Search

Abstract: Although foliar reflectance in the visible wavelengths is largely understood, species-specific relations between leaf spectral properties, pigment content and carbon exchange, and interdependence of these fundamental drivers that ultimately produce large-scale signals complicate understanding of and upscaling in remote sensing applications. We recorded seasonal patterns in foliar reflectance in relation to leaf photosynthetic, biochemical, structural and optical properties in two co-occurring tree species, red oak (Quercus rubra) and paper birch (Betula papyrifera). Over the course of a growing season, we monitored the timing of phenological events, i.e. bud break, near-complete leaf expansion and leaf fall, on mature trees. On a monthly basis, maximum rate of carboxylation (V cmax) and maximum rate of electron transport (J max) were estimated from leaf-level gas exchange measurements in the upper crown for three individuals per species. Thereafter, visible and near infrared spectral properties, nitrogen content and specific leaf area were determined for sampled sunlit leaves. These data were compared with color indices extracted from digital images of sampled leaves throughout the growing season. Studied leaf traits significantly varied between the two species and throughout the growing season. Paper birch was characterized by relatively early bud break and rapid leaf expansion. Hence, interactions between species and day of year could be partly contributed to contrasting spring phenology of paper birch and red oak. Spectral vegetation indices, Chlorophyll Normalized Difference Index (Chl NDI), Photochemical Reflectance Index (PRI) and in particular Red Edge Position (λRE), gave a good indication of leaf physiology over the course of the growing season, more specifically of photosynthesis and leaf nitrogen on an area basis (N area). On the other hand, color indices performed rather poorly at tracking key leaf functional traits in this study. Overall, dark green leaves characterized by low Intensity (I, derived from HSI color space) displayed highest photosynthetic activity and highest values of spectral vegetation indices. [Copyright &y& Elsevier]

Inside orchards, turbulent coherent structures dominate the transport of heat, momentum, and moisture between the canopy and the atmosphere. Integrated quadrant analysis is a method to visualize the trajectory of individual turbulent coherent structures using in situ data from three-dimensional anemometry. In this paper, integrated quadrant analysis is used to characterize the turbulent transport of heat and momentum from two orchard experiments: one in the interrow space (the Canopy Horizontal Array Turbulence Study from Dixon, California in May and June 2007) and one in the crown of a tree (the Vertical Array Cherry Experiment from Linden, California in November 2019). By using the integrated quadrant analysis (IQA) method, this paper demonstrates the importance of the cross-wind velocity component in maintaining the turbulent coherent structures. Results from integrated quadrant analysis in three dimensions support the idea that the microfront is collocated with the boundary of a sweep and an ejection in a convective boundary layer. Moreover, in both orchards, there are preferred planar trajectories for individual coherent structures that do not depend on wind regimes. The statistical profile of the turbulence quantities, as well as individual coherent structures, are not appreciably different in the interrow space or within the crown. • IQA provides the 3D coherent structure trajectory in two orchard experiments. • Individual coherent structures show characteristic planar trajectories. • Turbulent coherent structures have peak intensity near the top of the canopy. • The cross-stream velocity is large during the passage of coherent structures. [ABSTRACT FROM AUTHOR]

• Thermal infrared (TIR) remote sensing provides a diagnostic of surface energy balance. • The Two-Source Energy Balance (TSEB) model uses TIR to map evapotranspiration. • This paper describes the development and applications of TSEB from field to globe. • Applications include drought monitoring, yield prediction, and water management. Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales. In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches. [ABSTRACT FROM AUTHOR]

• Permafrost extent and ALT changes on the TP were evaluated. • The SOC stocks and storage in TP permafrost areas were estimated. • Thawed and perennially frozen state of SOC in permafrost areas is distinguished. • Dynamics of thawed SOC in TP permafrost areas from 1901−2020 were assessed. Large amounts of frozen carbon stored in the permafrost of the Tibetan Plateau (TP) are gradually thawing due to climate warming. The thaw of frozen carbon allows more active soil organic carbon (SOC) on the TP to participate in the global carbon cycle, which has usually been neglected in previous studies of permafrost carbon. This paper assesses the thawed SOC stock and its historical dynamics in TP permafrost areas based on a data-driven scheme. The results show that the current permafrost area of the TP is 1.14 × 106 km2, and the SOC stock in the top 10 m of permafrost areas is 47.36 Pg. The active layer of the TP permafrost has a regional average thickness of 2.37 m during the baseline period (2000−2020), storing 18.19 Pg of SOC, accounting for about 38.4 % of the total SOC stock in the top 10 m, with the remaining more than 60 % perennially frozen in permafrost. The dynamics of thawed SOC are calculated based on baseline period SOC pools. Despite significant fluctuations, there is a general trend of 0.074 Pg SOC thaw per decade over 1901−2020 as the active layer has deepened. Significantly, the thaw trend of frozen SOC after 1976 is more prominent, reaching 0.420 Pg decade−1. Moreover, this paper reconstructs the SOC pool at the beginning of the last century by incorporating historical deep carbon emissions (1 − 10 m) in the current SOC pool. It recalculates the thaw trend of frozen carbon based on this and finds that the general thaw trend of SOC from 1901−2020 is more significant, with a rate of 0.092 Pg decade−1. Additionally, the dynamics of SOC pools in surface soils (0 − 1 m) of TP permafrost areas are also discussed. Our study highlights the importance of permafrost carbon thaw and provides valuable information for TP carbon studies. [ABSTRACT FROM AUTHOR]

• Over 60 y, the scope of phenological research published in AFM has evolved. • Agricultural applications were once dominant; climate change is now a recurring theme. • Review focuses on Phenocam, or phenology camera, papers published in AFM. • Documents history and evolution of the PhenoCam Network, and lessons learned. • Opportunities for machine learning/computer vision to advance Phenocam science. Over the last twenty years, phenology—the study of seasonal life cycle events—has emerged as a key subfield of global change biology. Phenology provides an integrated measure of the organismal response to climate change and is a key driver of the functional responses of ecosystems to climate change. Since I established the PhenoCam Network in 2008, over 200 papers have been published using Phenocam technology, and these papers have added to our understanding of phenology as both an indicator of climate variability and change and a key aspect of ecosystem function. This review examines: (1) the changing phenological research landscape, as represented by phenology-themed papers in Agricultural and Forest Meteorology (AFM), over the last 60 y; (2) the contributions of phenocams and the PhenoCam Network, as reported in the pages of AFM, to the study of phenology; and (3) the lessons I have learned from developing this grassroots effort, and how other researchers might benefit from the PhenoCam Network's successes and failures. Key conclusions to emerge from this review include: (1) the enormous, value-added power of research networks; (2) the importance of both interpersonal relationships and serendipity, in the metamorphosis of ideas into results; and (3) the potential for open, freely-available data to be transformative, in ways that cut across disciplinary, socioeconomic, and demographic barriers. Finally, the development of the PhenoCam Network has been a collaborative, multidisciplinary experiment in team science, and the commitment of my team members and the enthusiasm of my collaborators have been critical to the success of these efforts. [ABSTRACT FROM AUTHOR]

• In-situ measurements of eddy covariance are used to examine the Bowen ratio over the Tibetan Plateau. • The Bowen ratio is found to be sensitive to soil moisture in arid and semiarid regions. • Vapor pressure deficit is the dominant factor influencing the Bowen ratio in arid and subhumid regions. Turbulent heat fluxes across the surface are an important mechanism of land-atmosphere coupling. But there is still a lack of sufficient observational measurements, particularly over the climate sensitive Tibetan Plateau (TP). This paper examines the partitioning between sensible and latent heat fluxes during growing season using the Bowen ratio as a diagnostic based on eddy covariance measurements from 12 observational sites located on the TP with the altitudes ranging between 3327 m and 4509 m above sea level. The results show an average Bowen ratio of 1.13, indicating that sensible heat flux is only slightly dominant in surface energy balance. For different climate zones, Bowen ratio varies from 0.56 to 1.05 in the semi-arid zone, 0.53 and 0.57 in the subhumid region, and 2.73 to 3.11 in the arid zone. The Bowen ratio shows sensitivity to soil dry and wet condition, with higher values during drier soil conditions. In the arid to semi-arid regions, the Bowen ratio shows a clear positive correlation with the Drought Soil Index (DSI) and a negative correlation with the Normalized Difference Vegetation Index (NDVI), suggesting sensitivity to soil moisture conditions. In the sub-humid climate zone, vapor pressure deficit (VPD) is the dominant factor influencing the Bowen ratio. In the wet and dry transition zone, soil moisture, VPD, NDVI and land-air temperature difference all have a role to play. [ABSTRACT FROM AUTHOR]

• We illuminate critical issues surrounding the collection and utilization of microclimate records in ecological studies. • Microclimatic responses to structural changes in ecosystems and landscapes have become a growing research focus. • Vapor pressure deficit is particularly emphasized for its applications in ecology. • We underscore the significance of microclimate in modeling major ecosystem processes. Drawing upon over 100 years of scholarly work on microclimate, we first present an overview of the history, key references, and critical issues surrounding the collection and utilization of microclimate records in ecosystem studies. We place particular emphasis on addressing specific and pressing issues related to the applications of microclimate at the community-ecosystem-landscape level, excluding those of controlled experiment such as growth chambers and greenhouses. Specifically, we: (1) highlight some key issues concerning the collection, quality assurance/quality control (QA/QC), and utilization of microclimatic data in ecosystem studies; (2) revisit microclimatic responses to the structural changes of ecosystems and landscapes; and (3) emphasize the significance of microclimate in understanding major ecosystem/landscape processes and functions. Vapor pressure deficit (VPD) is particularly emphasized for its calculation and use because of its burgeoning applications in the literature. Case studies for each of the three thematic topics are provided with selected references to demonstrate challenges and solutions. As the scientific community gears up to enhance microclimatic stations, we envision significant increases in the use of smart sensors, wireless access, networking, open databases, and computational capabilities. Understanding and addressing some of the issues raised in this synthesis paper may help advance microclimate research and foster collaboration with other relevant disciplines, such as ecosystem science. [ABSTRACT FROM AUTHOR]

• Compared to the latest report, the fraction of fires ignited by lighting was smaller. • Ignitions by lightning were more frequent amongst larger fire occurrences. • During the compound events of 2003 and 2017, lightning was a relevant fire cause. • Remote sensing data was able to reproduce the seasonality of lightning-ignited fires. Fire databases typically contain information regarding the location, timing, and duration of fire occurrences, as well as the cause (natural or human-induced). These databases allow for the analysis and understanding of the circumstances surrounding the ignition and propagation of wildfires, being highly relevant when addressing fire suppression and management plans, or for improvement of prevention policies. In Portugal, a large number of fires in the official database have unknown causes, which limits the understanding about the relevance of each fire cause, in particular the role of lightning on the ignition of fires over the country. The objective of this paper is twofold: first, we investigated the spatial-temporal variability of lightning-induced fires from 2003 to 2020. Then, we evaluate the feasibility of using remote sensing data as a surrogate for identifying fire causes, through validation with the fire database. Our results revealed that lightning-ignited fires represent an even smaller fraction of all fire events than mentioned in the latest official report about fire causes in the country, accounting for only 1 % of the fire occurrences and 4.6 % of the total burned area in the 18-year period under analysis. The months of June to September comprise 91 % of all lightning fires, which occur more frequently in the northern, remote regions of the country and at relatively high altitudes. Moreover, lightning ignitions originate fires larger than the global average and contributed significantly to the total area burned during the extreme 2003 and 2017 fire seasons. Its importance in these extreme years suggests that lightning can trigger large fires when in conjunction with compound events such as droughts and heatwaves. When compared with in-situ databases, the application of remote sensing data reproduced the seasonality of lightning-ignited fires, but failed to account for the contribution of smaller fires, which represent the majority of occurrences in the Portuguese fire database. Finally, we discuss the implications of our findings for the improved assessment of fire risks, causes, and impacts in Portugal. [ABSTRACT FROM AUTHOR]

Highlights • A novel post-processing method is developed to harness GCM forecasts of reference crop evapotranspiration. • The method is applied to post-process outputs from the Australian Bureau of Meteorology's seasonal prediction model. • The method generates reliable and skilful ensemble forecasts of monthly and seasonal reference crop evapotranspiration. Abstract Long-range forecasts of climatic variables are generated by climate centres around the world using global climate models (GCMs). This paper investigates ensemble forecasting of reference crop evapotranspiration (ETo) based on GCM outputs. The Penman-Monteith formula is used to calculate raw forecasts of ETo from GCM forecasts of solar radiation, temperature, wind speed, and vapor pressure. The Bayesian joint probability (BJP) modelling approach is applied to post-process raw monthly forecasts, separately for different lead times (month 1, 2 and 3 ahead). The Schaake shuffle is then employed to link the ensemble members of post-processed forecasts for all lead times to give a temporal structure. Forecasts of seasonal ETo total are obtained by aggregating the monthly forecasts. For comparison purposes, seasonal forecasts are also derived directly by post-processing raw seasonal forecasts without going through the monthly steps. Three case studies are presented for post-processing forecasts from the Australian Community Climate and Earth System Simulator-Seasonal (ACCESS-S1). Both raw forecasts and observations of monthly and seasonal ETo are found to be reasonably normally distributed. The post-processed forecasts of monthly and seasonal ETo are skilful in reference to climatology forecasts and statistically reliable in ensemble spread. The indirect and direct ways of generating forecasts of seasonal ETo total show similar skill and reliability, demonstrating the effectiveness of the Schaake shuffle. In this paper, the proposed post-processing method is evaluated through leave-one-out cross validation. The method can be easily adapted for post-processing raw GCM forecasts in real-time to produce ensemble forecasts of monthly and seasonal ETo. [ABSTRACT FROM AUTHOR]

Lightning-caused wildfires are responsible for substantial losses of lives and property worldwide. Convective storms can create large numbers of ignitions that can overwhelm suppression efforts. Both long- and short-term risk planning could benefit from daily, spatially-explicit forecasts of lightning ignitions. We fitted a logistic regression generalised additive model to lightning-caused ignitions in the state of Victoria, Australia. We proposed a new method for model selection that complemented existing methods and further reduced the number of variables in the model with minimal change to predictive power. We introduced an approach for deconstructing ignition forecasts into contributions from the individual covariates, which could allow model output to be more readily integrated with existing intuitive understandings of ignition likelihood. Our method of model selection reduced the number of variables in the model by 37.5% with little change to the predictive power. The final model showed good predictive ability (AUC 0.859) and we demonstrated the utility of the model for short term forecasting by comparing model predictions with observed lightning-caused fires over three time periods, two of which had extreme fire conditions, while the third was randomly chosen from our validation dataset. The model presented in this paper shows good predictive power and advancements in model output could allow fire managers to more easily interpret model forecasts. [ABSTRACT FROM AUTHOR]

The immediate effects of asymmetric warming (i.e., day- and night-time warming) on terrestrial ecosystems have been well documented, but the time-lag effects remain poorly understood. In this paper, we investigated the global inter-annual hysteretic responses of vegetation to asymmetric warming over the period of 1982–2013. The net primary production (NPP) was employed as the indicator of vegetation growth, and accumulated monthly average daily maximum temperature and minimum temperature (ATmax and ATmin) were used to reflect the asymmetric warming condition. Additionally, partial correlation analyses were conducted to examine the correlations between NPP and ATmax/ATmin on a monthly scale. Furthermore, the best time lags that ATmax/ATmin had on NPP and the optimal correlations between ATmax/ATmin and NPP were analyzed by time-lag analyses. The results showed that (i) vegetation responded to the asymmetric warming with near 12-month delays at a global scale, and vegetation exhibited larger lags in responding to recent warming over temperature-limited areas or semiarid and subhumid regions compared to other places; (ii) compared with ATmin, ATmax had longer time-lag effects on biomes over mid-high latitudes (45 °N–90 °N, 23 °S–60 °S) and high altitudes (i.e., the Tibetan Plateau and the Brazil Plateau) and smaller delay impacts on biomes in other regions; (iii) with hysteretic impacts considered, ATmax correlated positively with vegetation in temperature-limited areas and negatively in heat-sufficient and water-deficit places, and the reverse was mostly true for ATmin. These phenomena may be associated with the intrinsic differences in the mechanisms that day- and night-time temperatures have on vegetation growth. Our paper gives new insights into the non-uniform responses of the terrestrial ecosystem to asymmetric warming. Looking ahead, terrestrial ecosystem models are highly recommended to incorporate such non-uniform time-lag impacts and distinguished correlations so as to improve their performances in future work. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation plays pivotal roles on land-atmosphere interactions, and even global climate change. However, limited attempts have been taken to elucidate the responses of vegetation activity to weather-related drivers (e.g., droughts, floods). This is especially in China with vast area and changeable meteorological conditions. In this paper, the performance of two satellite-based indices, namely vegetation condition index (VCI) and vegetation health index (VHI) were analyzed to detect the vegetation responses to weather-related variations. The dynamics of vegetation activity in China were further examined for the period 1982–2013. We found widely-distributed vegetation stresses in the entire country for a long period (about an average of two months per year) from the VCI and the VHI. In addition, both the two indices indicated increasing vegetation activities over most parts of China during 1982–2013. However, there is no consensus between the two indices at spatial pattern and regional totals. This discrepancy can be due to the negative-correlation assumption of the VHI between the VCI and bright temperature. However, we found that the relationship between the VCI and temperature could be changeable in different regions, especially in China with complex topography, diverse climate conditions and different vegetation types. The findings of this paper highlight the necessity to account for dominant controls on vegetation growth when using the VCI and the VHI to analyze vegetation activity. [ABSTRACT FROM AUTHOR]

• Connectivity of riparian vegetation, transpiration and evaporation was assessed. • Evaporation is lower (18 %−34 %) near high riparian vegetation density (NDVI ≥ 0.6). • Riparian vegetation actively lower evaporation up to 90 m from the reservoir margin. • Riparian vegetation transpiration and reservoir evaporation ratios are equivalent. • 275,000 people may be supplied per day by the evapotranspiration losses. Understanding the combined open water reservoir evaporation and riparian vegetation transpiration (evapotranspiration) is important for water resource management in semiarid regions like the Brazilian Dryland region. Existing research has explored reductions in open water evaporation due to riparian vegetation transpiration, but evidence for this interdependence in this region is not well understood. The aim of this study was to estimate the connectivity (impact and maximum distance of action) between riparian vegetation and its transpiration and reservoir evaporation, as well as the ratio of the volume losses by transpiration and evaporation, using Landsat 5 and 8 in a Brazilian Dryland environment. Evaporation and transpiration were estimated using the Surface Energy Balance System for Water and the Surface Energy Balance System models, respectively, from 1985 to 2020. A Class A Pan and the Penman-Monteith equation were used, for reference reservoir evaporation and vegetation transpiration, respectively. The occurrence of riparian vegetation was associated with the Normalized Difference Vegetation Index. Both models performed well, with root mean square error ≤ 0.80 mm/day. The riparian vegetation transpiration demonstrated a high negative correlation (R²>0.7) with evaporation, suggesting the vegetation acts to reduce evaporation (average of 30 %) up to a maximum distance of 90 m from the reservoir shoreline. This is explained by the adjacent riparian vegetation-induced reduction in radiation inputs due to shading at the reservoir margin, a lowering of wind speeds and increases in air humidity from transpiration. The ratio of riparian vegetation transpiration to reservoir evaporation within a buffer of 150 m is approximately equal. Combined riparian vegetation and reservoir evapotranspiration equates to the daily water supply for ∼275,000 people. The paper explains and quantifies the connectivity between riparian vegetation transpiration and reservoir evaporation, thereby providing a model for improved reservoir water balance estimates, which can aid regional water resources planning, development, and management. [ABSTRACT FROM AUTHOR]

Constructed wetlands (CWs) are natural-like systems for wastewater treatment capable to remove both pollutants and nutrients without additional energy demand. In these systems, gaseous compounds are released into the atmosphere through microbial processes. Among these gases carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) are the most dangerous because they act as greenhouse gases (GHGs) and are well known as contributory factors to cause global warming. In this paper we reviewed 224 scientific articles (from 1980 to 2016) from the literature in order to analyze the most important factors that drive the quantity and type of GHGs production and emission from different CWs systems. Wastewater flow and composition, hydroperiod, environmental conditions and plant presence and species used to vegetate CWs have been considered. CWs typologies influence GHGs fluxes with lower CH 4 emissions from subsurface flow CWs than free water surface (FWS) ones and higher N 2 O emissions from vertical subsurface flow (VSSF) CWs than FWS ones. The inlet wastewater COD/N ratio of 5:1 has been found as the best ratio to obtain in the same time the lowest N 2 O emission and the highest nitrogen removal in FWS CWs. The inlet wastewater C/N ratio of 5:1 allows to obtain the lowest CO 2 and CH 4 emissions in VSSF CW treatment. Intermittent CWs bed wastewater loading decreases CH 4 and promote CO 2 and N 2 O emissions. Temperature is positively correlated with CO 2 , CH 4 and N 2 O emissions and solar radiation with CO 2 and CH 4 emissions. GHGs flux is affected by plant presence and species, and it is influenced both by the phenology and density of vegetation. Plant presence significantly increases the CO 2 emission respect to unvegetated condition in all CWs types, and increases N 2 O and CH 4 emissions in VSSF CWs. Considering the HSSF CWs plant presence significantly reduce the CH 4 emissions. Plant species richness effect on CH 4 emission has been investigates in a limited number of papers with not unique results, probably due to the different plant species and number used by authors, which may have influenced the CWs microbial population and activity. Considering plant species Zizania latifolia determine significant higher CH 4 and N 2 O emissions than Phragmites australis . No significant different CH 4 and N 2 O emissions have been found between P. australis and Typha latifolia . Significant lower N 2 O emissions determine the T. angustifolia than P. australis. Although plant presence, in some case, increases CW GHG emissions respect to unvegetated situation, the vegetation fixes atmospheric carbon by photosynthesis; as a consequence CWs act, in most cases, as sink of CO 2(eq) . [ABSTRACT FROM AUTHOR]

Reducing plant growth to limit their susceptibility to diseases has been proposed as a way to reduce pesticide use, but reducing crop growth may have detrimental effect on yield. In this paper, we test the hypothesis of a trade-off between maintaining grape yield and reducing grapevine susceptibility to powdery mildew ( Erysiphe necator ) and grey mould ( Botrytis cinerea ), two major diseases of the grapevine ( Vitis vinifera L.). Grapevine susceptibility to these two diseases was measured by relevant features of grapevine vegetative development identified in previous studies: leaf biomass at flowering for powdery mildew and pruning mass for grey mould. Data were collected during a 3-year field experiment in a vineyard located in the south of France, in which pests and disease were controlled by spraying pesticides. The two pathogens studied in this paper were chosen because they differ in terms of their biology (biotroph vs necrotroph) and their interaction with the grapevine (the highest grapevine susceptibility occurs early in the cycle for powdery mildew and late in the cycle for grey mould), in order to give genericity to the results. Results confirmed the hypothesis of a trade-off between maintaining grape yield and reducing grapevine susceptibility to both pathogens through reduced vegetative growth, but provided evidence that win–win situations (high yield, low susceptibility) do exist. Moreover, we found a synergy between reducing grapevine susceptibility to powdery mildew and grey mould. These results suggest opportunities to reduce fungicide use when a win–win situation occurs as the risk of yield and quality losses may be lower in those years. Inter-annual variation in water stress at flowering was found to be a key driver of the balance between grape yield and grapevine susceptibility to both pathogens through their effect on the source–sink balance of the grapevine. Water stress at flowering appeared as a relevant indicator to inform the probability of occurrence of a win–win situation. Our results suggest that it could also be used to adapt management practices like irrigation, cover cropping or a combination of both, to reach a win–win situation. The relevance of these findings to vineyards in similar semi-arid environments is discussed. [ABSTRACT FROM AUTHOR]

• New LoRaWAN-based tree tilting monitoring system is established for urban forestry. • Different modes of tree failure were recorded during the hit of typhoon mangkhut. • Proposed temporal wind tipping curve well presented the entire tree failure process. • Transition from elastic to elastoplastic response was revealed before tree falling. Managing urban forests in densely populated cities with narrow streets and compact environments is challenging. This paper describes a city-scale, low power-consumption, and real-time tree monitoring system, called the Internet of Tree Things (IoTT). The IoTT system includes tree motion sensors for capturing tree movement in terms of the tilt angle, LoRaWAN gateways for wireless data transmission established in an economical way, and servers for data storage, big data analysis capability, and data visualization. The performance of IoTT under extreme weather conditions was fully examined during the devastating strike of super typhoon Mangkhut in September 2018. In addition, trees having different failure modes, such as uprooting, trunk fracture, and root ball sliding, were recorded by the IoTT and are presented in this paper. The corresponding biomechanistic behavior of the trees can be better revealed using the newly proposed temporal wind tipping curves and associated polar diagrams of the tilt angles. At the beginning of the passage of Mangkhut, the trees exhibited elastic responses, and the swaying gradually increased with the wind speed. After the wind speed continued to get stronger and once the elastic limit of the tree was exceeded (yield), clustered plastic deformation developed parallel to the dominant wind direction. This behavior can be considered as a meaningful indication of tree damage, indicating a potentially hazardous tree situation and, most importantly, can be used as real-time, supplemental information to support arborists in better decision-making in urban forest management. [ABSTRACT FROM AUTHOR]

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO 2 Exchange (NEE) taken at the Alice Holt forest CO 2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 g C m −2 day −1 to 2.38 g C m −2 day −1 . [ABSTRACT FROM AUTHOR]

Abstract Hoffmann et al. suspected a likely overestimation of carbon (C) sink reported in our paper (Eze et al., 2018) entitled "Upland grasslands in Northern England were atmospheric carbon sinks regardless of management regime". They attributed this to potential sources of error associated with the estimation of C fluxes from closed-chamber measurements. We have explained why we think that the C sink reported in our paper was not overestimated as suspected by Hoffmann et al. However, we acknowledge the potential for error due to unavoidable operational and data limitations. [ABSTRACT FROM AUTHOR]

• The net ecosystem carbon balance was extended for harvest residues (HR). • The balance term related to HR can be in the range of other cumulative carbon terms. • Therefore, it is imperative to consider HR in the carbon balance of croplands. • Soil carbon loss/gain are mostly controlled by the fast-decomposing carbon pools. Over the past two decades, major efforts have been made to quantify the extent to which and under what conditions croplands are sources or sinks for carbon. For this purpose, the net carbon stock change of the study site is typically quantified based on net CO 2 fluxes monitored with an eddy covariance or chamber system, on measured C import by organic fertilizer and C export by harvest. While in cropland studies this balance is usually referred to as net biome productivity (NBP), we prefer to use the term net ecosystem carbon balance (NECB) here. NECB is basically the sum of the stock change of plant carbon (ΔPC) and soil organic carbon (ΔSOC). In the standard approach, the assumption is that at an annual cropland site the bulk of plant biomass is typically removed at harvest, and that ΔPC can therefore be neglected. In this case, ΔSOC equals NECB. In this paper we show that this assumption is problematic, particularly if in crop rotation systems the budget is determined over a single cropping period. The present contribution extends the concept of NECB to include harvest residues (HR) and applies it to a case study over a maize - winter wheat - winter wheat rotation at a cropland site in southwest Germany. In all three cropping periods, the sign of NECB was opposite that of ΔSOC. Accordingly, neglecting HR led to an incorrect result concerning the question whether the site is a sink or a source for soil carbon. Our findings demonstrate that for croplands HR must be included to obtain an accurate and meaningful carbon balance. [ABSTRACT FROM AUTHOR]

• Decade plus time series assess trends in CO 2 fluxes in a changing world. • Network gradients assess biophysical response functions at the ecosystem scale. • Long records capture rare and extreme events on CO 2 fluxes. • Paired sites examine management and disturbance effects. • Network trains and validates regional upscaling of CO 2 fluxes. For over two decades, the AmeriFlux network has quantified the breathing of the biosphere, as defined by measuring the flux densities of carbon dioxide and water vapor exchange between ecosystems and the atmosphere. The network includes natural and managed ecosystems that span broad climatic and ecological gradients. In this review, we provide an overview of key discoveries by this network and highlight important open questions. Such discoveries are possible through the network's shared and open access database that is used to interpret these fluxes; it includes a comprehensive set of carbon dioxide and water vapor flux measurements and a complementary set of biophysical variables, including meteorological and soil conditions, along with structural and functional properties of the ecosystems. Topics covered in this paper include interannual variability, trends and magnitude of carbon dioxide fluxes, and the response of these fluxes to biophysical drivers and extreme events. We examine results both within individual sites and across natural gradients. A subset of long-term flux measurements produced by the network provide quantitative insights into how whole ecosystems are responding to climate variability and global change, including warming, rising CO 2 and changes in rainfall distribution. By reviewing and synthesizing these insights, we underscore the value of long-term, bottom-up research coalitions. In closing, we hope this review can guide the future research directions necessary to deepen understanding of climate impacts on ecosystems in the decades ahead. [ABSTRACT FROM AUTHOR]

• A new contactless heat pulse method was tested for sap flow measurement. • An external microwave device was able to generate an internal heat pulse. • Heat pulse velocity was determined by infrared thermography. • The assessed heat pulse velocity was strongly correlated with the controlled flow rate. • The relationship was the same for varying stem diameters. Xylem sap flow measurement is a key method to quantify plant water use and assess the responses to environmental conditions and climatic change. However, available methods are generally invasive and of limited portability. This paper presents a non-invasive approach called TIMFLOW that combines microwave heat pulse and infrared thermography, while having a high portability and versatility potential. The methodology was tested in laboratory conditions for black poplar (Populus nigra) stems of various diameters (10–45 mm) and for the known sap flow velocity range (10–100 cm h −1). The heat pulse was generated by microwaves with a power amplifier supplying a bi-quad antenna at 2.45 GHz frequency located near the stem. The scene was filmed using a relatively low-cost light and compact InfraRed (IR) thermography camera. A stem temperature map was used to determine the heat pulse propagation velocity. The calculated heat velocity was highly correlated with the applied flow velocity with a unique relationship regardless of the diameter. The latter result confirms the equation of Marshall (1958) which links the sap velocity to the heat velocity with a vessel fraction of around 25 % within samples. The feasibility of outdoor measurements was also successfully tested. The assumed potentials and limitations of the proposed methodology are discussed. In summary, the study demonstrates the concept and validates, in woody stems, this new methodology for non-invasive portable sap flow velocity measurement. [ABSTRACT FROM AUTHOR]

• Irrigation changes precipitation. • Increased irrigation produces shallower planetary boundary layer. • Increased irrigation increases latent heat flux and reduces sensible heat flux. • Increased irrigation increases equivalent temperature and reduces air temperature. Land use land cover change, including irrigation, impacts weather and climate. In this paper a precipitation event that occurred on the morning of 23 July 2018 during the Great Plains Irrigation Experiment (GRAINEX) is investigated. Six Weather and Research Forecasting (WRF) model-based experiments were conducted, which involved the increase or decrease of soil moisture by 5 % and up to 15 % over the irrigated croplands. These changes approximated soil moisture content in response to different levels of irrigation applications. An additional experiment, where irrigated land use was changed to grassland, was conducted to capture pre-irrigation land use and its impacts. It was found that regardless of strength of irrigation, precipitation decreased. In addition, the model did not produce precipitation over non-irrigated land use. When grassland replaced irrigated agriculture, increases in precipitation were estimated. With increased irrigation, latent heat flux increased compared to the control simulation and decreased when irrigation decreased. On the other hand, sensible heat flux was decreased compared to control when irrigation increased. The planetary boundary layer over irrigated land use was shallower than over non-irrigated land use while over grassland it was higher than irrigated but lower than non-irrigated land use. The changes in precipitation, the surface energy balance, and the planetary boundary layer served as a reminder of irrigation's complex effects on the atmosphere. Additional analysis of other precipitation events during GRAINEX would be helpful to better understand the effects of irrigation. [ABSTRACT FROM AUTHOR]

• Climate-sensitive nonlinear mixed-effects height-diameter model was developed for 14 tree species in mixed forests. • The inclusion of climate and species diversity improved the H-D model. • The impact of environmental gradients on tree height growth was simulated. • The sensitivity of multiple species in mixed forest to environmental gradient changes varies with species. Models based on height-diameter (H-D) are helpful for estimating forest biomass and carbon storage on a large scale. Climate change has an impact on allometric height-diameter growth. H-D models explaining climatic effects are still inadequate due to the complexity of mixed forests. This paper developed climate-sensitive mixed-effects models for 14 tree species based on 316 natural mixed forest plots in Northeastern China and compared the response of tree height growth to environmental gradients for multiple species. The results showed that the species heterogeneity leads to the differences in scaling exponents in varying degrees. The analysis revealed that incorporating mean annual temperature (MAT), mean annual precipitation (MAP), dominant tree height (HT), basal area of the larger trees (BAL), and Shannon diversity index significantly enhanced the predictive ability of the model. By exploring the tree height change patterns of each tree species under different gradients of environmental factors, we found that increases in temperature, precipitation, site quality, and competition intensity positively affected height growth in mixed forests. In contrast, increases in species diversity inhibited height growth. The degree to which environmental factors regulate the H-D allometric growth of trees in mixed forests is related to species specificity. This paper provides theoretical support for tree height estimates of major tree species in mixed forests under global climate change by introducing environmental factors to improve the H-D model of 14 tree species. [ABSTRACT FROM AUTHOR]

Plant canopy spectral reflectance is measured at many observation towers to estimate the canopy’s biophysical and physiological features. When a wide-angle downward-facing spectrometer is used for tower-based measurements of reflected irradiance, it also captures reflections from the tower and near-horizon light from the sky within the field of view, leading to incorrect canopy reflectance estimates. However, the effects of this contamination have not been quantified. Therefore, we developed a retractable masking device to allow more accurate measurement of canopy reflectance by excluding the contamination. We continuously observed canopy reflectance of a Japanese larch ( Larix kaempferi ) forest from a flux tower by using the device alternately in masked and unmasked modes. In this paper, we describe the design of the masking device and demonstrate how tower contamination affected the measured canopy reflectance. We found that the canopy reflectance in the visible spectral region was overestimated by a maximum of more than 3 times the actual value due to the tower contamination, but that the near-infrared reflectance was affected much less. The contamination effects were substantial and complex, and the magnitude varied depending on the wavelength, solar zenith and azimuth angles, weather conditions, and canopy phenological status. Consequently, vegetation indices calculated from the spectral data were greatly affected. For example, the contaminated Normalized Difference Vegetation Index was 8–22% lower than that obtained with the masking device. We conclude that to accurately measure canopy reflectance via wide-angle spectroradiometers or photosynthetically active radiation sensors, a masking device similar to the one described in this paper is strongly recommended. [ABSTRACT FROM AUTHOR]

Soil temperature is a key factor which affects both soil-forming processes and the hydrological balance of water exchange between the atmosphere and the ground. The measured temperature waveforms in the top layer of forest soils demonstrate very characteristic diurnal variations. This paper presents a method of determining the amplitude of the diurnal temperature variation using the Fourier transform. Temperature measurements were carried out in the top layer of soil at a depth of approx. 0.08 m within 4 homogeneous, even-aged stands of different age-classes (from a 12-year-old thicket to a 117-year-old mature stand) and at 3 inter-forest meadows. The main aim of this paper was to relate all parameters in a simple empirical model of diurnal temperature variations in the top layer of soil under a stand to biometric features of stands. The parameters describing the model depend to a large extent on the soil exposure factor and the total biomass of above-ground tree parts. The soil exposure factor and the total biomass of a stand depend solely on the number of trees per hectare, the mean height of a stand, and the mean tree diameter measured at breast height (i.e. at a height of 130 cm), all of which are very easy to measure from the ground. The analyses also show that the amplitude values, particularly in an open field, also depend on the mass of organic matter accumulated in soil. The presented model was verified using measurement results in spruce stands of the Istebna ecotype (the Silesian Beskid Mts.). The model and parametrisation proposed for spruce stands depends solely on easily measurable biometric features and should be readily adaptable to stands composed of other tree species, using appropriate coefficients that differentiate these stands from spruce stands. The necessary improvements to the presented empirical model will be possible after a series of measurements taken within various types of stands. A thorough understanding of the factors determining temperature variation in woodlands may considerably expand the knowledge of the water exchange balance within forest complexes, as well as the estimation of site productivity. [ABSTRACT FROM AUTHOR]

• Pronounced diurnal patterns found for both UAV- and Airborne-based far-red SIF. • Consistent spatial patterns of UAV- and Airborne-based far-red SIF over different crops. • FluorSpec far-red SIF is linearly related to HyPlant far-red SIF. • Far-red sun-induced chlorophyll fluorescence can be reliably measured from a UAV. Sun-induced chlorophyll fluorescence (SIF) is a promising proxy of the dynamic photosynthetic process. Unmanned Aerial Vehicles (UAVs) are flexible and cost-effective for acquiring SIF data at high temporal and spatial resolution. The UAV-based point spectrometer FluorSpec was designed to measure SIF within agricultural fields. To correctly understand SIF values and further photosynthetic research, the ability of the UAV-based FluorSpec to provide reliable SIF information within agricultural fields needs evaluation. In this paper, the UAV-based FluorSpec was compared with the high-performance airborne imaging spectrometer HyPlant using diurnal far-red SIF measurements over different crop types (i.e. two varieties of winter wheat, two varieties of spring barley, bean, and maize), which were acquired by almost simultaneous airborne and UAV flights during a clear sky day in 2019. After improving the footprint geolocation of FluorSpec measurements using concurrent red-green-blue (RGB) images, we compared the FluorSpec and HyPlant SIF measurements, their diurnal developments, and spatial distributions for different crop types. The results from both systems show consistent, clear diurnal patterns that are positively correlated with photosynthetically active radiation (PAR) over most crop types. Similar SIF spatial patterns were shown within crop fields as well. UAV-based FluorSpec SIF showed a good linear correlation with HyPlant SIF with an R 2 up to 0.76. The good agreement confirms that the UAV-based FluorSpec system is able to measure meaningful SIF values at the field scale and thus stimulates SIF applications in agriculture. The systematic errors up to 0.3 mW m−2 sr−1 nm−1 from the linear regression between the two systems indicate that the UAV-based FluorSpec system should be improved by considering the main sources of uncertainty discussed in this paper. Future studies with dedicated experiments are recommended to assess the systematic uncertainties of UAV-based FluorSpec derived SIF information. [ABSTRACT FROM AUTHOR]

• CFD is conducted for an isolated, L -, U - and rectangular-shaped shelterbelt. • Porous canopy model provides sufficiently accurate mean wind speed and TKE results. • The dimensions of shelterbelt width, length and opening are systematically varied. • General considerations regarding efficient shelterbelt arrangements are deduced. Shelterbelts can locally reduce wind speed in open terrains, moderating wind induced damage for agriculture and ameliorating growing conditions for crops. In this paper, Computation Fluid Dynamics (CFD) is adopted to clarify the effects of the characteristic parameters and arrangement of shelterbelts on their windbreak efficiency. The Darcy-Forchheimer canopy model is adopted to represent the vegetation, so modeling it as an isotropic porous medium. The shelterbelts are immersed in wind representative of the one expected in the atmospheric boundary layer and the numerical model is firstly validated against full-scale data. The parameters governing the shelterbelt aerodynamic performance, including shelterbelt width, shelterbelt length and opening length, are systematically varied, so providing a clear overview of the effects induced by their modification. The results show that the sheltering effect is enhanced as the shelterbelt width increases, while the effects of shelterbelt length are less pronounced in comparison. When there is opening inside the shelterbelt, the mean wind speed and turbulence kinetic energy behind the canopy is significantly increased due to the passage of airflow. General considerations regarding efficient shelterbelt arrangements are deduced by a comparison among L -, U - and rectangular-shaped types. [ABSTRACT FROM AUTHOR]

• Average start of the photosynthetic period in China was on the 123rd day of the year. • The average start of the photosynthetic period advances at a rate of 4.3 d (10 a)−1. • 64% of the region is temperature controlled, others are precipitation controlled. • Importance of temperature and precipitation determined by pre-season precipitation. • Climate change increases NPP by advancing the start of the photosynthetic period. In the context of global warming, the advancement of spring phenology in northern and temperate regions due to increased temperatures has been widely reported. Early and delayed start of the photosynthetic period (SOP) directly affects the vegetation net primary productivity (NPP). However, the interrelationship between climate change, the SOP, and the NPP is unclear. In this paper, we use the dynamics of decadal daily solar-induced chlorophyll fluorescence data to calculate the response of Chinese vegetation photosynthetic phenology to climate change and its impact on the NPP over the last 20 years. The results found that over the last 20 years, the average SOP in China was on the 123rd day of the year, and the SOP has advanced at an average rate of 4.3 d (10 a)−1, with a faster trend of SOP advancement in highland and high-altitude areas. 64% of SOP in China is controlled by temperature; 36% of the SOP in China is controlled by precipitation, and the relative importance of temperature and precipitation was reversed as the precipitation gradient decreased, with SOP dominated by temperature when pre-season precipitation ≧300 mm, and SOP dominated by precipitation when pre-season precipitation ≦300 mm. Finally, we find that climate change indirectly increases vegetation NPP by advancing SOP. Our study emphasizes the importance of precipitation on phenology. It provides a scientific basis for understanding and predicting the response of spring photosynthetic phenology to climate change and the contribution of spring phenology to carbon estimation in terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

The Penman energy balance equation has become a common approach to estimating the evaporation of plant canopies. Unfortunately, implementation of the energy balance equation requires various assumptions and empiricisms, including "crop coefficients", so that in practice the Penman equation is used as a framework in which to apply empirical approximations. These limitations are reviewed in this paper. An alternative to the energy balance approach suggested by Penman was a "sink strength" model in which the gradient in vapor pressure was considered the prime driver of plant water loss. In this paper, the sink strength model is developed for determining plant canopy transpiration based on explicitly defined properties of the plants. The key variables are the carbon assimilation pathway of photosynthesis and the biochemical composition of the plant material being synthesized. These two parameters define the transpiration efficiency coefficient and radiation use efficiency that are required in the calculation of transpiration. Also, it is necessary to define a weighted vapor pressure gradient for transpiration when making estimates of the amount of transpiration on a daily basis. The derived expression of the sink strength model was used to compare predicted transpiration with measured values of four turf grass species measured over three years. A close linear correlation was observed for all grasses (P < 0.0001). However, there was an indication that the grasses may vary slightly in their radiation use efficiencies, which with measurement of photosynthetic capacity would further improve the predicted transpiration rates. The ability to directly compute transpiration from explicitly defined variables based on plant traits offers an attractive alternative to the Penman energy balance approach. [ABSTRACT FROM AUTHOR]

This paper focuses on calculating Evaporative Fraction (EF) and energy balance components, applying the Surface Energy Balance System (SEBS) model combined with remote sensing products and meteorological data over an agricultural rotating cropland from 2008 to 2011. The model is validated by comparing SEBS results with observed EF and surface fluxes obtained using an Eddy Covariance (EC) technique together with meteorological instrumentation. Three different approaches of the model are evaluated: SEBS-0 (original algorithm), SEBS-SM (algorithm modified with soil moisture), and SEBS-NDVI (SEBS-0 modified with the Normalized Difference Vegetation Index - NDVI - and the surface temperature - Tsurf). Based on current knowledge of the close relationship between EF and NDVI, a modified SEBS-0 algorithm, SEBS-NDVI, is proposed in this study. This new approach is developed so as to improve results for EF and latent heat flux (LE), and this paper presents the results of all three SEBS approaches used in this study. Modelled Rn is found to be in good agreement with observed data (R²=0.75), although SEBS-calculated G gave less satisfactory results (R²=0.38) and its seasonal dynamics shows discrepancies with observed data. An evaluation of SEBS-0 shows a clear underestimation of H (R²=0.54) and a marked overestimation of EF and LE. Comparison with ground-based data yielded the best correlation applying SEBS-NDVI, avoiding overestimation of EF and LE obtained with SEBS-0 and SEBS-SM. Results show that the proposed SEBS-NDVI, using a scale factor related to NDVI and Tsurf, is able to reproduce satisfactorily the EF (R²=0.65) and LE (R²=0.70) seasonal pattern better than the two previous approaches for our study plot. [ABSTRACT FROM AUTHOR]

Highlights: [•] Overview of currently available measurement methods to separately assess E and T. [•] Models validated for their ability to partition between E and T are discussed briefly. [•] 52 papers were found to attempt ET partitioning. [•] E/ET ratio was found to exceed 30% in 32 of the studies. [•] Despite measurement variability ∼30% of the papers show over 90% agreement for ET = E + T. [ABSTRACT FROM AUTHOR]

Abstract: This paper evaluates four methods to obtain accurate averaged flux estimates under conditions of non-stationary turbulence. In Part I (Van Kesteren et al., 2012), we introduced and evaluated these four combined methods for 30-min averaging intervals, notably the flux-variance method, the Bowen-variance method, the structure-parameter method, and the energy-balance method. The aim of this paper, Part II, is to validate the accuracy of the 1-min flux estimates of the CO2 flux, FCO 2, and the evapotranspiration/latent-heat flux, L v E. Furthermore, we use the 1-min fluxes to investigate flux and vegetation responses under conditions of non-stationary turbulence. Using several validation methods, we show that both the eddy-covariance method and the energy-balance method are unsuitable for estimating fluxes over 1-min averaging intervals. The three other combined methods are more successful in determining 1-min fluxes. The random error is approximately half that of the eddy-covariance method, but still some issues limit the success. The Bowen-variance method has a +0.09 systematic error and moreover, 30% of the data had to be omitted, because the method requires more stringent conditions. Furthermore, the flux-variance method has a −0.15 systematic error. The structure-parameter method performs best of all methods and accurately resolves 1-min fluxes. With this method, we do a final validation with a different data set and show that also under dry conditions the method accurately resolves FCO 2, although L v E was more difficult to resolve. In the last part, the structure-parameter method is successfully applied under conditions of non-stationary turbulence. We show that L v E and FCO 2 have a different step response upon abrupt changes in solar radiation, because different processes drive these fluxes. Also, we observe a 2-min time lag between solar radiation and 1-min fluxes and show the relevance of taking this into account for determining light–response curves of the plants for both 1-min and 30-min averaging intervals. Furthermore, we show the relevance of 1-min fluxes for studying the light–response curves of plants for conditions with different temperature and humidity. Finally, we show that accurate estimates of 1-min averaged canopy resistances can be determined via the resistance expressions for sensible heat and L v E. As such, we show that vegetation can indeed modify its canopy resistance significantly within several minutes. [Copyright &y& Elsevier]

Abstract: The wind speed in a forest is one of the main factors deciding upon the amount of water balance between the atmosphere and the soil. Inside a forest complex, the distribution of vertical wind speed has a characteristic S-shape. This paper presents a simplified, empirical model of such an S-shaped wind profile within a stem layer of an Istebna spruce stand. Measurements of the wind speed were taken at four levels in 9 homogeneous, even-aged stands of different age-class: from a 10-year-old natural seeding to a 121-year-old mature stand. Recorded data unambiguously confirmed the existence of the secondary maximum inside the stem layer. The main aim of this paper was to make all model parameters conditional on biometrics features of tree stand as well as the geographical location of research sites on the slopes in a mountain valley. All parameters describing the simplified, empirical model of S-shaped wind profile within the stem layer strictly depend on the well-known total basal area TBA [m2 h−1]. TBA is determined by only an average number of trees per hectare and a mean diameter at a breast height, i.e. 130cm above ground, which are easy to be measured with high accuracy ‘from ground’. Presented data analysis shows that the estimated wind speed at the secondary maximum and a mean wind speed within a stem layer are contingent upon a biomass density in the crown layer and its elevation above ground. A biomass density can be equated with the leaf area index (LAI). The presented simplified, empirical model of the vertical wind speed profile for a spruce stand and its parameterization scheme, which is to be measured in an effortless way, should be easily adaptable to stands composed of other tree species, taking into consideration specific differences between spaces. The necessary improvements of the presented empirical model would be possible after some series of measurements taken within various types of tree stands. A thorough understanding of the biological factors creating the observed S-shaped wind profile ought to considerably expand the knowledge of the forest ‘ventilation’ process, which determines vaporization from the soil as well as the CO2 advection. [Copyright &y& Elsevier]

Abstract: Significant progress has been made in the use of ensemble agricultural and climate modelling, and observed data, to project future productivity and to develop adaptation options. An increasing number of agricultural models are designed specifically for use with climate ensembles, and improved methods to quantify uncertainty in both climate and agriculture have been developed. Whilst crop–climate relationships are still the most common agricultural study of this sort, on-farm management, hydrology, pests, diseases and livestock are now also examined. This paper introduces all of these areas of progress, with more detail being found in the subsequent papers in the special issue. Remaining scientific challenges are discussed, and a distinction is developed between projection- and utility-based approaches to agro-climate ensemble modelling. Recommendations are made regarding the manner in which uncertainty is analysed and reported, and the way in which models and data are used to make inferences regarding the future. A key underlying principle is the use of models as tools from which information is extracted, rather than as competing attempts to represent reality. [Copyright &y& Elsevier]

Abstract: Vegetation is one of the most important components of the global ecosystem. It plays a vital role for global energy balances and climate modulations. Timely and precisely monitoring vegetation changes has become an increasing need. Among various satellite-based earth observation systems, the Landsat and ASTER sensors are most commonly used in mesoscale vegetation observations. Nevertheless, the quantitative relationship between the two sensors in measuring vegetation has not been investigated in detail. Therefore, this paper aims to investigate how well ASTER vegetation measurements replicate ETM+ vegetation measurements, and more important, how much difference there is in the measurements between the two sensors. The study utilised three date-coincident image pairs of the two sensors, covering the subtropical coastal areas of southeastern China. The approach was achieved by evaluating the consistence of the normalised difference vegetation index (NDVI) data and the NDVI-estimated vegetation area between the two sensors. A further inter-type comparison among forest, paddy and grass has also been carried out to examine whether there is any variation in the consistence between different vegetation types. Results suggest that the ASTER sensor produces similar vegetation measurements to ETM+ in the study areas. Nevertheless, differences have also been observed. The study found an overall lower spectral NDVI measurement of ASTER than ETM+ based on their differences in mean NDVI value and estimated vegetation area. This suggests that the ASTER sensor is less sensitive to vegetation characteristics. Compared with ETM+, the ASTER sensor produces lower NDVI values by up to 8.1% and estimates less vegetation area by up to 4.1%. Inter-type analysis indicates that among the three test vegetation types, forest has the highest degree of agreement in NDVI measurements between the two sensors, whereas grass has the greatest difference in mean NDVI value between both sensors. All these differences are worth considering when the vegetation measurement results of ASTER have to be used together with those of ETM+ for a synergistic scientific application. This often occurs when using the ASTER sensor data for Landsat data continuity in scientific applications in order to fill the gaps in observation. As such, a data conversion between the two sensors is recommended. This paper demonstrated that the conversion using the model equations obtained in this study could reduce the root mean square errors and improve data agreement between the two sensor NDVIs. The study shows that the revealed differences in vegetation measurements between both sensors are due largely to the difference in the relative spectral response functions of the NDVI-related near-infrared bands between the two sensors. [Copyright &y& Elsevier]

• Lysimeter measurements were used to assess EC technique and STSEB model in vineyard. • Residual technique was shown appropriate to force the EB closure in our conditions. • Good performance confirms STSEB as an attractive model to estimate ET a in vineyards. • Daily ET a was modeled with average errors of ±0.6 mm d−1. • Error <10% was observed between cumulated ET a estimates and lysimeter data. Estimation of crop water needs plays a key role in the water resource management in arid and semi-arid regions. Actual evapotranspiration (ET a) becomes the key term in both water and energy balances at this point. In this work we focus on vineyard due to the significance of this crop for La Mancha region, Spain, with the greatest concentration of vineyards in the world. Eddy-covariance (EC) technique has been traditionally used for ground observations of ET a. One of the aims of this work is to assess the feasibility of an EC system under the challenging conditions of a small drip-irrigated vineyard in a semi-arid environment. Two-source energy balance modelling allows for ET a estimation, as well as soil evaporation (E) and canopy transpiration (T) partitioning, using radiometric temperatures as a basis. A Simplified version of this Two-Source Energy Balance approach (STSEB) has been previously tested in a variety of crops and environments. The second goal of this paper is to explore now the performance of the STSEB model in a vineyard structure. Two experiments were carried out during the growing season of 2014 and 2015 in a ˜4 ha row-crop drip-irrigated Tempranillo vineyard, in a semi-arid location in Castilla-La Mancha, Spain. As a novelty in this work, a 9-m2 monolithic large weighting lysimeter was available. An eddy-covariance flux tower was deployed together with net radiation and soil heat flux instrumentation. The residual technique was selected to force the closure after an analysis of the energy imbalance based on the comparison EC-Lysimeter. Good agreement between adjusted EC measurements and lysimeter data (RMSE of ±0.09 mm h−1 and ±0.5 mm d−1 at hourly and daily scales, respectively) supports the validity of eddy-covariance technique to monitor turbulent fluxes and accurate ET a in vineyards. A set of 5 thermal-infrared radiometers were assembled to guaranty an appropriate thermal characterization of the vineyard structure required in the STSEB approach. Surface energy fluxes were modeled every hour with average estimation errors lower than ±30 W m−2 for net radiation and soil heat flux, and around ±50 W m−2 for sensible and latent heat fluxes, with systematic deviations lower than 25 W m−2 in all fluxes. Comparison with lysimeter data showed an average underestimation of 0.04 mm d-1 with a RMSE value of ±0.6 mm d-1 in modeled daily ET a. In terms of accumulated ET a for the full experiments, an underestimation of 12% in 2014 and an overestimation of 7% in 2015 were observed. These results reinforce the feasibility of the STSEB approach to monitor hourly, daily and accumulated ET a in row-crops such as vineyards. Although no ground measurements were available to assess the partitioning, separated E–T values were obtained for the full campaigns, showing a significant ratio E/ET a of 0.35−0.40 for soil evaporation. [ABSTRACT FROM AUTHOR]

• Machine learning algorithms have been applied to estimate wheat yield for Australia. • Combining climate and satellite data achieves high performance for yield prediction. • The unique and overlapping information of climate and satellite data is quantified. • Optimal prediction performance can be achieved two-month lead time before maturity. Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction. [ABSTRACT FROM AUTHOR]

• Full suppression of systematic errors proportional to rainfall intensity. • Precision measurements of precipitation over a considerably wider range of rainfall rates, even of extreme rainfall. • Smaller tipped water volume may be used to achieve higher resolution and accuracy. • Versatile algorithm based on a lookup table that can be effortlessly implemented in digital data loggers. • The proposed algorithm was successfully verified in field measurements. Tipping bucket rain gauges are the most popular devices for determining rainfall intensity and precipitation depth. Even though application of a tipping bucket gauge presupposes calibration, they are susceptible to various random, mechanical, and systematic errors. In general, precipitation measurements may be underestimated by 5% to 40%. This paper presents a method of determining volumetric correction that compensates for systematic errors caused by variable rainfall intensity. The main goal of this study was to develop a suitable mathematical model that can be readily implemented in real time during field measurements, including the possibility of compensation of each individual tip. The developed algorithm is based on a "tip interval" recording method; this is in contrast to the standard measurement method, which is based on "tip count" and nominal tip volume. Such a solution may be applied to any kind of tipping-bucket rain gauge and can be effortlessly implemented in modern digital data loggers. In addition, smaller canisters may be used in a seesaw-like mechanism that provides accurate measurements over a considerably wider range of rainfall rates, even including extreme rainfall. During meticulous laboratory experiments, various tipping buckets with nominal volumes of 3 cm3, 4 cm3, 5 cm3, 10 cm3 and 200 cm3 were tested over a wide range of simulated precipitation rates from 8 mm·h−1 to 500 mm·h−1. The extent of error reduction was 5.2%, 11.4%, 17.7%, 25.8% and 37.7% for rainfall intensities of 50 mm·h-1, 100 mm·h-1, 200 mm·h-1, 300 mm·h-1 and 500 mm·h-1, respectively (assuming that the nominal volume of the tipping bucket was 4 cm3 and the collection area of the rain gauge was 200 cm2). Besides scrupulous laboratory tests, the presented algorithm was verified during field measurements at 29 research sites located in a temperate climate region in two vegetation zones: foothills and lower montane. The results of this research will considerably enhance the accuracy of the precipitation data that are essential in various hydrological studies. [ABSTRACT FROM AUTHOR]

Highlights • The grassland fire risk was assessed in remote and data lacking areas. • The grassland fire risk knowledge and experience was transferred from one region to another. • This method could conveniently be used for risk mapping and updating. • It will reduce grassland fire risk management costs. Abstract A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods. [ABSTRACT FROM AUTHOR]

Highlights • We proposed abnormal data rejection range that α times of supplied energy in Bowen ratio(B 0) and inverse analysis method(IABM). • We found the IABM has equally performance for estimation of evapotranspiration as well as B 0 method. • We found number of the rejection data were larger in B 0 method than in the IABM. • We found the proposed rejection range covers the previous researches of them. Abstract First, this paper proposes a definition of the abnormal data that occur as the Bowen ratio (B 0) approaches −1. The data are defined as α times of the heat flux supply (Rn - G), thus indicating the rejection range for the B 0. The method is applied to observed data. Second, we compare the results of the B 0 method with those of the inverse analysis of Bowen ratio method (IABM). The IABM can estimate the sensible and latent heat flux using temperature and humidity observed at a single height above the ground by optimization. Third, the estimation of the monthly evapotranspiration required for water resources and irrigation planning is adequate for α = 1˜3 in the B 0 method and IABM , and the initial humidity for IABM is adequate for 80% of the observed humidity plus 20%. Fourth, the features of the analysis of both methods regarding the direction of latent heat flux (lE) and sensible heat flux (H) associated with temperature difference (ΔT) and vapor pressure (Δe) clarified. And we found that the number of the rejected data in hourly and monthly is remarkably large in B 0 method than IABM. Based on above results, the IABM has almost the same performance compared with the B 0 method excluding initial value determination in the optimization process. [ABSTRACT FROM AUTHOR]

Highlights • Combined-Source Variational Data Assimilation Scheme was evaluated over a forest savanna. • Evaluate the potential of using global meteorological forcing data to drive the model. • An error propagation analysis indicate that the model is sensitive to uncertainties in wind speed. Abstract In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSE daily was 54 W/m2, which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSE daily = 82 W/m2, GLDAS: RMSE daily = 151 W/m2). However, LE RMSE values at 8-day temporal scale for GMA are RMSE 8-days = 32 W/m2, similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations. [ABSTRACT FROM AUTHOR]

Highlights • The agent-based model was created using the basic knowledge of gust, wind, and tree. • The Agent-based model demonstrated wind damage similar to the observed damage in forests. • The model described damage propagation and forest edge effects. • The model showed some phenomena, which were similar to the previous research. • Possibilities of improving the agent-based model were discusses. Abstract Powerful storms, consisting of strong gusts and winds, damage forests. Therefore, foresters need forest management strategies to reduce the damage risk. This paper focused on the damage patterns within the forest as the final results of multiple tree-wind dynamic interactions in time and space during a storm. Recent developments in computer technology allow for the possibility of simulating the complex and dynamic phenomena of damage during a storm but are extremely time consuming. To simplify the simulations without losing the crucial aspects of wind damage in forests, we introduced a computer simulation model using the agent-based modelling (ABM) technique, which capture the phenomena and interactions of individuals called 'agents'. We created an ABM for forest wind damage simulation, coupling together an accepted understanding of wind gusts in forests, tree bending moments, and damage propagation. The model was tested with variations in three conditions: trees acclimated and unacclimated to their wind environment; three levels of gust strength; and three tree planting densities. The ABM was able to replicate damage patterns and demonstrate damage propagation within the forest and the effects of forest edges. The difference in the rate of damage in the forest between acclimated and unacclimated edges became similar with an increase in the gust intensity, and a decrease in tree density through a reduction in the shelter effect of the forest. The ABM could be improved in the future by parametrizing the variation in individual tree resistance, and the variation in gust and wind strength, as well as adding more information on local environmental conditions such as topography and soil variation, and storm characteristics such as duration and intensity. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We analysed environmental gradients between forest and fields and their effects. • We found S-shaped microclimatic gradients in transition zones of 50–80 m. • Aboveground biomass was lower within 65 m of the transition zone. • Soil carbon and nitrogen contents were elevated at the edge. Abstract Human-driven fragmentation of landscapes leads to the formation of transition zones between ecosystems that are characterised by fluxes of matter, energy and information. These transition zones may offer rather inhospitable habitats that could jeopardise biodiversity. On the other hand, transition zones are also reported to be hotspots for biodiversity and even evolutionary processes. The general mechanisms and influence of processes in transition zones are poorly understood. Although heterogeneity and diversity of land use of fragments and the transition zones between them play an important role, most studies only refer to forested transition zones. Often, only an extrapolation of measurements in the different fragments themselves is reported to determine gradients in transition zones. In this article, we analyse environmental gradients and their effects on biota and matter dynamics along transects between managed continental temperate forests and agricultural land for one year. Accordingly, we found S-shaped microclimatic gradients in transition zones of 50–80 m between arable lands and forests. Aboveground biomass was lower within 65 m of the transition zone, 30 m in the arable land and 35 m in the forest. Soil carbon and nitrogen contents were elevated close to the transition zone's zero line. This paper contributes to a quantitative understanding of agricultural landscapes beyond individual ecotopes, and towards connected ecosystem mosaics that may be beneficial for the provision of ecosystem services. [ABSTRACT FROM AUTHOR]

Highlights • Analyze the effect of plant parameters on airflow partition around single plants. • Develop a model of airflow distribution around single plants. • Provide a transition method of airflow from single plants to multiple plants. Abstract The biocontrol measurement is the most effective method for land desertification control in arid and semi-arid areas where is limited by water and poor soil, and thus. Optimizations are needed for the existing biocontrol methods for anti-desertification and soil-erosion control so that the scope and scale of the biocontrol can be reduced. The premise for the optimization work is to reveal the distribution law of airflow fields around vegetation. Current studies are lack of the spatial express of airflow speed on a 2-D surface and there is no report on the transition of airflow fields from single plant to multiple plants. Based on detail experiments in a wind tunnel for the airflow fields around a single plant, a single-row forest belt with different plant spacing, a multi-row forest belt with different numbers of rows but the same plant spacing, and a double-row forest belt with various arrangements, this paper developed the horizontal model (Eq. (9)) for the airflow fields around single plant comprehensively analyzing the effect of plants characteristic parameters (such as crown width (W), height (H), porosity (β) etc.) on the horizontal and vertical air flow field partition around single plants and proposed a transition model (Eqs. (13) or 14) for airflow fields from single plant to multiple plants. These researches lay the theoretical foundations for optimum biocontrol plant configurations to address anti-desertification and soil erosion control. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We model the motion of trees in the wind using realistic 3D tree models derived from terrestrial laser scanning data. • We test the model against long-term field data collected in Wytham Woods, Oxford. • The model proved accurate and could be used in other natural forests or for open grown trees. Abstract Wind damage is an important driver of forest structure and dynamics, but it is poorly understood in natural broadleaf forests. This paper presents a new approach in the study of wind damage: combining terrestrial laser scanning (TLS) data and finite element analysis. Recent advances in tree reconstruction from TLS data allowed us to accurately represent the 3D geometry of a tree in a mechanical simulation, without the need for arduous manual mapping or simplifying assumptions about tree shape. We used this simulation to predict the mechanical strains produced on the trunks of 21 trees in Wytham Woods, UK, and validated it using strain data measured on these same trees. For a subset of five trees near the anemometer, the model predicted a five-minute time-series of strain with a mean cross-correlation coefficient of 0.71, when forced by the locally measured wind speed data. Additionally, the maximum strain associated with a 5 ms−1 or 15 ms-1 wind speed was well predicted by the model (N = 17, R2 = 0.81 and R2 = 0.79, respectively). We also predicted the critical wind speed at which the trees will break from both the field data and models and find a good overall agreement (N = 17, R2 = 0.40). Finally, the model predicted the correct trend in the fundamental frequencies of the trees (N = 20, R2 = 0.38) although there was a systematic underprediction, possibly due to the simplified treatment of material properties in the model. The current approach relies on local wind data, so must be combined with wind flow modelling to be applicable at the landscape-scale or over complex terrain. This approach is applicable at the plot level and could also be applied to open-grown trees, such as in cities or parks. [ABSTRACT FROM AUTHOR]

Highlights • Develop a probabilistic forecasting model for canopy temperature by weather variables. • Allow model parameter to vary periodically for better model performance. • Compare the model performances with existing models by case studies. • Test contribution of individual predictors by evaluating performance of sub-models. Abstract Continuous measurement of canopy temperature is an important indicator of plant water status of crops and the ability to predict canopy temperature will assist in the implementation of this technology for guiding crop irrigation scheduling. By noting that canopy temperature is related to its environmental weather variables which change over time of the day and have different effect or contribution to canopy temperature, this paper presents a probabilistic model to predict canopy temperature by using weather variables which can be obtained from weather model predictions. Unlike the existing models which consider only the linear correlation, the proposed model allows the model parameters to vary according to a periodic function which is designed to capture the variation over the time of the day. The continuity of parameter changes is guaranteed by varying the model parameters periodically and smoothly. Two case studies using cotton experiments from Australia and the United States are conducted to compare the model performance with the existing published models. Using the predictions of canopy temperature an index of crop stress is also predicted in order to evaluate its influence in the irrigation scheduling. Results show that the proposed model is superior to the existing published models in its ability to predict canopy temperature into the future and has utility in assessing when crop stress will occur, to assist with irrigation scheduling. Further evaluations suggest that the air temperature is a dominate weather variable for forecasting canopy temperature but the inclusion of the other weather variables still improves the forecast. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A successful EC experiment was conducted on a 3765 m a.s.l. tropical grassland. • The páramo is found to be a source of carbon in the study period. • NEE and GPP light-response models for this alpine-tundra site are presented. • ETa and Kc for páramo tussock grasslands are determined via H 2 O vapor detection. Abstract Atmospheric carbon (CO 2) exchange, evapotranspiration (ET) processes, and their interactions with climatic drivers across tropical alpine grasslands are poorly understood. This lack of understanding is particularly evident for the páramo, the highest vegetated frontier in the northern Andes, the main source of water for inter-Andean cities, and a large carbon storage area. Studies of CO 2 and ET fluxes via the standard Eddy Covariance (EC) technique have never been applied to this region, limiting the understanding of diurnal / nocturnal exchanges and budget estimations. In this paper, we report the first EC analysis conducted on the Andean páramo (3765 m a.s.l.); this analysis measured CO 2 , ET, and micrometeorological variables over two years (2016–2018) to understand their interactions with climatic / biophysical controls. The páramo was found to be a source of CO 2 and exhibited a net positive exchange (mean = +99 ± 30 gC m−2 per year). The light-responses of net CO 2 exchange and the primary productivity were correlated and model-parameterized. Evapotranspiration was 635 ± 9 mm per year (51% of the annual rainfall total), and we obtained crop coefficients for the dominant vegetation (Tussock grass) based on reference-ET models FAO56 and ASCE-ERWI (0.90 and 0.78, respectively). We also compared our results to those from other high-altitude (alpine) and high-latitude grasslands (tundra). Finally, we demonstrate that our measurement period is representative of the páramo's longer-term climate dynamics. Our investigation contributes to the body of knowledge on the land surface-atmosphere processes of the tropical Andes and supports decision-making about ecosystem services management and the preservation of this vulnerable biome. [ABSTRACT FROM AUTHOR]

Highlights • Machine learning techniques were accurate in predicting wind damage to trees. • Random forests proved the most accurate and discriminating methodology. • Models were sensitive to removal of site and stand but not tree characteristics. • All models were able to accurately replicate a mechanistic wind risk model. • Machine learning techniques could help the management of wind damage to forests. Abstract This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory plots damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle-Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage. [ABSTRACT FROM AUTHOR]

Highlights • Lower λ in frozen state was accounted for small initial freeze moisture content. • Soil thermal conductivity λ decreased with increase in freeze-thaw cycle times. • The critical soil moisture content was about 0.195 m3 m−3. • The critical degree of saturation was 0.37. • The contribution of soil moisture content to λ was different at different state. Abstract Soil thermal conductivity (λ) is one of the essential parameters relating to heat exchange, and it also plays a key role in verifying soil thermal hydrodynamics in permafrost regions. In this paper, the characteristic of in situ λ was analyzed based on data measured from June 2004 to December 2008 at Tanggula district on the Qinghai–Tibet Plateau. The result showed that diurnal λ strongly influenced by variation of soil moisture content. The daily λ exhibited distinct seasonal variation; on average, the largest value of λ occurred in summer, followed by the autumn and spring season, while the smallest value occurred in winter. As a whole, λ values in the unfrozen state were larger than those in the frozen state. Unsaturated soil and the huge difference in soil moisture content between the unfrozen state and initial freeze resulted in the lower λ in the frozen state. For the study area, the critical value of local soil saturation degree was about 0.37, the corresponding critical soil moisture content was about 0.195 m3 m−3. And soil moisture content was the main factor controlling in situ λ. Finally, an empirically-derived model was proposed for predicting daily λ , and which showed good performance in the study area. [ABSTRACT FROM AUTHOR]

Highlights • The association between WTs and wildfire activity in Spain is tested. • Spatial and seasonal variations in the association according to WT were detected. • Wildfires are triggered mainly by a few WTs, particularly E, SW and C. • Southern advections play an important role in fire size, promoting larger fires. Abstract In this paper we present a detailed spatial assessment of the association between atmospheric synoptic conditions (the weather types, WT) and fires on the Spanish mainland. The WT approach allows the continuum of atmospheric fields to be compressed into a few comprehensive patterns, providing meaningful information on the movement of air masses, wind direction and, ultimately, wind speed. We analyzed a total of 291,547 individual fires from the official Spanish Fire database (1974–2014) and corresponding daily WT patterns on the Iberian Peninsula from the daily NCEP/NCAR Reanalysis database, 1951–2015. Underlying spatial patterns of association among fire and weather types were explored by means of self-organizing maps (SOM) coupled to hierarchical clustering. Our analyses have detected that fires tend to occur and become largermore likely under certain atmospheric conditions suggesting that specific weather patterns can promote or boost fire events. From a pyrogeographical point of view, the contribution of WTs to wildfires on the Spanish mainland seems to be highly diverse, and displays several temporal and spatial patterns. The northwest region responds mostly to eastern flows during summer and southern during winter. Fires in the northern Mediterranean coast are promoted by northerly flows while in southern regions fires are linked to C or SE extreme heat episodes during summer. Overall, fires activity usually increases in the lee ward of mountain ranges depending on the prevalent flow from WTs, and thus related to adiabatic heating of the air flow. [ABSTRACT FROM AUTHOR]