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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 hydrologic 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.7784632) encompasses 40 years of data between 1 January 1981 and 31 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 2 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”.

Die Hydrologie stellt für Wasserbauprojekte eine zentrale Dimensionierungsgrundlage dar. Für die Abschätzung von Hochwasserabflüssen definierter Jährlichkeit (HQx) und Hochwasservolumina werden in der Schweiz verschiedene Methoden und Verfahren angewendet. Eine Arbeitsgruppe der KOHS hat sich mit finanzieller Unterstützung des BAFU vom April 2020 bis November 2021 mit der Analyse der heutigen Praxis und bestehender Methoden für die Bemessungsabflüsse auseinandergesetzt (Bild 1). Im Schlussbericht zuhanden des BAFU wurden u.a. Vorschläge zur Verbesserung der Qualität von Hochwasserabschätzungen erarbeitet und konkrete Massnahmenvorschläge für die evaluierten Defizite ausgearbeitet

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 o

Changing climate and human demographics in the world’s mountains will have increasingly profound environmental and societal consequences across all elevations. Quantifying current human populations in and near mountains is crucial to ensure that any interventions in these complex social-ecological systems are appropriately resourced, and that valuable ecosystems are effectively protected. However, comprehensive and reproducible analyses on this subject are lacking. Here, we develop and implement an open workflow to quantify the sensitivity of mountain population estimates over recent decades, both globally and for several sets of relevant reporting regions, to alternative input dataset combinations. Relationships between mean population density and several potential environmental covariates are also explored across elevational bands within individual mountain regions (i.e. “sub-mountain range scale”). Globally, mountain population estimates vary greatly—from 0.344 billion (<5% of the corresponding global total) to 2.289 billion (>31%) in 2015. A more detailed analysis using one of the population datasets (GHS-POP) revealed that in ∼35% of mountain sub-regions, population increased at least twofold over the 40-year period 1975–2015. The urban proportion of the total mountain population in 2015 ranged from 6% to 39%, depending on the combination of population and urban extent datasets used. At sub-mountain range scale, population density was found to be more strongly associated with climatic than with topographic and protected-area variables, and these relationships appear to have strengthened slightly over time. Such insights may contribute to improved predictions of future mountain population distributions under scenarios of future climatic and demographic change. Overall, our work emphasizes that irrespective of data choices, substantial human populations are likely to be directly affected by—and themselves affect—mountainous environmental and ecological change. It

Mountainous regions are globally important, in part because they support large populations and are biodiverse. They are also characterized 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 socioeconomic 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 multidisciplinary 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 socioeconomic variables in the mountains. We propose principles and ways of connecting existing initiatives, supporting emerging areas, and developing new mountain observatory 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, Group on Earth Observations (GEO) Mountains, a GEO Work Programme Initiative.

In large areas of the world, runoff and other hydrological variables are controlled by the spatial and temporal variation of the 0°C isotherm, which is central for the temporal storage of precipitation as snow or ice. This storage is of crucial importance for the seasonal distribution of snow and ice melt, a major component of the movement of water in the global water cycle. This chapter provides an introduction to the role of snow and ice in the hydrosphere by discussing topics including snowpack characteristics, snow observation approaches, the energy balance of snow-covered areas, and modeling of snowmelt. Furthermore, the role of glaciers and glacial mass balances, including modeling glacier discharge, is discussed. An overview of the hydrology of snow- and ice-covered catchments is given, and the influence of snow, glaciers, river ice, seasonally frozen soils, and permafrost on discharge is discussed. Finally, the impacts of climate change on snow and ice are discussed.

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.

Die in diesem Projekt entwickelte Methodik erlaubt es, auf der Basis von kontinuierlichen Langzeitsimulationen verschiedene Abflussverläufe von Hochwassern mit gegebenen Wiederkehrperioden durch realistische Ganglinien wiederzugeben. Der vorliegende Bericht beschreibt zum einen die Entwicklung dieser Methodik und zum anderen erste Auswertungen der Resultate aus dem Projekt „Extremhochwasser an der Aare“ (EXAR) für 19 Stauanlagen unter Bundesaufsicht im Einzugsgebiet der Aare. Der Vorteil der entwickelten Methodik ist, dass sich realitätsnahe repräsentative Ganglinien für eine Sicherheitsabschätzung zu definierten Jährlichkeitsbereichen ergeben. Dies kann zu realistischeren Abschätzungen führen als die sonst häufig verwendeten synthetischen Ganglinien, welche typischerweise nur durch einen oder zwei Parameter definiert werden. In einem ersten Schritt wurden aus den vorliegenden EXAR-Daten bivariate Jährlichkeiten bezüglich Abflussspitze und Hochwasservolumen berechnet und die entsprechenden Hochwasserganglinien bestimmten Jährlichkeitsbereichen (z.B. HQ100, HQ1’000, HQ5’000) zugeordnet. Innerhalb jedes Jährlichkeitsbereiches wurden dann die Ganglinien über funktionelles Clustering gruppiert. Dieses Clustering basiert auf einer Beschreibung der Ganglinien durch Funktionen, was bedeutet, dass die Ganglinien nicht nur nach bestimmten Charakteristika wie Abflussspitze oder Hochwasservolumen gruppiert werden, sondern die gesamte Form der Ganglinien in den Clustering-Prozess miteinbezogen wird. Aus jedem Cluster wurde anschliessend ein funktioneller Boxplot konstruiert, welcher wiederum die Form der Ganglinien im Cluster statistisch aggregiert darstellt. Die sich daraus ergebenden repräsentativen Ganglinien sollen den jeweils gewählten Jährlichkeitsbereich gut abdecken. Die Mittellinie des funktionellen Boxplots (was in etwa einem Median eines klassischen Boxplots entspricht) dient dann als repräsentative Ganglinie und entspricht einer tatsächlichen Ganglinie des Ausgangsd

The cryosphere (including, snow, glaciers, permafrost, lake and river ice) is an integral element of high-mountain regions, which are home to roughly 10% of the global population. Widespread cryosphere changes affect physical, biological and human systems in the mountains and surrounding lowlands, with impacts evident even in the ocean. Building on the IPCC’s Fifth Assessment Report (AR5), this chapter assesses new evidence on observed recent and projected changes in the mountain cryosphere as well as associated impacts, risks and adaptation measures related to natural and human systems. Impacts in response to climate changes independently of changes in the cryosphere are not assessed in this chapter. Polar mountains are included in Chapter 3, except those in Alaska and adjacent Yukon, Iceland, and Scandinavia, which are included in this chapter.

The cryosphere (including, snow, glaciers, permafrost, lake and river ice) is an integral element of high mountain regions, which are home to roughly 10% of the global population. Widespread cryosphere changes affect physical, biological and human systems in the mountains and surrounding lowlands, with impacts evident even in the ocean. Building on the IPCC’s 5th Assessment Report (AR5), this chapter assesses new evidence on observed recent and projected changes in the mountain cryosphere as well as associated impacts, risks and adaptation measures related to natural and human systems. Impacts in response to climate changes independently of changes in the cryosphere are not assessed in this chapter. Polar mountains are included in Chapter 3, except those in Alaska and adjacent Yukon, Iceland and Scandinavia, which are included in this chapter.

Even in regions considered as densely monitored, most catchments are actually ungauged. Prediction of discharge in ungauged catchments commonly relies on parameter regionalization. While ungauged catchments lack continuous discharge time series, a limited number of observations could still be collected within short field campaigns. Here we analyze the value of such observations for improving parameter regionalization in otherwise ungauged catchments. More specifically, we propose an ensemble modeling approach, where discharge predictions from regionalization with multiple donor catchments are weighted based on the fit between predicted and observed discharge on the dates of the available observations. It was assumed that a total of 3 to 24 observations from a single hydrological year were available as an additional source of information for regionalization. This informed regionalization approach was tested with discharge observations from 10 different hydrological years in a leave‐one‐out cross validation scheme on 579 catchments in the United States using the HBV runoff model. Discharge observations helped to improve the regionalization in up to 94% of the study catchments in 8 out of 10 discharge sampling years. Sampling years characterized by exceptionally high peak discharge, or high annual or winter precipitation were less informative for regionalization. In the least informative years, model efficiency increased with an increasing number of observations. In contrast, in the most informative sampling year, 3 discharge observations provided as much information for regionalization as 24 discharge observations. Overall, discharge observations were most effective in informing regionalization in arid catchments, snow‐dominated catchments, and winter‐precipitation‐dominated catchments.

Flood hydrograph shapes contain valuable information on the flood-generation mechanisms of a catchment. To make good use of this information, we express flood hydrograph shapes as continuous functions using a functional data approach. We propose a clustering approach based on functional data for flood hydrograph shapes to identify a set of representative hydrograph shapes on a catchment scale and use these catchment-specific sets of representative hydrographs to establish regions of catchments with similar flood reactivity on a regional scale. We applied this approach to flood samples of 163 medium-size Swiss catchments. The results indicate that three representative hydrograph shapes sufficiently describe the hydrograph shape variability within a catchment and therefore can be used as a proxy for the flood behavior of a catchment. These catchment-specific sets of three hydrographs were used to group the catchments into three reactivity regions of similar flood behavior. These regions were not only characterized by similar hydrograph shapes and reactivity but also by event magnitudes and triggering event conditions. We envision these regions to be useful in regionalization studies, regional flood frequency analyses, and to allow for the construction of synthetic design hydrographs in ungauged catchments. The clustering approach based on functional data which establish these regions is very flexible and has the potential to be extended to other geographical regions or toward the use in climate impact studies.

Design flood estimates for a given return period are required in both gauged and ungauged catchments for hydraulic design and risk assessments. Contrary to classical design estimates, synthetic design hydrographs provide not only information on the peak magnitude of events but also on the corresponding hydrograph volumes together with the hydrograph shapes. In this study, we tested different regionalization approaches to transfer parameters of synthetic design hydrographs from gauged to ungauged catchments. These approaches include classical regionalization methods such as linear regression techniques, spatial methods, and methods based on the formation of homogeneous regions. In addition to these classical approaches, we tested nonlinear regression models not commonly used in hydrological regionalization studies, such as random forest, bagging, and boosting. We found that parameters related to the magnitude of the design event can be regionalized well using both linear and nonlinear regression techniques using catchment area, length of the main channel, maximum precipitation intensity, and relief energy as explanatory variables. The hydrograph shape, however, was found to be more difficult to regionalize due to its high variability within a catchment. Such variability might be better represented by looking at flood-type specific synthetic design hydrographs.

Flood hydrograph shapes contain valuable information on the flood-generation mechanisms of a catchment. To make good use of this information, we express flood hydrograph shapes as continuous functions using a functional data approach. We propose a clustering approach based on functional data for flood hydrograph shapes to identify a set of representative hydrograph shapes on a catchment scale and use these catchment-specific sets of representative hydrographs to establish regions of catchments with similar flood reactivity on a regional scale. We applied this approach to flood samples of 163 medium-size Swiss catchments. The results indicate that three representative hydrograph shapes sufficiently describe the hydrograph shape variability within a catchment and therefore can be used as a proxy for the flood behavior of a catchment. These catchment-specific sets of three hydrographs were used to group the catchments into three reactivity regions of similar flood behavior. These regions were not only characterized by similar hydrograph shapes and reactivity but also by event magnitudes and triggering event conditions. We envision these regions to be useful in regionalization studies, regional flood frequency analyses, and to allow for the construction of synthetic design hydrographs in ungauged catchments. The clustering approach based on functional data which establishes these regions is very flexible and has the potential to be extended to other geographical regions or towards the use in climate impact studies.

Accurate estimates of flood peaks, corresponding volumes, and hydrographs are required to design safe and cost-effective hydraulic structures. In this paper, we propose a statistical approach for the estimation of the design variables peak and volume by constructing synthetic design hydrographs for different flood types such as flash-floods, short-rain floods, long-rain floods, and rain-on-snow floods. Our approach relies on the fitting of probability density functions to observed flood hydrographs of a certain flood type and accounts for the dependence between peak discharge and flood volume. It makes use of the statistical information contained in the data and retains the process information of the flood type. The method was tested based on data from 39 mesoscale catchments in Switzerland and provides catchment specific and flood type specific synthetic design hydrographs for all of these catchments. We demonstrate that flood type specific synthetic design hydrographs are meaningful in flood-risk management when combined with knowledge on the seasonality and the frequency of different flood types.