Systems Analysis, Integrated Assessment and Modelling
In SIAM, we develop and apply models and formal techniques in order to understand, demonstrate, and predict the behavior of natural, technical, social and economical systems that pertain to water and other natural resources. Read more
Sendek, A., Baity-Jesi, M., Altermatt, F., Bader, M. K. F., Liebhold, A. M., Turner, R. M., … Brockerhoff, E. G. (2022). Fewer non-native insects in freshwater than in terrestrial habitats across continents. Diversity and Distributions. doi:10.1111/ddi.13622, Institutional Repository
Aim: Biological invasions are a major threat to biodiversity in aquatic and terrestrial habitats. Insects represent an important group of species in freshwater and terrestrial habitats, and they constitute a large proportion of non-native species. However, while many non-native insects are known from terrestrial ecosystems, they appear to be less represented in freshwater habitats. Comparisons between freshwater and terrestrial habitats of invader richness relative to native species richness are scarce, which hinders syntheses of invasion processes. Here, we used data from three regions on different continents to determine whether non-native insects are indeed under-represented in freshwater compared with terrestrial assemblages. Location: Europe, North America, New Zealand. Methods: We compiled a comprehensive inventory of native and non-native insect species established in freshwater and terrestrial habitats of the three study regions. We then contrasted the richness of non-native and native species among freshwater and terrestrial insects for all insect orders in each region. Using binomial regression, we analysed the proportions of non-native species in freshwater and terrestrial habitats. Marine insect species were excluded from our analysis, and insects in low-salinity brackish water were considered as freshwater insects. Results: In most insect orders living in freshwater, non-native species were under-represented, while they were over-represented in a number of terrestrial orders. This pattern occurred in purely aquatic orders and in orders with both freshwater and terrestrial species. Overall, the proportion of non-native species was significantly lower in freshwater than in terrestrial species. Main conclusions: Despite the numerical and ecological importance of insects among all non-native species, non-native insect species are surprisingly rare in freshwater habitats. This is consistent across the three investigated regions. We review hypotheses concerning species traits and invasion pathways that are most likely to explain these patterns. Our findings contribute to a growing appreciation of drivers and impacts of biological invasions.
Gao, H., Han, C., Chen, R., Feng, Z., Wang, K., Fenicia, F., & Savenije, H. (2022). Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai-Tibet Plateau. Hydrology and Earth System Sciences, 26(15), 4187-4208. doi:10.5194/hess-26-4187-2022, Institutional Repository
Increased attention directed at frozen soil hydrology has been prompted by climate change. In spite of an increasing number of field measurements and modeling studies, the impact of frozen soil on hydrological processes at the catchment scale is still unclear. However, frozen soil hydrology models have mostly been developed based on a bottom-up approach, i.e., by aggregating prior knowledge at the pixel scale, which is an approach notoriously suffering from equifinality and data scarcity. Therefore, in this study, we explore the impact of frozen soil at the catchment scale, following a top-down approach, implying the following sequence: expert-driven data analysis → qualitative perceptual model → quantitative conceptual model → testing of model realism. The complex mountainous Hulu catchment, northeast of the Qinghai-Tibet Plateau (QTP), was selected as the study site. First, we diagnosed the impact of frozen soil on catchment hydrology, based on multi-source field observations, model discrepancy, and our expert knowledge. The following two new typical hydrograph properties were identified: The low runoff in the early thawing season (LRET) and the discontinuous baseflow recession (DBR). Second, we developed a perceptual frozen soil hydrological model to explain the LRET and DBR properties. Third, based on the perceptual model and a landscape-based modeling framework (FLEX-Topo), a semi-distributed conceptual frozen soil hydrological model (FLEX-Topo-FS) was developed. The results demonstrate that the FLEX-Topo-FS model can represent the effect of soil freeze-Thaw processes on hydrologic connectivity and groundwater discharge and significantly improve hydrograph simulation, including the LRET and DBR events. Furthermore, its realism was confirmed by alternative multi-source and multi-scale observations, particularly the freezing and thawing front in the soil, the lower limit of permafrost, and the trends in groundwater level variation. To the best of our knowledge, this study is the first report of LRET and DBR processes in a mountainous frozen soil catchment. The FLEX-Topo-FS model is a novel conceptual frozen soil hydrological model which represents these complex processes and has the potential for wider use in the vast QTP and other cold mountainous regions.
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters but cancel out the noise. They can thus be seen as thermodynamic state variables, for general stochastic models. For many scientific applications, we need strictly more summary statistics than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use the inner dimension of deep neural network based Autoencoders as summary statistics. To create an incentive for the encoder to encode all the parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models.
In simplified models of glasses we clarify the existence of two different kinds of coexisting activated dynamics, with one of the two dominating over the other. One is the energy barrier hopping that is typically used to understand activation, and the other, which we call entropic activation, is driven by the scarcity of convenient directions in phase space. When entropic activation dominates, the height of the energy barriers is no longer the primary factor governing the system's slowdown. In our analysis, dominance of one mechanism over the other depends on temperature and the shape of the density of states. We also find that at low temperatures a phase transition between the two kinds of activation can occur. Our observations are used to provide a scenario that can harmonize the facilitation and thermodynamic pictures of the slowdown of glasses into a single description.
Vaghefi, S. A., Muccione, V., Neukom, R., Huggel, C., & Salzmann, N. (2022). Future trends in compound concurrent heat extremes in Swiss cities - an assessment considering deep uncertainty and climate adaptation options. Weather and Climate Extremes, 38, 100501 (18 pp.). doi:10.1016/j.wace.2022.100501, Institutional Repository
The interaction of multiple hazards across various spatial and temporal scales typically causes compound climate and extreme weather events. Compound concurrent hot day and night (CCHDNs) extremes that combine daytime and nighttime heat are of greater concern for health than individual hot days (HDs) or hot nights (HNs), even though their frequency is lower. We utilize a bottom-up exploratory approach to investigate how adaptation options and various unfolding future scenarios alleviate the impacts of the heatwaves and affect the frequency and intensity of CCHDNs. We use climate observations (1981-2020) and Switzerland's future climate model scenarios (CH2018) to analyze historical and future trends of the individual hot day followed by a hot night (HDNs, first metric), and the length and frequency of CCHDNs (second and third metrics) in the near-future (2020-2050) and far-future (2070-2100). Results show more frequent and lengthier HDNs in cities under all emission scenarios, notably significant under high emissions scenarios. The highest increase of HDNs occur in i) Lugano with 65.8 days (decade-1) in the historical period and 110 (371) days (decade-1) in near-future (far-future), ii) Geneva with historical 48 days (decade-1) to 108 (362) (decade-1), iii) Basel with 48-74 (217) days in the future, followed by iv) Bern with 15-44 (213) days and v) Zürich with 14-50 (217) days (decade-1) in the near-future and far-future, respectively. We consistently project that the CCHDNs in April-October become more likely and intense in all cities under all emission scenarios, with higher increases under the RCP8.5 scenario and after the 2050s. The frequency of compound extreme heatwaves (exceeding both historical thresholds of night and day temperatures) may increase by 3.5-7.8-fold and become 3.3-5.3-fold lengthier in all cities of Switzerland in the far-future. We find that the adaptation options targeting higher tolerance to increased minimum temperatures contribute more to reducing compound extreme events' frequency and intensity than adaptation options that address the maximum daily temperature.
Insects that live entirely or partly in freshwater have a much lower proportion of invasive species than insects that live on land. This is shown in a study by the Swiss Federal Institute for Forest, Snow and Landscape Research...
Insects that live entirely or partly in freshwater have a much lower proportion of invasive species than insects that live on land. This is shown in a study by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL in collaboration with the Swiss Federal Institute of Aquatic Science and Technology Eawag and an international team of researchers.
Bridging the gap between data science and mechanistic modelling for a better understanding of community composition.
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.
Mechanistic modelling of the macroinvertebrate community composition in rivers.
We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We use machine learning methods to predict the effects of chemicals on aquatic species.
Development of a semi-distributed hydrological model with a “flexible” approach. Testing and comparing of different model structures to combine modeling and experimenting into a learning process.