Department Systems Analysis, Integrated Assessment and Modelling

This study investigates the drought of three major terminal lakes: Great Salt Lake, Salton Sea, and Lake Urmia, driven by socio-hydrological lock-in—a phenomenon characterized by feedback loops between human activities and environmental processes. Previous research has linked this drying to socio-hydrological lock-in, where rational actions by individuals collectively lead to suboptimal outcomes, exacerbating water scarcity and ecological degradation. Despite existing studies, a critical knowledge gap remains in understanding how these feedback mechanisms operate across different socio-economic and ecological contexts. This research seeks to address this gap through an integrative analysis of historical data and case studies, highlighting the interplay between increased water demand for agriculture, population growth, and governance challenges. The resultant shrinkage of these lakes exposes saline playas, generating salt-rich dust with severe ecological and health impacts, including soil salinization and respiratory illnesses. By comparing the socio-hydrological lock-in across diverse regions, this study provides insights into preventing and mitigating these cycles. It emphasizes the necessity for holistic water management strategies that integrate both supply and demand-side measures. The key contribution of this research lies in its systematic exploration of the feedback mechanisms driving socio-hydrological lock-ins, offering a foundation for developing sustainable water resource management policies globally. This work aims to influence policy and practice by highlighting the need for integrated, long-term strategies that balance economic pursuits with environmental sustainability, ensuring resilience in water management amidst mounting environmental challenges.

Assessment of potential impacts of chemicals on the environment traditionally involves regulatory standard data requirements for acute aquatic toxicity testing using algae, daphnids, and fish (e.g., Organisation for Economic Co-operation and Development [OECD] test guidelines 201, 202, and 203, respectively), representing different trophic levels. In line with the societal goal to replace or reduce vertebrate animal testing, alternative bioassays were developed to replace testing with fish: the fish cell line RTgill-W1 acute toxicity assay (OECD test guideline 249) and the zebrafish embryo acute toxicity test (zFET, OECD test guideline 236). However, previous studies revealed the lower sensitivity of the RTgill-W1 cell line assay and zFET for some neurotoxic chemicals and allyl alcohol, which is presumably biotransformed in fish to the more toxic acrolein (which is predicted well through the cell line assay). To provide an additional alternative to acute fish toxicity, in this study we analyzed historic ecotoxicity data for fish and daphnids from the EnviroTox Database. We found a considerable variability in acute fish median lethal concentration and acute daphnids median effect concentration values, particularly for neurotoxic chemicals. Comparing sensitivity of these taxonomic groups according to different neurotoxicity classification schemes indicates that fish rarely represent the most sensitive trophic level of the two. Exceptions here most prominently include a few cyclodiene compounds, which are no longer marketed, and a chemical group that could be identified through structural alerts. Moreover, daphnids are more sensitive than fish to acrolein. This analysis highlights the potential of the Daphnia acute toxicity test, which is usually a standard regulatory data requirement, in safeguarding the environmental protection level provided by the RTgill-W1 cell line assay and the zFET. This research, rooted in decades of efforts to replace the fish acute toxicity test, shifts the focus from predicting fish toxicity one-to-one to emphasizing the protectiveness of alternative methods, paving the way for further eliminating vertebrate tests in environmental toxicology.

We review the main methods used to study spin glasses. In the first part, we focus on methods for fully connected models and systems defined on a tree. These include the replica method, the Thouless-Anderson-Palmer formalism, the cavity method, and the dynamical mean-field theory. In the second part, we deal with the description of low-dimensional systems, mostly in three spatial dimensions, which are mostly studied through numerical simulations. We conclude by mentioning some of the main open problems in the field.

Weunveil the multifractal behavior of Ising spin glasses in their low-temperature phase. Using the Janus II custom-built supercomputer, the spin-glass correlation function is studied locally. Dramatic fluctuations are found when pairs of sites at the same distance are compared. The scaling of these fluctuations, as the spin-glass coherence length grows with time, is characterized through the computation of the singularity spectrum and its corresponding Legendre transform. A comparatively small number of site pairs controls the average correlation that governs the response to a magnetic field. We explain how this scenario of dramatic fluctuations (at length scales smaller than the coherence length) can be reconciled with the smooth, self-averaging behavior that has long been considered to describe spin-glass dynamics.

Recent successes with machine learning (ML) models in catchment hydrology have highlighted their ability to extract crucial information from catchment properties pertinent to the rainfall–runoff relationship. In this study, we aim to identify a minimal set of catchment signatures in streamflow that, when combined with meteorological drivers, enable an accurate reconstruction of the entire streamflow time series. To achieve this, we utilize an explicit noise-conditional autoencoder (ENCA), which, assuming an optimal architecture, separates the influences of meteorological drivers and catchment properties on streamflow. The ENCA architecture feeds meteorological forcing and climate attributes into the decoder in order to incentivize the encoder to only learn features that are related to landscape properties minimally related to climate. By isolating the effect of meteorology, these hydrological features can thus be interpreted as landscape fingerprints. The optimal number of features is found by means of an intrinsic dimension estimator. We train our model on the hydro-meteorological time series data of 568 catchments of the continental United States from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset. We compare the reconstruction accuracy with models that take as input a subset of static catchment attributes (both climate and landscape attributes) along with meteorological forcing variables. Our results suggest that available landscape attributes can be summarized by only two relevant learnt features (or signatures), while at least a third one is needed for about a dozen difficult-to-predict catchments in the central United States, which is mainly characterized by a high aridity index. The principal components of the learnt features strongly correlate with the baseflow index and aridity indicators, which is consistent with the idea that these indicators capture the variability of catchment hydrological responses. The correlation analysis further indicates that soil-related and vegetation attributes are of importance.

The presence or absence of sex can have a strong influence on the processes whereby species arise. Yet, the mechanistic underpinnings of this influence are poorly understood. To gain insights into the mechanisms whereby the reproductive mode may influence ecological diversification, we investigate how natural selection, genetic mixing, and the reproductive mode interact and how this interaction affects the evolutionary dynamics of diversifying lineages. To do so, we analyze models of ecological diversification for sexual and asexual lineages, in which diversification is driven by intraspecific resource competition. We find that the reproductive mode strongly influences the diversification rate and, thus, the ensuing diversity of a lineage. Our results reveal that ecologically-based selection is stronger in asexual lineages because asexual organisms have a higher reproductive potential than sexual ones. This promotes faster diversification in asexual lineages. However, a small amount of genetic mixing accelerates the trait expansion process in sexual lineages, overturning the effect of ecologically-based selection alone and enabling a faster niche occupancy than asexual lineages. As a consequence, sexual lineages can occupy more ecological niches, eventually resulting in higher diversity. This suggests that sexual reproduction may be widespread among species because it increases the rate of diversification.