Mean Daily Air Temperature Research Articles

Regional Association between Mean Air Temperature and Case Numbers of Multiple SARS-CoV-2 Lineages throughout the Pandemic.

The association between mean air temperature and new SARS-CoV-2 case numbers throughout the ongoing coronavirus disease 2019 (COVID-19) pandemic was investigated to identify whether diverse SARS-CoV-2 lineages may exhibit diverse environmental behaviors. The number of new COVID-19 daily cases in the province of Verona was obtained from the Veneto Regional Healthcare Service, whilst the mean daily air temperature during the same period was retrieved from the Regional Agency for Ambient Prevention and Protection of Veneto. A significant inverse correlation was found between new COVID-19 daily cases and mean air temperature in Verona up to Omicron BA.1/BA.2 predominance (correlation coefficients between −0.79 and −0.41). The correlation then became positive when the Omicron BA.4/BA.5 lineages were prevalent (r = 0.32). When the median value (and interquartile range; IQR) of new COVID-19 daily cases recorded during the warmer period of the year in Verona (June–July) was compared across the three years of the pandemic, a gradual increase could be seen over time, from 1 (IQR, 0–2) in 2020, to 22 (IQR, 11–113) in 2021, up to 890 (IQR, 343–1345) in 2022. These results suggest that measures for preventing SARS-CoV-2 infection should not be completely abandoned during the warmer periods of the year.

Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016

In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean Ta from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual Ta models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R2 = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R2CV = 0.4–0.6, RMSECV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global Ta model was also obtained using daytime Terra LST data (R2 = 0.6 and RSE = 2 °C; in its validation: R2CV = 0.5, RMSECV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). Ta and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the Ta time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of Ta in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated Ta and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in Ta and generating mean Ta maps at a global level (1 km2 spatial resolution) in the Hurd Peninsula of Livingston Island.

From Intra-plant to Regional Scale: June Temperatures and Regional Climates Directly and Indirectly Control Betula nana Growth in Arctic Alaska

Tundra shrubs reflect climate sensitivities in their growth-ring widths, yet tissue-specific shrub chronologies are poorly studied. Further, the relative importance of regional climate patterns that exert mesoscale precipitation and temperature influences on tundra shrub growth has been explored in only a few Arctic locations. Here, we investigate Betula nana growth-ring chronologies from adjacent dry heath and moist tussock tundra habitats in arctic Alaska in relation to local and regional climate. Mean shrub and five tissue-specific ring width chronologies were analyzed using serial sectioning of above- and below-ground shrub organs, resulting in 30 shrubs per site with 161 and 104 cross sections from dry and moist tundra, respectively. Betula nana growth-ring widths in both habitats were primarily related to June air temperature (1989–2014). The strongest relationships with air temperature were found for ‘Branch2’ chronologies (dry site: r = 0.78, June 16, DOY = 167; moist site: r = 0.75, June 9, DOY = 160). Additionally, below-ground chronologies (‘Root’ and ‘Root2’) from the moist site were positively correlated with daily mean air temperatures in the previous late-June (‘Root2’ chronology: r = 0.57, pDOY = 173). Most tissue-specific chronologies exhibited the strongest correlations with daily mean air temperature during the period between 8 and 20 June. Structural equation modeling indicated that shrub growth is indirectly linked to regional Arctic and Pacific Decadal Oscillation (AO and PDO) climate indices through their relation to summer sea ice extent and air temperature. Strong dependence of Betula nana growth on early growing season temperature indicates a highly coordinated allocation of resources to tissue growth, which might increase its competitive advantage over other shrub species under a rapidly changing Arctic climate.

HOTRUNZ: an open-access 1 km resolution monthly 1910–2019 time series of interpolated temperature and rainfall grids with associated uncertainty for New Zealand

Abstract. Long time series of temperature and rainfall grids are fundamental to understanding how these environmental variables affect environmental or ecological patterns and processes such as plant distributions, plant and animal phenology, wildfires, and hydrology. Ideally such temperature and rainfall grids are openly available and associated with uncertainties so that data-quality issues are transparent to users. We present a History of Open Temperature and Rainfall with Uncertainty in New Zealand (HOTRUNZ) that uses climatological aided natural neighbour interpolation to provide monthly 1 km resolution grids of total rainfall, mean air temperature, mean daily maximum air temperature, and mean daily minimum air temperature across New Zealand from 1910 to 2019. HOTRUNZ matches the best available temporal extent and spatial resolution of any open-access temperature and rainfall grids that include New Zealand and is unique in providing associated spatial uncertainty in the variables' units. The HOTRUNZ grids capture the dynamic spatial and temporal nature of monthly temperature and rainfall and the uncertainties associated with the interpolation. We also demonstrate how to quantify and visualise temporal trends across New Zealand that recognise the temporal and spatial variation in uncertainties in the HOTRUNZ data. The HOTRUNZ data are openly available at https://doi.org/10.7931/zmvz-xf30 (Etherington et al., 2021).

Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean (Glycine max [L.] Merrill) Cultivar Augusta

Genotype and weather conditions play crucial roles in determining the volume and stability of a soybean yield. The aim of this study was to identify the key meteorological factors affecting the harvest date (model M_HARV) and yield of the soybean variety Augusta (model M_YIELD) using a neural network sensitivity analysis. The dates of the start of flowering and maturity, the yield data, the average daily temperatures and precipitation were collected, and the Selyaninov hydrothermal coefficients were calculated during a fifteen-year study (2005–2020 growing seasons). During the experiment, highly variable weather conditions occurred, strongly modifying the course of phenological phases in soybean and the achieved seed yield of Augusta cultivar. The harvesting of mature soybean seeds took place between 131 and 156 days after sowing, while the harvested yield ranged from 0.6 t·ha−1 to 2.6 t·ha−1. The sensitivity analysis of the MLP neural network made it possible to identify the factors which had the greatest impact on the tested dependent variables among all the analyzed factors. It was revealed that the variables assigned ranks 1 and 2 in the sensitivity analysis of the neural network forming the M_HARV model were total rainfall in the first decade of June and the first decade of August. The variables with the highest impact on the Augusta soybean seed yield (model M_YIELD) were the mean daily air temperature in the second decade of May and the Seljaninov coefficient values calculated for the sowing–flowering date period.

An Integrated Framework for Mapping Nationwide Daily Temperature in China

Air temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (Ts) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate Ta in view of the relationship between Ta and Ts. In this article, we proposed a two-phase integrated framework to estimate daily mean Ta nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy.

Consistency assessment between summer simmer index and other heat stress indices (WBGT and Humidex) in Iran's climates

This study aimed to assess the applicability of summer simmer index (SSI) in different climates of Iran and compare it with other common heat stress indices including WBGT and Humidex. The daily mean air temperature and relative humidity were obtained from the Meteorological Organization of Iran for the 60-year period between 1957 and 2016. Statistical tests including Pearson correlation coefficient and linear regression tests were used to consistency assessment between Summer Simmer Index and wet-bulb globe temperature (WBGT) and Humidex indices. The results of the present study showed that the SSI is significantly correlated with both WBGT and Humidex indices (R2 > 0.932, p < 0.001). In addition, the obtained correlation between SSI and Humidex was higher than correlation between SSI and WBGT in all the climates. Moreover, SSI in the Mediterranean region shows less correlation with both WBGT and Humidex indices (R2 = 0.932 and 0.964 for WBGT and Humidex, respectively). The highly correlation coefficients were observed between SSI and WBGT and Humidex indices, and also between SSI and other environmental parameters; moreover interpreting of SSI is easy, so it can be suggested that SSI can be used as an alternative index for WBGT or Humidex in assessment of heat stress in different climates of Iran.

The effect of weather conditions on the spring barley variety ‘Yaromir’ and its productivity in the Ryazan region

The current study was carried out in 2017–2021 in the Competitive Variety Testing on the basis of the ISPA of the FSBSI “FRAC VIM”. The soil of the experimental plot was dark gray forest, heavy loamy in granulometric composition, of medium fertility. The hydrothermal conditions of the vegetation periods differed significantly in terms of the mean daily air temperature and the sum of effective temperatures, and the amount of precipitation. The years of 2018 and 2021 were characterized with very dry conditions with HThC of 0.51–0.64. The years of 2017, 2019 were arid with HThC of 0.72–0.85. The years of 2020 was humid, with a moisture coefficient of 1.34. The purpose of the current study was to establish the effect of weather conditions of the Ryazan region on the interstage periods, the yield structure and productivity of the spring barley variety ‘Yaromir’. There has been found a correlation between the length of vegetation period with the amount of precipitation and HThC in the interstage periods of ‘germination-tillering’ (r = +0.571 and r = +0.607, respectively), ‘tillering-coming into ear’ (r = +0.735 and r = +0.655, respectively) and ‘filling-ripening’ (r = +0.881 and r = +0.967, respectively). Strong positive correlations were found between ‘amount of precipitation’ and the yield structure elements of the variety, namely ‘number of productive stems per 1 m2 ’ (r = +0.969), ‘productive tilling capacity’ (r = +0.947), ‘plant height’ (r = +0.827). Mean correlations were found between ‘amount of precipitation’ and ‘length of head’ (r = +0.562) and ‘number of kernels per head’ (r = +0.453). Negative mean correlations were found between ‘amount of precipitation’ and ‘1000-kernel weight’ (r = -0.654). There has been established that the average daily air temperature of the vegetation period had no effect on the formation of the yield structure elements of this variety. There have been identified average correlations between ‘sum of effective temperatures’ and ‘productive tillering’ (r = +0.352) and ‘length of head’ (r = +0.538). Weather conditions did not have a significant effect on grain productivity of the variety. There has been noted a slight effect of precipitation and HThC in the period ‘tillering-coming into ear’. The minimum variation in the productivity over the years (Cv = 3.5%) and revealing 95.1% of productivity potential indicates that this variety is characterized by stable productivity and adaptability to the weather conditions of this region.

Development of a Simple Empirical Yield Predition Model Based on Dry Matter Production in Sweet Pepper

Abstract The development of models for yield prediction in greenhouse sweet peppers may help improve yield and labour productivity. We aimed to monitor the growth and yield of hydroponically grown sweet pepper plants without destructive sampling. First, we constructed a prediction model and validated it in a cultivation experiment. In the developed model, daily node appearance and light use efficiency were predicted from daily mean air temperature and daytime carbon dioxide (CO2) concentration. The daily light interception was obtained by non-destructive leaf area estimation. Second, we validated the model through the cultivation experiment. The predicted total dry matter production at 200 days after transplanting (DAT), 1,379 g/m2, fell within the range of the observed value, 1,353 ± 46 g/m2 (mean ± SE). The predicted and observed yields at 200 DAT were 7.90 kg/m2 and 7.73 ± 0.82 kg/m2, respectively. We approximately predicted node appearance, total dry matter production, and fruit yield, while partially succeeding in predicting leaf area index and dry matter partitioning to fruit. Our non-destructive prediction model can be an effective tool for growers and to improve the yield of sweet pepper production.

Air Temperature Affects the Hospital Admission for Cardiovascular Diseases among Rural Residents in Dingxi City

Objective To explore the effect of air temperature on the hospitalization of rural residents with cardiovascular diseases and its lag effect in Dingxi city. Methods The meteorological data and air pollution data of Dingxi city from 2018 to 2019,as well as the daily hospitalization data of rural residents due to cardiovascular diseases,were collected.The distributed lag non-linear models were employed to analyze the relationship between daily mean air temperature and the number of inpatients with cardiovascular diseases.Meanwhile,stratified analysis was carried out according to gender,age,and disease. Results There was a non-linear relationship between air temperature and the number of hospitalized rural residents with cardiovascular diseases in Dingxi city.The exposure-response curve approximated a bell shape.The curves for different cardiovascular diseases appeared similar shapes,with different temperature thresholds.Low temperature(-7 ℃) and moderately low temperature(0 ℃) exhibited a cumulative lag effect on the number of patients hospitalized with cardiovascular diseases.With a cumulative lag of 7 days at -7 ℃ and 14 days at 0 ℃,the RR values peaked,which were 1.121(95% CI=1.002-1.255) and 1.198(95% CI=1.123-1.278),respectively.With a cumulative lag of 14 days at 0 ℃,the RR values were 1.034(95% CI=1.003-1.077) and 1.039(95% CI=1.004-1.066) for the number of hospitalized patients with ischemic heart disease and heart rhythm disorders,respectively.The cumulative lag effects of moderately high temperature(17 ℃) and high temperature(21 ℃) on ischemic heart disease,heart rhythm disorders,and cerebrovascular disease all peaked on that day.Specifically,the RR values at 17 ℃ and 21 ℃ were 1.148(95% CI=1.092-1.206) and 1.176(95% CI=1.096-1.261) for ischemic heart disease,1.071(95% CI=1.001-1.147) and 1.112(95% CI=1.011-1.223) for heart rhythm disorders,and 1.084(95% CI=1.025-1.145) and 1.094(95% CI=1.013-1.182) for cerebrovascular disease,respectively.There was no cumulative lag effect of air temperature on the number of hospitalized patients with heart failure.In addition,stratified analysis showed that low temperature(-7 ℃) and moderately low temperature(0 ℃) affected the number of hospitalized female patients with cardiovascular diseases,and only moderately low temperature(0 ℃) affected males.The cumulative lag effect of high temperature on females was higher than that on males.Air temperature exhibited a stronger impact on female patients than on male patients. Additionally,the population aged<65 years old was more sensitive to low temperature and high temperature than that aged ≥65 years old. Conclusions Air temperature changes increase the hospitalization risk of rural residents with cardiovascular diseases in Dingxi city,which presents a lag effect.The effects of air temperature on patients hospitalized due to cardiovascular diseases varied among different etiologies,genders,and ages.It is necessary to emphasize on the impact of temperature changes on health in residents,especially for key populations such as females,people aged<65 years old,and those with ischemic heart disease.

Comparison of Early-Twentieth-Century Arctic Warming and Contemporary Arctic Warming in the Light of Daily and Subdaily Data

AbstractA review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW.Significance StatementIt is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming.

Dynamics of seasonal changes in introduced plants in Eastern Transbaikalia

The article presents a comparative analysis of the seasonal development of introduced plants in the weather conditions of Eastern Transbaikalia for the growing periods from 2014 to 2019. The results presented are taken from 6-year phenological observations conducted in the arboretum of the Institute of Natural Resources, Ecology and Cryology SB RAS. We constructed the phenological spectra of the studied objects by year. We studied the main phenological indicators of vegetation the East Asian forest-steppe and Manchurian-Daurian preboreal species. We analyzed the meteorological data: the mean daily air temperatures and precipitation for the months of the growing season (2014–2019), and the sum of active temperatures and precipitation for this period. The data on the dynamics of the development of shrubby plant species in conditions of Eastern Transbaikalia for 2014–2019 indicate that plants respond adaptively to changes in weather conditions. Hereditary and physiological characteristics showed that the introduced plants are characterized as cold-resistant and drought-resistant. Corylus heterophylla and Armeniaca sibirica are most sensitive to temperature extremes in spring, in contrast to Euonymus maackii, which were stable. Low above-zero air temperatures in spring and a large amount of precipitation encourage plants to pass the development stages (phenophases) faster than in dry and warm weather (Corylus heterophylla, Armeniaca sibirica). At the same time, low temperatures (frosts) in spring with a wet period at the time of flowering negatively affect the further fruit formation in Corylus heterophylla individuals. Rainy or cloudy days reduce the color intensity of the autumn leaves, and cool, dry, and sunny weather contributes to the autumn color scheme. Armeniaca sibirica and Corylus heterophylla are variable in terms of the onset of the main phenophases; individuals of the Euonymus maackii species have minimal individual variability. The longest growing seasons on the phenological spectrum were registered in 2014 and 2018–2019 (in Corylus heterophylla individuals – 190 days), which contributes to the accumulation of more phytomass. The shortest growing season was registered in 2015 (Armeniaca sibirica – 150 days).

Daily Temperature Effect on Seedling Growth Dynamic of Three Invasive Alien Species.

A greater relative growth rate (RGR) is positively correlated with a species’ ability to deploy a larger leaf area either due to a greater total number of leaves (LN) in the canopy or due to an average size of individual leaves (LA). This study aimed to analyze and compare, (1) the temporal (i.e., daily) RGR, leaf production rate (LPR), and leaf area production rate (LAPR) changes during the early growth stages of three among the most invasive species in the world, namely, Ailanthus altissima, Phytolacca americana, and Robinia pseudoacacia. (2) the interspecific differences in the relationship between RGR, LPR, LAPR, and mean daily air temperature. Our results show that growth dynamics as a function of temperature differ between invasive alien species (IAS). While these differences are partly explained by differences due to the growth form of the investigated species, the three IAS have a different behavior to adjust RGR, LPR, and LAPR with air temperature changes even within the same growth form, and in agreement with species habitat requirements in their native range. In conclusion, the results help disentangle the relative role of RGR, LPR, and LAPR in defining non-native species growth responses to mean daily air temperature also in relation to a species’ growth form.

Impact of Accumulated Temperature on Wetland Vegetation Area in燩oyang燣ake

Accumulated temperature, which is now widely used in agronomy, is an important ecological factor to the growth of plants, but few relative studies have been found on the vegetation area of floodplain grasslands in Poyang Lake. This research used the classification and regression tree (CART) to classify normalized vegetation area index derived from MODIS LAI (Moderate Resolution Imaging Spectroradiometer Leaf Area Index) images from 2008 to 2014, according to different climate indexes, such as mean daily air temperature (n), accumulated temperature (jw), daily maximum temperature (g), daily minimum temperature (d), accumulative precipitation (j), water level (s) and average water level for 20 days preceding (a). The results showed that: (1) The accumulated temperature and the 20-day average WL (water level) were found to have the greatest impact on variation in wetland vegetation area, and they both dominated the classification process twice; (2) Two classification thresholds of accumulated temperature were 790°C and 1784°C, approximately corresponding to the beginning of April and mid-May; (3) 790°C could also be used as a threshold to select remote sensing images to analysis the annual variability of vegetation, i.e. while accumulated temperature is lower than 790°C, remote sensing images of similar accumulated temperature rather than similar date should be selected from different years for comparison. We also found that, effects of different hydrological factors on area of floodplain grasslands showed stage characteristics: (1) From January to March, water level changes slowly with less rainfall, as a result, the 20-day average WL which can interpret the hydrologic characteristics smoothly showed significant importance in this stage; (2) While entering April, intense rainfall make accumulative precipitation to be the dominating factor of classification; (3) From late April to mid-May, in condition of accumulative precipitation higher than 405 mm, daily water level is of most importance, because to the flood recession process as well as rapid water level fluctuations.

1-km gridded ensemble dataset of daily mean air temperature and precipitation for assessment of meteorological impacts under historical and future climate

1-km gridded ensemble dataset of daily mean air temperature and precipitation for assessment of meteorological impacts under historical and future climate

Influence of seasonal and climatic variables on coffee berry borer (Hypothenemus hampei Ferrari) flight activity in Hawaii

Coffee berry borer (CBB, Hypothenemus hampei Ferrari) is the most serious insect pest of coffee worldwide, yet little is known about the effect that weather variables have on CBB flight activity. We sampled flying female CBB adults bi-weekly over a three-year period using red funnel traps baited with an alcohol lure at 14 commercial coffee farms on Hawaii Island to characterize seasonal phenology and the relationship between flight activity and five weather variables. We captured almost 5 million scolytid beetles during the sampling period, with 81–93% of the trap catch comprised of CBB. Of the captured non-target beetles, the majority were tropical nut borer, black twig borer and a species of Cryphalus. Two major flight events were consistent across all three years: an initial emergence from January-April that coincided with early fruit development and a second flight during the harvest season from September-December. A generalized additive mixed model (GAMM) revealed that mean daily air temperature had a highly significant positive correlation with CBB flight; most flight events occurred between 20–26°C. Mean daily solar radiation also had a significant positive relationship with flight. Flight was positively correlated with maximum daily relative humidity at values below ~94%, and cumulative rainfall up to 100 mm; flight was also positively correlated with maximum daily wind speeds up to ~2.5 m/s, after which activity declined. Our findings provide important insight into CBB flight patterns across a highly variable landscape and can serve as a starting point for the development of flight prediction models.

THE LOW TEMPERATURE INFLUENCE ON THE DEEP EXCAVATION SUPPORTING SYSTEM

The practical experience to dig an excavation shows that this process is extended in time and can last for several months engulfing different seasons. An additional accounting of processes occurring in excavation shoring and surrounding soil body during wintertime, when the air temperature drops below zero, is important in such circumstances. This paper describes the results of a field survey of shoring of a deep excavation, soil body and buildings of a surrounding development as the air temperature was changing during the 2020-2021 winter. The survey was performed by monitoring the horizontal and vertical positions of structures using inclinometers and tacheometers. The heaving properties of the clay soil positioned at the excavation digging level were determined in laboratory conditions. The soil frost depth was determined by a numerical simulation of temperature distributions in the "atmosphere-excavation-soil body" system. All this helped understand the nature of the processes observed. The observation results showed that, as the temperature was decreasing, the excavation shoring structures moved into the excavation to a distance of up to 8 mm, and the surrounding development buildings shrank by up to 5 mm. The laboratory study results showed in this context that soils bordering on the shoring structures and foundations of surrounding buildings were not heaving. Thereupon, the strains, which were registered in this actual situation, can not be explained by the frost-heaving phenomenon. The observed strain can be explained by shrinkage of the sprung structure material resulting from a decreasing mean daily air temperature. This effect was confirmed by test results and several surveys presented in the paper. Based on the study conclusions practical guidelines were formulated for excavation digging with consideration to seasonal air temperature fluctuations causing shrinkage and expansion of sprung structures in the shoring structures of excavation of any depth.

Measured and predicted freeze-thaw days frequencies in climate change conditions in central Poland.

The rate of progression of geomorphological phenomena is greatly influenced by freeze-thaw processes. In the face of air temperature increasing over the past few decades, a question of the future impact of these processes arises, notably in the temperate and cold climate zones. Using the mean, maximum and minimum daily air temperature data in the period 1951–2018 obtained from three weather stations located in the vicinity of Jeziorsko reservoir (central Poland), we have determined the mathematical correlation, described with a polynomial function, between the mean monthly air temperature and the monthly number of freeze-thaw days (FTD). A freeze-thaw day is a day when the maximum air temperature is above 0 °C while the minimum air temperature equals or is below this threshold. The number of FTDs within the study area averaged 64–71 and demonstrated a downward trend of 2–4 FTDs/10 years. The study period (1951–2018), includes a clearly marked distinct sub-period (1991–2018), when the reservoir was in operation, which experienced 58–68 FTDs. Considering the assumed rise in temperature, one should expect a further, though slightly slower, decline in the future number of FTDs. Depending on the accepted model of the temperature increase, which for the area of Poland (Central Europe) in the perspective of 30 years oscillates between +1.1 to +1.3 °C, the number of FTDs within the study area is expected to decline by −4.5 to −5.3 FTD, i.e. 6–7% and 5.4–5.5 FTD i.e. 8–9% respectively.

Daily Field Observations of Retrogressive Thaw Slump Dynamics in the Canadian High Arctic

With observed increases in retrogressive thaw slump (RTS) number, rates, and size in recent decades, there is a need to understand these highly dynamic landforms as they impact surrounding ecosystems and infrastructure. There is a general lack of detailed (e.g., daily) field observations of change in RTSs; we help fill this gap by monitoring three RTSs for much of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP), and cumulative thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable, and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat largely driven by sediment distribution in the RTS floor could occur within a single period of increased air temperature. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. A statistically significant correlation was found between daily mean and mean cumulative retreat with MDAT (p &gt; 0.001) and TDD (p &gt; 0.001 and &gt; 0.0001) but not with total daily precipitation. Correlating mean cumulative retreat and TDD using polynomial regression generated R2 values greater than 0.99 for all three sites. Both cumulative retreat and TDD account for past and current conditions, as well as lag responses, within the monitoring period. The high R2 values for the correlation of mean cumulative retreat and TDD suggest the potential for accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however modelling is currently restricted to individual RTSs and only within short time scales. Monitoring RTSs on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales and highlights the importance of all system inputs when considering RTS retreat dynamics.

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM

This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as model input to estimate daily flow discharge. After model training and verification, two experimental simulations were conducted with hypothetical inputs instead of observed meteorological data to clarify the response of the trained model to the inputs. The first numerical experiment showed that even without input precipitation, the trained model generated flow discharge, particularly winter low flow and high flow during the snow melting period. The effects of warmer and colder conditions on the flow discharge were also replicated by the trained model without precipitation. Additionally, the model reflected only 17–39% of the total precipitation mass during the snow accumulation period in the total annual flow discharge, revealing a strong lack of water mass conservation. The results of this study indicated that a deep learning method may not properly learn the explicit physical relationships between input and target variables, although they are still capable of maintaining strong goodness-of-fit results.