Search Results (127)

Apiculture is among the most weather-dependent sectors of agriculture; however, quantifying the impact of meteorological factors remains challenging. Beehive weight has long been recognized as an important indicator of colony health, strength, and food availability, as well as foraging activity. Atmospheric influences on hive weight dynamics have been a subject of research since the early 20th century. This study aims to estimate hourly hive weight variation by applying linear time-series models to hive weight data collected from active apiaries during intensive foraging periods, considering atmospheric predictors. We employed a rolling 24 h forward ARIMAX and SARIMAX model, incorporating meteorological variables as exogenous factors. The median estimates for the study period resulted in model RMSE values of 0.1 and 0.3 kg/h. From numerous meteorological variables, the hourly maximum temperature was found to be the most significant predictor. ARIMAX model results also exhibited a strong diurnal cycle, pointing out the weather-driven seasonality of hive weight variations. Full article

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

Surface ozone is a pollutant linked to higher risks of cardiopulmonary diseases with long-term exposure. Timely forecasting of ozone levels helps authorities implement preventive measures to protect public health and safety. However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. The model is trained and tested at the Wanshouxigong site in Beijing. The ICEEMDAN method decomposes the ozone time series data to extract trends and obtain intrinsic mode functions (IMFs) and a residual (Res). Fourier period analysis is employed to elucidate the periodicity of the IMFs, which serves as the basis for selecting the prediction model (BiLSTM or persistence model) for different IMFs. Extensive experiments have shown that a hybrid model of ICEEMDAN, BiLSTM, and persistence model is able to achieve a good performance, with a prediction accuracy of R² = 0.86 and RMSE = 18.70 µg/m³ for the 36th hour, outperforming other models. Full article

Evapotranspiration (ET) is an important part of agricultural water consumption, yet little is known about nocturnal evapotranspiration (ETN) patterns. An eddy covariance system was used to observe ET over five consecutive years (2020–2024) during the growing season in a dry farming area of the Loess Plateau. Daytime and nocturnal evapotranspiration were partitioned using the photosynthetically active radiation threshold to reveal the changing characteristics of ETN at multiple time scales and its control variables. The results showed the following: (1) In contrast to the non-significant trend in ETN on the diurnal and daily scales, monthly ETN dynamics exhibited two peak fluctuations during the growing season. (2) The contribution of ETN to ET exhibited seasonal characteristics, being relatively low in summer, with interannual variations ranging from 10.9% to 14.3% and an annual average of 12.8%. (3) The half-hourly ETN, determined by machine learning methods, was driven by a combination of factors. The main driving factors were the difference between surface temperature and air temperature (Ts-Ta) and net radiation (Rn), which have almost equivalent contributions. Regression analysis results suggested that Ta was the main factor influencing ETN/ET at the monthly scale. This study focuses on the nighttime water loss process in dry farming fields in Northwest China, and the results provide a basis for rational allocation and efficient utilization of agricultural water resources in arid regions. Full article

The near-surface air temperature lapse rate (SATLR) is a crucial parameter in climate, hydrology, and ecology research conducted in mountainous regions. However, existing research has difficulty characterizing its dynamic changes on an hourly scale. Obtaining data with high spatiotemporal resolution in complex terrains using existing methods poses challenges. This study introduces a hierarchical method for estimating SATLR at high spatiotemporal resolutions based on Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) land surface temperature (LST) data and machine learning techniques. Based on reconstructed FY-4A AGRI LST data, this study downscales the 4 km resolution data to a 1 km resolution using machine learning. It then estimates the spatial distribution of near-surface air temperature (SAT) and normalized near-surface air temperature (nSAT) by integrating station observations. Subsequently, high spatiotemporal resolution SATLRs are estimated, and their spatial and temporal distribution characteristics in the Beijing–Tianjin–Hebei mountainous region are analyzed. The results indicate that the SATLR exhibits a predominant distribution of 2~6 °C/km annually across the study area. However, in specific regions such as Taihang Mountains in the southwest, Damajun Mountain in the northwest, and certain areas of central Beijing City, the SATLR exceeds 6 °C/km in depth. Conversely, in Chengde City in the northeast and Huapiling in Damajun Mountain in the northwest, the SATLR is shallower than 2 °C/km. Seasonally, the average SATLR displays significant variation, with 3~5 °C/km being prevalent in spring, summer, and autumn, and 2~4 °C/km in winter. Moreover, the diurnal SATLR patterns from the second to fifth altitude grades exhibit consistency throughout the year and across seasons, albeit with varying overall values at different altitudes. Notably, the SATLR of the first altitude grade demonstrates stability within a day at lower elevations. Full article

A detailed characterization of evapotranspiration (ET) patterns is of paramount importance for optimizing irrigation scheduling and enhancing water-use efficiency in the North China Plain. To delve into this, a two-season study was conducted at the National Experimental Station for Precise Agriculture in Beijing. Using 12 weighing lysimeters, the study compared two summer maize varieties with contrasting canopy sizes: Jingke 968 (JK), characterized by a large canopy, and CF 1002 (CF), with a small canopy. The comprehensive analysis yielded the following significant findings: (1) The daily average ET rates exhibited consistent trends across cultivars, yet with notable disparities in magnitude. JK consistently demonstrated higher water consumption throughout the growth seasons. In the first season, at the V13–R1 stage, the peak daily ET of JK and CF reached 5.91 mm/day and 5.52 mm/day, respectively. In the second season, during the R1–R3 stage, these values were 5.21 mm/day for JK and 5.22 mm/day for CF, highlighting the nuanced differences in water use between the varieties under varying growth conditions. (2) Regardless of canopy size, the hourly ET fluctuations across different growth stages followed similar temporal patterns. However, the most striking inter-varietal differences in ET emerged during the R1–R3 reproductive stages, when both cultivars had achieved peak canopy development (leaf area index, LAI > 4.5). Notably, the ET differences between JK and CF adhered to a characteristic diurnal “increase–decrease” pattern. These differences peaked during mid-morning (09:00–11:00) and early afternoon (13:00–15:00), while minimal divergence was observed at solar noon. This pattern suggests complex interactions between canopy structure, microclimate, and plant physiological processes that govern water loss over the course of a day. (3) Analysis of the pooled data pinpointed two critical time periods that significantly contributed to the cumulative ET differences between the varieties. The first period was from 12:00–17:00 during the R1–R3 (anthesis) stage, and the second was from 08:00–16:00 during the R3–R5 (grain filling) stage. JK maintained significantly higher transpiration rates (Tr) compared to CF, especially during the morning hours (09:00–12:00). On average, the Tr of JK exceeded that of CF by 5.3% during the pre-anthesis stage and by 16.0% during the post-anthesis stage. These observed Tr differentials strongly indicate that canopy architecture plays a pivotal role in modulating stomatal regulation patterns. Maize varieties with large canopies, such as JK, demonstrated enhanced morning photosynthetic activity, which likely contributed to increased transpiration. At the same time, both varieties seemed to employ similar midday water conservation strategies, possibly as an adaptive response to environmental stress. In summary, this study has comprehensively elucidated the intricate relationship between the leaf area index and the evapotranspiration of summer maize across multiple timescales, encompassing periodic, daily, and hourly variations. The findings provide invaluable data-driven insights that can underpin the development of precise and quantitative irrigation strategies, ultimately promoting sustainable and efficient maize production in the North China Plain. Full article

In recent years, with the rapid socioeconomic development of Wuxi City, the frequent occurrence of severe air pollution events has attracted widespread attention from both the local government and the public. Based on the real-time monitoring data of criteria pollutants and GDAS (Global Data Assimilation System) reanalysis data, the spatiotemporal variation patterns, meteorological influences, and potential sources of major air pollutants in Wuxi across different seasons during 2019 (pre-COVID-19) and 2023 (post-COVID-19 restrictions) are investigated using the Pearson correlation coefficient, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models. The results demonstrate that the annual mean PM_2.5 concentration in Wuxi decreased significantly from 39.6 μg/m³ in 2019 to 29.3 μg/m³ in 2023, whereas the annual mean 8h O₃ concentration remained persistently elevated, with comparable levels of 104.6 μg/m³ and 105.0 μg/m³ in 2019 and 2023, respectively. The O₃ and particulate matter (PM) remain the most prominent air pollutants in Wuxi’s ambient air quality. The hourly mass concentrations of criteria pollutants, except O₃, exhibited characteristic bimodal distributions, with peak concentrations occurring post-rush hour during morning and evening commute periods. In contrast, O₃ displayed a distinct unimodal diurnal pattern, peaking between 15:00 and 16:00 local time. The spatial distribution patterns revealed significantly elevated concentrations of all monitored species, excluding O₃, in the central urban zone, compared to the northern Taihu Lake region. The statistical analysis revealed significant correlations among PM concentrations and other air pollutants. Additionally, meteorological parameters exerted substantial influences on pollutant concentrations. The PSCF and CWT analyses revealed distinct seasonal variations in the potential source regions of atmospheric pollutants in Wuxi. In spring, the Suzhou–Wuxi–Changzhou metropolitan cluster and northern Zhejiang Province were identified as significant contributors to PM_2.5 and O₃ pollution in Wuxi. The potential source regions of O₃ are predominantly distributed across the Taihu Lake-rim cities during summer, while the eastern urban agglomeration adjacent to Wuxi serves as major potential source areas for O₃ in autumn. In winter, the prevailing northerly winds facilitate southward PM_2.5 transport from central-northern Jiangsu, characterized by high emissions (e.g., industrial activities), identifying this region as a key potential source contribution area for Wuxi’s aerosol pollution. The current air pollution status in Wuxi City underscores the imperative for implementing more stringent and efficacious intervention strategies to ameliorate air quality. Full article

Enhancing greenspace cooling efficiency (GCE) is a cost-effective nature-based solution to improve the urban thermal environment. The spatiotemporal patterns of GCE and their driving factors have been investigated mainly based on land surface temperature in a spatial comparison perspective. However, the diurnal change in GCE based on air temperature (AT) and its non-linear responses to meteorological factors are far from thoroughly understood. Taking the subtropical Chinese city of Changsha as an example, we quantified the hourly GCE based on AT in the hottest month of 2020, investigated its diurnal changes, and uncovered its non-linear responses to meteorological change using the Generalized Additive Model. The results showed that (1) the hourly GCE displayed a U-shaped temporal pattern with an average of 0.0128 °C%⁻¹. The nighttime GCE (0.0134 °C%⁻¹) was significantly higher than the daytime GCE (0.012 °C%⁻¹). (2) Meteorological factors (i.e., temperature, relative humidity, and wind speed) significantly and non-linearly impacted GCE. (3) The responses of GCE to changes in relative humidity and wind speed followed an inverted U-shaped pattern, with the maximum values appearing at a relative humidity of 70% and a wind speed of 6m/s, respectively. GCE responded to temperature change more complexly, i.e., a negative response (<28 °C), then a positive response (30–35 °C), and finally a negative response (>35 °C). These findings extend our understanding of the diurnal variations of GCE and the non-linear responses to meteorological change and can help effective urban greenspace planning and management in Changsha, China, and other cities with similar climates in an era of rapid climate change. For example, expanding greenspace coverage as well as optimizing greenspace spatial configuration should be a priority action in areas where the AT is higher than 35 °C currently and will be in the future. Full article

Soil temperature, a key factor in subsurface geochemical processes, is influenced by environmental and geological dynamics. This study analyzed hourly soil temperature variations at depths of 10 to 100 cm near the Sakurajima volcano, alongside concurrent ambient temperature measurements. By applying temperature models and statistical methods, we characterized both seasonal and short-term thermal dynamics, including soil-atmosphere thermal coupling. Our findings revealed a depth-dependent thermal diffusivity, establishing distinct thermal regimes within the soil profile. The soil’s strong thermal buffering capacity, evidenced by increasing amplitude attenuation and temporal lag with depth, allowed us to identify optimal instrument placement depths (80–100 cm) for minimal diurnal temperature influence. We also quantified the relationship between ambient temperature fluctuations and soil thermal response at various depths, as well as the impact of these temperature variations on soil permeability. These results enhance our understanding of subsurface thermal behaviour in volcanic environments and offer practical guidance for environmental monitoring and geohazard studies. Full article

Efficient solar energy capture is crucial for renewable energy development, particularly in equatorial regions with consistent solar radiation. This study evaluated the impact of the azimuth angle of the solar panels on photovoltaic energy production in Loja, Ecuador. Three photovoltaic systems with east and west orientations were installed, and data were continuously collected from June 2021 to May 2022. Descriptive and comparative statistical analyses, including one-way ANOVA and Kruskal–Wallis tests, were employed to assess the differences in energy production between the systems. Additionally, an analysis of average hourly energy production was conducted to better understand diurnal variations and their relationship with energy demand. Results showed no significant differences in energy production between east- and west-oriented systems, although east-facing panels showed a slight advantage in certain months, between October and December. Seasonal variations were found to have a greater influence on energy production than orientation, suggesting that climatic factors should be prioritized when designing solar installations in equatorial areas. The findings indicate that azimuth angle is not a decisive factor for optimizing energy efficiency in Loja, Ecuador. Moreover, the diurnal analysis demonstrated a typical daily curve with midday peaks, misaligned with morning and evening demand, which could affect future design strategies. Full article

Sea surface currents (SSCs) play a pivotal role in material transport, energy exchange, and ecosystem dynamics in coastal marine environments. While traditional methods to obtain wide-range SSCs, such as satellite altimetry, often struggle with limited performance in coastal regions due to waveform contamination, deriving SSCs from sequential ocean color data using maximum cross-correlation (MCC) has emerged as a promising approach. In this study, we proposed a novel SSC estimation method, called tide-restricted maximum cross-correlation (TRMCC), and implemented it on hourly ocean color data obtained from the Geostationary Ocean Color Imager II (GOCI-II) and the global tide model FES2014 to derive SSCs in coastal seas and turbid estuaries. Cross-comparison over three years with buoy data, high-frequency radar, and numerical model products shows that TRMCC is capable of obtaining high-resolution SSCs with good accuracy in coastal and estuarine areas. Both large-scale ocean circulation patterns in seas and fine-scale surface current structures in estuaries can be effectively captured. The deriving accuracy, especially in coastal and estuarine areas, can be significantly improved by integrating tidal current data into the MCC workflow, and the influence of invalid data can be minimized by using a flexible reference window size and normalized cross-correlation in the Fourier domain technique. Seasonal SSC structure in the Bohai Sea and diurnal SSC variation in the Yangtze River Estuary were depicted via the satellite method, for the first time. Our study highlights the vast potential of TRMCC to improve the understanding of current dynamics in complex coastal regions. Full article

Because PM_2.5 concentrations are not regularly monitored in Mbarara, Uganda, this study was implemented to test whether correlations exist between weather parameters and PM_2.5 concentration, which could then be used to estimate PM_2.5 concentrations. PM_2.5 was monitored for 24 h periods once every week for eight months, while weather parameters were monitored every day. The mean dry and wet season PM_2.5 concentrations were 70.1 and 39.4 µg/m³, respectively. Diurnal trends for PM_2.5 levels show bimodal peaks in the morning and evening. The univariate regression analysis between PM_2.5 and meteorological factors for the 24 h averages yields a significant correlation with air pressure when all data are considered, and when the data are separated by season, there is a significant correlation between PM_2.5 concentration and wind speed in the dry season. A strong correlation is seen between diurnal variations in PM_2.5 concentration and most weather parameters, but our analysis suggests that in modeling PM_2.5 concentrations, the importance of these meteorological factors is mainly due to their correlation with underlying causes including diurnal changes in the atmospheric boundary layer height and changes in sources both hourly and seasonally. While additional measurements are needed to confirm the results, this study contributes to the knowledge of short-term and seasonal variation in PM_2.5 concentration in Mbarara and forms a basis for modeling short-term variation in PM_2.5 concentration and determining the effect of seasonal and diurnal sources on PM_2.5 concentration. Full article

Statistical characteristics of atmospheric circulation patterns (ACPs) and associated diurnal variation characteristics (DVCs) of precipitation in summer (June–August) from 2015 to 2019 over the complex terrain in northern Xinjiang (NX), northwestern arid region of China, were investigated based on NCEP FNL reanalysis data and Weather Research and Forecasting model simulation data from Nanjing University (WRF-NJU). The results show that six different ACPs (Type 1–6) were identified based on the Simulated ANealing and Diversified RAndomization (SANDRA), exhibiting significant differences in major-influencing synoptic systems and basic meteorological environments. Types 5, 3, and 2 were the most prevalent three patterns, accounting for 21.6%, 19.7%, and 17.7%, respectively. Type 5 mainly occurred in June and July, while Types 3 and 2 mainly occurred in August and July, respectively. From the perspective of DVCs, Type 1 reached its peak at midnight, while Type 5 was most frequent in the afternoon and morning. The overall DVCs of hourly precipitation intensity and frequency demonstrated a unimodal structure, with a peak occurring at around 16 Local Solar Time (LST). Basic meteorological elements in various terrain regions exhibit significant diurnal variation, with marked differences between mountainous and basin areas under different ACPs. In Types 3 and 6, meteorological elements significantly influence precipitation enhancement by promoting the convergence and uplift of low-level wind fields and maintaining high relative humidity (RH). The Altay Mountains region and Western Mountainous regions experience dominant westerly winds under these conditions, while the Junggar Basin and Ili River Valley regions benefit from counterclockwise water vapor transport associated with the Iranian Subtropical High in Type 6, which increases RH. Collectively, these factors facilitate the formation and development of precipitation. Full article

The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval. Full article

We used NASA’s high-resolution (1/48° or 2.3 km, hourly) Estimating the Circulation and Climate of the Ocean (ECCO) estimates of salinity at a 1 m depth from November 2011 to October 2012 to detect semi-diurnal and diurnal internal tides (ITs) in the Andaman Sea and determine their characteristics in three 2° × 2° boxes off the Myanmar coast (box A), central Andaman Sea (box B), and off the Thailand coast (box C). We also used observed salinity and temperature data for the above period at the BD12-moored buoy in the central Andaman Sea. ECCO salinity data were bandpass-filtered with 11–14 h and 22–26 h periods. Large variations in filtered ECCO salinity (~0.1 psu) in the boxes corresponded with near-surface imprints of propagating ITs. Observed data from the box B domain reveals strong salinity stratification (halocline) in the upper 40 m. Our analyses reveal that the shallow halocline affects the signatures of propagating semi-diurnal ITs reaching the surface, but diurnal ITs propagating in the halocline reach up to the surface and bring variability in ECCO salinity. In box A, the semi-diurnal IT characteristics are higher speeds (0.96 m/s) with larger wavelengths (45 km), that are closer to theoretical mode 2 estimates, but the diurnal ITs propagating in the box A domain, with a possible source over the shelf of Gulf of Martaban, attain lower values (0.45 m/s, 38 km). In box B, the propagation speed is lower (higher) for semi-diurnal (diurnal) ITs. Estimates for box C are closer to those for box A. Full article