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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biometeorology".

Deadline for manuscript submissions: closed (25 August 2022) | Viewed by 25744

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Special Issue Editors

Dr. Demetrios E. Tsesmelis

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Guest Editor

1. Laboratory of Technology and Policy of Energy and Environment, School of Applied Arts and Sustainable Design, Hellenic Open University, 26335 Patras, Greece
2. Department of Technology Products and Services, NEURPUBLIC S.A., 18545 Piraeus, Greece
Interests: integrated water resources management; drought management; contingency planning; drought vulnerability; desertification vulnerability; composite index; water and land degradation
Special Issues, Collections and Topics in MDPI journals

Dr. Nikolaos Skondras

E-Mail Website1 Website2
Guest Editor

Global Water Partnership-Mediterranean (GWP-Med), 10556 Athens, Greece
Interests: integrated water resources management; non-conventional water resources; water treatment; wastewater treatment; composite index; water scarcity; water stress; resilience; vulnerability
Special Issues, Collections and Topics in MDPI journals

Dr. Nikolaos Proutsos

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Guest Editor

Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organisation “DEMETER”, 11528 Athens, Greece
Interests: forest micrometeorology; agrometeorology; aridity; drought; crop water requirements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Weather and climate variabilities impact plants’ and animals’ physiological processes. Agrometeorology is the science that studies the physical connections and interactions between weather/climate and agriculture, including croplands, forests, and livestock. Those processes and impacts are critical for the conservation and protection of natural ecosystems and also to sustain the productivity of agricultural systems. The influence of temperature, humidity, radiation, wind, and other related meteorological and biometeorological parameters in small temporal and spatial scales is within the scope of this Special Issue. Evapotranspiration (ET) and other mass and energy fluxes are also the main parameters in agrometeorology. In particular, the estimation of ET is essential for determining precise agricultural water requirements, irrigation scheduling, and the balance between water supply (available water resources) and demand. The available water resources play a key role in the agricultural sector, while an increasing share of overconsumption of vulnerable areas is due to the intensification of this sector and mainly because of over-irrigation. In arid and semi-arid areas, dependence on groundwater for water supply is significantly higher compared to other areas. It should be mentioned that water users of these areas continue to overexploit stocks during droughts without sufficiently considering their limited availability; this lack of agricultural water management further contributes to the environmental degradation of these areas. In addition, drought is an “insidious” natural hazard due to the reduction, at an unsuspected time, of the expected rainfall in an area or hydrological basin.

This Special issue focuses on agrometeorology aspects including recent experimental and modeling works in rural microenvironments and broader regions. Topics of interest include but are not limited to:

Micrometeorology micrometeorological measurements;
Evapotranspiration and other mass (carbon, water, etc.) and energy fluxes;
Radiation availability for photosynthesis and optical characteristics of plants;
Radiation distribution in canopies;
Droughts and impacts on plants;
Agricultural water management;
Irrigation management;
Weather factors’ effect on phytopathology and plant diseases;
Harmful organisms;
Effects of temperature and water availability on plants and animals;
Weather impact on honey production and bees’ behavior and productivity;
Impacts of climate and climate change on forests and agricultural crops;
Relations between climate attribute and biodiversity;
Hydrological processes;
Evapotranspiration models and evaluation;
Agroclimatology;
Comparison of biometeorological and bioclimatic indices;
Methods and data validation;
Remote sensing and crop modeling;
Future projections;
Aridity and changes of climate;
Impacts of vegetation on rural microclimate;
Changes in phenology of plants and animals;
Plant ecophysiology.

Dr. Demetrios E. Tsesmelis
Dr. Nikolaos Skondras
Dr. Nikolaos Proutsos
Guest Editors

Manuscript Submission Information

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Keywords

soil–plant–atmosphere continuum
micrometeorology
forest ecosystems
climate change
agricultural production
droughts
drought impacts
irrigation management
evapotranspiration
radiation fluxes
plant growth
plant–weather relations

Published Papers (12 papers)

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Research

17 pages, 4663 KiB

Open AccessArticle

Soil Management and Microclimate Effects on Ecosystem Evapotranspiration of Winter Wheat–Soybean Cropping in Northern Alabama

by Maheteme Gebremedhin, Jacob Brown and Ian Ries

Atmosphere 2022, 13(10), 1653; https://doi.org/10.3390/atmos13101653 - 11 Oct 2022

Viewed by 1008

Abstract

Nearly all the current winter wheat–soybean cropping systems occurring in the southeastern United States (SE) region are rainfed, as such, precipitation (P) underpins energy partitioning. We investigated, using the eddy covariance technique, the seasonal and interannual variability and rate and trend of energy partitioning, i.e., sensible and evapotranspiration (ET), for rainfed soybean and winter wheat cover cropping at Winfred Thomas Agricultural Research Station (WTARS) in Hazel Green, Alabama. Yearlong cumulative ET of 493, 743, and 746 mm during 2007, 2008, and 2009 relative to cumulative precipitation of 567, 1280, and 1356 mm y⁻¹ resulted in a higher mean ET/P ratio of 0.87, in 2007, medium 0.58 in 2008, and lowest 0.55 in 2009. Mean daily ET for the cover crop and soybean ranged between 1.70 and 2.44 mm d⁻¹ and 1.82 to 2.83 mm d⁻¹, respectively. Overall, our findings suggest total and seasonal precipitation distribution were major controlling factors in the partitioning of the energy and water budgets. This study provides evidence that changes in rainfall frequency and intensity in the SE will likely alter the regional croplands hydrology with implications on water resource management decisions in rainfed agriculture. Full article

(This article belongs to the Special Issue Agrometeorology)

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14 pages, 2912 KiB

Open AccessArticle

Climate Change Impact on the Duration of Great Vegetation Period and Vegetation Period of Beetroot and Watermelon in Slovakia

by Ján Čimo, Vladimír Kišš, Elena Aydın, Jakub Pagáč, Monika Božiková, Matúš Bilčík and Martin Minárik

Atmosphere 2022, 13(10), 1641; https://doi.org/10.3390/atmos13101641 - 09 Oct 2022

Cited by 1 | Viewed by 1476

Abstract

Climate change brings to the whole world numerous challenges such as an increase in the global temperature, weather fluctuations, periods of drought and heat alternating the local floods. While the majority of the effects are negative for agricultural production, some can be beneficial. Our work presents the evaluation of the changes in the duration of the great vegetation period (delineated with the beginning and end of days with an average temperature T ≥ 5.0 °C) and the vegetation periods of watermelon (Citrullus lanatus Thumb.) and beetroot (Beta vulgaris L.). Data sets on the average monthly air temperatures for the period 1961–2020 from one hundred agroclimatic stations in Slovakia were selected for the estimation of the future average air temperatures using statistical methods (linear trendline). Based on the temperature requirements of the selected crops, the potential maximum duration of the vegetation period was estimated for several decades from 2041 up to 2100. The results clearly showed prolongation of the vegetation periods and changes of their zonation in Slovakia. In 2011–2020, the duration of the beetroot vegetation period in the southernmost part of Slovakia (Danubian Lowland) was 15–20 days longer than in decade 1971–1980. It is expected, that this value will rise by another 10–15 days in decade 2091–2100. Since 1971–1980, watermelon vegetation period duration increased by 5–10 days when compared to decade 2011–2020. It is expected that by 2091–2100, its duration will prolong by another 30–35 days. Full article

(This article belongs to the Special Issue Agrometeorology)

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14 pages, 1234 KiB

Open AccessArticle

Nutrient Accumulation in Cover Crops under Contrasting Water Regimes in the Brazilian Cerrado

by Alberto do Nascimento Silva, Walter Quadros Ribeiro Junior, Maria Lucrecia Gerosa Ramos, Cristiane Andrea de Lima, Adilson Jayme-Oliveira, Antonio Marcos Miranda Silva and Arminda Moreira de Carvalho

Atmosphere 2022, 13(10), 1617; https://doi.org/10.3390/atmos13101617 - 03 Oct 2022

Viewed by 1120

Abstract

Brazilian Cerrado has a dry period, and the inclusion of new species for diversification in the production system needs to be drought-tolerant. This work aimed to evaluate biomass and nutrient accumulation in species with potential as cover crops and grain crops under different water levels. Irrigation treatments were obtained through an irrigation bar with sprinklers with increasing water flows to create a continuous gradient. The experimental design randomized complete blocks in split plots with four replications. The main plots were composed of four water regimes (167 mm, 268 mm, 381 mm and 432 mm), and the subplots were formed by the following cover crops: Amaranthus cruenthus, Chenopodium quinoa and Pennisetum glaucum, the latter already used for this purpose. Amaranthus cruenthus and P. glaucum recorded the highest dry biomass (10.16 and 9.75 Mg ha⁻¹, respectively). Dry biomass production and the cellulose contents decreased with the reduction of water availability for all species. A. cruenthus was the species that most accumulated P (37.42 kg ha⁻¹), K (416.92 kg ha⁻¹), Mg (30.88 kg ha⁻¹), S (43.53 kg ha⁻¹), Fe (2.22 kg ha⁻¹), B (0.124 kg ha⁻¹) and Zn (0.240 kg ha⁻¹). Amaranthus cruenthus produced the highest yield. Under high and low water availability conditions, A. cruenthus presents potential as a cover crop and grain cash crop, in addition to accumulating more nutrients; P. glaucum has potential as a cover crop and C. quinoa only for grain production. Full article

(This article belongs to the Special Issue Agrometeorology)

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23 pages, 8220 KiB

Open AccessArticle

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

by Robson Argolo dos Santos, Everardo Chartuni Mantovani, Elpídio Inácio Fernandes-Filho, Roberto Filgueiras, Rodrigo Dal Sasso Lourenço, Vinícius Bof Bufon and Christopher M. U. Neale

Atmosphere 2022, 13(9), 1518; https://doi.org/10.3390/atmos13091518 - 17 Sep 2022

Cited by 3 | Viewed by 2173

Abstract

The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ET_rF (Evapotranspiration Fraction) of sugar can crop using the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model with data from the Sentinel-2 satellites constellation. In order to achieve this goal, images from the MSI sensor (MultiSpectral Instrument) from the Sentinel-2 and the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors from the Landsat-8 were acquired nearly at the same time between the years 2018 and 2020 for sugar cane crops. Images from OLI and TIR sensors were intended to calculate ET_rF through METRIC (target variable), while for the MSI sensor images, the explanatory variables were extracted in two approaches, using 10 m (approach 1) and 20 m (approach 2) spatial resolution. The results showed that the algorithms were able to identify patterns in the MSI sensor data to predict the ET_rF of the METRIC model. For approach 1, the best predictions were XgbLinear (R² = 0.80; RMSE = 0.15) and XgbTree (R² = 0.80; RMSE = 0.15). For approach 2, the algorithm that demonstrated superiority was the XgbLinear (R² = 0.91; RMSE = 0.10), respectively. Thus, it became evident that machine learning algorithms, when applied to the MSI sensor, were able to estimate the ET_rF in a simpler way than the one that involves energy balance with the thermal band used in the METRIC model. Full article

(This article belongs to the Special Issue Agrometeorology)

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17 pages, 2651 KiB

Open AccessArticle

Atmospheric Factors Affecting Global Solar and Photosynthetically Active Radiation Relationship in a Mediterranean Forest Site

by Nikolaos D. Proutsos, Aristotle Liakatas, Stavros G. Alexandris, Ioannis X. Tsiros, Dimitris Tigkas and George Halivopoulos

Atmosphere 2022, 13(8), 1207; https://doi.org/10.3390/atmos13081207 - 31 Jul 2022

Cited by 7 | Viewed by 1997

Abstract

Light availability and its composition in components affecting plant growth as photosynthetically active radiation (PAR), are of critical importance in agricultural and environmental research. In this work, radiation data for the period 2009–2014 in a forest site in Greece were analyzed to identify the effect of meteorological variables on the formation of the photosynthetically active to global solar radiation ratio. The temporal changes of the ratio are also discussed. Results showed that the ratio values are higher in summer (0.462) and lower in autumn (0.432), resulting in an annual average of 0.446. In addition, for the investigated site, which was characterized by relatively high water content in the atmosphere, the atmospheric water content and clearness were found to be the most influential factors in the composition of the global solar radiation in the wavelengths of PAR. On the contrary, temperature and related meteorological attributes (including relative humidity, vapor pressure deficit and saturation vapor pressure) were found to have minor effect. Full article

(This article belongs to the Special Issue Agrometeorology)

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15 pages, 4422 KiB

Open AccessArticle

Winter Potato Water Footprint Response to Climate Change in Egypt

by Amal Mohamed Abdel-Hameed, Mohamed EL-Sayed Abuarab, Nadhir Al-Ansari, Hazem Sayed Mehawed, Mohamed Abdelwahab Kassem, Hongming He, Yeboah Gyasi-Agyei and Ali Mokhtar

Atmosphere 2022, 13(7), 1052; https://doi.org/10.3390/atmos13071052 - 30 Jun 2022

Cited by 4 | Viewed by 1785

Abstract

The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ET_C) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m³ ton⁻¹ in 1990 to 120 m³ ton⁻¹ in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity. Full article

(This article belongs to the Special Issue Agrometeorology)

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14 pages, 3446 KiB

Open AccessEditor’s ChoiceArticle

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB Model to Micrometeorology Environment in Begonia Fimbristipula Hance

by Pei Zhang, Zhengmeng Chen, Fuzheng Wang, Rong Wang, Tingting Bao, Xiaoping Xie, Ziyue An, Xinxin Jian and Chunwei Liu

Atmosphere 2022, 13(6), 890; https://doi.org/10.3390/atmos13060890 - 30 May 2022

Cited by 3 | Viewed by 1554

Abstract

The high quality and efficient production of greenhouse vegetation depend on micrometeorology environmental adjusting such as system warming and illumination supplement. In order to improve the quantity, quality, and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of the greenhouse environment and reduce the running costs. The parameters from the color information of the plant canopy reflect the internal physiological conditions, thus, the RGB model has been widely used in the color analysis of digital pictures of leaves. We take photographs of Begonia Fimbristipula Hance (BFH) growing in the greenhouse at a fixed time every day and measure the meteorological factors. The results showed that the color scale for the single leaf, single plant, and the populated canopy of the BFH photographs all have skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of the canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode, and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized. The models can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology. Full article

(This article belongs to the Special Issue Agrometeorology)

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13 pages, 721 KiB

Open AccessArticle

Quantifying Agricultural Drought Severity for Spring Wheat Based on Response of Leaf Photosynthetic Features to Progressive Soil Drying

by Kai Zhang, Bo Zhang and Funian Zhao

Atmosphere 2022, 13(4), 531; https://doi.org/10.3390/atmos13040531 - 28 Mar 2022

Cited by 4 | Viewed by 1835

Abstract

Agricultural drought definition focuses on the effect of water deficit during the crop growth period on the final crop yield. However, it is difficult to quantify the dynamic process for agricultural drought precisely during the crop growing season and then relate its impact to the final crop yield. This study was conducted to quantify agricultural drought severity for spring wheat (Triticum aestivum L.) at the jointing stage based on the response of leaf physiological parameters to progressive soil drying. The leaf potential and gas exchange parameters were observed daily using a DewPoint Potential Meter (WP4) and portable photosynthetic apparatus (LI-6400) at the jointing stage of spring wheat for two different water treatments: well water supply and natural drought, respectively. The results showed that the leaf photosynthetic features’ response to available soil water could be classified into five main stages, as the available soil water thresholds were at 0.41, 0.2, 0.12, and 0.04, respectively. We defined those five stages as no agricultural drought, mild agricultural drought, moderate agricultural drought, severe agricultural drought, and extremely severe agricultural drought based on the different mechanisms of the net photosynthesis rate’s response to progressive soil drying. The parameters of three stomatal conductance models, i.e., Ball–Berry, Leuning, and Medlyn, had two apparently different groups of values divided by moderate agricultural drought. This study combined atmosphere–soil–crop as a unit to quantify agricultural drought severity during the crop growth period could be used to model crop growth and development under water deficit conditions and calculate agricultural drought indices in drought research and management. Full article

(This article belongs to the Special Issue Agrometeorology)

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17 pages, 6837 KiB

Open AccessArticle

Evaluation of Ground-Based Models for Estimating Surface Albedo with In-Situ Radiometric Measurements across China

by Gang Chen, Mi Zhou, Shixiang Gu, Jinming Chen and Lei Wu

Atmosphere 2022, 13(3), 412; https://doi.org/10.3390/atmos13030412 - 03 Mar 2022

Viewed by 1371

Abstract

Surface albedo is an essential parameter in many solar radiation applications. Although several models are available, it remains debatable whether they are applicable to other locations. Using long-term daily measurements of radiation acquired by observational networks in China, this study examined the applicability of six existing albedo models: Ineichen model (IeM), Gueymard model (GM), Dong model (DeM), Iziomon-Mayer model (IMM), Morton model (MM), and Zhou model (ZeM). The evaluation results of model performance through statistical analysis showed that among the available ground albedo models, ZeM had the best overall performance at 12 selected stations, IeM was shown to provide acceptable estimations for locations where albedo records are readily available. The statistical results of individual station have shownthat the number of input parameters is not the only key factor for determining the predictive performance of ground albedo models. In other words, a simple model has potential for accurate estimation of ground albedo with appropriate model parameters. Therefore, the simple two-parameter DeM was selected to re-calibrate with in-situ radiometric measurements, which can be adopted as a surrogate for ZeM to predict surface albedo in China. Full article

(This article belongs to the Special Issue Agrometeorology)

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17 pages, 2800 KiB

Open AccessEditor’s ChoiceArticle

Evaluating Probability Distribution Functions for the Standardized Precipitation Evapotranspiration Index over Ethiopia

by Estifanos Addisu Yimer, Bert Van Schaeybroeck, Hans Van de Vyver and Ann van Griensven

Atmosphere 2022, 13(3), 364; https://doi.org/10.3390/atmos13030364 - 22 Feb 2022

Cited by 11 | Viewed by 3631

Abstract

Drought indices are used to identify and monitor drought events. The standardized precipitation evapotranspiration index (SPEI) is a widely used index based on accumulated water balance. There is, however, no broad consensus on which probability distribution is the most appropriate for water balances. This issue is investigated for Ethiopia using 125 meteorological stations spread across the country. Based on long-term series, a selection was made among the generalized extreme value, Pearson type 3, and generalized logistics (Genlog) distributions. Additionally, the effect of using actual, instead of potential, evapotranspiration and a limited amount of data (10, 15, 20, and 25 years) is explored. Genlog is found to be the best distribution for all accumulation periods. Furthermore, there is a considerable difference amongst the SPEI values estimated from the three distributions on the identification of extreme wet or extreme dry periods. Next, there are significant differences between standardized precipitation actual evapotranspiration index (SPAEI) and SPEI, signifying the importance of drought index selection for proper drought monitoring. Finally, time series of 20 or 25 years of data lead to almost similar SPEI values to those estimated using more than 30 years of data, so could potentially be used to assess drought in Ethiopia. Full article

(This article belongs to the Special Issue Agrometeorology)

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16 pages, 2419 KiB

Open AccessEditor’s ChoiceArticle

Sensible Heat and Latent Heat Flux Estimates in a Tall and Dense Forest Canopy under Unstable Conditions

by Francesc Castellví, Noman Ali Buttar, Yongguang Hu and Kamran Ikram

Atmosphere 2022, 13(2), 264; https://doi.org/10.3390/atmos13020264 - 04 Feb 2022

Cited by 3 | Viewed by 2666

Abstract

A method to estimate the sensible heat flux (H) for unstable atmospheric condition requiring measurements taken in half-hourly basis as input and involving the land surface temperature (LST), H_LST, was tested over a tall and dense aspen stand. The method avoids the need to estimate the zero-plane displacement and the roughness length for momentum. The net radiation (Rn) and the latent heat flux (λE) dominated the surface energy balance (SEB). Therefore, λE was estimated applying the residual method using H_LST as input, λE_R-LST. The sum of H and λE determined with the eddy covariance (EC) method led to a surface energy imbalance of 20% Rn. Thus, the reference taken for the comparisons were determined forcing the SEB using the EC Bowen ratio (BREB method). For clear sky days, H_LST performed close to H_BREB. Therefore, it showed potential in the framework of remote sensing because the input requirements are similar to current methods widely used. For cloudy days, H_LST scattered H_BREB and nearly matched the accumulated sensible hear flux. Regardless of the time basis and cloudiness, λE_R-LST was close to λE_BREB. For all the data, both H_LST and λE_R-LST were not biased and showed, respectively, a mean absolute relative error of 24.5% and 12.5% and an index of agreement of 68.5% and 80%. Full article

(This article belongs to the Special Issue Agrometeorology)

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17 pages, 2173 KiB

Open AccessArticle

Evaluating the Degradation of Natural Resources in the Mediterranean Environment Using the Water and Land Resources Degradation Index, the Case of Crete Island

by Demetrios E. Tsesmelis, Christos A. Karavitis, Kleomenis Kalogeropoulos, Efthimios Zervas, Constantina G. Vasilakou, Nikolaos A. Skondras, Panagiotis D. Oikonomou, Nikolaos Stathopoulos, Stavros G. Alexandris, Andreas Tsatsaris and Constantinos Kosmas

Atmosphere 2022, 13(1), 135; https://doi.org/10.3390/atmos13010135 - 14 Jan 2022

Cited by 15 | Viewed by 2720

Abstract

Natural resources degradation poses multiple challenges particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation. Full article

(This article belongs to the Special Issue Agrometeorology)

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