Edoardo Cremonese

As drought frequency and intensity increase globally, understanding ecosystem resilience and vegetation response to prolonged dry periods is crucial. However, while several resilience metrics exist, they are rarely assessed across different contexts and ecosystem types. This is particularly relevant when using remote sensing products, which enable large-scale monitoring.We propose a five-step methodology to identify suitable metrics for assessing ecosystem resilience to droughts:s1) standardizing drought indices and detrending vegetation spectral indices;s2) identifying the appropriate aggregation timescale for drought and vegetation indices, via a correlation analysis;s3) defining drought events and a set of possible ecosystem resilience metrics;s4) selecting metrics capable of separating vegetation responses to drought from other ecosystem processes, employing a "surrogate period experiment";s5) assessing the selected metrics across different classes of interest.We tested the methodology by applying it to locations where drought impacts have been documented across the Mediterranean Basin. We examined monthly timeseries of the Standardized Precipitation–Evapotranspiration Index (SPEI) at 3-, 6-, and 12-months time-scales and four vegetation spectral indices (Normalized Difference Vegetation Index NDVI, Enhanced Vegetation Index EVI, the near infrared reflectance of vegetation NIRv, and the kernel NDVI kNDVI).SPEI-12 and NDVI/kNDVI provided the most informative combinations for detecting drought impacts. Eight literature-based resilience metrics were evaluated, with Productivity Loss and Recovery metrics presenting the most consistent trends across locations and drought definitions.Our methodology provides a transferable framework for event-based assessments of ecosystem resilience in drought-sensitive areas and those becoming so. Moreover, our findings support the relevance of these metrics for large-scale assessments of drought resilience in Mediterranean ecosystems.

This study evaluates the performance of Sentinel-2 derived fAPAR in capturing vegetation stress under dry conditions in Southern Italy, a drought-prone Mediterranean region. The Sentinel-2 product was compared with MODIS, VIIRS, and Copernicus Global Land Service fAPAR datasets in terms of monthly averages and anomalies, aggregated over the diverse land cover types presented in the study area. The performance of the products was assessed in relation with the reference dry periods defined with common drought indicators. Despite its shorter timeline, the Sentinel-2 product demonstrated good consistency with other fAPAR datasets in both temporal dynamics and drought-related anomalies, particularly with the Copernicus Global Land Service fAPAR. These findings support Sentinel-2 use for high-resolution drought monitoring, as the ability to capture fine-scale heterogeneity is particularly valuable in heterogeneous agricultural landscapes, where drought impacts may vary substantially across short distances. However, further research is needed to address uncertainties and temporal limitations associated with Sentinel-2 observations.

Forest ecosystems play a crucial role in mitigating climate change through carbon (C) sequestration (i.e., photosynthesis). However, the increasing frequency and intensity of climate extremes, especially heatwaves and drought events, are affecting the balance between C uptake and C loss in terrestrial ecosystem, resulting in decreasing CO2 sequestration from the atmosphere and storage as plant biomass. Reaching the ecosystem tipping point, with the consequent transition from carbon sinks to carbon sources, is a concrete risk. Climate change has the potential to alter different forest processes in complex ways, showing high variability across space and time, and impacting ecological processes at different spatial scales, from the individual trees to the whole ecosystem. The structure of forests and the diversity in vegetation distribution may act as a form of insurance against these environmental changes, but the understanding of the mechanisms behind forest resistance and resilience to climate change require further investigation. The primary objective of the present study is to establish a quantitative link between the functional and structural properties of a European larch forest located in the Western Alps, within the Aosta Valley (Italy) at an altitude of around 2100 meters a.s.l. (Larix decidua Mill., IT-Trf, LTER site: LTER_EU_IT_078). To achieve this goal, the study take advantage of different methods. The main approach is represented by the eddy covariance method to measure C fluxes at two levels: 1) whole forest ecosystem and 2) understory vegetation. Biometric measurements of tree growth, performed in 2020, aimed at quantifying the carbon allocated to the wood biomass as structural carbohydrates. Additionally, satellite and UAV-based LiDAR observations were used to analyse the structure and composition of various vegetation layers. Results revealed that only a small fraction of the total CO2 sequestered by the ecosystem contributes to tree stem growth. Furthermore, due to the openness of the larch forest stand, the understory vegetation accounts for approximately 30% of the total ecosystem's gross primary productivity. These findings underscore the intricate relationship between forest structure, overall ecosystem function, and the specific roles played by various vegetation components.

Under the Paris Agreement, countries are encouraged to preserve and enhance existing carbon sinks, especially forests, thereby including the LULUCF (Land Use, Land Use Change and Forestry) sector in international climate mitigation targets. In particular, Europe has set the target to reach climate neutrality, i.e. a balance between anthropogenic emissions by sources and removals by sinks, by 2050. A prerequisite to reach these goals is an accurate and credible estimation of both these large fluxes. However, recent works highlighted the uncertainty related to the quantification of the land sector mitigation potential, one of the most challenging emission sectors. Moreover, the definition of climate mitigation policies often occur at the local level, where details on CO2 removals from forests and other land uses are traditionally lacking. Indeed, local authorities (e.g. cities and Regions) can be more effective in the transition to a sustainable economy compared to higher level authorities such as Nations. In this study, we tested a data-driven method based on eddy covariance (EC) data to quantify the current LULUCF role to the regional carbon sink of the Aosta Valley Region (Italy), by the integration of different approaches. Our model is based on eddy covariance measurements of CO2 fluxes, MODIS NDVI (250m), daily gridded meteorological variables at 100m spatial resolution, and a land cover map at 250m spatial resolution. A Random Forest model was used to up-scale the point eddy covariance data to the Regional level, by testing different sets of drivers (air temperature, VPD, Snow (presence/absence), NDVI, solar radiation,...). Our model was then compared to independent data derived from the National Forest Inventory (NFI), and a process-based model. Preliminary results show that forests and other ecosystems of the Region remove nearly 70% of the total anthropogenic emissions in this area. The discrepancies between the different methods will be discussed by exploring the different advantages and flaws and the spatio-temporal variability of the different approaches. Such an assessment of the local carbon budget and its uncertainties will provide a solid base for Climate-smart management of the territory and thus for reaching the carbon neutrality targets.

Field spectroscopy is a powerful tool for understanding plant carbon uptake and for providing a link between local flux measurements and global satellite remote sensing. In fact, optical remote sensing techniques can capture valuable information on both phenology and physiology. Even if the concept is of interest for the scientific community, the integration of field spectroscopy techniques in flux networks is still challenging and largely unresolved. Further, mainly due to the lack of available instruments, obtained relationships between remote sensing measurements and gross primary productivity (GPP) are often site specific and so far poorly exploited.JB devices (FloX and RoX) are field spectrometer systems acquiring in situ radiometric measurements with standardized routines. They have been installed over the last five years across several ecosystems (e.g. croplands, forests, grasslands), often at eddy covariance stations. Recently, an effort was made to standardize the data processing and to align it to flux networks. The development of an open source processing chain was made and the definition of all the metadata needed to correctly interpret the measurements, including devices specification and setup, is ongoing.With this contribution, we aim to advance understanding on the relationship between optical proximal sensing information and GPP using a selection of eight diverse sites equipped with JB devices and belonging to flux networks (ICOS or FLUXNET). Selected sites represent different vegetation types, including broadleaf forest, needle leaf forests, croplands, and grasslands. The time series length varies from one season to 4 years, depending on the time the instrument was installed. From the hyperspectral data, a subset of vegetation indices (e.g. indices related to biomass, chlorophyll content, carotenoids, and sun induced chlorophyll fluorescence (SIF, NIRv), and was computed where available (i.e. only on six of the equipped FloX sites). The data output was qualitatively checked and aggregated to the same temporal resolution as for the flux data (30 mins). Several models were tested to investigate this relationship, exploited both on half-hourly interval and on daily aggregation. Overall, good results were found. In general, SIF and NIRv were found to be the best predictor for GPP at half-hourly scales (r2 =0.75 over croplands and broadleaf forests). When the analysis was computed on daily aggregation, the indices related to chlorophyll content showed the best agreement with GPP. Significant differences were found according to vegetation types, where needle leaf forests were giving the poorest results. Our analysis demonstrates the valuable information carried by field spectroscopy data in the context of understanding GPP dynamics, supporting the hypothesis that the link between optical sensing and fluxes can be better interpreted with a growing number of field spectrometer data available at flux sites.

Water conflicts generally stem from an imbalance between water demand and availability; as such, they are often studied as a result of meteorological droughts – that is, a lack of precipitation or streamflow. By shifting water availability from wet winters to dry summers, when demand peaks, we hypothesized that snow water resources represent a crucial precursor of this imbalance, and thus play an important, but unexplored role in escalating drought-related water crises and conflict. To shed light on the nexus between snow droughts and increased water challenges, we draw lessons from the extraordinarily warm, dry, and prolonged 2021-2022 snow drought in the Italian Alps, from the consequent spring-to-summer water deficit, and from the relative seeds of conflict. To this end, we compared the spatial distribution of snow water resources deficit with the distribution and type of municipal mandatory water restrictions, under the assumption that the former are proxies of a future deficit in availability, while the latter are proxies of an imbalance between this availability and needs. We found initial evidence that the location and magnitude of the deficit in snow water resources observed across the Italian Alps in winter 2022 (-60% or more at peak accumulation) did result in seeds of institutional conflicts later in spring and summer. These findings can aid institutions and policymakers in understanding the mechanisms behind emerging water conflicts and their implications, and so design ad-hoc water policies, especially in a warming climate.

Both natural and managed ecosystems contribute to mitigating climate change through the process of CO2 sequestration from the atmosphere. However, projections of future global climate indicate that extreme weather events will become more frequent and more intense in the coming years. And since heat waves and droughts can alter the ecosystems functioning, and increase the vulnerability of carbon sinks, this trend represents a potential risk for their contribution to reaching global climate change mitigation goals. Further efforts are therefore needed to assess the resistance and resilience of different ecosystems and land uses to climate change. In this context, Alpine mountain ecosystems face a double challenge, on the one hand, warming in the Alps is occurring twice as fast as in other regions of the planet and drought events are increasingly frequent, on the other, socio-economic changes have led to partial land abandonment, with effects on the composition and distribution of plant species.The main objective of this study is to analyze the impacts of extreme heat and drought events on the functioning of two different ecosystems in the Alps, a European larch forest (Larix decidua Mill.) and an abandoned subalpine pasture dominated by Nardus stricta, both located in the Aosta Valley region (Italy) at about 2100 m asl and thus experiencing the same climate conditions. The eddy covariance method was used to measure the carbon and water fluxes between the ecosystem and the atmosphere. Radiometric vegetation indices (eg. NDVI), and field observations related to plant phenology were used to explain the role of timing in determining the carbon and water fluxes impacts. Finally, functional traits of plant species were used to interpret the divergent ecosystem responses from an adaptive perspective. The results show that the different heat and drought events observed during the ten-year study period (2012-2022) had a variable impact on the different ecosystems monitored, also based on the timing of the extreme event in relation to the phenology of the vegetation and the presence/absence of the snowpack, with impacts generally more severe for the grassland compared to the forest. The contrasting responses observed will be discussed by exploring the linkage between the functioning of the whole ecosystem and the adaptive strategies of individual plant species.

Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant-survey-based approaches applied along chronosequences provide invaluable information on plant communities, the “space-for-time” approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonization dynamics using a so-called “real-time” approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the southwestern European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonization by plants and in subsequent growth rates and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., neighboring vegetation cover and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, which is perhaps due to insufficient spatial resolution of predictor variables. Notably, the identity of pioneer plant species was highly variable, and initial plant community composition had a much stronger influence on plant assemblages than elapsed time since deglaciation. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather determined by local ecological context. We discuss the importance of scale in deciphering the complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefield systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields.