Contrasting impact of extreme soil and atmospheric dryness on the functioning of trees and forests.

Recent studies indicate an increase in the frequency of extreme compound dryness days (days with both extreme soil AND air dryness) across central Europe in the future, with little information on their impact on the functioning of trees and forests. This study aims to quantify and assess the impact of extreme soil dryness, extreme air dryness, and extreme compound dryness on the functioning of trees and forests. For this, >15 years of ecosystem-level (carbon dioxide and water vapor fluxes) and 6-10 years of tree-level measurements (transpiration and growth) each from a montane mixed deciduous forest (CH-Lae) and a subalpine evergreen coniferous forest (CH-Dav) in Switzerland, is used. The results showed extreme air dryness limitation on CO2 fluxes and extreme soil dryness limitations on water vapor fluxes. Additionally, CH-Dav was mainly affected by extreme air dryness whereas CH-Lae was affected by both extreme soil dryness and extreme air dryness. The impact of extreme compound dryness on net CO2 uptake (about 75 % decrease) was more due to higher increased ecosystem respiration (40 % and 70 % increase at CH-Dav and CH-Lae, respectively) than decreased gross primary productivity (10 % and 40 % decrease at CH-Dav and CH-Lae, respectively). A significant negative impact on evapotranspiration and transpiration was only observed at CH-Lae during extreme soil and compound dryness (about 25 % decrease). Furthermore, with some differences, the tree-level impact on tree water deficit, transpiration, and growth were consistent with the ecosystem-level impact on carbon uptake and evapotranspiration. Finally, the impact of extreme dryness showed no significant relationship with tree allometry (diameter and height) but across different tree species. The projected future is likely to expose these forest areas to more extreme and frequent dryness conditions, thus compromising the functioning of trees and forests, thereby calling for management interventions to increase the adaptive capacity and resistance of these forests.

Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Long-term changes in forest response to extreme atmospheric dryness.

Atmospheric dryness, as indicated by vapor pressure deficit (VPD), has a strong influence on forest greenhouse gas exchange with the atmosphere. In this study, we used long-term (10-30 years) net ecosystem productivity (NEP) measurements from 60 forest sites across the world (1003 site-years) to quantify long-term changes in forest NEP resistance and NEP recovery in response to extreme atmospheric dryness. We tested two hypotheses: first, across sites differences in NEP resistance and NEP recovery of forests will depend on both the biophysical characteristics (i.e., leaf area index [LAI] and forest type) of the forest as well as on the local meteorological conditions of the site (i.e., mean VPD of the site), and second, forests experiencing an increasing trend in frequency and intensity of extreme dryness will show an increasing trend in NEP resistance and NEP recovery over time due to emergence of long-term ecological stress memory. We used a data-driven statistical learning approach to quantify NEP resistance and NEP recovery over multiple years. Our results showed that forest types, LAI, and median local VPD conditions explained over 50% of variance in both NEP resistance and NEP recovery, with drier sites showing higher NEP resistance and NEP recovery compared to sites with less atmospheric dryness. The impact of extreme atmospheric dryness events on NEP lasted for up to 3 days following most severe extreme events in most forests, indicated by an NEP recovery of less than 100%. We rejected our second hypothesis as we found no consistent relationship between trends of extreme VPD with trends in NEP resistance and NEP recovery across different forest sites, thus an increase in atmospheric dryness as it is predicted might not increase the resistance or recovery of forests in terms of NEP.

ForestClim-Bioclimatic variables for microclimate temperatures of European forests.

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.

Spatiotemporal evolution of global long-term patterns of soil moisture.

Surface soil moisture (SM) is essential for existence of biotic lifeform and geophysical processes. However, with increasing global warming due to climatic changes, its spatiotemporal evolution is uncertain and largely unknown. In this study we detected long-term (40 years; 1981-2020) SM patterns of global vegetated areas through spatial timeseries clustering using the state-of-the-art ERA5-Land dataset. In addition, we also analyzed long-term patterns of precipitation (P), evapotranspiration (bare soil evaporation (BSe) and vegetation transpiration (VT)), and normalized difference vegetation index (NDVI). Our results indicate that surface SM (0-7 cm depth) of about 48 % and 9 % of the global vegetated area is showing drying and wetting pattern over the past 40 years, respectively. The detected soil drying, and wetting patterns were largely consistent across different soil depth, with 90 % and 80 % pattern similarity of surface soil layer with 2nd soil layer (7-28 cm) and 3rd soil layer (28-100 cm), respectively. About 80 % of areas with drying soil pattern also showed increasing evapotranspiration and/or decreasing precipitation. Specifically, decreasing P, increasing BSe and VT pattern were detected for 11 % of the soil drying pattern area. Similarly, increasing BSe and VT pattern, only decreasing P and only increasing VT pattern were detected for 17 %, 25 % and 12 % of soil drying areas, respectively. Both decreasing precipitation and increasing evapotranspiration patterns showed about 40 % similarity with decreasing soil moisture patterns. Across different landcover types, broadleaved forests, and cropland areas showed largest drying pattern. Under the future global warming scenario, the global soil water is expected to decrease as evapotranspiration would increase with inconsistent trend of global precipitation change. Our findings are of utmost importance for global soil water resource conservation and management.

Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany.

This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ( N O 2 ) and ozone ( O 3 ) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) N O 2 concentrations by 23 % (& 16 % ) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ O 3 concentration increased by 4 % between March 21 and May 31, 2020, and decreased by 3 % in June 2020, compared to 2019. In the winter and spring, the degree of N O X saturation of ozone production is stronger than in the summer. This implies that future reductions in N O X emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI N O 2 concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced N O X emissions from agricultural soils.

ForestTemp - Sub-canopy microclimate temperatures of European forests.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

What Are the Different Measures of Mobility Telling Us About Surface Transportation CO2 Emissions During the COVID-19 Pandemic?

The COVID-19 pandemic led to widespread reductions in mobility and induced observable changes in atmospheric emissions. Recent work has employed novel mobility data sets as a proxy for trace gas emissions from traffic by scaling CO2 emissions linearly with those near-real-time mobility data. Yet, there has been little work evaluating these emission numbers. Here, we systematically compare these mobility data sets to traffic data from local governments in seven diverse urban and national/state regions to characterize the magnitude of errors that result from using the mobility data. We observe differences in excess of 60% between these mobility data sets and local traffic data. We could not find a general functional relationship between the mobility data and traffic flow over all the regions and observe higher deviations from using such general relationships than the original data. Finally, we give an overview of the potential errors that come from estimating CO2 emissions using (mobility or traffic) activity data. Future work should be cautious while using these mobility metrics for emission estimates.

Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index.

Groundwater is a vital source of freshwater in both urban and rural regions of the world. However, its injudicious abstraction and rapidly increasing contamination are posing a severe threat for sustainable water supply worldwide. Geographical Information System (GIS)-based groundwater quality evaluation using Groundwater Quality Index (GQI) has been proved to be a cost-effective tool for assessing groundwater quality and its variability at a larger scale. However, the conventional GQI approach is unable to deal with uncertainties involved in the assessment of environmental problems. To overcome this limitation, a novel hybrid framework integrating Fuzzy Logic with the GIS-based GQI is proposed in this study for assessing groundwater quality and its spatial variability. The proposed hybrid framework is demonstrated through a case study in a hard-rock terrain of Southern India using ten prominent groundwater-quality parameters measured during pre-monsoon and post-monsoon seasons. Two conventional GIS-based GQI models GQI-10 (using all the ten groundwater-quality parameters) and GQI-7 (using seven 'concerned/critical' groundwater-quality parameters) as well as hybrid Fuzzy-GIS-based GQI (FGQI) models (using seven critical parameters) were developed for the two seasons and the results were compared. The Trapezoidal membership functions classified the model input parameters into 'desirable', 'acceptable' and 'unacceptable' classes based on the experts' knowledge and water quality standards for drinking purposes. The concentrations of Ca2+, Mg2+, and SO42- in groundwater were found within the WHO desirable limits for drinking water throughout the year, while the concentrations of seven parameters (TDS, NO3--N, Na+, Cl-, K+, F- and Hardness) exceed their permissible limits during pre-monsoon and post-monsoon seasons. A comparative evaluation of GQI models revealed that the FGQI model predicts groundwater quality better than the conventional GQI-10 and GQI-7 models. GQI modeling results suggest that the groundwater of most of eastern and southern parts (∼60% in pre-monsoon season; ∼90% in post-monsoon season) of the study area is unsuitable for drinking. Further, the groundwater quality deteriorates during post-monsoon seasons compared to pre-monsoon seasons, which indicates an increased influx of contaminants from different industries, mining areas, waste disposal sites and agricultural fields during monsoon seasons. This finding calls for the strict enforcement of regulations for proper handling of effluents from various contamination sources in the study area. It is concluded that the fuzzy logic-based decision-making approach (FGQI) is more reliable and pragmatic for groundwater-quality assessment and analysis at a larger scale. It can serve as a useful tool for the water planners and decision makers in efficiently monitoring and managing groundwater quality at watershed or basin scales.

Thermal atomic layer deposition of gold nanoparticles: controlled growth and size selection for photocatalysis.

Gold nanoparticles have been extensively studied for their applications in catalysis. For Au nanoparticles to be catalytically active, controlling the particle size is crucial. Here we present a low temperature (105 °C) thermal atomic layer deposition approach for depositing gold nanoparticles on TiO2 with controlled size and loading using trimethylphosphino-trimethylgold(iii) and two co-reactants (ozone and water) in a fluidized bed reactor. We show that the exposure time of the precursors is a variable that can be used to decouple the Au particle size from the loading. Longer exposures of ozone narrow the particle size distribution, while longer exposures of water broaden it. By studying the photocatalytic activity of Au/TiO2 nanocomposites, we show how the ability to control particle size and loading independently can be used not only to enhance performance but also to investigate structure-property relationships. This study provides insights into the mechanism underlying the formation and evolution of Au nanoparticles prepared for the first time via vapor phase atomic layer deposition. Employing a vapor deposition technique for the synthesis of Au/TiO2 nanocomposites eliminates the shortcomings of conventional liquid-based processes opening up the possibility of highly controlled synthesis of materials at large scale.