• Energy Research

AbstractCurrent analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

AbstractExtreme precipitation and drought events are predicted to become more intense and more frequent over the Amazon rainforest. Because changes in forest dynamics could prompt strong feedback loops to the global climate, it is of crucial importance to gain insight into the response of tropical forests to these recurring extreme climatic events. Here, we evaluated the Amazon forest stability (resistance and resilience) to drought in the context of past dry and wet climatic events using MODIS EVI satellite imagery and cumulative water deficit anomalies. We observed large spatial differences in the occurrence of extreme climatic events from 1980 to 2019, with an increase in drought frequency in the central and northern Amazon and drought intensity in the southern Amazon basin. An increasing trend in the occurrence of wet events was found in the western, southern, and eastern Amazon. Furthermore, we found significant legacy effects of previous climatic events on the forest drought response. An extreme drought closely preceding another drought decreased forest resilience, whereas the occurrence of a recent drier‐than‐usual event also decreased the forest resistance to later droughts. Both wetter‐than‐usual and extreme wet events preceding an extreme drought increased the resistance of the forest, and with similar effects sizes as dry events, indicating that wet and dry events have similarly sized legacy effects on the drought response of tropical forests. Our results indicate that the predicted increase in drought frequency and intensity can have negative consequences for the functioning of the Amazon forest. However, more frequent wet periods in combination with these droughts could counteract their negative impact. Finally, we also found that more stable forests according to the alternative stable states theory are also more resistant and resilient to individual droughts, showing a positive relationship between the equilibrium and non‐equilibrium stability dynamics.

Urban green spaces are known to provide ample benefits to human society and hence play a vital role in safeguarding the quality of life in our cities. In order to optimize the design and management of green spaces with regard to the provisioning of these ecosystem services, there is a clear need for uniform and spatially explicit datasets on the existing urban green infrastructure. Current mapping approaches, however, largely focus on large land use units (e.g., park, garden), or broad land cover classes (e.g., tree, grass), not providing sufficient thematic detail to model urban ecosystem service supply. We therefore proposed a functional urban green typology and explored the potential of both passive (2 m-hyperspectral and 0.5 m-multispectral optical imagery) and active (airborne LiDAR) remote sensing technology for mapping the proposed types using object-based image analysis and machine learning. Airborne LiDAR data was found to be the most valuable dataset overall, while fusion with hyperspectral data was essential for mapping the most detailed classes. High spectral similarities, along with adjacency and shadow effects still caused severe confusion, resulting in class-wise accuracies <50% for some detailed functional types. Further research should focus on the use of multi-temporal image analysis to fully unlock the potential of remote sensing data for detailed urban green mapping.

Extensive green roofs are a promising type of urban green that can play an important role in climate proofing and ultimately in the sustainability of our cities. Despite their increasingly widespread application and the growing scientific interest in extensive green roofs, their aesthetics have received limited scientific attention. Furthermore, several functional issues occur, as weedy species can colonize the roof, and extreme roof conditions can lead to gaps in the vegetation. Apart from altering the function of a green roof, we also expect these issues to influence the perception of extensive green roofs, possibly affecting their acceptance and application. We therefore assessed the preferences of a self-selected convenience sample of 155 Flemish respondents for visual aspects using a discrete choice experiment. This approach, combined with current knowledge on the psychological aspects of green roof visuals, allowed us to quantify extensive green roof preferences. Our results indicate that vegetation gaps and weedy species, together with a diverse vegetation have a considerable impact on green roof perception. Gaps were the single most important attribute, indicated by a relative importance of ca. 53%, with cost coming in at a close second at ca. 46%. Overall, this study explores the applicability of a stated preference technique to assess an often overlooked aspect of extensive green roofs. It thereby provides a foundation for further research aimed at generating practical recommendations for green roof construction and maintenance.