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Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the link between vegetation and surface fluxes by combining the yearly average MODIS leaf area index (LAI) with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation of the LAI with water and energy fluxes depends on the vegetation type and aridity. Under water-limited conditions, the link between the LAI and the water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no link between the LAI and water and energy fluxes. In contrast to water and energy fluxes, we found a strong spatial correlation between the LAI and gross primary productivity that was independent of vegetation type and aridity. This study provides insight into the link between vegetation and surface fluxes. It indicates that for modelling or extrapolating surface fluxes, the LAI can be useful in savanna and grassland, but it is only of limited use in deciduous broadleaf forest and evergreen needleleaf forest to model variability in water and energy fluxes.

Quantifying relative contributions of plant transpiration (T) and soil evaporation to evapotranspiration (ET) is crucial to better understand how vegetation influences and controls ET, the largest efflux of the terrestrial water balance. Here, we derive estimates of transpiration fraction (T/ET) using consistent isotope‐based ET partitioning methods for 13 sites spanning five ecosystem types of the continental US, capturing 56 snapshots of T/ET during the growing season. We found transpiration dominated the ET flux across all sites with a mean T/ET of 0.81 ± 0.08 (±standard error). Sites and dates with higher vegetation indices exhibited higher T/ET and transpiration rates, with the latter increasing 0.30 mm/day per unit Leaf Area Index and 2.9 mm/day per unit Normalized Difference Vegetation Index. Counter to expectations, antecedent precipitation had no effect on T/ET. Despite the breadth of ecosystems and conditions represented, evaporation exceeded transpiration only once, suggesting that evaporation rarely dominates ET in the growing season.

Fire weather indices are used to assess the effect of weather on wildfire behaviour and to support fire management. Previous studies identified the high daily severity rating percentile (DSRp) as being strongly related to the total burnt area (BA) in Portugal, but it is still poorly understood how this knowledge can support fire management at a smaller spatial scale. The aims of this study were to (1) assess whether the 90th DSRp (DSR90p) threshold is adequate for estimating most of the BA in mainland Portugal; (2) analyse the spatial variability of the DSRp threshold that explains a large part of BA, at higher resolution; and, (3) analyse whether vegetation cover can justify the DSRp spatial variability. We used weather reanalysis data from ERA5-Land, wildfire and land use data from Portuguese land management departments for an extended summer period (15 May to 31 October) from 2001 to 2019. We computed and related DSRp to large wildfires (BA > 100 ha) and land use to clarify the effectiveness of the DSRp for estimating BA in Portugal and assess how vegetation influences it. Results revealed that the DSR90p is an adequate indicator of extreme fire weather days and BA in Portugal. In addition, the spatial pattern of the DSRp associated with most of the total BA shows variability at the municipality scale. Municipalities where large wildfires occur with more extreme weather conditions have most of the BAs in forests and are in coastal areas. By contrast, municipalities where large wildfires occur with less extreme weather conditions are predominantly covered by shrublands and are situated in eastern and inland regions. These findings are a novelty for fire science in Portugal and should be considered by fire managers and fire risk assessors.

Previous studies have demonstrated that vegetation‐generated turbulence can enhance erosion rate and reduce the velocity threshold for erosion of non‐cohesive sediment. This study considered whether vegetation‐generated turbulence had a similar influence on natural cohesive sediment. Cores were collected from a black mangrove forest with aboveground biomass and exposed to stepwise increases in velocity. Erosion was recorded through suspended sediment concentration. For the same velocity, cores with pneumatophores had elevated turbulent kinetic energy compared to bare cores without pneumatophores. However, the vegetation‐generated turbulence did not increase bed stress or the rate of resuspension, relative to bare cores. It was hypothesized that the short time‐scale fluctuations associated with vegetation‐generated turbulence were not of sufficient duration to break cohesion between grains, explaining why elevated levels of turbulence associated with the pneumatophores had no impact on the erosion threshold or rate. Plain Language Summary Mangrove habitat grows by retaining sediment. To restore these systems, it is necessary to understand how vegetation influences the transport and retention of sediment. This study used sediment cores collected from the interior of a mangrove forest to study how the aboveground roots, called pneumatophores, influence hydrodynamic conditions and sediment transport, and in particular the onset and rate of sediment erosion. Individual pneumatophores generate eddies that enhance turbulence, compared to conditions without pneumatophores. In sandy soil, vegetation‐generated turbulence can enhance erosion. However, in this study, vegetation‐generated turbulence did not increase the rate of erosion for natural cohesive (muddy) sediment, suggesting that the mangrove forest interior has naturally greater resistance to erosion and sediment loss. Key Points For the same velocity, cores with pneumatophores had higher turbulent kinetic energy (TKE) compared to cores without pneumatophores Unlike sands, the inception of erosion and erosion rates for cohesive sediment were better predicted by bed shear stress than by TKE Modelers should parameterize erosion within vegetation differently for cohesive and non‐cohesive sediment

Vegetation influences the atmosphere in complex and nonlinear ways, such that large-scale changes in vegetation cover can drive changes in climate on both local and global scales. Large-scale land surface changes have been shown to introduce excess energy to one hemisphere, causing a shift in atmospheric circulation on a global scale. However, past work has not quantified how the climate response scales with the area of vegetation. Here, the response of climate to linearly increasing the area of forest cover in the northern midlatitudes is systematically evaluated. This study shows that the magnitude of afforestation of the northern midlatitudes determines the local climate response in a nonlinear fashion, and the authors identify a threshold in vegetation-induced cloud feedbacks—a concept not previously addressed by large-scale vegetation manipulation experiments. Small increases in tree cover drive compensating cloud feedbacks, while latent heat fluxes reach a threshold after sufficiently large increases in tree cover, causing the troposphere to warm and dry, subsequently reducing cloud cover. Increased absorption of solar radiation at the surface is driven by both surface albedo changes and cloud feedbacks. This study shows how atmospheric cross-equatorial energy transport changes as the area of afforestation is incrementally increased. The results highlight the importance of considering both local and remote climate effects of large-scale vegetation change and explore the scaling relationship between changes in vegetation cover and resulting climate impacts.

Alternate bars migrate downstream during floods due to coupled erosion and deposition on both sides of alluvial river channels. During low discharge periods, vegetation can grow on the tops of these bars, reducing migration rates and increasing bar wavelengths and bar heights. We explore two specific effects of above-ground vegetation on flow and transport. First, above-ground roots and groundcover can reduce bedload transport rates due to near-bed roughness, an effect not explored in most previous studies. Second, vegetation bodies (i.e. the above-ground trunk, stem, branches, and leaves) generate hydraulic drag. We model vegetation influences on alternate bar evolution using previously proposed equations which consider both vegetation body and above-ground root effects. We investigated three scenarios: vegetation body effects only, above-ground root effects only, and the full vegetation system (i.e., body and above-ground roots together). We find that vegetation body and root effects both reduce the bar migration rate and increase the bar wavelength. Reduced flow velocities over the bars due to vegetation body effects tend to enhance velocities and localized erosion on the opposite side of the channel, which in turn increases relative bar heights. Bar morphology and migration rate are most sensitive to vegetation growth rates at lower flood discharges where bar-top vegetation persists from year to year and older vegetation has stronger impacts on flow and transport. Higher peak floods tend to remove and reset vegetation growth, resulting in little sensitivity to growth rate.

Aim To verify which vegetation and environmental factors are the most important in determining the spatial and temporal variability of average and maximum values of radiation use efficiency (RUEann and RUEmax, respectively) of cold and temperate forests. Location Forty‐eight cold and temperate forests distributed across the Northern Hemisphere. Major taxa studied Evergreen and deciduous trees. Time period 2000–2011. Methods We analysed the impact of 17 factors as potential determinants of mean RUE (at 8 days interval, annual and interannual level) and RUEmax (at annual and interannual level) in cold and temperate forests by using linear regression and random forests models. Results Mean annual RUE (RUEann, c. 1.1 gC/MJ) and RUEmax (c. 0.8 gC/MJ) did not differ between cold and temperate forests. However, for cold forests, RUEann was affected by temperature‐related variables, while for temperate forests RUEann was affected by drought‐related variables. Leaf area index (LAI) was important for both forest types, while N deposition only for cold forests and cloud cover only for temperate forest. RUEmax of cold forests was mainly driven by N deposition and LAI, whereas for temperate forests only a weak relationship between RUEmax and CO2 concentration was found. Short‐term variability of RUE was strongly related to the meteorological variables and varied during the season and was stronger in summer than spring or autumn. Interannual variability of RUEann and RUEmax was only weakly related to the interannual variability of the environmental drivers. Main conclusions Cold and temperate forests show different relationships with the environment and vegetation properties. Among the RUE drivers observed, the least anticipated was N deposition. RUE is strongly related to short‐term and seasonal changes in meteorological variables among seasons and among sites. Our results should be considered in the formulation of climate zone‐specific tools for remote sensing and global models.

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O3 and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO2 concentrations are controlled by the changes in O3 concentrations, increasing where O3 concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution.

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Urban areas are characterized by very intense and complex interactions between built objects and natural elements, especially in terms of the impact they have on the local microclimate. One of the most important aspects of this interaction is the way in which vegetation influences incident solar radiation, which has a major impact on energy consumption, comfort and the overall sustainability of urban areas. In this study, we focus on the city of Zagreb, where we conduct a detailed analysis of the impact of urban vegetation on solar radiation, comparing areas with and without high vegetation. For this purpose, we calculated a digital surface model (DSM) from high-resolution LiDAR data. LiDAR technology enables very good mapping of both the terrain and above-ground features such as buildings and vegetation. The creation of two different DSMs - one with high vegetation and one without - allows us to isolate the specific effects of vegetation on solar radiation. The analysis was performed in WhiteboxTools using the TimeInDaylight command, which calculates the daylight in a landscape over a given period of time. This command takes into account the position of the sun and topographic features in the DSMs for accurate modeling of solar radiation. The result shows that solar radiation is different in areas with and without dense vegetation. The study found that tall vegetation, such as trees, greatly reduces solar radiation on the ground and surrounding buildings. A reduced impact of solar radiation can lead to cooler surface temperatures and a lower cooling demand in summer, contributing to higher thermal comfort in urban areas. Without vegetation, radiation would be much higher, increasing heat build-up and thus the extent of the urban heat island.