Climate change heavily impacts smallholder farming worldwide. Cross-scale vulnerability assessment has a high potential to identify nested measures for reducing vulnerability of smallholder farmers. Despite their high practical value, there are currently only limited examples of cross-scale assessments. The presented study aims at assessing the vulnerability of smallholder farmers in the Northeast of Brazil across three scales: regional, farm and field scale. In doing so, it builds on existing vulnerability indices and compares results between indices at the same scale and across scales. In total, six independent indices are tested, two at each scale. The calculated indices include social, economic and ecological indicators, based on municipal statistics, meteorological data, farm interviews and soil analyses. Subsequently, indices and overlapping indicators are normalized for intra- and cross-scale comparison. The results show considerable differences between indices across and within scales. They indicate different activities to reduce vulnerability of smallholder farmers. Major shortcomings arise from the conceptual differences between the indices. We therefore recommend the development of hierarchical indices, which are adapted to local conditions and contain more overlapping indicators for a better understanding of the nested vulnerabilities of smallholder farmers.

We present the data recorded by eight institutions within the PYROPHOB project, running from 2020 to 2024 at two forest research sites in the south-west of Brandenburg, Germany. The aim of the project was to monitor abiotic and biotic parameters of forest regrowth under different management regimes on former wildfire sites in northeastern Germany. The observations comprised intermittent and continuous measurements or surveys on meteorological parameters (rainfall, temperature, vapour pressure deficit), soil (soil type and texture, soil chemistry and leaching, water content, soil temperature), deadwood, stand structure, vegetation regrowth, abundance of selected fauna (moths, beetles, mammals), UAV-based remote sensing (stand structure, spatial overview), and photo monitoring (succession). Thus, the multitude of collected data allows not only for detailed analyses of these observables separately, but also considering their interaction for a more multidisciplinary view on forest recovery after a wildfire. The data are available under the following DOIs: https://doi.org/10.23728/b2share.08a6a8b56f774a08a984871e7ef1b5a8 (Part 1, Schmehl et al., 2025a), under embargo until 31 March 2026 (Part 2, Schmehl et al., 2025b), https://doi.org/10.23728/b2share.de1661761da5481f938caf460e4f0ca9 (Part 3.1, Schmehl et al., 2025c), and https://doi.org/10.23728/b2share.51bdf4b6dc854873b6ff44fdddbf4c3b (Part 3.2, Schmehl et al., 2025d).

We present the data recorded by eight institutions within the PYROPHOB project, running from 2020 to 2024 at two forest research sites in the south-west of Brandenburg, Germany. The aim of the project was to monitor abiotic and biotic parameters of forest regrowth under different management regimes on former wildfire sites in northeastern Germany. The observations comprised intermittent and continuous measurements or surveys on meteorological parameters (rainfall, temperature, vapour pressure deficit), soil (soil type and texture, soil chemistry and leaching, water content, soil temperature), deadwood, stand structure, vegetation regrowth, abundance of selected fauna (moths, beetles, mammals), UAV-based remote sensing, and photo monitoring. Thus, the multitude of collected data allows not only for detailed analyses of these observables separately, but also considering their interaction for a more multidisciplinary view on forest recovery after a wildfire. The data are available under the following DOIs: 10.23728/b2share.5821b57eae3b45619c2263205b1c9815 (Part 1, (Schmehl et al., 2025a)), under embargo until 2026-03-31 (Part 2, (Schmehl et al., 2025b)), 10.23728/b2share.d49631857d1345c48be9e36b62214e6d (Part 3.1, (Schmehl et al., 2025c)), and 10.23728/b2share.51bdf4b6dc854873b6ff44fdddbf4c3b (Part 3.2, (Schmehl et al., 2025d)).

Urban surface sealing limits infiltration and thus increases the formation of runoff during heavy rain events. Green infrastructure (GI) measures can be used to reduce urban flood risk by promoting decentralized infiltration, water storage and evapotranspiration. With a scenario-based modelling study, we investigate the impact of green infrastructure on urban runoff formation, flood water depths and the resulting damage to buildings; comparing it with the impact of the conventional drainage system. The study area is located in the city of Berlin, in a heavily sealed 3.3 km² urban catchment. Design rain storms with a duration of one hour and totals between 15 and 100 mm are considered. The green infrastructure scenarios include different spatial extents and combinations of bioretention systems, green roofs and pervious pavement. The Storm Water Management Model is used for the urban runoff generation and the 2D-hydrodynamic module of TELEMAC for surface runoff concentration. Building damage is modelled with the Flood Damage Estimation Tool, a recursive partitioning tool developed with survey data representative of building damage caused by pluvial floods. Flood mitigation is investigated regarding absolute and relative reduction and also space efficiency of the GI types. Relative flood mitigation reduces at all modelling steps with increasing rain totals. In contrast, absolute runoff reduction increases with increasing rain totals while the area with maximum water level > 10 cm decreases the most at the 49 mm event and building damage reduces most at 25–30 mm. Bioretention systems achieve the highest spatial efficiency, however, green roofs and pervious pavements do not impede the former land use.

Urban surface sealing limits infiltration and thus increases the formation of runoff during heavy rain events. Green infrastructure (GI) measures can be used to reduce urban flood risk by promoting decentralized infiltration, water storage and evapotranspiration. With a scenario-based modelling study, we investigate the impact of green infrastructure on urban runoff formation, flood water depths and the resulting damage to buildings; comparing it with the impact of the conventional drainage system. The study area is located in the city of Berlin, in a heavily sealed 3.3 km² urban catchment. Design rain storms with a duration of one hour and totals between 15 and 100 mm are considered. The green infrastructure scenarios include different spatial extents and combinations of bioretention systems, green roofs and pervious pavement. The Storm Water Management Model is used for the urban runoff generation and the 2D-hydrodynamic module of TELEMAC for surface runoff concentration. Building damage is modelled with the Flood Damage Estimation Tool, a recursive partitioning tool developed with survey data representative of building damage caused by pluvial floods. Flood mitigation is investigated regarding absolute and relative reduction and also space efficiency of the GI types. Relative flood mitigation reduces at all modelling steps with increasing rain totals. In contrast, absolute runoff reduction increases with increasing rain totals while the area with maximum water level > 10 cm decreases the most at the 49 mm event and building damage reduces most at 25–30 mm. Bioretention systems achieve the highest spatial efficiency, however, green roofs and pervious pavements do not impede the former land use.