Abteilung Systemanalyse, Integrated Assessment und Modellierung

Universal access to safe drinking water is called for under Sustainable Development Target 6.1 (SDG 6.1). Yet quantitative evidence on disease reductions associated with achieving SDG 6.1 and the service dimensions that most effectively reduce diarrhoeal diseases remains limited and inconsistent. Here, using multiple indicator cluster survey data from 24 low- and middle-income countries, we show that household use of safely managed drinking water services (SMDWS) reduces diarrhoea risks in children under 5 years old compared with use of services not fulfilling all of the SMDWS criteria. Drinking water availability when needed and absence of faecal contamination at the point of use showed consistent protective associations with child diarrhoea. We observed less evidence of the protective effect of an improved drinking water source, access on premises and absence of faecal contamination at the point of collection. Our findings reaffirm the importance of SMDWS in protecting against child diarrhoea, while highlighting uncertainties in estimates of the magnitude of the disease burden, which could be prevented by achieving SDG 6.1.

Mechanistic effect models are important research and decision-support tools in ecology and ecological risk assessment (ERA). A way to foster model utilization and increase confidence in their results in ERA is to compare different models developed to represent the same experimental system. Here, we present the results of such a ring study conducted to examine the feasibility and capability of four aquatic system models (ASMs) to represent mesocosm data and evaluate whether such models could be used as an extension of mesocosm experiments in support of ERA. The ring study methodology is described in a companion article published alongside this article. The four models we tested were AQUATOX, CASM, StoLaM+, and Streambugs. These ASMs were calibrated to represent the biological communities in mesocosms with (treatment) and without (control) the application of azoxystrobin. Mesocosm data were also available to perform model control and treatment validation. We show that the models, especially two of them, performed very well in reproducing the control dynamics according to the calibration criteria we set up, and the control validation simulations confirmed those scores. Models also reproduced the more evident effects (including indirect ones). We could identify similarities among the models' challenges, crucial data needs, and common knowledge gaps about mesocosm communities. The ring study also highlighted some weaknesses in the defined model performance criteria, which did not account for model differences and resulted in favoring some models over others. Despite the detailed model comparison, we could not quantify to what extent different performances were due to model structures, implemented processes, or calibration techniques. This ring study established a basis for adjusting models to mesocosm studies and proposed possible solutions towards advancing capabilities in using ASMs to inform ERA.

Wet grasslands are among the most effective terrestrial ecosystems for long-term carbon storage, yet their stability can be threatened by climate change and altered groundwater regimes. This study applies the MONICA agroecosystem model enhanced through dynamic coupling with the individual-based grassland model GRASSMIND to evaluate carbon, nitrogen, and biomass dynamics in Brandenburg's wet grasslands (2021–2100) under three groundwater table scenarios and RCP 2.6, 4.5, and 8.5 climate pathways. Shallow groundwater conditions (∼30 cm) were associated with smaller soil organic carbon (SOC) loss (' 0.6 Mg C ha⁻¹ yr⁻¹), suppressed nitrous oxide emissions, and limited nitrate leaching. In contrast, deep or unmanaged water tables increased aerobic decomposition and mineral nitrogen losses, especially under RCP 8.5, suggesting a critical climate–hydrology threshold around mid-century. Coupling MONICA with GRASSMIND allows species composition shifts to be represented and provides an exploratory assessment of how trait-based competition may influence biomass projections in wet grassland modelling. This study highlights that groundwater level management exerts a stronger control over ecosystem stability than climate forcing alone. These findings underscore the need for adaptive water regime strategies and model-based planning tools to safeguard the climate regulation potential of wet grasslands.

Although large-sample hydrology data sets are increasingly used to advance predictions in ungauged basins, the influence of landscape data quality on model regionalization remains insufficiently explored. This study investigates whether geological catchment attributes derived from maps of increasing detail—global, continental, and regional—improve parameter transfer and model regionalization. To ensure robustness across model approaches, we applied both a semi-distributed process-based hydrological model using hydrological response units (HRUs) and a data-driven Long Short-Term Memory (LSTM) model. The analysis covered a total of 130 catchments in the Moselle (Central Europe) and Garonne (southwestern France) basins. We conducted five model experiments differing only in the representation of geological information: a benchmark without geology, a benchmark with random geology classes, and configurations based on the global-, continental-, and regional-scale geological maps. Model performance was evaluated using a modified Nash-Sutcliffe (NSE) metric for daily streamflow, as well as Pearson correlation and relative bias for three streamflow signatures: baseflow index, slope of the flow duration curve, and half-flow date. Across both basins and modeling frameworks, increasing geological detail consistently improved predictive performance under space–time evaluation. While differences in NSE were modest, improvements were pronounced for streamflow signatures: only models using the more detailed geological information, especially the regional map, consistently reproduced spatial variability in baseflow and flow regime characteristics. These findings highlight the importance of integrating high-quality geological data into hydrological modeling, particularly for improving predictions in ungauged basins through more reliable parameter transfer and regionalization.

Protecting aquatic ecosystems from pesticide pollution is a critical regulatory task. Benthic diatoms, being part of aquatic biofilms, are commonly used to indicate nutrient pollution. However, their potential as bioindicators for pesticides and other co-occurring environmental stressors remains poorly assessed. To address this, we conducted a flow-through chamber experiment designed to reflect realistic field exposure scenarios. We exposed biofilms to 44 treatments combining environmentally relevant pesticide mixtures including herbicides, fungicides, insecticides (44 levels of measured Toxic Units (TU_mix) ranging from 2 × 10^–5 to 4), in combination with three environmental stressors (light intensity, temperature, nutrient levels) over a 25-day period. Upon termination, we examined the diatom community composition using Next-Generation Sequencing along with community-level biofilm descriptors (β-diversity, effective photosynthetic activity, chlorophyll-a content, ash-free dry weight, bacterial abundance). While we did not find any significant responses to pesticides at the community level, we identified species-specific patterns of sensitivity or tolerance to pesticides and other experimental factors, thereby identifying them as potential bioindicators. These candidate species represent a first step toward a diatom-based index for pesticide stress assessment in Swiss streams. These findings highlight the need for realistic, multifactorial experiments to assess the impact of stressors in aquatic ecosystems.

Study regionThe Urumqi Glacier No.1 (UGN1) and Dongkemadi Glacier (DG) catchments in China, both highly glacierized alpine catchments, provide critical water resources for downstream rivers and are sensitive to climate change impacts.Study focusAccurate simulation of glacier runoff processes remains challenging due to limited observational data. Here, we introduce FLEXG-iso, an isotope-aided glacier hydrological model that couples stable water isotopes with glacier mass balance (GMB) to simulate runoff generation and partition runoff components in these catchments. We also assess the transferability of model structures and parameters between UGN1 and DG catchments.New hydrological insights for the regionResults demonstrate that incorporating stable water isotopes and GMB data enhances the identification of parameters controlling snow and ice accumulation and melt. FLEXG-iso provides more robust partitioning of runoff components, distinguishing throughflow and groundwater, even with limited tracer availability, and improves runoff simulation accuracy (KGE > 0.8) when transferring optimal parameter sets. These findings highlight the potential of isotope-aided modeling to improve both calibration and transferability of glacier hydrological models, offering a robust framework for understanding runoff dynamics in high mountain regions under climate change.