Spatiotemporal Trends Research Articles

Time series analysis of landsat data for urban heat island monitoring in Semarang City

The urban heat island (UHI) condition has received significant attention because it has the potential to negatively impact life quality and comfort, especially livability and level of comfort. Semarang City, the capital of Central Java, has been attracting many people to live and causing land use conversion to built-up areas. This study aims to observe the land surface temperature (LST) trend and to analyse the UHI spatiotemporal pattern in Semarang based on satellite time series data within 10 years. Land cover and LST were extracted from Landsat data of 2008, 2013, and 2019. Based on the results, it was observed that the vegetated area has reduced from 20.347 Ha to 7.241 Ha, while the average LST value has increased from 29.24°C to 31.31°C in 2008 and 2019 respectively. We also found that the UHI pattern, which was initially detected only in a few areas in 2008 and was focused in the city centre, has gradually spread to the east and south of Semarang in 2019. The result also shows the spatiotemporal trend of land cover conversion and UHI areas, which can be beneficial for mitigation planning in the future.

Spatiotemporal climate variability and trends in the Upper Gelana Watershed, northeastern highlands of Ethiopia

The aim of this study was to evaluate the performance of CHIRPS and TAMSAT satellite rainfall data over the Upper Gelana watershed, where gauged meteorological data to understand the nature of the climate are scarce. In addition, variability and trends in rainfall and temperature were examined from 1983 to 2021. To evaluate satellite rainfall, categorical and continuous validation statistics were used. Trends were analyzed using Mann-Kendall, Sen's Slope estimator, and innovative trend analysis (ITA) methods. The study also utilized time-series geostatistical analysis techniques. The validation statistics show that TAMSAT performs better on the daily timescale, while the two products have comparable performance on the monthly timescale. TAMSAT was chosen for rainfall analysis because of its higher resolution and performance. The results reveal high inter-annual spatiotemporal variability and strong irregularities in monthly rainfall. The Mann-Kendall test indicates statistically significant positive trends in kiremt and annual rainfall, but belg rainfall exhibits an insignificant negative trend. In the kiremt season, we found a 96.1, 101.6, and 104.8 mm decadal rate of rainfall increment in the lower weina dega (LWD), upper weina dega (UWD), and dega agroecological zones, respectively. In contrast, belg season rainfall declined by 16.4, 16.2, and 14.0 mm per decade in the LWD, UWD, and dega agroecology zones, respectively. The pixel-wise trend analysis also revealed trends and magnitudes of monthly, seasonal, and annual rainfall that vary across the study area. In both LWD and UWD annual minimum and maximum temperatures, respectively, showed significant decreasing and increasing trends, but in dega agroecology the trends were insignificant. The findings of rainfall and temperature trends using the ITA method demonstrated its ability to discover some hidden trends that were not detected by the MK test.

The spatiotemporal trend of human brucellosis in China and driving factors using interpretability analysis

Human brucellosis has reemerged in China, with a distinct change in its geographical distribution. The incidence of human brucellosis has significantly risen in inland regions of China. To gain insights into epidemic characteristics and identify factors influencing the geographic spread of human brucellosis, our study utilized the Extreme Gradient Boosting (XGBoost) algorithm and interpretable machine learning techniques. The results showed a consistent upward trend in the incidence of human brucellosis, with a significant increase of 8.20% from 2004 to 2021 (95% CI: 1.70, 15.10). The northern region continued to face a serious human situation, with a gradual upward trend. Meanwhile, the western and southern regions have experienced a gradual spread of human brucellosis, encompassing all regions of China over the past decade. Further analysis using Shapley Additive Explanations (SHAP) demonstrated that higher Gross Domestic Product (GDP) per capita and increased funding for education have the potential to reduce the spread. Conversely, the expansion of human brucellosis showed a positive correlation with bed availability per 1000 individuals, humidity, railway mileage, and GDP. These findings strongly suggest that socioeconomic factors play a more significant role in the spread of human brucellosis than other factors.

The temporal and spatial evolution of green finance and carbon emissions in the Pearl River Delta region: An analysis of impact pathways

The issue of carbon emissions has become increasingly urgent in tandem with the rapid growth of the economy in the Pearl River Delta region. This paper uses kernel density estimation and standard deviation ellipses based on Pearl River Delta data from 2012 to 2021 to explore the spatiotemporal evolution trends of green financing and carbon emissions. Additionally, it uses a spatial Durbin model to examine the spatial effects of green finance on carbon emissions. The results indicate the following: Firstly, over the years, green finance shows an overall increasing trend, with an internal spatial pattern characterized by higher levels in the northeast and lower levels in the southwest. In contrast, carbon emissions are decreasing, with an internal spatial pattern characterized by higher levels in the southwest and lower levels in the northeast. Secondly, more and more green financing initiatives are aiming towards the Pearl River Delta's northeast, suggesting a more significant concentration of such initiatives there than elsewhere. At the same time, carbon emissions seem to converge on the southwest of the Pearl River Delta, which could indicate that this area generally has greater carbon emission levels. Finally, the spatial effect results reveal that improving green finance levels can significantly inhibit carbon emission levels in the Pearl River Delta region. Evidence of spatial spillover effects indicates that the carbon emissions in neighboring areas are influenced to some extent. This study on green finance and carbon emissions' spatiotemporal characteristics and spatial impact gives a development path and theoretical support for sustainable development goals in the global economy.

Spatiotemporal Analysis of Stranded Loggerhead Sea Turtles on the Croatian Adriatic Coast.

This study investigates the spatiotemporal trends of loggerhead turtles along the Croatian Adriatic coast by using stranding data and post-mortem analyses. Information on 620 loggerhead turtles (Caretta caretta), collected in the period between 2010 and 2022, has been analysed. Seasonal stranding variations reveal distinct patterns, indicating season-specific abundance and age-specific mortality in different areas, particularly in the key neritic habitat of the northern Adriatic. The analysis identifies four critical areas in the northeast and central Adriatic showing high stranding densities and provides regional managers with a tool with which to effectively conserve and manage this species. Fishing-induced mortality, collision with vessels, and potential cold stunning are identified as major threats to loggerhead turtles. Post-mortem investigations reveal that longline fishing gear and collisions with vessels are significant age-specific mortality contributors, underscoring the need for targeted conservation efforts in high-risk areas. The study acknowledges potential biases in strandings records but highlights the importance of post-mortem investigations in understanding mortality causes. The findings provide valuable insights for improving conservation strategies, emphasizing the importance of focused surveillance and conservation efforts in identified high-risk locations to mitigate human-turtle interactions.

Wastewater-based Epidemiology to Investigate Spatio-Temporal Trends in Alcohol Consumption in Aotearoa, New Zealand.

Alcohol is an influential drug that has extensive societal impact. In Aotearoa New Zealand, there are a number of worrying trends related to alcohol consumption including rates of alcohol-related harm and violence and heavy episodic drinking. To understand and address these issues, alcohol consumption rates and related trends need to be understood. A wastewater-based epidemiology study was carried out over the course of six months (April-September 2021), sampling one week per month from ten catchment areas covering ~40 % of the national population. The average alcohol consumption was found to be 12.2 mL/person/day. Temporal and geographical trends in alcohol consumption were found; there was higher alcohol consumption on the weekends compared to weekdays, alcohol consumption was inversely related to settlement size, there was increased alcohol consumption due to public holidays and alcohol consumption was not seen to increase during COVID restrictions, however there was a notable change in the weekly trend during lockdowns. This application of alcohol WBE to Aotearoa New Zealand represents a comprehensive national study to understand alcohol consumption and its influences. The knowledge obtained will allow appropriate services and public policies to be reviewed to best serve communities.

Global, Regional, and National Change Patterns in the Incidence of Low Back Pain From 1990 to 2019 and Its Predicted Level in the Next Decade.

Objectives: To analyze and describe the spatiotemporal trends of Low back pain (LBP) burdens from 1990 to 2019 and anticipate the following decade's incidence. Methods: Using data from the Global Burden of Disease (GBD) 2019 Study, we described net drifts, local drifts, age effects, and period cohort effects in incidence and forecasted incidence rates and cases by sex from 2020 to 2029 using the Nordpred R package. Results: LBP remained the leading cause of the musculoskeletal disease burden globally and across all socio-demographic index (SDI) regions. China is the top country. For recent periods, high-SDI countries faced unfavorable or worsening risks. The relative risk of incidence showed improving trends over time and in successively younger birth cohorts amongst low-middle-, middle- and high-middle-SDI countries. Additionally, the age-standardized incidence rates (ASIR) of LBP in both sexes globally showed a decreasing trend, but the incident cases would increase from 223 to 253 million overall in the next decade. Conclusion: As the population ages, incident cases will rise but ASIR will fall. To minimise LBP, public awareness and disease prevention and control are needed.

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

Spatiotemporal Trends in Group A Streptococcal Pharyngitis in the United States.

Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States, with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the United States is poorly characterized. We used outpatient claims data from individuals with private medical insurance between 2010 and 2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits. The South had the most visits per person (yearly average, 39.11 visits per 1000 people; 95% confidence interval, 36.21-42.01) and the West had the fewest (yearly average, 17.63 visits per 1000 people; 95% confidence interval, 16.76-18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times. The burden and timing of GAS pharyngitis varied across the continental United States, with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures.

Assessment of ambient particulate matter and trace gases in Istanbul: Insights from long-term and multi-monitoring stations

In this study, the long-term spatio-temporal variation, trends, and source increments of particulate matter (PM10 and PM2.5) and gaseous pollutants (SO2, NO, NO2, CO, O3) measured from 2015 to 2021 across 37 air quality monitoring stations in the European and Asian sides of Istanbul were investigated. The average PM10 concentration on the European side is higher than the Asian side. However, the average PM2.5 in both regions showed the same values. The results show that the industries near urban sites mainly affect PM10 concentrations in Istanbul. Ship emissions (from the Istanbul Strait) make the largest incremental contribution to SO2 and CO (211% and 139%, respectively) among other pollutants. PM10, PM2.5, SO2, and NO2 concentrations show significant (p < 0.01) negative temporal trends of 6.02%, 6.97%, 5.38%, and 3.28%, respectively, most notable in the winter season. However, the CO and O3 concentration trends are stable and increasing. According to the correlation coefficient (R) and the coefficient of divergence (COD) analysis, there is a heterogeneous distribution of pollutants at the intra-urban scale. PM2.5 concentrations are dominated by diverse pollution sources such as traffic, shipping and regional sources.

Global burden of maternal disorders attributable to malnutrition from 1990 to 2019 and predictions to 2035: worsening or improving?

Maternal malnutrition is a major global public health problem that can lead to serious maternal diseases. This study aimed to analyze and predict the spatio-temporal trends in the burden of maternal disorders attributable to malnutrition, and to provide a basis for scientific improvement of maternal malnutrition and targeted prevention of maternal disorders. Data on maternal disorders attributable to malnutrition, including number of deaths, disability-adjusted life years (DALYs), population attributable fractions (PAFs), age-standardized mortality rates (ASMRs), and age-standardized DALY rates (ASDRs) were obtained from the Global Burden of Disease Study 2019 to describe their epidemiological characteristics by age, region, year, and type of disease. A log-linear regression model was used to calculate the annual percentage change (AAPC) of ASMR or ASDR to reflect their temporal trends. Bayesian age-period-cohort model was used to predict the number of deaths and mortality rates to 2035. Global number of deaths and DALYs for maternal disorders attributable to malnutrition declined by 42.35 and 41.61% from 1990 to 2019, with an AAPC of -3.09 (95% CI: -3.31, -2.88) and -2.98 (95% CI: -3.20, -2.77) for ASMR and ASDR, respectively. The burden was higher among younger pregnant women (20-29 years) in low and low-middle socio-demographic index (SDI) regions, whereas it was higher among older pregnant women (30-39 years) in high SDI region. Both ASMR and ASDR showed a significant decreasing trend with increasing SDI. Maternal hemorrhage had the highest burden of all diseases. Global deaths are predicted to decline from 42,350 in 2019 to 38,461 in 2035, with the ASMR declining from 1.08 (95% UI: 0.38, 1.79) to 0.89 (95% UI: 0.47, 1.31). Maternal malnutrition is improving globally, but in the context of the global food crisis, attention needs to be paid to malnutrition in low SDI regions, especially among young pregnant women, and corresponding measures need to be taken to effectively reduce the burden of disease.

Beetlehangers.org: harmonizing host–parasite records of Harmonia axyridis and Hesperomyces harmoniae

Citizen science is an increasingly powerful tool to gather large quantities of biological data and to engage the public. The number of citizen science initiatives has rapidly increased in the last 15 years. Invasive alien species such as the harlequin ladybird, Harmonia axyridis (Coleoptera, Coccinellidae), provide a particularly good opportunity for broad-scale use of such initiatives. Harmonia axyridis is parasitized by a fungus, Hesperomyces harmoniae (Ascomycota, Laboulbeniales), that can be found throughout the range of its host. Here we present Beetlehangers.org, a website and data repository where we combine observations of He. harmoniae from literature, online databases, and citizen science initiatives, including new records of both Ha. axyridis and He. harmoniae. Whereas Ha. axyridis is now present in 86 countries across six continents (including seven new country records), the distribution of its parasite He. harmoniae comprises 33 countries in five continents (including two new country records since its description in 2022). We explore spatiotemporal trends of He. harmoniae in light of these records. Finally, we discuss challenges and new opportunities for citizen science in relation to species interactions such as these and provide future perspectives for the website as a home for future Laboulbeniales research and outreach.

Long-Term Spatiotemporal Investigation of Various Rainfall Intensities over Central India Using EO Datasets

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

This study systematically reviews 55 landscape studies that use computer vision methods to interpret social media images and summarizes their spatiotemporal distribution, research themes, method trends, platform and data selection, and limitations. The results reveal that in the past six years, social media–based landscape studies, which were in an exploratory period, entered a refined and diversified phase of automatic visual analysis of images due to the rapid development of machine learning. The efficient processing of large samples of crowdsourced images while accurately interpreting image content with the help of text content and metadata will be the main topic in the next stage of research. Finally, this study proposes a development framework based on existing gaps in four aspects, namely image data, social media platforms, computer vision methods, and ethics, to provide a reference for future research.

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran, and they play an important role in controlling the climate of the country’s northern regions. The land surface temperature (LST) is an important variable that affects the ecosystem of this area. This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500–4000 m above sea level. MODIS data were extracted for the period of 2000–2021, and the Mann–Kendall nonparametric test was applied to evaluating the changes in the LST. The results indicated a significant increasing trend for the monthly average LST in May–August along the southern aspect. Both the northern and southern aspects showed decreasing trends for the monthly average LST in October, November, and March and an increasing trend in other months. At all elevations, the average decadal change in the monthly average LST was more severe along the southern aspect (0.60°C) than along the northern aspect (0.37°C). The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months. At the same elevation, the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months. With increasing elevation, the lapse rate between the northern and southern aspects disappeared. Climate change was concluded to greatly decrease the difference in LST at different elevations for April–July.

Evaluating genotyping-in-thousands by sequencing as a geneticmonitoring tool for a climate sentinel mammal using non-invasive and archival samples.

Genetic tools for wildlife monitoring can provide valuable information on spatiotemporal population trends and connectivity, particularly in systems experiencing rapid environmental change. Multiplexed targeted amplicon sequencing techniques, such as genotyping-in-thousands by sequencing (GT-seq), can provide cost-effective approaches for collecting genetic data from low-quality and quantity DNA samples, making them potentially useful for long-term wildlife monitoring using non-invasive and archival samples. Here, we developed a GT-seq panel as a potential monitoring tool for the American pika (Ochotona princeps) and evaluated its performance when applied to traditional, non-invasive, and archival samples, respectively. Specifically, we optimized a GT-seq panel (307 single nucleotide polymorphisms (SNPs)) that included neutral, sex-associated, and putatively adaptive SNPs using contemporary tissue samples (n = 77) from the Northern Rocky Mountains lineage of American pikas. The panel demonstrated high genotyping success (94.7%), low genotyping error (0.001%), and excellent performance identifying individuals, sex, relatedness, and population structure. We subsequently applied the GT-seq panel to archival tissue (n = 17) and contemporary fecal pellet samples (n = 129) collected within the Canadian Rocky Mountains to evaluate its effectiveness. Although the panel demonstrated high efficacy with archival tissue samples (90.5% genotyping success, 0.0% genotyping error), this was not the case for the fecal pellet samples (79.7% genotyping success, 28.4% genotyping error) likely due to the exceptionally low quality/quantity of recovered DNA using the approaches implemented. Overall, our study reinforced GT-seq as an effective tool using contemporary and archival tissue samples, providing future opportunities for temporal applications using historical specimens. Our results further highlight the need for additional optimization of sample and genetic data collection techniques prior to broader-scale implementation of a non-invasive genetic monitoring tool for American pikas.

Environmental and social inequities in continental France: an analysis of exposure to heat, air pollution, and lack of vegetation.

Cumulative environmental exposures and social deprivation increase health vulnerability and limit the capacity of populations to adapt to climate change. Our study aimed at providing a fine-scale characterization of exposure to heat, air pollution, and lack of vegetation in continental France between 2000 and 2018, describing spatiotemporal trends and environmental hotspots (i.e., areas that cumulate the highest levels of overexposure), and exploring any associations with social deprivation. The European (EDI) and French (FDep) social deprivation indices, the normalized difference vegetation index, daily ambient temperatures, particulate matter (PM2.5 and PM10), nitrogen dioxide, and ozone (O3) concentrations were estimated for 48,185 French census districts. Reference values were chosen to characterize (over-)exposure. Hotspots were defined as the areas cumulating the highest overexposure to temperature, air pollution, and lack of vegetation. Associations between heat overexposure or hotspots and social deprivation were assessed using logistic regressions. Overexposure to heat was higher in 2015-2018 compared with 2000-2014. Exposure to all air pollutants except for O3 decreased during the study period. In 2018, more than 79% of the urban census districts exceeded the 2021 WHO air quality guidelines. The evolution of vegetation density between 2000 and 2018 was heterogeneous across continental France. In urban areas, the most deprived census districts were at a higher risk of being hotspots (odds ratio (OR): 10.86, 95% CI: 9.87-11.98 using EDI and OR: 1.07, 95% CI: 1.04-1.11 using FDep). We studied cumulative environmental exposures and social deprivation in French census districts. The 2015-2018 period showed the highest overexposure to heat between 2000 and 2018. In 2018, the air quality did not meet the 2021 WHO guidelines in most census districts and 8.6 million people lived in environmental hotspots. Highly socially deprived urban areas had a higher risk of being in a hotspot. This study proposes for the first time, a methodology to identify hotspots of exposure to heat, air pollution, and lack of vegetation and their associations with social deprivation at a national level.

Extracting Spatio-Temporal Trends in Medical Research Prioritization Through Natural Language Processing of Case Report Abstracts.

Medical research prioritization is an important aspect of decision-making by researchers and relevant stakeholders. The ever-increasing availability of technology and data has opened doors to new discoveries and new questions. This makes it difficult for researchers and relevant stakeholders to make well-informed decisions about the research areas they want to support and the nations they should look for collaborations. It is, therefore, useful to look at the spatio-temporal trends of medical research prioritization to gain insight into popular and neglected areas of research as well as the allocation of prioritization of each nation. In this study, we develop a system that collects, classifies, and summarizes case report abstracts according to the location, time, and disease category of the report. The additional classifications allow us to visualize and monitor the trends in medical research prioritization by location, time, and disease category.

Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.

Spatiotemporal variability and trends of droughts in the Mediterranean coastal region of Türkiye

AbstractDrought, defined as the period when water availability considerably remains below normal levels for a certain time period, has devastating social, economic and environmental impacts. It is significant to closely monitor the temporal and spatial variability of drought in order to implement policies to mitigate the drought risk and ensure the sustainable management of water resources. In this study, three different drought indices, standard precipitation index (SPI), standardized precipitation‐evapotranspiration index (SPEI) and reconnaissance drought index (RDI), are calculated for 3‐, 6‐ and 12‐month time scales from the precipitation and temperature data measured in 11 meteorological observation stations within the Mediterranean coastal region of Türkiye during 1972–2020. Initially, two absolute homogeneity tests were applied, namely the standard normal homogeneity test (SNHT) and the Pettitt test, to detect abrupt changes in drought index time series. Then, trend analysis was carried out with the innovative trend analysis (ITA) and improved visualization of ITA (IV‐ITA) methods to determine the temporal variation of the drought in the region. A graphical representation is developed for the drought classifications based on IV‐ITA. As a result of the SNHT and Pettitt test, significant change points were found at a 5% significance level in 91.92% and 85.86% of all indices, respectively. In both tests, breaks were detected at similar times (between 1972 and 1974) at Marmaris station, while at Alanya station, changes were found between 1972 and 1973 for the SNHT and in 2008 for the Pettitt test. The trend differences between extreme conditions (extremely dry and wet) decreased with an increase in time scale, distinctively. The ITA trends were almost identical for 3‐month time scales, and they started to differ with time scale increases. Particularly, the presence of negative differences in data points contributes to the augmentation of drought periods, despite a few instances of extreme cases and minor fluctuations that can also be observed in IV‐ITA. The findings obtained from the study are expected to provide valuable information about the recent shifts in drought in the Mediterranean basin.