Spatiotemporal Variability Research Articles

Influence of spatiotemporal and meteorological variation on Norwegian atmospheric pollen seasonality

Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.

Dynamic analysis of carbon emissions from construction and demolition activities in Japan: Revealed by high-resolution 4D-GIS modeling

To meet the 2050 decarbonization target of the global buildings and construction sector, more attention is needed to reduce carbon emissions from construction and demolition. However, current national carbon accounting studies for these activities remain limited in spatial granularity and localized applicability. This study developed a bottom-up spatiotemporal database of carbon emissions from building construction and demolition in Japan via integrating a geographic information system-based building stock model, statistical data, and survey information. Focusing on municipal-level emissions, the Logarithmic Mean Divisia Index approach was used to decompose spatiotemporal variations and identify the contributing factors. Results indicate that carbon emissions from Japan's construction and demolition activities fell by more than 50% between 2005 and 2020, largely due to declining new/demolished-to-stock ratio, suggesting a transition to a stock-based society. Central cities' reliance on carbon-intensive buildings positively contributed to spatial variations in their construction emissions, underscoring the importance of sustainable materials and timber designs. Differences between prefectures in demolition emission intensity highlighted the strategic placement of recycling facilities in key regions to curb transportation-related emissions. Overall, these findings provided data reference for local governments to devise tailored policies for managing construction and demolition emissions.

Rock moisture reinforces belowground water storage under different precipitation scenarios and vegetation coverage

Rock moisture is rarely deliberated but crucial for hydrologic regulation in semiarid loess hilly regions. In this study, using in-situ monitoring observation and simulation, soil and rock moisture spatiotemporal variations under planting alfalfa and caragana were analyzed and sketched, and the water storage contributions by weathered bedrock layers (WBLs) under different rainwater scenarios were then simulated and estimated. The results showed that WBLs had apparent spatiotemporal changes in water distribution and storage with rainfall events. Under water scarcity scenarios, WBLs increased the variabilities of water storage, with an average increase of 89.9 % in alfalfa and 14.2 % in caragana, providing wider water supply boundaries for vegetation. However, higher volumetric water content was presented under relatively abundant water scenarios. The average soil water content was 0.08 cm3 cm−3 in alfalfa and 0.09 cm3 cm−3 in caragana, while the average rock water content was 0.12 cm3 cm−3 in alfalfa and 0.13 cm3 cm−3 in caragana. Profile water consumption goes to surface soil evaporation and plant transpiration over an entire growing season, while extrawater would be stored in WBLs to maintain vegetation growth during the water-scarce seasons. Moreover, water storage had a positive linear relationship with the thickness of WBLs. Rock water storage increased by 6.9 % in alfalfa and 3.7 % in caragana after adding 20 cm WBLs, while it increased by 23.6 % in alfalfa and 9.9 % in caragana after adding 50 cm WBLs. Furthermore, various exogenous rainwater simulations showed that differences in water storage were significantly increased with WBLs thickness. This study would provide new insights into the deep-rooted plant distribution planning in semiarid loess hilly regions, and could also be helpful for the rational water resources utilization and management in shallow soil areas.

The impact of environmental variables on surface Conductance: Advancing simulation with a nonlinear Machine learning model

Surface conductance (Gs) is a key factor in the Penman-Monteith (PM) equation; the interaction between environmental variables such as CO2 concentration, air temperature (TA), vapor pressure deficit (VPD), soil water content (SWC), and net radiation (R) affects Gs, evapotranspiration and thus impacts the hydrological cycle. These interactions are highly nonlinear and vary among different vegetation types. However, conventional Gs simulation models use fixed interactions between environmental variables in their equations for all vegetation types. Moreover, the characterisation and parameterisation of conventional Gs models is highly uncertain due to the high spatiotemporal variability in key environmental variables and plant parameters, which inhibits their generalisation. This study investigates whether Gs could be estimated more accurately by nonlinear statistical techniques that capture the multiple interactions between the environmental variables that affect Gs for each vegetation type. We compare mixed generalized additive model (MGAM) for Gs simulation with semi-empirical and empirical models at 20 eddy covariance flux tower sites with four different vegetation types at daily and monthly timescales. The results show that the Nash-Sutcliffe Efficiency (NSE) in Gs simulation increased by up to 50% in MGAM model in comparison to the semi-empirical and empirical models. The MGAM model highlighted the interactive effects of CO2, VPD, and SWC for crops and grasses. The interactive effects of CO2, VPD, and TA were important for trees and grasses. The results from this study expand our understanding of the ability of Gs simulation models to identify and include the interactive effects of crucial environmental variables on plant transpiration and hydrological processes.

Spatio-Temporal Variation in Cyanobacteria and Epiphytic Algae of Thalassia testudinum in Two Localities of Southern Quintana Roo, Mexico

The leaves of Thalassia testudinum provide an ideal substrate for the establishment of small-sized algae with different morphologies that are abundant and diverse. There are few studies on epiphytism in Mexico, and most of them are floristic lists. The objective of this study was to analyze the taxonomic and morphofunctional composition of epiphytes in three climatic seasons, and their relationship with the phorophyte T. testudinum in two localities, El Uvero and Santa Rosa, in the south of Quintana Roo; three transects and fifteen quadrants were set in June and December (2014) and April (2015). A total of 84 epiphytic species were identified: 27 corresponded to Cyanobacteria, 10 to Phaeophyceae, 9 to Chlorophyta, and 38 to Rhodophyta. The highest specific richness was observed in Santa Rosa (73 species). The dry and summer rains seasons share a higher number of species compared to the winter rainy season. The crusty algae Hydrolithon farinosum was the dominant and most persistent species; in addition, filamentous algae presented great specific richness and coverage due to their morphology and reproductive strategies, which allowed them to successfully establish themselves on the phorophyte. This is related to the ecological succession of the epiphytes and seagrass phenology.

Spatiotemporal variations of ecosystem services in the Aral Sea basin under different CMIP6 projections

The Aral Sea, located in Central Asia, has undergone significant reduction in surface area owing to the combined impacts of climate change and human activities. This reduction has led to a regional ecological crisis and profound repercussions on ecosystem services. Investigating the spatiotemporal variations and synergistic trade-offs of ESs in the Aral Sea basin is crucial for fostering the integrated development of the region’s socioeconomic ecology. This study utilizes the Future Land-Use Simulation and InVEST models to analyze future land-use scenarios, integrating CMIP6 projections to assess the quality of four key ecosystem services: water production, soil conservation, carbon storage, and habitat quality over two timeframes: the historical period (1995–2020) and the projected future (2021–2100). Employing Spearman correlation, the study explores the trade-offs and synergies among these ecosystem services. Findings reveal that the primary forms of land-use change in the Aral Sea basin are the reduction in water area (− 49.59%) and the rapid expansion of urban areas (+ 504.65%). Temporally, habitat quality exhibits a declining trend, while carbon storage shows an increasing trend, and water production and soil retention fluctuate initially decreasing and then increasing. Spatially, water production and carbon storage demonstrate an increasing trend from the northwest to the southeast. Habitat quality exhibits a higher spatial pattern in the southeast and south, contrasting with lower spatial patterns in the north and west. Low-level soil conservation is predominantly distributed in the northwest, while medium to low-level soil conservation is prevalent in the east of the basin. The trade-off and synergy analysis indicates that between 1995 and 2020, a trade-off relationship existed between carbon storage and habitat quality and water production, whereas synergies were observed between soil conservation and carbon storage, water production and habitat quality, and soil conservation. The correlation between water production and soil conservation emerges as the strongest, whereas the correlation between carbon storage and habitat quality appears to be the weakest. The dynamic spatiotemporal changes, trade-offs, and collaborative relationships of ESs constitute major aspects of ecosystem service research, holding substantial implications for the effective management of the regional ecological environment.

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

Spatio-temporal variation of aerosol loading and planetary boundary layer height before, during and after COVID-19 lockdown over the major cities in India

Spatio-temporal variation of aerosol loading and planetary boundary layer height before, during and after COVID-19 lockdown over the major cities in India

Spatio-temporal variations of habitat quality in the Greater Bay Area around Hangzhou Bay, based on LUCC and simulation

Context Habitat quality (HQ) is vital for achieving sustainable regional development. Understanding the spatial patterns and temporal evolution of HQ in the context of land use–cover change (LUCC) is crucial for promoting ecological civilisation and high-quality growth, especially for regions with rapid economic development. Aims In order to analyse the impact of LUCC on habitat quality in the coastal areas with frequent human activities. Methods This study focused on the Greater Bay Area around Hangzhou Bay and analysed the HQ on the basis of LUCC data during 2010–2020 by using the InVEST model and spatial autocorrelation analysis. Additionally, land-use data for multiple scenarios in 2030 were predicted using the PLUS model, and the variations in land use and HQ in the study region during 2020–2030 were assessed. Key results During 2010–2020, the construction land in the region expanded by 1932.79 km2, primarily at the expense of cropland and water areas. The mean HQ values were 0.6287, 0.6181 and 0.6037 for 2010, 2015 and 2020 respectively, indicating a continuous decline. Spatially, HQ exhibited strong clustering during this period. However, there was a clear trend of fragmentation and reduction in ‘high–high’ cluster areas along the coast, mostly owing to the transformation of water areas and wetlands into construction land and cropland. In the projected scenarios (natural development, ND; economic development, ED; crop protection, CP; and ecological protection, EP) for 2030, the mean HQ values are estimated to be 0.5881, 0.5837, 0.5915 and 0.5965 respectively. Compared with 2020, there will be a certain decrease in HQ, with the EP scenario showing the lowest decrease of 0.0052. Conclusions The HQ changes were closely linked to LUCC, the construction-land expansion was the main cause of HQ destruction in the Greater Bay Area around Hangzhou Bay. To alleviate the trend of declining HQ, it is essential to select appropriate development scenarios for each city in the region and coordinate the development of the cities. Implications These findings provide valuable insights for promoting sustainable economic growth in the Greater Bay Area around Hangzhou Bay.

Detecting spatial and spatio-temporal clusters of rape in India, 2011–2020

The study aims to identify the spatial and spatio-temporal patterns of rape in India from 2011 to 2020 using crime data aggregated at the district level. The study also tries to understand persistent patterns in the spatial variation of rape incidence across the country during the study period. The annual rape data from 2011 to 2020 were extracted from the National Crime Records Bureau. Retrospective spatial and temporal cluster analyses were performed using the purely spatial (Kulldorff’s and Mann–Whitney scan statistic) and space–time scan statistic (Kulldorff’s based on the discrete Poisson model). Cluster frequency analysis was performed. Results showed the spatial heterogeneity in the pattern of rape crime across the country as well as its localization among geographically contiguous regions across time and space. The detected primary spatial clusters (P < 0.05) persisted mainly in the Central, North, and North Eastern zones of the country over the ten year study period. The primary spatio-temporal cluster (LLR = 5560.09, P < 0.001) appeared between the time frame 2014 to 2016 and was concentrated mainly in the Northern zone. Thirty-seven districts of India (5.78%) contributed to spatial clusters in all years of the study period. The heterogeneous distribution of rape across India could be due to the spatio-temporal variations in the determinants of rape (such as socioeconomic status, demographics, urbanisation, and gender equality) across the country. Identification of the localized rape clusters could result in understanding the contextual factors of rape and, thereby be beneficial to national and regional rape control strategies.

Campanian Ignimbrite tephra reveals asynchronous vegetation responses to abrupt climate change in the eastern Mediterranean region

The timing and rate of ecosystem response to abrupt climate change is a product of numerous complex interactions between biotic and abiotic drivers. Palaeoecological studies from long sedimentary records, particularly those that span periods of dynamic climate such as the last glacial cycle, can help to contextualise ecosystem responses to climate variability through time. Detailed studies that compare proxy data from multiple sites, with high chronological precision, have the potential to ascribe mutual climate drivers, and, therefore, track spatiotemporal variability in ecosystem responses. Here, we interrogate the vegetation impact of past climate change in the eastern Mediterranean, using three sub-centennially resolved pollen archives from Greece. The widespread Campanian Ignimbrite (CI/Y-5; ca. 39.85 ka BP) tephra marker is used as an isochron to directly correlate pollen records from Ioannina (NW Greece), Tenaghi Philippon (NE Greece), and Megali Limni (NE Aegean). Our results reveal spatiotemporal variability in the timing of vegetation response in the Mediterranean to climate forcing across Heinrich Stadial 4 (40.2–38.3 ka BP), a period of known abrupt climatic change. We identify a decline in tree pollen in all three sites, likely related to the onset of enhanced regional aridity, with vegetation at Tenaghi Philippon responding prior to the CI/Y-5, in contrast to at Megali Limni and Ioannina, where much of the vegetation change occurs following tephra deposition.

Analysis of spatiotemporal variations in b-values before the 6.8-magnitude earthquake in Luding, Sichuan, China, on September 5, 2022

Analysis of spatiotemporal variations in b-values before the 6.8-magnitude earthquake in Luding, Sichuan, China, on September 5, 2022

The multiscale variability of global extreme wind and wave events and their relationships with climate modes

Extreme winds and waves threaten human life, property security, infrastructure, and marine engineering. Persistent extreme wind events (EWiEs) and extreme wave events (EWaEs) can cause more significant impacts and destruction than short-term or instantaneous extreme events. This study defines an EWiE (EWaE) as a prolonged discrete event characterized by abnormally high wind speeds (significant wave heights). The climatological characteristics, spatiotemporal variability, and long-term trends of EWaEs and EWiEs globally are studied utilizing ERA5 reanalysis datasets from 1940 to 2021. The climatological mean characteristics of EWiEs and EWaEs are generally similar: extratropical regions exhibit high intensities, high counts, and short durations, whereas tropical regions are characterized by weak intensities, low counts, and long durations. The count and duration of EWaEs have significantly increased globally, with the greatest rate in the equatorial Pacific Ocean, where the count of events has increased by more than 2.1/decade and the duration by more than 7.2 h/decade. Furthermore, the Southern Annular Mode (SAM) exhibits a significant positive correlation with the count and duration of EWaEs in the eastern Pacific and Southern Oceans. In the Southern Hemisphere at 0–30°S, the Atlantic Multidecadal Oscillation (AMO) shows a significant positive correlation with the count of EWaEs and EWiEs.

Mapping surface hoar from near-infrared texture in a laboratory

Abstract. Surface hoar crystals are snow grains that form when water vapor deposits on the snow surface. Once buried, surface hoar creates a weak layer in the snowpack that can later cause large avalanches to occur. The formation and persistence of surface hoar are highly spatiotemporally variable, making its detection difficult. Remote-sensing technology capable of detecting the presence and spatial distribution of surface hoar would be beneficial for avalanche forecasting, but this capability has yet to be developed. Here, we hypothesize that near-infrared (NIR) texture, defined as the spatial variability of reflectance magnitude, may produce an optical signature unique to surface hoar due to the distinct shape and orientation of the grains. We tested this hypothesis by performing reflectance experiments in a controlled cold laboratory environment to evaluate the potential and accuracy of surface hoar mapping from NIR texture using a near-infrared hyperspectral imager (NIR-HSI) and a lidar operating at 1064 nm. We analyzed 41 snow samples, three of which were surface hoar and 38 of which consisted of other grain morphologies. When using NIR-HSI under direct and diffuse illumination, we found that surface hoar displayed higher NIR texture relative to all other grain shapes across numerous spectral bands and a wide range of spatial resolutions (0.5–50 mm). Due to the large number of spectral- and spatial-resolution combinations, we conducted a detailed samplewise case study at 1324 nm spectral and 10 mm spatial resolution. The case study resulted in the median texture of surface hoar being 1.3 to 8.6 times greater than that of the 38 other samples under direct and diffuse illumination (p &lt; 0.05 in all cases). Using lidar, surface hoar also exhibited significantly increased NIR texture in 30 out of 38 samples, but only at select (5–25 mm) spatial resolutions. Leveraging these results, we propose a simple binary classification algorithm to map the extent of surface hoar on a pixelwise basis using both the NIR-HSI and lidar instruments. The NIR-HSI under direct and diffuse illumination performed best, with a median accuracy of 96.91 % and 97.37 %, respectively. Conversely, the median classification accuracy achieved with lidar was only 66.99 %. Further, to assess the repeatability of our method and demonstrate its mapping capacity, we ran the algorithm on a new sample with mixed microstructures, with an accuracy of 99.61 % and 96.15 % achieved using NIR-HSI under direct and diffuse illumination, respectively. As NIR-HSI detectors become increasingly available, our findings demonstrate the potential of a new tool for avalanche forecasters to remotely assess the spatiotemporal variability of surface hoar, which would improve avalanche forecasts and potentially save lives.

Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks

The land surface of a watershed acts as a large reservoir, with its catchment water storage capacity (CWSC) influencing rainfall-runoff relationship. Estimating CWSC at global grid scale is challenging due to calibration complexity, limited spatial continuity, and data scarcity. To address this, a deep learning-based approach incorporates spatial reconstruction and temporal transfer for capturing spatio-temporal variations in watershed characteristics. The study focuses on the Global Runoff Data Centre dataset and presents a grid-based hydrological model. Findings demonstrate accurate identification of CWSC distribution, with the model achieving an R 2 of 0.92 and the runoff Kling–Gupta efficiency of 0.71 during validation. According to the CMIP6 projections, the global CWSC is anticipated to undergo a significant increase at a rate of 1.7 mm per decade under the SSP5-8.5 emission scenario. Neglecting spatio-temporal CWSC variability globally overestimates climate change’s impact on runoff, potentially reducing the projected long-term increase by up to 41%.

Enhancing mobile aerosol monitoring with CE376 dual-wavelength depolarization lidar

Abstract. We present the capabilities of a compact dual-wavelength depolarization lidar to assess the spatiotemporal variations in aerosol properties aboard moving vectors. Our approach involves coupling the lightweight Cimel CE376 lidar, which provides measurements at 532 and 808 nm and depolarization at 532 nm, with a photometer to monitor aerosol properties. The assessments, both algorithmic and instrumental, were conducted at ATOLL (ATmospheric Observatory of LiLle) platform operated by the Laboratoire d'Optique Atmosphérique (LOA), in Lille, France. An early version of the CE376 lidar co-located with the CE318-T photometer and with a multi-wavelength Raman lidar were considered for comparisons and validation. We developed a modified Klett inversion method for simultaneous two-wavelength elastic lidar and photometer measurements. Using this setup, we characterized aerosols during two distinct events of Saharan dust and dust smoke aerosols transported over Lille in spring 2021 and summer 2022. For validation purposes, comparisons against the Raman lidar were performed, demonstrating good agreement in aerosol properties with relative differences of up to 12 % in the depolarization measurements. Moreover, a first dataset of CE376 lidar and photometer performing on-road measurements was obtained during the FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) field campaign deployed in summer 2019 over the northwestern USA. By lidar and photometer mapping in 3D, we investigated the transport of released smoke from active fire spots at William Flats (northeast WA, USA). Despite extreme environmental conditions, our study enabled the investigation of aerosol optical properties near the fire source, distinguishing the influence of diffuse, convective, and residual smoke. Backscatter, extinction profiles, and column-integrated lidar ratios at 532 and 808 nm were derived for a quality-assured dataset. Additionally, the extinction Ångström exponent (EAE), color ratio (CR), attenuated color ratio (ACR), and particle linear depolarization ratio (PLDR) were derived. In this study, we discuss the capabilities (and limitations) of the CE376 lidar in bridging observational gaps in aerosol monitoring, providing valuable insights for future research in this field.

Assessment of long-term spatio-temporal variability of hot extremes and associated physical mechanism over India

Assessment of long-term spatio-temporal variability of hot extremes and associated physical mechanism over India

Spatio-temporal trends in long-term seasonal groundwater level of South-western Punjab using non-parametric statistical tests.

To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann-Kendall (MK), Sens's SlopeEstimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

Spatio-temporal variations of reference evapotranspiration using Innovative and Mann–Kendall trend analysis under limited weather data in semi-arid region of Indian Punjab

Spatio-temporal variations of reference evapotranspiration using Innovative and Mann–Kendall trend analysis under limited weather data in semi-arid region of Indian Punjab