Non-steady-state closed dynamic accumulation chambers are widely used to measure the respiration of terrestrial ecosystems, thanks to their low cost, low energy consumption and simple transportability, that allow measurements even in hostile and remote environments. However, the assessment of the accuracy and precision associated with the measurement system (independently of possible disturbances due to chamber-soil interactions) is rarely reported. This information is instead necessary for basic quality control, to compare data obtained by different devices and regression models and to provide Confidence Intervals (CIs) on the carbon flux values. This study quantifies the uncertainty associated with emission flux measurements, with a focus on very low fluxes. Calibration tests using different accumulation chambers and CO₂ sensors were performed, and fluxes were calculated by means of different models (parametric, non-parametric and flux models). The results of this work show that the linear regression model has the best reproducibility when compared to the other tested models, regardless of the sensor used and the chamber volumes, while the second order polynomial regression has the best accuracy. We remark the importance of building a calibration curve in the range of the expected flux values, with an interval between the lowest and highest imposed flux that should not exceed two orders of magnitude. To evaluate the reproducibility of the measurement, performing replicates for each imposed flux value is essential. We also show that it is necessary to carefully identify the best time interval for interpolating the CO₂ concentration curve in order to guarantee reproducibility and accuracy in flux estimates.

The dynamics of carbon dioxide fluxes in the high-altitude Alpine Critical Zone is only partially understood. The complex geomorphology induces significant spatial heterogeneity, and a strong interannual variability is present in the often-extreme climatic and environmental conditions of Alpine ecosystems. To explore the relative importance of the spatial and temporal variability of CO₂ fluxes, we analysed a set of in-situ measurements obtained during the summers from 2018 to 2021 in four sampling plots, characterised by soils with different underlying bedrock within the same watershed in the Nivolet plain, in the Gran Paradiso National Park, western Italian Alps. Multi-regression models of CO₂ emission and uptake were built using measured meteo-climatic and environmental variables considering either individual years (aggregating over plots) or individual plots (aggregating over years). We observed a significant variability of the model parameters across the different years, while such variability was much smaller across different plots. Significant changes between the different years mainly concerned the temperature dependence of respiration (CO₂ emission) and the light dependence of photosynthesis (CO₂ uptake). These results suggest that spatial upscaling can be obtained from site measurements, but long-term flux monitoring is required to properly capture the temporal variability at interannual scales.

Precipitation is crucial for water resource renewal, but climate change alters their frequency and amounts, challenging societies for correct and effective water management. However, modifications of precipitation dynamics appear to be not uniformly distributed, both in space and time. Even in relatively small areas, precipitation shows the coexistence of positive and negative trends. Local topography seems to be a strong driver of precipitation changes. Understanding precipitation changes and their relationship with local topography is crucial for society’s resilience. Taking advantage of a dense and long-lasting (1920–2019) meteorological monitoring network, we analyzed the precipitation changes over the last century in a sensitive and strategic area in the Mediterranean hotspot. The study area corresponds to northern Tuscany (Italy), where its topography comprises mountain ridges and coastal and river plains. Forty-eight rain gauges were selected with continuous annual precipitation time series. These were analyzed for trends and differences in mean annual precipitation between the stable period of 1921–1970 and the last 30-year 1990–2019. The relationship between precipitation changes and local topography was also examined. The results show the following highlights: (i) A general decrease in precipitation was found through the century, even if variability is marked. (ii) The mountain ridges show the largest decrease in mean annual precipitation. (iii) The precipitation change entity over the last century was not homogenous and was dependent on topography and geographical setting. (iv) A decrease in annual precipitation of up to 400 mm was found for the mountainous sites.