ORCID Profile
0000-0002-6155-837X
Current Organisations
University of Bern
,
Universitat Bern
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Publisher: Copernicus GmbH
Date: 15-06-2023
Abstract: Abstract. We present CAMELS-CH (Catchment Attributes and MEteorology for large-s le Studies - Switzerland), a large-s le hydro-meteorological data set for hydrological Switzerland in Central Europe. This domain covers 331 basins within Switzerland and neighboring countries. About one third of the catchments are located in Austria, France, Germany and Italy. As an Alpine country, Switzerland covers a vast ersity of landscapes, including mountainous environments, karstic regions, and several strongly cultivated regions, along with a wide range of hydrological regimes, i.e. catchments that are glacier-, snow- or rain-dominated. Similar to existing data sets, CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes. CAMELS-CH (Höge et al., 2023, available at: 0.5281/zenodo.7957061) encompasses 40 years of data between 1st January 1981 and 31st December 2020, including daily time series of stream flow and water levels, and of meteorological data such as precipitation and air temperature. It also includes daily snow water equivalent data for each catchment starting from 2nd September 1998. Additionally, we provide annual time series of land cover change and glacier evolution per catchment. The static catchment attributes cover location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers. This Swiss data set complements comparable publicly accessible data sets, providing data from the "water tower of Europe".
Publisher: Copernicus GmbH
Date: 02-09-2022
DOI: 10.5194/NHESS-22-2891-2022
Abstract: Abstract. Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from in idual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping.
Publisher: Copernicus GmbH
Date: 22-03-2022
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-9859
Abstract: & & Over recent years, numerous open catchment datasets have been published.& In 2017, the first CAMELS (catchment attributes and meteorology for large-s le 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.& & & & & & & & & & strong& References& /strong& & & & & Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-s le 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 s le studies-Chile dataset, Hydrology and Earth System Sciences, 22, 5817& #8211 , 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& #8211 , 2020& & & & Newman, A., Clark, M., S son, K., Wood, A., Hay, L., Bock, A., Viger, R., Blodgett, D., Brekke, L., Arnold, J.: Development of a large-s le 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& & & & & & &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1384
Abstract: Rare to very rare floods (associated to return periods of 1'000& #8211 '000 years) can cause extensive human and economic damage. Still, their estimation is limited by the comparatively short streamflow records available. Some of the limitations of commonly used estimation methods can be avoided by using continuous simulation (CS), which considers many simulated meteorological configurations and a conceptual representation of hydrological processes. CS also avoids assumptions about antecedent conditions and their spatial patterns.We present an implementation of CS to estimate rare and very rare floods at multiple sites in a large river basin (19 locations in the Aare River basin, Switzerland area: 17'700& #8201 km& #178 ), using exceedingly long simulations from a hydrometeorological model chain (Viviroli et al., 2022). The model chain consisted of three components: First, the multi-site stochastic weather generator GWEX provided 30 meteorological scenarios (precipitation and temperature) spanning 10'000 years each. Second, these weather generator simulations were used as input for the bucket-type hydrological model HBV, run at an hourly time step for 80 catchments covering the entire Aare River basin. Third, runoff simulations from the in idual catchments were routed for a representation of the entire Aare River system using the routing system model RS Minerve, including a simplified representation of main river channels, major lakes and relevant floodplains. The final simulation outputs spanned about 300'000 years at hourly resolution and cover the Aare River outlet, critical points further upstream as well as the outlets of the hydrological catchments. The comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations were well represented. This implied that meaningful information on floods with low probability can be inferred. Although uncertainties were still considerable, the explicit consideration of important flood generating processes (snow accumulation, snowmelt, soil moisture storage) and routing (bank overflow, lake regulation, lake and floodplain retention) was a substantial advantage compared to common extrapolation of streamflow records.The suggested approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is particularly valuable in a large river basin where the complex interaction of flows from in idual tributaries and lake regulations are typically not well represented in the streamflow records. The framework is also suitable for estimating more common, i.e., more frequently occurring floods.ReferenceViviroli D, Sikorska-Senoner AE, Evin G, Staudinger M, Kauzlaric M, Chardon J, Favre AC, Hingray B, Nicolet G, Raynaud D, Seibert J, Weingartner R, Whealton C, 2022. Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin. Natural Hazards and Earth System Sciences, 22(9), 2891& #8211 , doi:10.5194/nhess-22-2891-2022
Publisher: Copernicus GmbH
Date: 22-03-2022
Abstract: Abstract. Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e., return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realisations of hourly precipitation and temperature scenarios of 10 000 years each. These realisations were then run through a bucket-type hydrological model for 79 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented, and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows to comprehensively explore possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from in idual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping.
No related grants have been discovered for Martina Kauzlaric.