Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities.

The European Alps are known as the 'water towers of Europe'. However, climatic and socioeconomic changes influence both water supply and demand, increasing the need to manage this limited and valuable resource properly to avoid user conflicts and water scarcity. Two major challenges emerge when assessing water scarcity in the Alps: Firstly, mountainous regions are very heterogeneous regarding water availability and demand over space and time, and therefore water scarcity assessments need to be done at low temporal and spatial scales. Secondly, the tight coupling of the natural and the social sphere necessitate an integrative approach considering dynamics and interactions of the social-ecological system. Hence, we applied the agent-based water supply and demand model Aqua.MORE, which is designed for catchment scale and sub-daily temporal resolution, to a case study site in the Italian Alps. In the model, the water supply, the local water managers and water users are represented by interacting model agents. We estimated the water supply by refining the annual runoff data provided by the InVEST water yield model for within-year variations. Local stakeholders contributed to the development of quantitative and spatially-explicit scenarios for land use and tourism evolution. To evaluate water supply and demand dynamics, we assessed six scenarios for the period of 2015 to 2050: three different socio-economic policy pathways, both alone and in combination with a climate change scenario. In all six scenarios, the water demand:supply (D:S) ratio continuously rises from 2015 to 2050.The highest D:S ratio values are prognosed at the beginning of the irrigation period in May. In all scenarios considering climatic changes, the D:S ratio exceeds 20% for several days, indicating potential water scarcity. The simulation results reinforce the importance of analysing water balances at a high temporal resolution and can support management processes and stakeholder dialogues for sustainable watershed management.

With flash flood events having been repeatedly observed in Central and Western Europe in recent years, there is a growing interest in how catchment physiographic properties and hydrological conditions are eventually controlling rapid and concentrated hydrological responses. Here we focus on a set of two nested catchments in Luxembourg (Europe) that have been exposed in 2016 and 2018 to flash flood events and study their seasonal runoff time transfer distributions. Both catchments are of similar size (∼ 30 km²) and have analogous hydrological distance distributions, but their geological bedrock and landscape features are notably different. The upper catchment (KOE) is dominated by a low land area (38 % of the catchment is located less than 30 m above the river network) consisting of variegated marly bedrock (middle Keuper Km3) and moderately steep Luxembourg sandstone outcrops (lower Liassic Li2). The lower catchment (HM) has its drainage network deeply cut into the Luxembourg sandstone, with half of it being covered by marly plateaus (Lower Liassic Li3, located between 80 and 100 m above the river network) featuring heavy clay soil. Based on data generated from a dedicated hydro-meteorological monitoring network, we calculated for 40 rainfall–runoff events observed between August 2019 and July 2021 the corresponding net rainfall transfer time distributions (TTDs) from the hillslopes to the catchment outlet. We then compared the TTD properties and related them to the catchment's hydrological state and rainfall properties. We observed a marked seasonality in TTDs for both catchments. The KOE catchment reacts fastest during the winter period (December–February), while its response time is most delayed and spread out during periods of catchment recharging (October–November) and drying (March–May). The HM catchment exhibits similar TTDs during the mid-October to mid-April period, but they diverge markedly during the remaining part of the year, with opposite variations. During the mid-April to mid-October period, the average response time increases progressively in the KOE catchment. This behavior is in stark contrast to the HM catchment, where response times are significantly shorter (peak discharge delay time decreases by −70 % ± 28 %) and more concentrated (runoff volume occurring in 1 h increases by +48 % ± 87 %) during the mid-April to mid-October, in comparison to the extended winter period. This opposite seasonality leads us to consider different control factors of the runoff transfer processes in relation with the topographic and geological layout of the catchment areas. In the KOE catchment, we found the TTD to be essentially driven by onset and cessation of hydrological connectivity on the flat marly terrain – the latter operating like a variable contributing area in terms of deep soil storage dynamics (except for one summer event). The HM section exhibits contrasted TTDs throughout the year, suggesting threshold-dependent hydrological processes. More specifically, particularly quick runoff transfers seem to dominate under dry conditions (mid-April to mid-October). Correlation analyses compared to the literature on runoff generation on the one hand and our descriptive knowledge of the catchments on the other hand suggest multiple causes for the triggering of these rapid flows. The fractured marly plateaus, but also the hydrophobic forest litter forming during dry conditions on steep slopes, stand as our main hypotheses in this respect. Moreover, the absence of a riparian zone, preventing any dampening of (observed) abrupt and massive flows during extreme precipitation events, also seems to be a key feature of the rapid runoff transfer. For improving our understanding and forecasting capabilities in Luxembourg (and more broadly in the nearby regions of Germany, Belgium, and France with similar physiographic and climate conditions), we recommend further studies focusing on catchments with fractured bedrock and limited riparian zones. Special attention may equally be given to the hypothesized responses of hydrophobic soil surfaces on steep hillslopes and marly soils to heavy precipitation events occurring after extended dry spells.

A key challenge in the sustainable management of freshwater is related to non-stationary processes and transboundary requirements. The assessment of freshwater is often hampered due to small-scale analyses, lacking data and with the focus on only its provision. Based on the ecosystem service (ES) concept, this study aims at quantitatively comparing potential water supply with the demand for freshwater in the European Alps and their surrounding lowlands. We propose an easy-to-use combination of different mapping approaches, including a large-scale hydrologic model to estimate water supply and the downscaling of regional data to the local scale to map demand. Our results demonstrate spatial mismatches between supply and demand and a high dependency of the densely populated lowlands from water providing mountain areas. Under expected climate variations and future demographic changes, our results suggest increasing pressures on freshwater in the south of the Alps. Hence, sustainable water management strategies need to assure the supply of freshwater under changing environmental conditions to meet the increasing water demand of urbanized areas in the lowlands. Moreover, national water management strategies need to be optimally concerted at the international level, as transboundary policies and frameworks can strengthen future water provision.