Comprehensive Spatiotemporal Analysis Research Articles

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. The spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

The Spatio-Temporal Distribution of Suicide-related Emergency Calls in a European City: Age and Gender Patterns, and Neighborhood Influences.

Objective: The aim of this study was to conduct a comprehensive spatio-temporal analysis of suicide-related emergency calls in the city of Valencia (Spain) over a six-year period. To this end we first examined age and gender patterns and, second, the influence of neighborhood characteristics on general and gender-specific spatio-temporal patterns of suicide-related emergency calls. Method: Geocoded data on suicide-related emergency calls between 2017 and 2022 (N = 10,030) were collected from the 112 emergency service in Valencia. Data were aggregated at the census block group level, used as a proxy for neighborhoods, and trimesters were considered as the temporal unit. Two set of analyses were performed: (1) demographic (age and gender) and temporal descriptive analyses and (2) general and gender-specific Bayesian spatio-temporal autoregressive models. Results: Descriptive analyses revealed a higher incidence of suicide-related emergency calls among females and an increase in calls among the 18-23 age group from 2020 onwards. The general spatio-temporal model showed higher levels of suicide-related emergency calls in neighborhoods characterized by lower education levels and population density, and higher residential mobility, aging population, and immigrant concentration. Relevant gender differences were also observed. A seasonal effect was noted, with a peak in calls during spring for females and summer for males. Conclusions: These findings highlight the need for comprehensive mental health targeted interventions and preventive strategies that account for gender-specific disparities, age-related vulnerabilities, and the specific characteristics of neighborhoods.

Spatiotemporal Analysis of Drought Characteristics and Their Impact on Vegetation and Crop Production in Rwanda

In recent years, Rwanda, especially its Eastern Province, has been contending with water shortages, primarily due to prolonged dry spells and restricted water sources. This situation poses a substantial threat to the country’s agriculture-based economy and food security. The impact may escalate with climate change, exacerbating the frequency and severity of droughts. However, there is a lack of comprehensive spatiotemporal analysis of meteorological and agricultural droughts, which is an urgent need for a nationwide assessment of the drought’s impact on vegetation and agriculture. Therefore, the study aimed to identify meteorological and agricultural droughts by employing the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI). VHI comprises the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), both derived from the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). This study analyzed data from 31 meteorological stations spanning from 1983 to 2020, as well as remote sensing indices from 2001 to 2020, to assess the spatiotemporal patterns, characteristics, and adverse impact of droughts on vegetation and agriculture. The results showed that the years 2003, 2004, 2005, 2006, 2013, 2014, 2015, 2016, and 2017 were the most prolonged and severe for both meteorological and agricultural droughts, especially in the Southern Province and Eastern Province. These extremely dry conditions led to a decline in both vegetation and crop production in the country. It is recommended that policymakers engage in proactive drought mitigation activities, address climate change, and enforce water resource management policies in Rwanda. These actions are crucial to decreasing the risk of drought and its negative impact on both vegetation and crop production in Rwanda.

How does energy utilization affect rural sustainability development in traditional villages? Re-examination from the coupling coordination degree of atmosphere-ecology-socioeconomics system

In the face of global environmental challenges and China's commitment to achieving carbon neutrality, understanding the impact of energy transition in rural areas, particularly in traditional villages, becomes crucial for sustainable development. Based on 703 traditional villages in Hunan Province from 2005 to 2020, this study conducts a comprehensive spatiotemporal analysis of energy utilization, and analyzes the influence of energy utilization on the coupling coordination of the Atmosphere-Ecology-Socioeconomics System. The findings reveal a fluctuating trend in traditional villages' coupling coordination degree (CCD), particularly higher in western Hunan Province, and a notable reduction in regional disparities over time. The traditional villages' atmospheric conditions are better than the social and ecological conditions, which reflects the importance of improving socioeconomics and ecological subsystems for coupling coordination development. The study identifies that all seven energy utilization factors have positive effects on CCD. Forest vegetation carbon stocks and total energy consumption are critical drivers of CCD, highlighting spatial heterogeneity in their impacts on the CCD across different regions. Additionally, from 2005 to 2020, the effects of Forest Understory Vegetation Carbon Stocks on coupling coordination changed from widespread to localized, while Total Energy Consumption's impact was consistently localized. This paper reevaluates the role of energy management in enhancing the quality of traditional village habitats and offers valuable insights into environmental management and sustainable development in traditional villages, providing compelling evidence for policymakers.

The Role of Anthropogenic Forcings on Historical Sea‐Level Change in the Indo‐Pacific Warm Pool Region

AbstractDetecting and attributing sea‐level rise over different spatiotemporal scales is essential for low‐lying and highly populated coastal regions. Using the Detection and Attribution Model Intercomparison Project (DAMIP) from the Coupled Model Intercomparison Project Phase 6, we evaluate the role of anthropogenic forcing in sea‐level change in the historical (1950–2014) period in the Indo‐Pacific warm pool region. We use three models that have at least 10 ensemble members, corresponding to different DAMIP simulations. We determined the changes in regional sea level from both natural and anthropogenic forcings. Our results demonstrate: (a) the emergence of an anthropogenic footprint on regional sterodynamic sea‐level change has a large spatiotemporal diversity over the Indo‐Pacific warm pool region with the earliest emergence in the western Indian Ocean; (b) a significant rise in dynamic sea level (DSL) (up to 25 mm) and thermosteric (up to 40 mm) sea level over the western Indian Ocean due to greenhouse gas forcing; (c) a positive Indian Ocean Dipole‐like pattern in the DSL changes over the tropical Indian Ocean; (d) a significant increase in the halosteric contribution to sea‐level rise in the Indo‐Pacific warm pool region, and (e) a pronounced rise of manometric sea level (up to 20 mm) over shallow oceans and coastal regions in recent decades. These results provide a comprehensive spatiotemporal analysis of the attribution of anthropogenic factors to sea‐level changes in the Indo‐Pacific warm pool region.

Spatiotemporal Dynamics of Forest Vegetation and Their Impacts on Soil Properties in the Forest-Steppe Zone of Central Russian Upland: A Remote Sensing, GIS Analysis, and Field Studies Approach

This article showcases the outcomes of a comprehensive spatiotemporal dynamic analysis conducted in forest vegetation areas within the forest-steppe zone of the Central Russian Upland (eastern Europe), spanning the period from 1970 to 2020. This study utilized high-resolution data from the Corona satellite system from the year 1970 as well as satellite imagery from the ArcGIS World Imagery database. Soil properties and their changes were assessed based on the analysis of soil bulk density (930 samples), soil organic carbon features, pH, available phosphorus, and the composition of salt extracts (1362 samples). We collected and analyzed 3920 soil samples in the field to study the impact of shelterbelts on soil moisture over a period of two years. For six selected key sites with a total area of 1722 km2, it was found that over a 50-year period, the area covered by forest vegetation increased from 14% to 24%. This expansion was primarily due to the planting and growth of young shelterbelts in the 1970s–1980s as well as widening anti-erosion shelterbelts on slopes and gullies. The average linear growth rate of forest vegetation boundaries was found to be 23.5 m (4.7 m per decade) for the entire study area. The expansion was highest on west-facing slopes, which was attributed to the higher moisture content from windward atmospheric precipitation events. However, alongside the increase in forest cover, degradation was also observed, particularly in old-age shelterbelts, which was attributed to increased fragmentation and mortality. A gradual increase in the extent of shelterbelt degradation was observed from the northwest to the southeast within the forest-steppe region, corresponding to areas with a drier climate. Additionally, the impact of shelterbelts on soil properties and soil cover was analyzed using four key sites and using fields and laboratory research methods. We detected a lateral uptake of substances from plowed soils into the soils of shelterbelts and vertical uptake from deep layers. The two-year observations (2020 and 2021) of soil moisture during the growing season (May–September) in two climatically contrasting forest-steppe areas revealed a more intensive accumulation of soil moisture in fields west of shelterbelts compared to those to the east of them, particularly within the 10 m zone near the shelterbelts. This can be attributed to arable fields on the windward side receiving more moisture compared to the leeward side. The formation of striped microstructures in the soil cover that occurred under the shelterbelts and on adjacent arable lands was influenced by various factors such as microclimatic conditions, vegetation types, ecological conditions for soil fauna, and human-induced soil processing and transformation along the shelterbelt boundaries. Shelterbelts and their adjacent areas in agro-landscapes are considered to be self-developing natural–anthropogenic geosystems with their own organizational structure. Therefore, their study is recommended as an integral part of modern geographical zoning.

Spatio-temporal comprehensive measurement of China's agricultural green development level and associated influencing factors.

Green development is an inevitable trend in the modernization of agriculture and rural areas, and promoting the green development of agriculture has always been an important measure for China's sustainable growth. However, due to the influence of diverse regional environments and the wide range of landscapes in China, a largely agricultural country, China is facing ongoing challenges in improving the overall level of agricultural green development and narrowing regional differences, which has recently garnered worldwide attention. This study aims to measure and analyze the agricultural green development level of 30 provinces in China (Tibet, Hong Kong, Macao, and Taiwan are not included in the target areas of this research due to a lack of data). Here, we applied GIS technology, an entropy-TOPSIS (technique for order of preference by similarity to ideal solution) model, quantitative analysis methods such as global spatial autocorrelation analysis, coldspot and hotspot analysis, and a spatial Durbin model to construct measurement models and index systems, after which we performed a comprehensive spatiotemporal analysis of China's agricultural green development level. Furthermore, the present study also analyzed the factors that influence agricultural green development in China. The present study demonstrated that: (i) between 2005 and 2020, China's overall level of agricultural green development exhibited a fluctuating upward trend, with significant improvement and enhancement in most provinces. However, the overall level of China's agricultural green development remains low, and differences at the provincial level are particularly prominent, with the main regions displaying the following descending development pattern: Eastern > Central > Western regions. (ii) The level of China's agricultural green development shows clear signs of spatial aggregation, characterized by spatial dependence and heterogeneity. Although this phenomenon is gradually weakening over time, the high levels of agricultural green development in the eastern regions and low levels in the western regions are likely to persist in the near future. (iii) Green agricultural structure, technology supply, agricultural mechanization level, and arable land area are the key factors influencing China's level of agricultural green development. Among these factors, technology supply, agricultural mechanization level, and arable land area have the largest direct impact, whereas green agricultural structure has a positive spatial spillover effect on the level of agricultural green development. Technology supply has both a positive direct impact and a negative indirect impact on the level of agricultural green development. Therefore, further improving technology supply and agricultural mechanization level can directly promote China's agricultural green development.

A Relational Data Model for Advancing Stormwater Infrastructure Management

To date, the knowledge benefits that can result from the growing abundance of measured stormwater data have yet to be fully realized within the industry. Obstacles due to data format and storage, retrieval, and quality control have limited the size and impact of accessing known data, resulting in data that are often siloed by project or objective, which inhibits advancements in our understanding, as well as decision making and information sharing across municipalities. Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI)’s observations data model (ODM) is a relational data model designed to organize disparate types of data; however, it does not provide avenues for the storage of stormwater monitoring–specific metadata. To facilitate stormwater infrastructure data analysis, the Villanova stormwater infrastructure data model (SIDM), constructed comparably to ODM1, was created by adding and removing tables from the ODM1 structure, along with other modifications to enable efficient data access. The novel data model presented here facilitates a comprehensive spatiotemporal analysis specific to stormwater systems to overcome traditional stormwater data siloes and harness the benefits of stormwater data through efficient avenues. The data model structure of the SIDM enables stormwater-specific data to be managed efficiently not only to provide data regarding function and performance but to advance the science around stormwater planning and management, as well as knowledge sharing across researchers, utilities, and municipalities. In this paper, we describe the logic of the design, benefits of the data model, and applications to specific green stormwater infrastructure installations. This has the potential to not only provide novel insight but open the door for applications of modeling advancement in physics-based models and beyond to artificial intelligence, data analysis, and data-informed decision making in the planning, design, operation, and maintenance of stormwater systems.

Efficient Dynamic Mesh Refinement Technique for Simulation of HPM Breakdown-Induced Plasma Pattern Formation

Numerical simulation of the complex plasma dynamics associated with high power, high-frequency microwave breakdown at high pressures, leading to the formation of filamentary plasma structures such as self-organized plasma arrays, is a computationally challenging problem. The widely used 2-D electromagnetic (EM)–plasma fluid model, which accurately captures the experimental observations, requires a runtime of several days to months to simulate standard problems due to stringent numerical requirements in terms of cell size and time step. This article presents a self-aware mesh refinement (MR) algorithm that uses a coarse mesh and a fine mesh that dynamically expand based on the plasma profile topology to resolve the sharp gradients in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -fields and plasma density in the breakdown region. The dynamic MR (DMR) technique is explained in detail, and its performance has been evaluated using a standard benchmark microwave breakdown problem. We observe a speedup of 8 (of the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(r^{3})$ </tex-math></inline-formula> , when the refinement factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> ) is 2) compared with a traditional single uniform fine-mesh-based simulation. The technique is scalable and performs better when the problem size increases. We also present a comprehensive spatio-temporal visual analysis to explain the complex physics of high-power microwave (HPM) breakdown, leading to self-organized plasma filaments as an application of the DMR technique.

Comprehensive spatio-temporal analysis of ambient air quality of Kolkata Municipal Corporation, Kolkata (West Bengal, India) during 2017–2020

Comprehensive spatio-temporal analysis of ambient air quality of Kolkata Municipal Corporation, Kolkata (West Bengal, India) during 2017–2020

Extreme Rainfall Indices in Southern Levant and Related Large-Scale Atmospheric Circulation Patterns: A Spatial and Temporal Analysis

This study aims to provide a comprehensive spatio-temporal analysis of the annual and seasonal extreme rainfall indices over the southern Levant from 1970 to 2020. For this, temporal and spatial trends of 15 climate extreme indices based on daily precipitation at 66 stations distributed across Israel and Palestine territories were annually and seasonally analyzed through the nonparametric Mann–Kendall test and the Sen’s slope estimator. The annual averages for frequency-based extreme indices exhibited decreasing trends, significantly for the Consecutive Dry Days. In contrast, the percentiles- and intensity-based extreme indices showed increasing trends, significant for extremely wet days, Max 1- and 3-day precipitation amount indices. The study area had expanding periods of extreme dry spells for spring and correspondingly shortening extreme wet spells for spring, winter and the combined winter–spring. Moreover, most of spring indices showed negative trends. Conversely, most winter indices displayed positive trends. Regarding the influence of large-scale circulation patterns, the North Sea Caspian pattern, the Western Mediterranean Oscillation, and ENSO were the primary regulators of the winter, spring, and autumn extreme indices, respectively. These findings contribute to a better understanding of extreme rainfall variability in the Levant region and could be utilized in the management of water resources, drought monitoring, and flood control.

Phylogenetic and Phylogeographic Analysis of the Highly Pathogenic H5N6 Avian Influenza Virus in China.

The clade 2.3.4.4b H5N8 avian influenza viruses (AIVs) have caused the loss of more than 33 million domestic poultry worldwide since January 2020. Novel H5N6 reassortants with hemagglutinin (HA) from clade 2.3.4.4b H5N8 AIVs are responsible for multiple human infections in China. Therefore, we conducted an epidemiological survey on waterfowl farms in Sichuan and Guangxi provinces and performed a comprehensive spatiotemporal analysis of H5N6 AIVs in China. At the nucleotide level, the H5N6 AIVs isolated in the present study exhibited high homology with the H5N6 AIVs that caused human infections. Demographic history indicates that clade 2.3.4.4b seemingly replaced clade 2.3.4.4h to become China’s predominant H5N6 AIV clade. Based on genomic diversity, we classified clade 2.3.4.4b H5N6 AIV into ten genotypes (2.3.4.4bG1–G10), of which the 2.3.4.4bG5 and G10 AIVs can cause human infections. Phylogeographic results suggest that Hong Kong and Jiangxi acted as important epicentres for clades 2.3.4.4b and 2.3.4.4h, respectively. Taken together, our study provides critical insight into the evolution and spread of H5N6 AIVs in China, which indicates that the novel 2.3.4.4b reassortants pose challenges for public health and poultry.

Spatio-Temporal Trends of Precipitation and Temperature Extremes across the North-East Region of Côte d’Ivoire over the Period 1981–2020

The northeast region of Côte d’Ivoire, where agriculture is the main economic activity, is potentially vulnerable to extreme climatic conditions. This study aims to make a comprehensive spatio-temporal analysis of trends in extreme indices related to precipitation and temperature for the Zanzan region of Côte d’Ivoire over the period of 1981–2020. The statistical significance of the calculated trends was assessed using the non-parametric Mann–Kendall test, while Sen’s slope estimation was used to define the amount of change. For extreme precipitations, the results showed a decreasing trend in annual total precipitations estimated at 112.37 mm and in daily precipitations intensity indices. Furthermore, the consecutive dry days’ index showed an increasing trend estimated at 18.67 days. Unlike the trends in precipitation extremes, which showed statistically non-significant trends, the trends in temperature extremes were mostly significant over the entire study area. The cold spells indices all show decreasing trends, while the warm spells show increasing trends. Drawing inferences from the results, it becomes clear that the study area may be threatened by food insecurity and water scarcity. The results are aimed to support climate adaptation efforts and policy intervention in the region.

Hazard risk awareness and disaster management: Extracting the information content of twitter data

• This study conducts a comprehensive spatiotemporal analysis on Twitter data. • We provide Hazard Risk Awareness (HRA) index for effective disaster management. • We utilize Statistical techniques along with Natural Language Processing (NLP). The use of social media platforms such as Twitter significantly increases during natural hazards. With the emergence of several social media platforms over the past decade, many studies have investigated the applications of these platforms during calamities. This study presents a comprehensive spatiotemporal analysis of textual content from millions of tweets shared on Twitter during Hurricane Harvey (2017) across several affected counties in southeast Texas. We propose a new Hazard Risk Awareness (HRA) Index, which considers multiple factors, including the number of tweets, population, internet use rate, and natural hazard characteristics per geographic location. We then map the HRA Index across southeast Texas. Utilizing a dataset of 18 million tweets, we employ Natural Language Processing (NLP) along with a set of statistical techniques to perform analysis on the textual data generated by Twitter users during Hurricane Harvey. This enables us to subdivide the tweet contents into several categories per county that would inform crisis management during the event. In all, our study provides valuable information at the county level before, during, and after Harvey that could significantly help disaster managers and responders to minimize the consequences of the event and improve the preparedness of the residents for it. Since HRA is derived based on the meteorological observations and some demographic information, depending on the availability of such dataset and the nature of the hazard (i.e., flood, wildfire, hurricane, and earthquake), this index can be calculated and employed for assessing the risk awareness of a community exposed to either of these natural hazards.

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

Different sensing methods provide valuable information for comprehensive monitoring strategies, which are crucial for the ecological management of lakes and watersheds. Subsequently, the resulting spatio-temporal information can be considered the fundamental knowledge for the water resources management of watersheds. Lake Urmia is deemed one of the most important aquatic habitats in Iran. It has been experiencing significant changes during recent years due to climate change, anthropogenic activities, and a lack of coherent management approaches. Hence, awareness of the hydro-ecological factors during the last few decades is critical for identifying the problems. In this research, the impacts of changes in key parameters such as precipitation, evapotranspiration, water surface temperatures, suspended sediment concentration, saline features, and vegetation are explored using satellite imagery. The primary purpose of this study is to evaluate the Lake Urmia crisis concerning human-involved and climate factors such as the agriculture sector and construction of the causeway. In this regard, a limbic-based Emotional Artificial Neural Network (EANN) is developed as a non-linear universal mapping and implemented for the first time to demonstrate the interactions between the considered hydro-ecological factors and the sensitivity of the two indicators the lake health. Providing a comprehensive spatio-temporal analysis is another objective of this study to detect the onset of deterioration in the parameters. The values of the efficiency criteria were measured to evaluate the sensitivity of the EANN models to the related inputs. The results of the model in scenario 4 with evapotranspiration, precipitation, runoff and vegetation as input variables led to higher performance with the best efficiency criteria, including DC = 0.868 and RMSE = 0.096. The quantitative results confirm that the combination of both climate and anthropogenic factors, including the agricultural sector's overdraft, leads to the most efficient EANN model and, consequently, is considered the leading cause of the crisis.

The New Distribution: Spatio-Temporal Analysis of Large Distribution Warehouse Premises in England and Wales

This research addresses the deficit of empirical investigation of changes in industrial and warehouse property markets in the UK. It uses business rates (rating list) data for England and Wales to reveal changes in the quantum and distribution of premises over the last decade. Spatio-temporal analysis using geographical information systems identifies where new industrial and warehouse premises have been developed and examines spatial changes in the distribution of premises between the two sectors. The research focuses on the development of new large distribution warehouses (LDWs) to investigate whether there is a new pattern of warehouse premises located in close proximity to junctions on the national highway network. Findings confirm the emergence of a dynamic distribution warehouse property market where “super sheds” have been developed in areas with high levels of multi-modal connectivity. The comprehensive spatio-temporal analysis of all industrial and warehouse premises in England and Wales reconfigures the previously recognised Midlands “Golden Triangle” of distribution warehouses to a “Golden Pointer” and reveals the emergence of a rival “Northern Dumbbell” of distribution warehouse premises in the North of England. Further analysis using isochrones confirms that 85% of the population of Great Britain is situated within four hours average heavy goods vehicle drive time of these two concentrations of super sheds and over 60% of all LDWs floorspace is within 30 minutes’ drive of intermodal rail freight interchanges.

Spatiotemporal analysis of land use pattern and stream water quality in southern Alberta, Canada

Alberta has over 70% of total irrigated land in Canada and 13 irrigation districts are mainly located along the rivers in southern Alberta. The runoff and irrigation return flow can carry excess nutrients, pesticides, and sediments, which adversely affect the river water quality. In the present study, the comprehensive spatiotemporal analysis of land use pattern and stream water quality in southern Alberta was conducted. The water quality monitoring on the South Saskatchewan River watershed within Alberta showed that most water quality indicators did not significantly change between 2003 and 2017. Land use maps demonstrated significant urban expansion and cropland decline in the study area from 2005 to 2015, while the irrigation area increased. Correlation analysis and redundancy analysis revealed that cropland, built-up land, and native grassland were three land use types that were positively correlated with water quality indicators. Stronger correlations between nitrogen concentration and built-up land were found in dry seasons compared with wet seasons. According to the results of cluster analysis, higher water quality indicator concentrations could be observed in groups with a higher proportion of cropland and built-up land. Significant relationships between land use patterns and water quality were found in this study, which demonstrated that further investigation is needed to identify the sources of water pollutants at a smaller scale.

Apoptosis is not conserved in plants as revealed by critical examination of a model for plant apoptosis-like cell death

BackgroundAnimals and plants diverged over one billion years ago and evolved unique mechanisms for many cellular processes, including cell death. One of the most well-studied cell death programmes in animals, apoptosis, involves gradual cell dismantling and engulfment of cellular fragments, apoptotic bodies, through phagocytosis. However, rigid cell walls prevent plant cell fragmentation and thus apoptosis is not applicable for executing cell death in plants. Furthermore, plants are devoid of the key components of apoptotic machinery, including phagocytosis as well as caspases and Bcl-2 family proteins. Nevertheless, the concept of plant “apoptosis-like programmed cell death” (AL-PCD) is widespread. This is largely due to superficial morphological resemblances between plant cell death and apoptosis, and in particular between protoplast shrinkage in plant cells killed by various stimuli and animal cell volume decrease preceding fragmentation into apoptotic bodies.ResultsHere, we provide a comprehensive spatio-temporal analysis of cytological and biochemical events occurring in plant cells subjected to heat shock at 40–55 °C and 85 °C, the experimental conditions typically used to trigger AL-PCD and necrotic cell death, respectively. We show that cell death under both conditions was not accompanied by membrane blebbing or formation of apoptotic bodies, as would be expected during apoptosis. Instead, we observed instant and irreversible permeabilization of the plasma membrane and ATP depletion. These processes did not depend on mitochondrial functionality or the presence of Ca2+ and could not be prevented by an inhibitor of ferroptosis. We further reveal that the lack of protoplast shrinkage at 85 °C, the only striking morphological difference between cell deaths induced by 40–55 °C or 85 °C heat shock, is a consequence of the fixative effect of the high temperature on intracellular contents.ConclusionsWe conclude that heat shock-induced cell death is an energy-independent process best matching definition of necrosis. Although the initial steps of this necrotic cell death could be genetically regulated, classifying it as apoptosis or AL-PCD is a terminological misnomer. Our work supports the viewpoint that apoptosis is not conserved across animal and plant kingdoms and demonstrates the importance of focusing on plant-specific aspects of cell death pathways.

Projected drought pattern under climate change scenario using multivariate analysis

Drought is generally associated with variation in multi geomorphic-climatic variables rather than a single drought driver. Hence, it is better to consider all possible drought drivers for comprehensive spatiotemporal analysis of historical and future drought in the region with an erratic, scant, and unstable climate like the south-west agricultural region of Pakistan. This study attempted to quantify the multivariable future drought projection based on project CMIP5 climatic data under the Representative Concentration Pathway (RCP 8.5) scenario. The drought projection under the climate change scenario was performed in the historical segment (HS: 1996–2018) and two future segments (2019–2034 and 2035–2050) using trend analysis and composite drought index (CDI). The overall results indicated that temperature and evapotranspiration tend to increase with an average relative value of ≥ 5% and in the case of precipitation, the ≥ 40% (relative average) decline was observed from HS to the future, which indicated an increase in numbers of drought in near future. Moreover, drought analysis also showed that the probability of occurrence and intensity of drought would increase in the near future, especially during 2019–2034. It was observed that the sub-regions, i.e., Sindh and Balochistan, would experience more intense long-term droughts as compared to Punjab. Hence, considering the future variation of drought, substantial attention is needed for contingency planning, mitigation, and adaptation strategies to minimize the impact of probable future drought on society in drought-prone agricultural areas of Pakistan.

Observed changes in precipitation during recent warming: The Czech Republic, 1961–2019

AbstractThis paper presents a comprehensive spatiotemporal analysis of precipitation patterns over the territory of the Czech Republic for the 1961–2019 period. Monthly, seasonal and annual series of precipitation totals and numbers of precipitation days were calculated for four altitudinal groups and the entire Czech Republic, based upon the daily precipitation totals recorded by 531 rain‐gauge stations run by the Czech Hydrometeorological Institute. Analysis of series of monthly, seasonal and annual precipitation totals revealed relatively stable fluctuations, while linear trends remained largely insignificant. However, wavelet analysis indicated significant interannual variability on a timescale of 4–8‐years in seasonal and annual series. The minimum in annual variation tended to appear in February (also in January and, at higher altitudes in particular, in April) with the maximum favouring July (but also June and August). The relative proportions of annual totals taken up by winter precipitation increased with altitude, while the proportions for summer precipitation decreased with altitude. Linear trends in the numbers of precipitation days exhibited the most pronounced decreases from April to June, reflected in negative precipitation trends in April–June and positive in July–September. The results obtained are also presented in the broader (central) European context.