Minimum Air Temperature Research Articles

Yield Prediction Models for ‘Royal Gala’ and ‘Fuji Suprema’ Apple Varieties Cultivated under a Subtropical Climate

Nitrogen (N) effect on crop yield depends on several factors such as soil type, climatic characteristics and orchard management, including plant density and N fertilization. These variables can be used to develop yield prediction models, which are scarce in the horticulture sector. This study aimed to evaluate the effect of nitrogen fertilization, orchards and cultivars and to predict the yields of ‘Royal Gala’ and ‘Fuji Suprema’ apples cultivated in a subtropical climate under different soil N availabilities. During the four seasons, nitrogen fertilization was applied a rates of 0, 25, 50, 100 and 150 kg N ha−1 year−1 for ‘Royal Gala’ and ‘Fuji Suprema’ apples located in southern Brazil. Yield, average fruit weight and leaf and fruit pulp N concentration were evaluated. Yield prediction models were developed based on the following variables: concentration of N in leaves and fruits, air temperature, chilling units, relative humidity and rainfall. “Cultivar” was the variable responsible for the greatest variation of yield, followed by “years/season”, and then the “orchard management. The N rates applied in the four seasons did not predict crop yield. In the model, “orchard” was the greatest determinant for leaf N concentration, and “season” was the main determinant for fruit-pulp N concentration. Mathematical model (3), based on leaf and fruit pulp N concentration, and certain climatic variables (minimum air temperature, annual rainfall and chilling hours < 7.2 °C) had the greatest potential for predicting yield in orchards of ‘Royal Gala’ and ‘Fuji Suprema’.

Long-term annual climate trends around the Breton Plots area, Alberta: is there any evidence of local climate change?

The objective of this research was to investigate the long-term trends in historical climate variables using the data collected near the classical Breton Plots (Alberta, Canada) and to determine if the data show any evidence of local climate change. The climate data used for the study were obtained from the Alberta Climate Information Service for the years 1901–2020. Various parametric statistical analyses were conducted to determine if monotonic trends occurred in the climate variables over time, and the analyses were conducted on the annual data as well as the 30 year climate normals. Large fluctuations in annual climate variables occurred, but a positive linear trend was observed in the average annual and growing season minimum air temperatures over time. Between 1901 and 2020 the annual minimum air temperature average increased at a rate of 0.3 °C for every 10 years. During the winter periods (December, January, and February, inclusive) from 1901 to 2020, the minimum air temperature average increased at an even higher rate of 0.5 °C every 10 years. Overall, the 30 year climate normals for minimum and maximum air temperatures increased for most seasons. The rates of increase were largest over the winter period, at 0.5 °C every 10 years for minimum air temperature and at 0.3 °C every 10 years for maximum air temperature. Strong linear increases occurred over time for growing degree days, number of frost-free days, total annual precipitation, growing season precipitation, and off-season precipitation.

Thermal Performance and Energy Analysis of Phase Change Material-Integrated Building with the Auxiliary Heating System in Different Climate Regions

Phase change material (PCM) embedded in the building contributes to enhancing indoor thermal comfort and reducing the heating load in winter by absorbing/releasing solar energy. The application effects of PCM incorporated into a single-space passive solar building with the auxiliary heating system (AHS) were investigated numerically in the six representative regions of western China. In the calculation and analysis by the enthalpy model, it was found that the effectiveness of PCM greatly depended on local climate conditions, thickness, and location of PCM. The results indicated that it was necessary to adopt the AHS for winter heating of the passive solar building. On this basis of using AHS, it showed a favorable thermal energy storage effect after the application of PCM resulting in the minimum indoor air temperature being maintained above 15°C within the comfort range stably. The integrated discomfort degree in each region was decreased by more than 96% compared to the original building, and the average temperature in Lhasa was 2°C higher than that in Urumqi. Furthermore, the highest rate of energy saving and carbon dioxide emission saving in the six regions was 19.7% and 17.4% in Lhasa, and the lowest were 4.3% and 4.2% in Urumqi, while other regions were relatively close, respectively. As a result, it can be stated that for a passive solar building using the AHS during the heating period, PCM was the most suitable for application in Lhasa, unsuitable in Urumqi, and generally suitable in other regions.

Eco-friendly management of wheat stripe rust through application of Bacillus subtilis in combination with plant defense activators

Stripe rust (SR) caused byPuccinia striiformisWestend. f. sp.triticiErikss (Pst) is one of the most important and destructive disease of wheat worldwide. In Pakistan, stipe rust appeared as epidemic and is causing huge losses to wheat production. However, wheat breeding programs are not sufficiently advanced to cope with the recently emergedPuccinia striiformisstrains. Under this scenario, current research was carried out for safe, effective and sustainable management of stripe rust of wheat. Seven wheat varieties include Sehar-06, Galaxy-13, Abdul Sattar-02, Faisalabad-08, Johar-16, TD-1 and Ujala-16 were planted at research farm of Muhammad Nawaz Shareef University of Agriculture, Multan, Pakistan during November 2019–20 and 2020–21 to determine their response toward Puccinia striiformis. The fungicide Tilt®, propiconazole, (T2) at the rate of 3 mL per 1500 mL water was used while the Bacillus subtilis (T1) was added at rate of 0.25 mL/1500 mL water with 15 g of dextrose, 0.25 g of chitosan and 0.25 g of salicylic acid in 1500 mL water to make a fine suspension. Our results showed that T1 reduced the disease effectively up to (8.18%) followed by T2 (10.7%) as compared to T0 (23.8%). The correlation between minimum air temperature, relative humidity and disease severity was highly significant while with maximum air temperature it was negatively non-significant. Also, wind speed, solar radiation and rainfall showed non-significant correlation with disease severity. After treatment, application of T1 and T2, minimum air temperature expressed a significant correlation with disease severity on varieties Sehar-2006, Galaxy-13 and Abdul Sattar-02 while non-significant correlation with disease severity on varieties Faisalabad-08, Johar-16, TD-1 and Ujala-16. Similarly, maximum air temperature, relative humidity, wind speed and solar radiation showed non-significant correlation with disease severity while rain fall was negatively non-significant. The current study showed that Bacillus subtilis is an ecofriendly management of stripe rust and its combination with plant defense activators enhance the efficacy and suppress disease. This management strategy is an innovative, and the results obtained will be helpful for better, ecofriendly and effective management of disease.

Tropical Forest Microclimatic Changes: Hurricane, Drought, and 15–20 Year Climate Trend Effects on Elevational Gradient Temperature and Moisture

The effects of hurricanes Irma and Maria and a severe drought on the temperature, precipitation, and soil moisture (under canopy and in the open) were calculated at 22 sites from 0–1045 m in northeastern Puerto Rico from 2001–2021, against the background short-term trend. Median and minimum air temperatures increased uniformly across the elevational gradient, 1.6 times as fast in the air under the canopy (+0.08 °C/yr) and 2.2 times as fast in the soil under the canopy (+0.11 °C/yr) as for air temperature in the open. There were no substantial moisture trends (average decrease <0.01 mm/yr). The peak effect of the hurricanes on under-canopy air temperature was the same as under-canopy soil temperature at 1000 m (+3, 0.7, 0.4 °C for maximum, median, minimum) but air maximum and minimum temperature peak effects were twice as high at 0 m (and soil temperatures stayed constant). Soil temperature hurricane recovery took longer at higher elevations. The peak effect of the hurricanes and the drought on the soil moisture was the same (but in opposite directions, ±0%), except for the wettest months where drought peak effect was larger and increasing with elevation. Differing patterns with elevation indicate different ecosystem stresses.

The impact of climate change on the water quality of Baiyangdian Lake (China) in the past 30 years (1991–2020)

Climate change significantly influenced the water quality of lakes in recent decades. This study investigated the effects of climate change on the water quality of Baiyangdian Lake (China) in the past 30 years (1991–2020) using correlation analysis, regression analysis, and the generalized additive model (GAM). The results show that water quality grade, chemical oxygen demand (COD), total phosphorus (TP) concentrations, and annual average and minimum air temperatures of the lake showed significant differences (p < 0.05) in the one-way ANOVA during the studied period. The concentration of dissolved oxygen (DO) and TP, annual average and minimum air temperatures, and annual precipitation decreased, while the COD and total nitrogen (TN) concentration, annual maximum temperature, and monthly maximum precipitation increased. The annual average and minimum air temperature affected all water quality variables and explained 12.3 %–54.5 % of variation deviation in correlation and GAM analyses, indicating that the changes of air temperature influenced the water temperature, which then affected the biochemical reaction rates leading to changes in water quality. The precipitation factors explained 10.5 % (TN) to 54.8 % (TP) of variation deviation, implying that the increase in precipitation improved water quality by diluting the COD concentration. However, excessive precipitation also accelerated the endogenous release of phosphorus in sediments by increasing the TP concentration. Additionally, extreme climate factors correlated with some water quality variables and explained 57.7 %–95.9 % of the total variances in correlation and regression analyses, suggesting that the extreme temperatures changed the nitrogen and DO concentration to aggravate lake pollution. However, the extreme precipitation purified the water through dilution. This study will facilitate to understand the impacts of climate change on water quality and find appropriate adaptation measures for ecosystem management of shallow lakes.

Streamflow Composition and the Contradicting Impacts of Anthropogenic Activities and Climatic Change on Streamflow in the Amu Darya Basin, Central Asia

Abstract Transboundary rivers are often the cause of water-related international disputes. One example is the Amu Darya River, with a catchment area of 470 000 km2, which passes through five countries and provides water resources for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water-related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in the snowmelt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in the glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in the snowmelt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in the glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2%–122.1%; however, the contribution of climate change ranges from −22.1% to −3.2%.

Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors

The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations; the role of T, n, RH, wind speed (U2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.

A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain

Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p&lt;0.01).

Increasing Risk of Spring Frost Occurrence during the Cherry Tree Flowering in Times of Climate Change

Climate change affects the agroecological conditions and persistence of cherry tree flowering. Detailed evaluation of minimum air temperature and occurrence of synoptic events occurrence during spring frosts within the cherry tree flowering in the Czech Republic (Central Europe) is missing. The main objective of this study was to evaluate the above-mentioned variables during the cherry tree flowering in different parts of the country from 1924 to 2012. Our question was how the frequency of frost days occurrence changed during the cherry tree flowering. A trend analysis was conducted with the Mann-Kendall test. The onset of the beginning of flowering and end of flowering shifted to an earlier date per the whole examined period (up to −13.9 and −8.1 days) and the period of flowering extended (up to 4.1 days). The shifts were more pronounced at higher elevations. During the period of the cherry tree flowering, the trend in change of the number of frost days was negative at the lowland station (−0.3 day) and positive at the highland station (+1.2 day). At all stations, “Ap3” synoptic event (anticyclone) occurrence during cherry tree flowering on days with the highest spring frost risk (Tmin at 2 m &lt; −1.1 °C) prevailed. The positive trend of frost-day occurrence and the negative trend of minimum air temperature in cherry tree flowering indicate that blossoms are more endangered at higher elevations.

Mapping and Analysis of Monthly Average Maximum and Minimum Air Temperature Distribution of Muğla Province in Geographic Information Systems (GIS) Environment with IDW Method

Muğla province, which is located in Southwest Anatolia and has a surface area of 13,338 km2, generally has a mountainous and rough terrain. In this study, it is aimed to map and analyze the distribution of monthly and annual maximum and minimum average temperature values of Muğla province by using the Inverse Distance Weighted (IDW) interpolation method. For this purpose, meteorological data of 13 stations of the General Directorate of Meteorology in Muğla and the stations of Gölhisar, Bozdoğan, Kaş, Elmalı, which are close to the borders of the province, were used. The average values of the annual and monthly maximum and minimum temperatures of these stations were transferred to the ArcGIS environment with the help of Geographic Information Systems (GIS) and their distribution was mapped with the IDW method. According to these maps, the highest average annual temperature in Muğla is in Milas and its surroundings, while the lowest is the provincial borders in the northeast of Muğla center and Seydikemer district. These two places are followed by Datça district, which is located on a long peninsula. The places with the highest annual minimum average temperatures are around Datça and Marmaris districts, while the lowest is the Burdur provincial border in the northeast of Kavaklıdere and Seydikemer. The maximum average temperature in the province is July (between 41.6-41.1°C in Milas and Köyceğiz), and the lowest is January (between 14-17°C in Muğla center and Kavaklıdere). The minimum average temperatures in the province are highest in June (between 37.7-34.6°C in Marmaris), and the lowest in January (between -7.9 and -6.6°C in Kavaklıdere). Elevation conditions, maritime influence and aspect conditions, and the intrusion of thermal pressure areas, especially in summer, have been effective in the distribution of maximum and minimum average temperature values in the province. These values may also vary depending on time during the year

A double sigmoidal model for snow-rain phase separation

This paper presents the use of a double sigmoidal model for precipitation phase partitioning in mountains. For the model development and evaluation, daily rainfall, minimum and maximum air temperatures, and snow depth data from twelve synoptic stations in the mountainous regions of Iran are used. The model performance is compared against six air temperature-based methods using the Taylor diagram, the coefficient of determination (R2), and the Root Mean Square Error (RMSE) performance indicators. Comparing the frequency of snowfall events estimated by the proposed model with those of the existing models and observation at the selected stations showed better performance of the proposed model in precipitation phase discrimination at the studied stations. The average R2 statistic that relates the frequency of snowfall occurrences estimated by the proposed model with the frequency of snowfall events observed at the studied stations is 0.989, which is the highest compared to other available models. Its average error across the stations is 4%, the lowest among the examined models. A split sample approach was used to ensure that the parameters of the proposed model are appropriately estimated. The result showed that the R2 and RMSE values of both calibration and validation subsets are not vastly different from each other, suggesting that the parameters of the two experiments are stable and the corresponding results are robust. The proposed model also performs better than other existing models in accurately estimating air temperature thresholds required for precipitation phase discrimination as well as the snow depth data in all studied stations.

Disaster event-based spring frost damage identification indicator for tea plants and its applications over the region north of the Yangtze River, China

Spring frost damage (SFD) is the main meteorological disaster limiting the tea industry in the region of the north of the Yangtze River (NYR) in China. Research regarding an SFD indicator for tea plants is of great significance for prevention and control of spring frosts, as well as for timely monitoring and early warning. Based on daily minimum air temperatures (Tmin) and spring frost disaster records in NYR from 1961 to 2020, the Receiver Operating Characteristic curve (ROC) was used to evaluate the accuracy of frost identification, and the critical temperature threshold for SFD to tea plants was determined by jointly comparing Overall Accuracy and the Youden Index. The results showed that the area under the ROC curve was 0.977, indicating that Tmin performed well in identifying frost events. The critical temperature threshold for spring frost in NYR was determined as Tmin of 3.7 °C, and 93 % of frost occurrences were correctly identified. Based on the determined critical threshold of SFD, the number of spring frost days, first frost date (FFD), and last frost date (LFD) in NYR from 1961 to 2020 were analyzed. The results showed that the number of spring frost days in NYR decreased from north to south. FFD appeared earlier and LFD ended later in northern NYR than in southern NYR. These results provide theoretical and technical support for the monitoring and evaluation of spring frosts in NYR, and also for implementing SFD prevention and control measurements for tea plants by use of a critical temperature threshold.

Common snowdrop as a climate change bioindicator in Czechia

The phenological response to climate change differs among species. We examined the beginning of flowering of the common snowdrop (Galanthus nivalis) in connection with meteorological variables in Czechia in the period 1923-2021. The long-term series were analyzed from phenological and meteorological stations of the Czech Hydrometeorological Institute (CHMI). Temporal and spatial evaluation (using Geographic Information System) in timing of beginning of flowering (BBCH 61) of G. nivalis was investigated under urban and rural settings. Furthermore, the detailed analysis of selected meteorological variables to onset of G. nivalis flowering was performed. Moreover, the trends (using Mann-Kendall test) and Pearson's correlation coefficients between phenological phase and meteorological variable were calculated. The main finding of this study was that the trend of the beginning of flowering of the common snowdrop during the studied period (1923-2021) is negative, and it varies in urban and rural environments. The results showed most significant acceleration of the beginning of flowering of G. nivalis by - 0.20dayyear-1 in urban area and by - 0.11dayyear-1 in rural area. Above that, a major turning point occurred between 1987 and 1988 (both, in phenological observations and meteorological variables), and the variability of the beginning of flowering is significantly higher in the second period 1988-2021. On top of, the study proved that the beginning of flowering of G. nivalis closely correlated with number of days with snow cover above 1cm (December-March) at both types of stations (urban and rural), and with mean air temperature in February, maximum air temperature in January, and minimum air temperature in March. The Mann-Kendall test showed a reduction in the number of days with snow cover above 1cm (December-March) during 99years period at Klatovy station (a long-term time series) by - 0.06dayyear-1, i.e., by - 5.94days per the whole period. Conversely, air temperatures increase (maximum and minimum air temperature by 0.03°Cyear-1 (2.97°C per the whole period) and average air temperature by 0.02°Cyear-1 (1.98°C per the whole period)). Thus, our results indicate significant changes in the beginning of flowering of G. nivalis in Czechia as a consequence of climate change.

ECONOMICAL EVALUATION OF MANGO (CV. KEITT) TREES UNDER COLOURED SHADE NETS

This study explored the economical viability of five different coloured shade nets (White, yellow, red, blue and black) on mango production (Mangifera indica L. cv. Keitt). This study investigated the climatic parameters in terms of light intensity, minimum and maximum air temperature, relative humidity and plant growth across 2019 and 2020 seasons. When compared to open field conditions, coloured shade nets resulted in higher maximum temperatures and relative humidity. The black net cover and open field treatments gave the lowest mango yield compared to all greenhouse net cover. When compared to the other treatments, the use of white or yellow nets resulted in a considerable increase in the number of fruits and total yield per plant. According to prevailed conditions, it could be concluded that optimum microclimate for growing mango in similar conditions could be obtained under white and yellow nets compared to the other net covers. Black greenhouse net cover gave the lowest productivity during the experimental duration. Production costs, total revenue, net return and ratio of total revenue to cost for Keitt mango in Egypt are presented in this study. The result shows that the net return for white net reached 15777 Egyptian pound (LE) and LE 15270 per m2 in 2019 and 2020 respectively, which was 2.63 times those grown in the open field (LE 5980 and LE 5081 per 2000 m2) in 2019 and 2020 respectively. However, Revenue to cost ratio was found higher in white net in both seasons which were 1.9 and 1.8 respectively.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.

Wintering and Cold Hardiness of the Small Tortoiseshell Aglais urticae (Linnaeus, 1758) (Nymphalidae, Lepidoptera) in the West and East of the Northern Palearctic

The geographic variability of the cold hardiness of poikilothermic animals is one of the keys to understanding the mechanisms of the formation of their ranges under climate change or anthropogenic introductions. A convenient object is the small tortoiseshell butterfly Aglais urticae, which is distributed from the Atlantic Ocean to the Pacific Ocean. On the edges of the distribution range, the difference between the averages of the absolute minimum air temperatures reaches 60 °C. The cold hardiness (supercooling point and lower lethal temperatures) of imago wintering in a supercooled state in the northeast of Russia was assessed in comparison to the previously studied European ones. Despite the huge difference in air temperatures, the mean supercooling points ranges in the east (−23...−29 °C) and the west (−17...−22 °C) differ by only 7 °C; the lower lethal temperatures for this species is near −30 °C. The identified cold hardiness is not enough for overwintering of A. urticae on the vast majority part of the species range in natural shelters above the level of snow cover. The inhabiting of A. urticae in regions with air temperatures below −30 °C is possible only when wintering under snow. This primitive behavioral adaptation probably does not require physiological changes and may not be unique to Lepidoptera.

Tropical nights (1976–2019) as an indicator of climate change in Ukraine

The article presents the results of an analysis of the number, frequency and intensity of tropical nights in Ukraine. We have selected such weather stations that are located at different latitudes and correspond with the north, west, east, and south as well as the central part of Ukraine. This was done in order to analyse the differences in tropical nights across latitudes. Also we have weather stations located at the approximately one latitude to analyse how tropical nights vary in longitude. Data from weather stations for 1976-2019 were under analysis. We can say that the increase in the minimum air temperature during summer is on the whole territory of Ukraine. This leads to the fact that since the mid-1990s tropical nights begin to be observed in the east of Ukraine, as well as in the south. After 2000, tropical nights also have begun to be observed in the centre and even in the west of Ukraine. Between two half-periods (1976-1997 and 1998-2019) the increase of number of tropical night events in the south of Ukraine (up to 300 cases) is much more significant than the increase in their number in the northwest (up to 20 cases). As well as the frequency, the intensity of tropical nights also increases. There are cases with minimum daily air temperatures above 25°C. The analysis of the North Atlantic Oscillation (NAC) and the Mediterranean (Cairo-Algeria) indices in the context of explaining the tropical night’s occurrence in Ukraine turned out to be quite effective.

Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India

Accurate estimation of reference evapotranspiration (ET0) is vital for hydrological studies and irrigation scheduling. This study aimed to estimate ET0 using 4 machine learning algorithms: random forest, support vector machine, light gradient boosting decision trees and extreme gradient decision trees. Daily data for 2001 to 2020 at 11 (arid and semi-arid) stations were used for modelling. Eighteen scenarios with different input combinations were evaluated using the data of maximum and minimum air temperature, mean relative humidity and wind speed, number of sunshine hours, solar radiation, and extra-terrestrial radiation data at these stations. The ET0 estimated using the FAO 56 Penman-Monteith equation was chosen as the target value for model fitting. The best input combination was found in the models that used all inputs, while the least accurate were the models that used temperature data only. The results showed that the support vector machine models outperformed the other models at most stations. The application of various input combinations indicated that the use of fewer inputs also gave reasonable accuracy in the modelling. In addition, wind speed and solar radiation were found to be important parameters for precise estimation.

Evaluation of different methods to estimate monthly reference evapotranspiration at Raichur region, India

The potential evapotranspiration (PET) is an important parameter to address water needs for growing crops. Estimation of potential evapotranspiration (PET) requires knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although potential evapotranspiration is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. Numerous potential evapotranspiration models have been developed and are being used, depending upon the availability of weather data. For the proposed study, the daily data of all weather parameters viz., maximum and minimum air temperature, maximum and minimum relative humidity, wind speed, actual sunshine hours and rainfall for estimating potential evapotranspiration for the duration of 31 years (1991-2021) were collected from the weather station at Main Agricultural Research Station (MARS), Raichur. A number of empirical formulae or approaches have been used for the determination of potential evapotranspiration (PET) from meteorological data. Availability of climatic data and accurately converting them in terms of water requirement are of great constraints. Judging the accuracy of different PET estimation models is a difficult task. The FAO 56-Penman Monteith model was considered as a standard reference for PET estimation in this investigation over the study location. The objective was to compare various PET models with standard reference model (FAO 56 Penman-Monteith model) and choose the best suitable model for estimating PET for the study area.