner, S., Körner, P., Kronenberg, R., and Schuetze, N.: CAMELS-SAX: A meteorological and hydrological dataset for spatially distributed modeling of catchments in Saxony, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14357, https://doi.org/10.5194/egusphere-egu23-14357, 2023.

The classification of river catchments has been an active field of study for decades and the recent surge in hydrological and environmental datasets promotes the formulation of new approaches to this endeavor. We present a novel method for catchment classification based on physical traits similarity using network science, where the relationship among the catchments is represented by the edges of a network. Under this framework we leverage the capability of networks to capture collective behaviors to find clusters of catchments with similar physical traits. The use of networks allows the adoption of similarity metrics other than the common euclidean distance, which is subjected to quality degradation in high dimensions but is still required in many traditional clustering algorithms. Also, a network of traits is built to investigate their similarity patterns and condense this information into a small number of interpretable traits categories. Such categories are used to provide a characterization of each cluster of catchments. The method has been tested on over 9000 river catchments across the contiguous United States, each one accompanied by traits such as climate or vegetation coverage, and anthropogenic features such as land use or proximity to developed areas. The resulting classification shows a remarkable geographical coherence supported by the characteristic traits categories. Additionally, we find that when hydrological indices (like statistics on streamflow or water temperature) are aggregated according to the clusters of catchments, different clusters show different hydrologic behaviors. This, along with the information from cluster characterization, allows us to establish a connection between hydrological behaviors and physical traits. Finally, this framework can be applied at multiple scales, from continental to regional. When tested on a regional scale, the method automatically modifies the network topology to reflect the traits patterns relevant to the area under investigation.

How to cite: Ciulla, F. and Varadharajan, C.: Interpretable Unsupervised Classification of River Catchments with Network Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10344, https://doi.org/10.5194/egusphere-egu23-10344, 2023.

Hydrological extreme events are generated by different sequences of hydro-meteorological drivers, the importance of which may vary within the sample of drought events and in space and time. Here, we investigate how the importance of different hydro-meteorological driver sequences varies by event magnitude, in space, and in time using large samples of catchments in Europe and the Alps. To do so, we develop an automated classification scheme for streamflow drought events, which assigns events to one of eight drought event types - each characterized by a set of single or compounding drivers. Our results show that (1) moderate droughts are mainly driven by rainfall deficits while severe events are mainly driven by snowmelt deficits; (2) rainfall deficit droughts and cold snow season droughts are the dominant drought event type in Western Europe and in Eastern and Northern Europe, respectively; (3) temporal changes in both drought intensity, deficit, and duration and generation processes are stronger in high- than in low-elevation catchments; and (4) in high-elevation catchments, snowmelt-deficit-induced droughts become more frequent, leading to increases in drought deficits. We conclude that climate impact assessments on droughts can profit from assessing changes in drought generation processes to improve the understanding of how drought magnitudes are changing in a warming world.

How to cite: Van Loon, A., Brunner, M., and Götte, J.: Spatial variability and temporal changes of drought generation processes over Europe and the Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17151, https://doi.org/10.5194/egusphere-egu23-17151, 2023.

Land surface temperature (LST) is a crucial state variable determining the interactions between the land surface and the atmosphere (i.e., energy, water, and carbon fluxes). Accordingly, several hydrological quantities, such as soil moisture content, vegetation water stress, gross primary production, and crop yield, correlate strongly with it. Thus, LST constitutes a critical variable in understanding the physics of multiple land surface processes. Decades of global satellite remotely sensed fields are now available, creating an unprecedented opportunity to understand better the LST spatiotemporal variability by diagnosing its spatial and temporal persistence, deriving spatial and temporal correlation lengths, identifying areas with similar spatiotemporal patterns, and determining the physical factors influencing this variability from regional to global scales. This presentation will address this gap in understanding by comprehensively analyzing the spatiotemporal variability of LST globally. Preliminary work regarding this topic has been performed using the

As part of our evaluation, we will first derive the Empirical Spatio-Temporal Covariance Functions (ESTCFs) for the global ~5x5 km Copernicus LST hourly product. A 1x1-arcdegree moving window will be defined over the globe to compute the ESTCFs, and an hourly time step between 2010 and 2022 will be used for the analysis. The analysis will focus exclusively on the daytime of summer months because spatial heterogeneity of LST will play the most significant role in summertime (e.g., daytime summer convection). To summarize the obtained ESTCFs, a parametric spatiotemporal covariance function model will be fit to each 1x1-arcdegree ESTCF. From this parametric fit, we will evaluate the persistence of the patterns, analyze the spatial and temporal correlation lengths, and evaluate the space-time interaction displayed for different locations. Additionally, clustering analysis will be applied directly to the derived parametric covariance functions to identify functionally similar areas. Finally, we will compare the derived empirical covariance functions to well-known factors spatiotemporal influencing LST variabilities such as land cover, surface thermal properties, topography, incoming solar radiation, and meteorological conditions.

How to cite: Torres-Rojas, L. and Chaney, N. W.: A comprehensive global analysis of the spatiotemporal variability of Land Surface Temperature, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10031, https://doi.org/10.5194/egusphere-egu23-10031, 2023.

Due to the impact of changing climate and human activities on hydrology, non-stationary research is becoming more popular. In the Wei River Basin, which is the largest tributary of the Yellow River, non-stationary has been studied for decades and has found non-stationary signals in discharge and precipitation records. However, these studies have mainly focused on the annual time series and ignored the seasonal signal.

In this study, to investigate the non-stationarity more comprehensively, two non-stationary tests have been applied including the Mann-Kendall test and the Heuristic segmentation algorithm. These tests were applied to runoff time series from 12 catchments and catchment averaged precipitation and temperature time series derived from 114 meteorological stations. Like other studies, our results, show that on the annual timescale, non-stationary signals (multiple change points and decreasing trends) are found in the runoff time series on most catchments along the mainstem, while the runoff time series of the Beiluo catchment does not show any non-stationarity signal. However, our results show that there is clearly a seasonal difference with change points occurring at contrasting times. Among all time series, about 40% show only single nonstationary signals (trend or change point), while the remainder exhibit multiple signals indicating the importance of using multiple tests. While the results show that non-stationary signals exist in all time series, further work is needed to quantify if or to what level are meteorological variables the driver of non-stationary signals in the runoff time series.  

How to cite: Yuan, X. and O'Loughlin, F.: Evaluating the seasonal non-stationarity in the Wei River Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1404, https://doi.org/10.5194/egusphere-egu23-1404, 2023.

When they are used for operational forecasting, hydrological models are almost always combined with some kind of updating procedures. Then a question arises: should the model parameters be calibrated with or without the updating procedures? Calibrating with the updating procedures often improves forecast efficiency, but it can also lead to parameter inconsistency and ultimately to a drop in performance in some cases.

In this study, we evaluate the pros and cons of making the parameters of a flood forecasting model vary with lead times. We investigate the dependencies of the model parameters to the lead times and determine where and when this procedure significantly improves forecast quality. A modified version of the GR5H hydrological model is used on 229 French catchments where 10,652 events were selected. The model is run at the hourly time step and combined with a simple updating procedure to produce forecasts at four lead times. The model parameters were estimated from a large screening of the parameter space (3 million runs for each catchment). Results show that the parameters related to fast catchment processes are the most dependant on lead times, indicating the need for more specific parameter estimation methods when modelling catchments prone to flash floods.

How to cite: Astagneau, P. C., Bourgin, F., Andréassian, V., and Perrin, C.: Estimating the parameters of a flood forecasting model: with or without updating procedures?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5492, https://doi.org/10.5194/egusphere-egu23-5492, 2023.

Large-scale hydrological modeling has gained a wealth of attention in the last decades, due to the importance of assessing the growing anthropogenic and climate change impacts on water resources. In the context of these studies, the Alpine Region has historically played a key role, being widely recognized as “Europe’s water tower” and given the complex combination of anthropogenic and climatic drivers influencing its hydrology. The application of hydrological modeling at the synoptic scale requires an accurate assessment of the climatic forcing, chiefly precipitation and temperature. Nowadays, a number of observation-derived gridded products providing precipitation and temperature over a regular grid are available to benchmark and support large-scale analyses. However, these products are often not tailored to potential hydrological applications and are based on data with different and often uncertain levels of accuracy and resolution. In this context, assessing the uncertainty due to the climatic forcing and its relationship with the hydrological response of different catchments becomes crucial in order to gain confidence in the simulations. In the present study, we analyze the ability of several gridded datasets (which are best suited to large-scale analyses) to reproduce observed streamflows of more than 200 reaches across the Italian Alps. The simulations have been conducted by feeding HYPERstreamHS, a distributed hydrological model specifically tailored for large-scale simulations, with the following gridded meteorological datasets: MESAN, COSMO reanalysis, APGD, MSWEP, E-OBS, MESCAN, and ERA5-Land. Hydrological coherence was first evaluated by means of the NSE and KGE efficiency indexes. Then, we attempted to break down the main drivers of hydrological coherence by classifying the analyzed catchments based on hydrological and geomorphological characteristics, and by analyzing the relative incidence on the uncertainty of temperature and precipitation, by means of ANOVA.

How to cite: Galletti, A., Avesani, D., Bellin, A., and Majone, B.: Analysis and attribution of the hydrological coherence of gridded precipitation and temperature datasets in the Italian Alpine Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15001, https://doi.org/10.5194/egusphere-egu23-15001, 2023.