Dr. Massimiliano Zappa

This study presents a data-driven reconstruction of daily runoff that covers the entirety of Switzerland over an extensive period from 1962 to 2023. To this end, we harness the capabilities of deep-learning-based models to learn complex runoff-generating processes directly from observations, thereby facilitating efficient large-scale simulation of runoff rates at ungauged locations. We test two sequential deep-learning architectures: a long short-term memory (LSTM) model, which is a recurrent neural network able to learn complex temporal features from sequences, and a convolution-based model, which learns temporal dependencies via 1D convolutions in the time domain. The models receive temperature, precipitation, and static catchment properties as input. By driving the resulting model with gridded temperature and precipitation data available since the 1960s, we provide a spatiotemporally continuous reconstruction of runoff. The efficacy of the developed model is thoroughly assessed through spatiotemporal cross-validation and compared against a distributed hydrological model used operationally in Switzerland.
The developed data-driven model demonstrates not only competitive performance, but also notable improvements over traditional hydrological modeling in replicating daily runoff patterns, capturing interannual variability, and discerning long-term trends. The resulting long-term reconstruction of runoff is subsequently used to delineate substantial shifts in Swiss water resources throughout the past decades. These are characterized by an increased occurrence of dry years, contributing to a negative decadal trend in runoff, particularly during the summer months. These insights are pivotal for the understanding and management of water resources, particularly in the context of climate change and environmental conservation. The reconstruction product is made available online.
Furthermore, the low data requirements and computational efficiency of our model pave the way for simulating diverse scenarios and conducting comprehensive climate attribution studies. This represents a substantial progression in the field, allowing for the analysis of thousands of scenarios in a time frame significantly shorter than those of traditional methods.

Stream water temperatures influence water quality, with effects on aquatic biodiversity, drinking-water provision, electricity production, agriculture, and recreation. Therefore, stakeholders would benefit from an operational forecasting service that would support timely action. Deep-learning models are well-suited to providing probabilistic forecasts at individual stations of a monitoring network. Here, we train and evaluate several state-of-the-art models using 10 years of data from 54 stations across Switzerland. Static catchment features, time of the year, meteorological observations from the past 64 d, and their ensemble forecasts for the following 32 d are included as predictors in the models to estimate daily maximum water temperature over the next 32 d. Results show that the temporal fusion transformer (TFT) model performs best, with a continuous rank probability score (CRPS) of 0.70 °C averaged over all lead times, stations, and 90 forecasts distributed over 1 year. The TFT is followed by the recurrent neural network encoder–decoder, with a CRPS of 0.74 °C, and the neural hierarchical interpolation for time series, with a CRPS of 0.75 °C. These deep-learning models outperform other simpler models trained at each station: random forest (CRPS = 0.80 °C), multi-layer perceptron neural network (CRPS = 0.81 °C), and autoregressive linear model (CRPS = 0.96 °C). The average CRPS of the TFT degrades from 0.38 °C at lead a time of 1 d to 0.90 °C at a lead time of 32 d, largely driven by the uncertainty of the meteorological ensemble forecasts. In addition, TFT water temperature predictions at new and ungauged stations outperform those from the other models. When analyzing the importance of model inputs, we find a dominant role of observed water temperature and future air temperature, while including precipitation and time of the year further improves predictive skill. Operational probabilistic forecasts of daily maximum water temperature are generated twice per week with our TFT model and are publicly available at https://www.drought.ch/de/impakt-vorhersagen-malefix/wassertemperatur-prognosen/ (last access: 20 March 2025). Overall, this study provides insights into the extended-range predictability of stream water temperature and into the applicability of deep-learning models in hydrology.

Seit dem Weckruf durch den Hitze- und Trockensommer 2003 hat in der Schweiz eine sehr intensive Auseinandersetzung mit dem Thema Trockenheit stattgefunden. Mehr als 20 Jahre später haben Forschung und Folgeereignisse bestätigt, dass auch das Wasserschloss Europas, die Alpen, von extremer Trockenheit betroffen sein können. Auch globale Studien weisen auf die zunehmende Gefahr von mehrjähriger Trockenheit hin, die heute noch kaum vorstellbare Folgen für Umwelt und Gesellschaft haben könnten. Die Schweiz ist nur ungenügend vorbereitet, doch seit 2012 arbeiten Forschung und Verwaltung enger zusammen um auf Trockenereignisse besser vorbereitet zu sein.

EXtreme drought in Switzerland: regional and local perspectives on a global challenge
Since the wake-up call of the heatwave and drought in Summer 2003, there has been a very intensive debate in Switzerland about drought. More than 20 years later, the understanding that Europe’s water tower can also be affected by extreme drought has been confirmed by research and subsequent events. The global outlook also points to the increasing risk of multi years mega droughts, which is likely to have consequences for the environment and society that are difficult to imagine today. The yearly increase of areas affected by megadroughts almost equal the area of Switzerland (~42’000 km2). Switzerland is not unprepared, because since 2012 research and administration have been working closely together on the foundations for coping with future extreme droughts. In May 2025 the Swiss confederation launches the new national drought early-warning and detection system. This will contribute to anticipate and mitigate future drought extremes that might affect among others the ecosystems, the energy system and the transportation of goods along the Rhine.

Study region: The European alpine space.
Study focus: Despite the advancements in hydrological modeling, there is a lack of comparative studies quantifying the performance differences between local and large-scale models, particularly in the context of drought and low flow assessment. This study addresses this gap by designing a framework to evaluate the European Flood Awareness System (EFAS), a continental system, in simulating low flow events across 101 alpine stations from 1999–2018, with detailed comparisons to two local systems — GR6J in France and PREVAH in Switzerland — at 34 of these stations.
New hydrological insights for the region: The results show that the local systems, PREVAH and GR6J, generally outperform the continental system EFAS for almost all stations. The performance gap between the systems increases as low flow conditions intensify, highlighting the importance of local systems for extreme low flow events. Despite EFAS not being specifically set up for low flows, it has shown an overall acceptable performance compared to the local hydrological systems, especially at locations that are not calibrated within the EFAS system, indicating its potential in ungauged areas. This study lays a foundation for understanding how a continental hydrological system like EFAS can complement local systems or fill the gap when a local system is unavailable to provide reliable predictions of low flow conditions.

Time series analyses of solute concentrations in streamwater and precipitation are powerful tools for unraveling the interplay of hydrological and biogeochemical processes at the catchment scale. While such datasets are available for many sites around the world, they often lack the necessary temporal resolution or are limited in the number of solutes they encompass. Here we present a multi-year dataset encompassing daily records of major ions and a range of trace metals in both streamwater and precipitation in three catchments in the northern Swiss Pre-Alps. These time series capture the temporal variability observed in solute concentrations in response to storm events, snow melt, and dry summer conditions. This dataset additionally includes stable water isotope data as an extension of a publicly available isotope dataset collected concurrently at the same locations, and together these data can provide insights into a range of ecohydrological processes and enable a suite of analyses into hydrologic and biogeochemical catchment functioning.

Climate model ensembles serve as an input to all impact studies that use sector-specific models (e.g., hydrological, ecological, crop models, fire hazard) at regional or local scales. These models require regionally scaled climate information to simulate the potential environmental and socio-economic sectoral impacts of climate change. Such simulations are based on comprehensive multi-member climate ensembles derived from the bias-adjusted and downscaled global and regional climate models. Due to limited computational resources, users of climate scenarios often can only include a small number of the ensemble members in their calculations, and therefore they often select them at random. A pre-selection of meaningful, consistent and case-specific members is therefore desired by the climate data users. In this work, we aim to fill this gap and present a novel user-tailored procedure for sub-selecting ensemble members for a variety of applications. Our method is based on the ranking of the climate change signal (CCS) calculated for a set of climate indices (e.g., mean temperature or number of hot days). Based on the CCS strength, three ensemble members representing the strongest, weakest, and median CCS are selected for each application. We also demonstrate the robustness of our approach in a specific hydrological impact model framework. Providing a systematic procedure not only assists impact modelers in selecting appropriate members, but also improves the consistency and comparability of different impact studies.

Mit HydCHeck wurde ein automatisiertes Screening-Tool entwickelt, um an einem beliebigen Standort entlang von Schweizer Fliessgewässern die anthropogene Beeinflussung des Abflusses beurteilen zu können. Die Resultate werden übersichtlich in einem Faktenblatt zusammengefasst. Ein Netzdiagramm zeigt den Beeinflussungsgrad von sieben Beurteilungskategorien (Wasserspeicherung-/regulierung, Wasserkraft, Abwasser, Bauwerke, Landwirtschaft, Trink-/Brauchwasser, Grundwasser). Des Weiteren wird mithilfe eines Ampelsystems Art und das Ausmass der hydrologischen Beeinträchtigung bewertet und anhand von fünf hydrologischen Kenngrössen dargestellt (Niedrigwasserregime, Mittelwasserregime, Hochwasserregime, Kurzzeiteffekte, Hydraulik). Die Resultate dienen als Indikator für die mögliche Beeinflussung des Abflusses am Untersuchungsstandort und geben Hinweise, wo aus hydrologischer Sicht ein ökologisches Defizit vorhanden sein könnte. Das hier vorgestellte Werkzeug ist ein Hilfsmittel für eine flächendeckende Übersicht und lenkt den Blick auf Standorte bzw. Gebiete, in denen eine genauere Beurteilung der hydrologischen Beeinflussung notwendig ist.

In der Schweiz werden Wasserkraftanlagen (WKA) mit einer Leistung von mehr als 300 kW systematisch erfasst und in der nationalen Wasserkraftstatistik (WASTA) zusammengetragen. Die WASTA bildet eine umfangreiche Datengrundlage für Analysen bezüglich Wasserkraft, auch im Kontext von Klimawandel, Biodiversitätsverlust und Energiewende. Nicht erfasst auf nationaler Ebene waren bis anhin die sogenannten Kleinstwasserkraftwerke, WKA mit einer Leistung von weniger als 300 kW. Kleinstwasserkraftwerke tragen zwar nicht wesentlich zur Schweizer Elektrizitätsversorgung bei, haben jedoch aufgrund ihrer hohen Anzahl Auswirkungen auf die Gewässerökologie, die mit den Klimawandel noch stärker unter Druck gerät.

Der neu erstellte Datensatz CH-Kleinstwasserkraftwerke umfasst 1'682 WKA mit einer Leistung von weniger als 300 kW. In den zwei Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW) sind zurzeit 2’388 WKA erfasst. Im Austausch mit den 26 Kantonen wurde jedoch klar, dass es sich nicht um eine vollständige Sammlung von Kleinstwasserkraftwerken handelt. Die 1'682 Kleinstwasserkraftwerke machen rund 70% aller erfassten WKA in der Schweiz aus, tragen jedoch mit weniger als 600 GWh/Jahr lediglich rund 1.5% zur schweizerischen Wasserkraftproduktion bei. In einem optimistischen Szenario, in dem die jährliche Produktionserwartung tendenziell überschätzt wird, liegt dieser Anteil um 0.3% höher. Von den erfassten 2’388 WKA erzeugen die 10% der grössten Anlagen über 90% der schweizerischen Wasserkraftproduktion. Der Datensatz CH-Kleinstwasserkraftwerke ist im Rahmen der ETH-Initiative SPEED2ZERO entstanden, die Klimadienstleistungen in den Bereichen Klimawandel, Biodiversitätsverlust und Energiewende erbringt (https://speed2zero.ethz.ch).

Study region: Four perialpine lakes in Switzerland, with different degrees of lake level management.
Study focus: Alpine regions are particularly sensitive to climate change due to the pronounced effect on snow and glacial melt. In this context, large perialpine lakes play a crucial role in modulating climate change impacts on water resources, which brings together diverse interests. However, climate change studies on river systems rarely include lakes or lake level management. An open question is how to incorporate lake level management effects into hydrologic simulations to project climate change impacts. We combine the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate lake level and outflow scenarios from 1981 to 2099, using the Swiss climate change scenarios CH2018.
New hydrological insights for the region: The hydrological projections at the end of the century show pronounced seasonal changes in lake levels, characterised by an increase in winter and a decrease in summer when water demand is highest. Without climate mitigation measures, this summer decrease ranges from −0.04 m for a regulated lake to −0.4 m for an unregulated lake. In addition, the simulations indicate more frequent drought events. The projected changes intensify with time and missing climate mitigation measures. Future work could focus on interannual variability to explore regulatory strategies under changing conditions.

Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.

Gegitterte Klimadaten mit einer regelmässigen räumlichen Auflösung sind eine wichtige Grundlage für Umweltstudien. Sie werden mithilfe statistischer Methoden aus Punktdaten von Messstationen erzeugt und haben insbesondere in topografisch komplexen Gebirgsregionen wie den Schweizer Alpen eine limitierte Genauigkeit. Dieser Artikel vergleicht vier für die Schweiz entwickelte gegitterte Datensätze unterschiedlicher Temperatur- und Niederschlagsvariablen mit unabhängigen Messdaten von 14 Dauerbeobachtungsflächen der Langfristigen Waldökosystemforschung (LWF) über einen Zeitraum von 20 Jahren. Die Resultate dienen als Entscheidungshilfe zur Wahl eines für eine grossflächige Anwendung zuverlässigen und homogenen Datensatzes. Die untersuchten Datensätze mit individuellen Stärken und Schwächen kommen für Anwendungen in der Schweiz infrage.

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 Anzahl der intensiven und räumlich ausgedehnten Trockenjahren hat in der Schweiz in den letzten Jahrzehnten massiv zugenommen. Bei langanhaltenden, ausgeprägten und verbreiteten Trockenperioden soll deshalb künftig für die Kantone beim Bund eine Berichterstattungspflicht gelten. Das Bundesamt für Umwelt (BAFU) beauftragte dazu die Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft (WSL) um die Trockenperioden zwischen 1991 und 2022 bezüglich Abfluss, Niederschlag und Bodenfeuchte zu analysieren. Die Trockenperioden wurden nach ihrer Dauer, Magnitude und räumlichen Verbreitung analysiert. Die folgenden Jahre wurden als wichtige Trockenjahre bezüglich ihrer Intensität und Ausdehnung bestimmt: 1991, 2003, 2011, 2015, 2018, 2022.

Das globale Klima verändert sich und damit einhergehend auch das Wasserdargebot der Schweizer Gewässer. In dieser Studie wurden anhand der CH2018 Klimaszenarien und den hydrologischen Projektionen Hydro-CH2018, Abflussszenarien für die Einzugsgebiete der Kraftwerke Göschenen und Meiental erstellt.

Hydropower plays a significant role in the transition towards a low-carbon power system, being a renewable energy source that can complement solar and wind power, which are highly intermittent. However, hydropower is itself dependent on local weather conditions and climate variability. Moreover, extreme climate conditions, such as hot-dry compound events, can have a major impact on hydropower production (HP). Here, we examine the impacts of hot-dry conditions on HP under current and future climate scenarios in Switzerland, a country where hydropower provides the biggest share (60%) of the total electricity production. Overall, our results point out that the impacts of hot-dry conditions on HP are case-specific. We found that hot-dry compound conditions during the warmer months negatively impact HP in power plants with little or no water storage capacity (run-of-river schemes). On the contrary, schemes with large, seasonal accumulation lakes and significant glacier resources will continue to be able to produce high amounts of HP during hot-dry conditions in summer, which is an important result for Alpine hydropower.

Für die Hochwasserabschätzung in der Praxis haben sich in der Schweiz einfache Schätzmethoden etabliert. Mit den Software-Produkten HAKESCH und HQx_meso_CH stehen Werkzeuge zur schnellen Hochwasserabschätzung mit vorhandenen Methoden zur Verfügung. Dennoch gibt es Unklarheiten bei der konkreten Anwendung und grosse Unsicherheiten bei den berechneten Hochwasserabflussspitzen.
Im Auftrag des Amts für Abfall, Wasser, Energie und Luft (AWEL) des Kantons Zürich hat die geo7 AG einheitliche Faktenblätter zu den Schätzmethoden und einen Leitfaden zur Plausibilisierung der Ergebnisse erarbeitet. Die Faktenblätter fassen pro Methode die wichtigsten Informationen zur Gültigkeit, Parametrisierung, Güte und Sensitivität der Ergebnisse grafisch anschaulich und übersichtlich auf je einer DIN A4 Seite zusammen. Der Leitfaden unterstützt in der Form eines Entscheidungsbaums die nachvollziehbare Ableitung eines robusten Mittelwerts der Hochwasserspitze aus den Einzelergebnissen der Schätzmethoden und bietet eine Hilfestellung zur Plausibilisierung der Ergebnisse. Die Faktenblätter und der Leitfaden richten sich an Fachpersonen mit Vorkenntnissen.
Eine Analyse zeigte ausserdem, dass nur fünf der heute verwendeten Methoden zur Hochwasserabschätzung (Kölla, Kölla_meso, Taubmann, Modifiziertes Fliesszeitverfahren, Clark-WSL) die Veränderung der Starkniederschläge durch den Klimawandel berücksichtigen können. Die Berücksichtigung des Klimawandels wird jedoch bei der Hochwasserabschätzung zunehmend wichtiger.