Micrometeorological Data Research Articles

Determination of outdoor thermal comfort thresholds for hot and semi-arid climates: A field study of residential neighbourhoods in Jaipur city

Outdoor thermal comfort (OTC) plays a vital role in enhancing the livability and well-being of urban residents, making it an essential area of research, particularly in the context of climate change. However, few studies have specifically focused on hot and semi-arid regions, especially within residential environments. This study seeks to address this gap by assessing OTC in various residential areas classified as Local Climate Zones (LCZs), ensuring a diverse range of survey locations to evaluate people's comfort and stress levels. Using the Thermal Sensation Vote (TSV) index, the study calibrated the Physiologically Equivalent Temperature (PET) scale for hot and semi-arid climates. In Jaipur, micrometeorological data was monitored alongside thermal perceptions, resulting in 2,428 valid responses gathered during both summer and winter seasons, between 09:00 and 21:00 h, when outdoor activity is common. The analysis revealed that microclimate parameters significantly influence thermal sensations in outdoor environments. Respondents showed high tolerance levels during summer, with variations in thermoneutrality, acceptability, preferences, and comfort across different built typologies. The threshold values identified in this study offer valuable insights for urban planners and designers, enabling them to enhance outdoor thermal comfort during the planning stages, ultimately creating more comfortable and livable urban spaces for residents.

Can changes in land use in a semi-arid region of Brazil cause seasonal variation in energy partitioning and evapotranspiration?

Changes to forests due to deforestation, or their replacement by agricultural areas, alter evapotranspiration and the partitioning of available energy. This study investigated seasonal variations in the energy balance and evapotranspiration in landscapes under different levels of anthropogenic intervention in the semi-arid region of Brazil. Micrometeorological data was obtained from September 2020 to October 2022 for three areas of the semi-arid region: preserved Caatinga (CAA, native vegetation), Caatinga under regeneration (REGE) and a deforested area (DEFA). Here, we use the Bowen ratio energy balance method. Measurements were taken of global solar radiation, air temperature, relative humidity, vapour pressure deficit, rainfall, net radiation, latent heat flux, sensible heat flux, soil heat flux, evapotranspiration, volumetric soil water content and Normalised Difference Vegetation Index. Sensible heat flux was the dominant flux in both areas with 66% for preserved Caatinga vegetation, 63% for Caatinga under regeneration and 62% deforested area. The latent heat flux was equivalent to 28% of the net radiation for preserved Caatinga vegetation, Caatinga under regeneration and deforested area. The evapotranspiration in turn responded as a function of water availability, being higher during the rainy seasons, with average values of 1.82 mm day−1 for preserved Caatinga vegetation, 2.26 mm day−1 for Caatinga under regeneration and 1.25 mm day−1 for deforested area. The Bowen ratio presented values > 1 in deforested area, preserved Caatinga vegetation and Caatinga under regeneration. Thus, it can be concluded that the change in land use alters the energy balance components, promoting reductions in available energy and latent and sensible heat fluxes during the rainy-dry transition in the deforested area. In addition, the seasonality of energy fluxes depends on water availability in the environment.

RisDes_Index: An Index for Analysing the Advance of Areas Undergoing Desertification Using Satellite Data

The proposal for a method of identifying the occurrence of desertification that has a strong association with in situ data leads to more assertive results when analysing the contribution of climate and social and economic factors to advancing the process. This study aimed to develop a methodology called the RisDes_Index to evaluate the evolution of the desertification process based on satellite data. The concept of the RisDes_Index method was based on the reflectance variables of the R, B and G bands, albedo and LAI of the Landsat 5/TM and Landsat 8/OLI satellites. Principal component analysis was used to assess the biophysical basis of the RisDes_Index by associating the results with micrometeorological data, physical and chemical properties, and vegetation cover data collected from five experimental sites in the semi-arid region of Brazil. These sites included one from a seasonally dry forest (i.e., the Caatinga), an agricultural cactus plantation, an area undergoing desertification, and two irrigated sugarcane crops (wetlands), one with and one without straw cover. The RisDes_Index was applied to all pixels of the images from 5 December 1991, 14 November 2001, 20 November 2009 and 6 October 2016 of an important desertification nucleus (DN) in the semi-arid region of Brazil, i.e., the DN of Cabrobó. The proposed RisDes_Index was able to identify areas with significant processes of desertification, which mainly occur in areas of sandy, acidic, bare soils with a high β value (Bowen ratio) and high soil temperature. The results of the RisDes_Index showed that in 5 December 1991, desertified areas comprised 38% of the total area of the DN of Cabrobó, expanding to 51% in 2016. Application of the RisDes_Index confirmed the advance of desertification in the DN of Cabrobó. This was due to a consequent increase in the water deficit and intensified deforestation to increase the areas of livestock farming. The RisDes_Index proved to be a robust method, as its estimation based on simple satellite products exhibited a strong association with biophysical variables of areas with different land uses and degradation levels. Thus, it is suggested that the RisDes_Index be applied in various regions of the world, with the idea of directing action to meet the advance of desertification.

Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling

Actual evapotranspiration (ETa) can be commonly estimated using numerical models based on i) weather and plant-based parameters, ii) from remotely sensed data and energy balance algorithms, and lately, iii) through the development and implementation of machine learning (ML) modeling techniques. In this work, supervised ML models were developed from a vineyard located in Talca, Chile, (i) to estimate actual evapotranspiration (ETa) (Model 1; M1) using the micrometeorological approach [Eddy Covariance; EC; sensible (H), latent (LE), soil heat fluxes (G) and net radiation (Rn)] and data from an automatic meteorological station (AMS) in reference conditions as ground-truth (inputs); (ii) to estimate energy balance components (Model 2; M2) from AMS data (inputs) and EC energy balance data as targets; (iii) to estimate ETa from the EC’s measured ETa data as target and thermal time data (degree hours; DH) calculated from air temperature with a base of 5 °C increments from 5 – 45 °C as inputs (Model 3; M3) and iv) to estimate energy balance components (targets from EC) from the same inputs of Model 3 (Model 4; M4). Results showed that the developed ML models had high accuracy and performance with no signs of over or under-fitting with a high correlation (R) and slope (b) close to unity (M1; R=0.94; b=0.89; M2; R=0.97; b=0.93; M3; R=0.97; b=0.89–0.95; M4; R=0.98; b=0.97). Furthermore, models were directly deployed over another vineyard located 22 km West of the modeled vineyard at 60 m lower over the sea level with significant performances and R values (R = 0.64–0.87; b = 0.66–1.00 for M1 to M4, respectively). These models could be used for precision irrigation to increase water use efficiency and better control canopy vigor, balance fruit and vegetative components, and ultimately improve berry and wine quality traits.

Evapotranspiration and crop coefficient of ‘Kent’ mango in an important fruit‐growing hub in Brazil

AbstractThe ‘Kent’ mango is one of the main cultivars produced in the São Francisco valley. However, due to a lack of data, water management was carried out using coefficients from the Tommy Atkins cultivar. Thus, aiming to achieve greater water management efficiency, the aim of this study was to evaluate the growth, radiation and energy balance, evapotranspiration and coefficients of the ‘Kent’ mango in the lower‐middle São Francisco valley in Brazil. The study was conducted in an orchard over two harvests between 2017 and 2018. The radiation and energy balance, evapotranspiration (ETc) and crop coefficients (Kc) of the mango were estimated from micrometeorological data. The mean reference evapotranspiration (ET0) and ETc values were 5.47 and 4.40 mm day−1 (vegetative growth, VG), 4.42 and 4.29 mm day−1 (floral induction, FI), 4.08 and 3.48 mm day−1 (floral induction + flowering, FI + FL), 4.51 and 3.63 mm day−1 (fruit drop, FD) and 6.09 and 4.46 mm day−1 (formation fruit + maturation fruit phase, FF + MF). Under the climate conditions of the São Francisco valley, Kc values of 0.80, 0.97, 0.85, 0.80 and 0.74 are recommended for the ‘Kent’ mango during the VG, FI, FI + FL, FD and FF + MF phases, respectively.

Growing Importance of Micro-Meteorology in the New Power System: Review, Analysis and Case Study

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides review including the relevant micro-meteorological techniques such asbservation, assimilation andumerical techniques, as well as artificial intelligence, presenting a relatively complete overview ofhe most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systemss analyzed inix different forms of power generationnd three typical scenariosf different stages in theower system, as well as integrated energy systemsnd disaster prevention and reduction. Finally, aase study in China is provided. This case takes wind power prediction as an example in aower system to compare the performance whenpplying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial–temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to theower system to some extent.

Feasibility analysis of dynamic improvement of current carrying capacity of overhead lines

The demand for electricity in China has been increasing year by year. However, the construction of transmission lines is costly and has significant composite short-term peak characteristics. This article aims to study the feasibility of improving the conductor’s current capacity, analyze data characteristics of typical conductor states and micrometeorological data, figure out the real-time conductor’s current capacity based on the calculation model, analyze the margin for improving the CCC, and lift the power supply potential.

Effect of Microclimate Modification on Growth and Yield of Basmati Rice under Punjab Conditions

The field experiments were conducted at the Research Farm, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana during kharif 2018. Pusa Basmati variety was transplanted on 5th July (D1) and 15th July (D2) under three spacing (25 cm x12 cm, 20 cm x 15 cm and 30 cm x 10 cm). The micrometeorological data on photosynthetically active radiation (PAR) was recorded at different phenological stages. The periodic biometric observations on leaf area index were recorded. Growing degree days (GDD) were calculated at different phenological stages. The results indicate that PAR interception and leaf area index was higher under wider spacing (30 cm x 10 cm) followed by closer spacing (20 cm x 15 cm and 25 cm x 12 cm). The leaf area index was higher under wider spacing (30 cm x 10 cm) followed by closer spacing (20 cm x 15 cm and 25 cm x 12 cm). Correlation coefficients were worked out between periodic number of tillers of rice and temperature (maximum and minimum) and sunshine hours/day. In both dates of transplanting, maximum temperature showed a significant negative correlation with tiller number in most of the cases. The sunshine hours in both dates of transplanting showed a positive and significant correlation under different treatments. The first date of transplanting reported higher grain yield (30.61 q/ha) as compared to second date of transplanting (27.20 q/ha). The higher grain yield (30.57 q/ha) was recorded in basmati rice transplanted under wider spacing (30 cm x 10 cm) in comparison to closer spacing of 20 cm x 15 cm (29.25 q/ha) and 25 cm x 12 cm (26.89 q/ ha). Hence, microclimate modification through dates of transplanting and spacing is cost effective measure for increasing basmati rice yield.

Detecting soil water redistribution in subsurface drip irrigated processing tomatoes using electrical resistivity tomography, proximal sensing and hydrological modelling

In this study, multiple soil-plant-atmosphere continuum (SPAC) monitoring methodologies, including electrical resistivity tomography (ERT), proximal thermal sensing techniques, and micrometeorological data, were combined with two-dimensional (2-D) soil hydrological modelling using HYDRUS 2-D to explore the soil water redistribution, and infer the relative crop water status in a subsurface drip irrigated (SDI) processing tomato field located in California (Yolo County, USA). Specifically, time-lapse ERT surveys were performed at two transects distributed parallel and perpendicular, respectively, to the SDI line, during an irrigation event. The ERT results were compared to HYDRUS 2-D outputs and the relative differences were explained in the form of local heterogeneities in electrical resistivity (ER) changes, as a proxy for soil water content (SWC) variations. Concurrent simultaneous soil wetting and root water uptake during the last irrigation event of the season caused negligible changes in ER in the active root zone. Slight differences in ER were observed in the top 20 cm along the dripline, confirming that the emitter spacing is small enough to create a wetted strip along the processing tomato bed. These changes were also compared to SWC values measured with time domain reflectometry soil moisture sensors. A comparison between HYDRUS 2-D and ERT confirmed negligible changes in ER during irrigation due to simultaneous wetting and root water uptake processes. In addition, a good correlation was observed between the proximal sensed and the ERT results. Finally, the findings of this study underscore the necessity of using multiple methods for improving our knowledge of the SPAC system under real field conditions.

Characterization of the Energy Balance of Wheat Grown under Irrigation in the Hot, Arid Environment of Sudan

An analysis of the crop microclimate is essential for assessing the climate’s appropriateness for cultivation. Here, the Bowen ratio (BR) was used to characterize the energy balance in an irrigated wheat field in a hot, arid environment in Sudan. The hourly BR was calculated using micrometeorological data, including net radiation (Rn) and soil heat flux (G), observed in the 2021–2022 and 2022–2023 growing seasons (December–March) and used to compute hourly daytime latent heat (LE) and sensible heat (H) fluxes during the days before and after irrigation. In both seasons, the observed significant evaporative cooling effect of irrigation led to a daily maximum temperature difference of 2.5–5.7 °C between the wheat field and a nearby meteorological station in a non-vegetated desert area. The energy balance calculation results showed that LE was dominant over H and G. Because BR tended to be negative, H was negative; thus, LE was larger than Rn because of sensible heat advection from the surrounding area. Further, a decrease in G after irrigation indicated an alteration in the soil’s thermal properties. These results provide insights into the micrometeorological response of irrigated wheat to a hot, arid environment and useful information for understanding soil–plant–atmosphere interactions in hot, dry environments.

Deep learning to extract the meteorological by-catch of wildlife cameras.

Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

High spatial variability in water use efficiency of terrestrial ecosystems throughout China is predominated by biological factors

Water use efficiency (WUE) in terrestrial ecosystems is a crucial indicator of regional resource use efficiency and the water-carbon balance relationship. However, spatial variability casts uncertainties in evaluating the water-carbon coupling, leaving the spatial patterns and underlying driving mechanisms of WUE obscure. To address this issue, our study conducted a comprehensive analysis of China's WUE from 2003 to 2020, utilizing 83 observed ecosystem fluxes and micrometeorological data from ChinaFLUX. We discovered the multi-year measured average WUE value was 2.13 ± 0.88 g C/kg H2O, with forest and cropland ecosystems demonstrating superior WUE (2.59 and 2.58 g C/kg H2O, respectively) compared to other ecosystem types. China's WUE exhibited marked spatial heterogeneity, characterized by geographic and climatic gradients, mainly decreasing from southeast towards northwest. Intriguingly, South China potentially exhibits maximal WUE theoretically. Yet, we observed an “oversaturation” effect in its WUE concerning gross primary productivity (GPP), peaking when GPP surpassed approximately 2200 g C m−2 yr−1. A clear dividing line between high- and low- WUE regions in China was observed at 400–500 mm isohyets. Our structural equation model, based on the "geography-climate-biology-flux coupling-WUE" cascade relationship, explained 93 % of WUE spatial variability. Here, GPP emerges as the primary direct factor of WUE variation, with climate conditions primarily affecting WUE through GPP. Our study provides valuable insights into the regional pattern of resource use efficiency and enriches the understanding of the integrated water-carbon cycle and carbon sinks.

A Dynamic Load Increasing Method for 220 kV Overhead Transmission Lines and Application

With the continuous development of China’s economy, the demand for electricity in society is increasing year by year, and it is necessary to continuously strengthen the construction of transmission capacity. This article aims to develop a terminal monitoring device that can monitor the status of transmission lines and the environment, analyze the monitoring data of overhead line status and micrometeorological data, and, based on the prediction model of the current carrying capacity of transmission lines, fully tap the power supply potential of transmission lines, which is of great significance for improving the transmission capacity of transmission channels.

First quantitative constraints on chlorine 36 dry deposition velocities on grassland: Comparing measurements and modelling results

Once released into the atmosphere, radionuclide dry deposition represents a major transfer process. It can be accurately characterized by its deposition velocity. However, this parameter is poorly constrained for most radionuclides, including chlorine 36. Chlorine 36 is a radionuclide of cosmogenic and anthropogenic origin. It may be discharged into the environment as gases and/or particles during the decommissioning of nuclear plants and the recycling of nuclear fuels. In this study, chlorine 36 deposition velocities are, for the first time, experimentally determined on grass downwind from the Orano La-Hague plant. The atmospheric chlorine 36 measurements were on average 50 nBq.m−3 for the gaseous fraction and 19 nBq.m3 for the particulate fraction. To measure the chlorine 36 transferred from the atmosphere to the grass, a method was devised for extracting the chlorides contained in solid matrices. With this method, chlorides were extracted with a mean efficiency of 83%. Chlorine 36 concentrations in the grass were on average 4 μBq.g−1, suggesting fast uptake of atmospheric chlorine 36. The yielded 36Cl dry deposition velocities varied with the season and were between 1 × 10−3 and 6 × 10−3 m s−1. The chlorine 36 depositions were modelled by adapting the existing deposition models and based on meteorological and micro-meteorological data. The dry deposition velocities calculated by the model showed less than one order of magnitude of difference with those determined experimentally. The deposition fluxes calculated by the model showed that the atmospheric depositions were predominantly gaseous chlorine 36 (>97%). However, on remote sites, the particulate fraction could be larger and have a greater influence on dry deposition. As chlorine 36 is a highly soluble and bioavailable element, these results will enable a better study of its behaviour in the environment and a more accurate evaluation of its dosimetric impact.

Bowen ratio and evapotranspiration dynamics of oil palm

Abstract. Asyura MF, June T, Salmayenti R. 2023. Bowen ratio and evapotranspiration dynamics of oil palm. Intl J Trop Drylands 7: 37-45. To optimize its biophysical processes, plant needs water and this requirement can be quantified as Evapotranspiration (ET). There are various methods for estimating ET from a vegetated surface, and Bowen ratio is one method commonly used for agricultural purposes. We use Bowen ratio method to estimate ET from oil palm (Elaeis guineensis Jacq.) plantation in PT. Perkebunan Nusantara VI, Batang Hari, Jambi Province, Indonesia for the period 2014-2015 using observed micrometeorology data and looked at the strength of micrometeorological components in influencing the daily and diurnal patterns, which include net radiation (Rnetto), heat fluxes (latent LE and sensible H), actual vapor pressure (ea), soil moisture (SM), wind speed (WS), air temperature (Ta), ground heat fluxes (G), and LE-H. We found that the daily pattern of Bowen ratio was strongly influenced by LE-H, LE, H, SM, shown by a correlation coefficient of -0.85, -0.75, 0.84, -0.58, respectively. The diurnal pattern of Bowen ratio was affected by ea with a correlation coefficient of -0.52, while daily ET was strongly influenced by Rnetto, LE-H, H, Bowen ratio with a correlation coefficient of 0.85, 0.95, -0.63, -0.75 respectively. WS, Ta, Rnetto, G, ea, RH and LE-H affected diurnal ET with a correlation coefficient of 0.72, 0.72, 0.96, 0.89, 0.51, -0.64 and 0.9, respectively. Within this period of 2014-2015 there were 7 months where rainfall was greater than ET (wet period December 2014 to April 2015, November 2015) and 5 months where rainfall was lower than ET (dry period May 2015 to October 2015). During this long dry period of 2015, caused by strong El Nino events, oil palms became vulnerable to drought.

Different spatial and temporal arrangements for validating the latent heat flux obtained using the MOD16 product in a forest in the Western Amazon

The Amazon rainforest is an important source of evapotranspiration and is essential in the global atmospheric circulation and hydrological cycle. However, measurements on vegetated surfaces are difficult, and methods such as remote sensing are promising in the micrometeorological data area. We aimed to assess the applicability of the MOD16 product to estimate evapotranspiration in a vegetated area. MOD16 data (LEMOD) derive from the Moderate Resolution Imaging Spectroradiometer sensor. For comparison, we used data from the eddy covariance system (LEEC) of the tower of the Large-scale Biosphere-Atmosphere Program in the Amazon of the Jaru Biological Reserve. To measure the linearity among variables, the Pearson correlation test (α=0.05) was used, and the T test was applied to assess the statistical significance between LEMOD and LEEC means (α=0.05); the root mean square error was also calculated. Regarding LEMOD, a similarity was found between the annual means of model and data, in which the best LEMOD estimates were obtained for annual averaging, when mean values were 96.51 W m–2 (0.7% lower than LEEC). LEMOD overestimates ranged from 1.05 W m–2 (1%) in 2007 to 15.78 W m–2 (18%) in 2005. Variations in short periods were not represented by the LEMOD product.

Accurate Wind Observation and Robust Control for Drones in the Field of Micro-meteorology⋆

Urban logistics has been the primary target for recent drone applications. However, these drones face varying weather conditions in their flight horizon that can limit their performance. From another perspective, this flight horizon is the focus of micro-meteorology data gathering for a city-wide accurate weather prediction system. Hence, these drones can carry out logistics while gathering micro-meteorology data. This paper focuses on the latter and aims to realize the drone capabilities required for forecasting micro-meteorology, especially observing highly accurate wind data and robust control capability against strong wind disturbances. The generation of wind maps using a 2D wind sensor mounted on a drone and wind gust estimation using a nonlinear disturbance observer (NDOB) are proposed as the capability for accurate wind observation. Next, the position and velocity control of the drone using super twisting sliding mode control (STSMC) and NDOB are designed for robust control. Lastly, numerical simulation and real-world experiments verify the effectiveness of the whole system proposed in this paper.

Determination of energy partition of a cucumber grown Venlo-type greenhouse in southeast China

Accurate determination of energy partition inside greenhouses is a key procedure in optimizing efficient water management. In this study, micro-meteorological data and energy fluxes of cucumber were measured during four planting seasons (2018–2021) in a Venlo-type greenhouse to analyze the energy manifestation in different growing stages. The results showed that the latent heat flux (λET) was the primary component of net radiation (Rn), accounting for 61.2–72.6 % of Rn during the whole growing seasons, followed by sensible heat flux (H, 19.8–24.3 % of Rn) and soil heat flux (G, 7.6–14.8 % of Rn). The λET increased while both H and G decreased as the crop grew. Especially, H was larger than λET at midday in the initial growing stage. Energy partition was intensely affected by the leaf area index (LAI). The λET/Rn increased linearly with the increase in LAI, while the H/Rn decreased linearly and G/Rn decreased exponentially until LAI reached 4 m2 m−2. Canopy conductance (Gc), Priestley-Taylor coefficient (α), and the decoupling coefficient (Ω) were calculated to evaluate the controlling factors over cucumber λET. The high values of the α (= 1.15 ± 0.05) and the Ω (= 0.65 ± 0.05) indicated that the cucumber λET was principally constrained by Rn. The Gc was calculated by inverting Penman-Monteith (PM) equation using aerodynamic conductance (Ga) values obtained by the heat transfer coefficient (hs) method based on accurate convection regimes. The Gc varied from 1.42 to 17.98 mm s−1 and high Gc values corresponded to high λET. The λET was estimated by the Ω model with the mean root mean square error (RMSE) and mean absolute error (MAE) equaled 32.65 and 25.73 W m−2, while the determination coefficient (R2) was 0.94. The H was reproduced by the Bulk Transfer (BT) model to validate Ga with the RMSE and MAE equaled 32.57 and 24.95 W m−2, the R2 was 0.86. The results also indicated that the simplified energy balance (EB) method was an optional approach to analyzing energy partition in greenhouses. Consequently, more energy could be saved by avoiding the excessive application of water and thus improving crop water use efficiency in greenhouses.

Carbon flux variation and associated biomass energy storage economic value implications in the Dinghushan Biosphere Reserve

Subtropical forests are a major carbon (C) storage pool and are therefore rich in biomass energy reservoirs. However, there have been no studies that quantify the C storage economic value, especially in the region of the Dinghushan Biosphere Reserve. This study is the first attempt to apply the eddy covariance technique to measure biomass energy storage and the associated economic value and demonstrates that CO2 flux measurement is a viable method for quantifying the ecosystem biomass energy storage. CO2 flux and micro-meteorological data were collected and analyzed to calculate the daily C sequestration and establish their inter-relationship. In addition, we used the C price estimates of Guangdong Province in China to determine the capital value of the C sequestered. The measured average annual precipitation was 1459.4 mm, the average temperature was 22.9 °C and the highest photosynthetic photon flux density (PPFD) was 334.92 μmol m−2s−1. CO2 flux and biomass energy storage varied seasonally. Photosynthetic photon flux density (PPFD) was highly correlated with gross primary production (GPP) (r2 = 0.99, p < 0.0001). Soil temperature was correlated with net ecosystem exchange (NEE) (r2 = 0.84, p < 0.0001). There was a positive correlation (r2 = 0.49, p < 0.0001) between soil moisture and NEE. The Dinghushan Biosphere Reserve sequestered an average of 5300 tCy−1 equivalent to RMB 3.3 million (USD 530,000). We conclude that economically, the Dinghushan Biosphere Reserve is highly significant to China's Guangdong Province because of its rich biomass energy reserves via C sequestration and our findings will stimulate the formulation of more aggressive conservation policies that will inevitably lead to better management of the reserve.

Turbulence characteristics within the atmospheric surface layer of the coastal region of Qatar

The atmospheric turbulence characteristics in the coastal region of Qatar are analyzed using the measurements conducted on the shoreline (26.08 N, 51.36 E). The micrometeorological data were collected, from August 2015 to September 2016, using sonic anemometers (20 Hz) at three heights and a weather station atop a 9-m tower. The turbulence characteristics are studied within the framework of Monin–Obukhov similarity theory (MOST), in the presence of the coastal inhomogeneities generated by the sea and land surfaces coming together. The results show the wind from the north-west prevails during the entire test period, with the wind speed higher than that from other directions. The non-dimensional standard deviations of velocity components are found to be consistent the results reported around the world and match suggested MOST scaling, with a relatively greater value for the dissipation rate of turbulent kinetic energy. The flux Richardson number shows a larger scatter under the super-stable and super-unstable regimes. Moreover, the non-dimensional standard deviation of temperature does not align with the suggested model under near-neutral and very stable regimes, and the gradient Richardson number shows some negative values under stable regimes. Two different atmospheric daily stability patterns, ‘orderly’ and ‘disheveled,’ are identified based on the wind conditions. The orderly stability pattern shows a daily descending and ascending trend during the sunrise and sunset periods, respectively, while the disheveled days follow a random pattern with no clear order. The two patterns are then related to the wind continuity and direction relative to the shoreline.