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We present CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland), a large-sample 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 diversity 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: https://doi.org/10.5281/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".

Discharge data used to calibrate and evaluate hydrological models can be highly uncertain and this uncertainty affects the conclusions that we can draw from modelling results. We investigated the role of discharge data uncertainty and its representation in hydrological model calibration to give recommendations on methods to account for data uncertainty. We tested five different representations of discharge data uncertainty in calibrating the HBV-model for three Swiss catchments, ranging from using no information to using full empirical probability distributions for each time step. We developed a new objective function to include discharge data uncertainty, as quantified by these distributions directly in calibration to hydrological time series. This new objective function provided more reliable results than using no data uncertainty or multiple realizations of discharge time series. We recommend using the new objective function in combination with empirical or triangular distributions of the discharge data uncertainty.

Mountainous regions are globally important as they support large populations, are biodiverse and are characterised by enhanced vulnerability to anthropogenic pressures and sensitivity to climate change. This importance necessitates the development of a global reference network of long-term environmental and socio-economic monitoring – mountain observatories. At present, monitoring is limited and unevenly distributed across mountain regions, globally. Existing thematic networks do not fully support the generation of multi-disciplinary knowledge required to inform decisions, enact drivers of sustainable development and safeguard against losses. In this paper, the Mountain Observatories Working Group, established by the Mountain Research Initiative (MRI) Science Leadership Council, identifies geographical and thematic gaps as well as recent advances in monitoring of relevant biophysical and socio-economic variables in the mountains. We propose principles and ways of connecting existing initiatives, supporting emerging areas and developing new mountain observatories networks regionally and, eventually, globally. Particularly in the data-poor regions, we aspire to build a community of researchers and practitioners in collaboration with the Global Network on Observations and Information in Mountain Environments, GEO Mountains, a GEO Work Programme Initiative.

openaire: EC/H2020/819202/EU//SOS.aquaterra Funding Information: M.K. received financial support from the Academy of Finland projects WASCO (grant 305471), WATVUL (grant 317320), and TREFORM (grant 339834); the Academy of Finland SRC project ?Winland?; the Emil Aaltonen Foundation project ?eat-less-water?; and the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement 819202). M.H. and M.T. received financial support from Maa- ja Vesitekniikan Tuki Ry. M.H. was also supported by the Aalto University Engineering doctoral program. We appreciate the help of Johannes Piipponen with livestock production data. M.K. D.V. and M.H. designed the research with support from all co-authors. M.K. and D.V. compiled the Holdridge life zone mapping. M.H. and M.K. performed the spatial analyses with support from D.V. M.K. led the writing of the manuscript with contributions from all co-authors. We declare no competing financial interests. Funding Information: M.K. received financial support from the Academy of Finland projects WASCO (grant 305471 ), WATVUL (grant 317320 ), and TREFORM (grant 339834 ); the Academy of Finland SRC project “Winland”; the Emil Aaltonen Foundation project “eat-less-water”; and the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement 819202 ). M.H. and M.T. received financial support from Maa- ja Vesitekniikan Tuki Ry . M.H. was also supported by the Aalto University Engineering doctoral program. We appreciate the help of Johannes Piipponen with livestock production data. Publisher Copyright: © 2021 The Authors Food production on our planet is dominantly based on agricultural practices developed during stable Holocene climatic conditions. Although it is widely accepted that climate change perturbs these conditions, no systematic understanding exists on where and how the major risks for entering unprecedented conditions may occur. Here, we address this gap by introducing the concept of safe climatic space (SCS), which incorporates the decisive climatic factors of agricultural production: precipitation, temperature, and aridity. We show that a rapid and unhalted growth of greenhouse gas emissions (SSP5–8.5) could force 31% of the global food crop and 34% of livestock production beyond the SCS by 2081–2100. The most vulnerable areas are South and Southeast Asia and Africa's Sudano-Sahelian Zone, which have low resilience to cope with these changes. Our results underpin the importance of committing to a low-emissions scenario (SSP1–2.6), whereupon the extent of food production facing unprecedented conditions would be a fraction.