Spatiotemporal Variability Research Articles

[Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021].

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time.

Knowledge of interspecific and spatiotemporal variation in demography-environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue-winged teal [Spatula discors], gadwall [Mareca strepera], green-winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961-2019 and generally declined through time in blue-winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green-winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross-seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross-seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working-lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources.

Spatiotemporal variation and scenario projections of heat wave during 1961–2100 in the Loess Plateau

Spatiotemporal variation and scenario projections of heat wave during 1961–2100 in the Loess Plateau

Investigating a novel feature of multi-phase rotated empirical orthogonal function to capture spatiotemporal temperature variations

Investigating a novel feature of multi-phase rotated empirical orthogonal function to capture spatiotemporal temperature variations

Indirect Impact of Eutrophication on Occurrence, Air-Water Exchange, and Vertical Sinking Fluxes of Antibiotics in a Subtropical River.

A significant challenge that warrants attention is the influence of eutrophication on the biogeochemical cycle of emerging contaminants (ECs) in aquatic environments. Antibiotics pollution in the eutrophic Pearl River in South China was examined to offer new insights into the effects of eutrophication on the occurrence, air-water exchange fluxes (Fair-water), and vertical sinking fluxes (Fsinking) of antibiotics. Antibiotics transferred to the atmosphere primarily through aerosolization controlled by phytoplankton biomass and significant spatiotemporal variations were observed in the Fair-water of individual antibiotics throughout all sites and seasons. The Fsinking of ∑AB14 (defined as a summary of 14 antibiotics) was 750.46 ± 283.19, 242.71 ± 122.87, and 346.74 ± 249.52 ng of m-2 d-1 in spring, summer, and winter seasons. Eutrophication indirectly led to an elevated pH, which reduced seasonal Fair-water of antibiotics, sediment aromaticity, and phytoplankton hydrophobicity, thereby decreasing antibiotic accumulation in sediments and phytoplankton. Negative correlations were further found between Fsinking and the water column daily loss of antibiotics with phytoplankton biomass. The novelty of this study is to provide new complementary knowledge for the regulation mechanisms of antibiotics by phytoplankton biological pump, offering novel perspectives and approaches to understanding the coupling between eutrophication and migration and fate of antibiotics in a subtropical eutrophic river.

The spatiotemporal variations of microbial community in relation to water quality in a tropical drinking water reservoir, Southmost China

The spatiotemporal variations of microbial community in relation to water quality in a tropical drinking water reservoir, Southmost China

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005–1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024–1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129–1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009–1.0036], percentage of adults aged 45–64 years old [RR = 1.0031, 95% CI 1.0024–1.0039], and particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PM}_{2.5}$$\\end{document}) concentrations [RR = 1.0126, 95% CI 1.0083–1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Spatiotemporal variations and influencing factors of methane emissions from livestock in China: A spatial econometric analysis.

In recent years, China has been implementing policies to improve the livestock industry in response to the global trend toward green and low-carbon development. These policies include the establishment of demonstration zones for high-standard agriculture, the relocation of farms to the north, etc. This study aims to investigate the impact of changes in the spatial structure of the livestock industry on methane emissions. It used panel data from 31 provinces in China from 2001 to 2021 and applied the IPCC methodology to quantify methane emissions at both the national and provincial levels. In addition, a spatial econometric model was used to analyze the impact of changes in the spatial structure of the livestock industry on methane emissions. The results show that methane from livestock in China decreased from 13.85 million tons in 2001 to 11.82 million tons in 2021. In addition, methane emissions from livestock in China show a significant spatial gradient and correlation. The Southwest has the highest methane emissions, accounting for 24 % of the total emissions. After controlling for spatial correlation and other factors in the model, it was found that the spatial structure of the livestock industry has a different influence on methane emissions both in the province and in neighboring provinces. To improve methane emission efficiency in the future, policies such as establishing functional zones for livestock farming, strengthening technological innovation and sharing for green development in agriculture, and promoting the optimization of agricultural and rural management structures should be implemented.

Contribution of marine macrophytes to pCO2 and DOC variations in human-impacted coastal waters

AbstractCarbon cycles in coastal waters are highly sensitive to human activities and play important roles in global carbon budgets. CO2 sink–source behavior is regulated by spatiotemporal variations in net biological productivity, but the contribution of macrophyte habitats including macroalgae aquaculture to atmospheric CO2 removal has not been well quantified. We investigated the variations in the carbonate system and dissolved organic carbon (DOC) in human-impacted macrophyte habitats and analyzed the biogeochemical drivers for the variations of these processes. Cultivated macroalgal metabolism (photosynthesis, respiration, calcification, and DOC release) was quantified by in situ field-bag experiments. Cultivated macroalgae took up dissolved inorganic carbon (DIC) (16.2–439 mmol-C m−2 day−1) and released DOC (1.2–146 mmol-C m−2 day−1). We estimated that seagrass beds and macroalgae farming contributed 0.8 and 0.4 mmol-C m−2 day−1 of the in situ total CO2 removal (5.7 and 6.7 mmol-C m−2 day−1, respectively) during their growing period in a semi-enclosed embayment but efficient water exchange (i.e., short residence time) in an open coastal area precluded detection of the contribution of macrophyte habitats to the CO2 removal. Although hydrological processes, biological metabolism, and organic carbon storage processes would contribute to the net CO2 sink–source behavior, our analyses distinguished the contribution of macrophytes from other factors. Our findings imply that macroalgae farming, in addition to restoring and creating macrophyte habitats, has potential for atmospheric CO2 removal.

Revealing climatic and groundwater impacts on the spatiotemporal variations in vegetation coverage in marine sedimentary basins of the North China Plain, China

The North China Plain (NCP) is one of the three great plains in China and also serves as a vital region for grain, cotton, and oil production. Under the influence of regional hydrothermal changes, groundwater overexploitation, and seawater intrusion, the vegetation coverage is undergoing continuous alterations. However, a comprehensive assessment of impacts of precipitation, temperature, and groundwater on vegetation in marine sedimentary regions of the NCP is lacking. Heilonggang Basin (HB) is located in the low-lying plain area in the east of NCP, which is part of the NCP. In this study, the HB was chosen as a typical area of interest. We collected a series of data, including the Normalized Difference Vegetation Index (NDVI), precipitation, temperature, groundwater depth, and Total Dissolved Solids (TDS) from 2001 to 2020. Then the spatiotemporal variation in vegetation was analyzed, and the underlying driving mechanisms of vegetation variation were explored in this paper. The results show that NDVI experiences a rapid increase from 2001 to 2004, followed by stable fluctuations from 2004 to 2020. The vegetation in the HB has achieved an overall improvement in the past two decades, with 76% showing improvement, mainly in the central and eastern areas, and 24% exhibiting deterioration in other areas. From 2001 to 2020, NDVI correlates positively with precipitation, whereas its relationship with temperature fluctuates between positive and negative, and is not statistically significant. There is a threshold for the synergistic change of NDVI and groundwater depth. When the groundwater depth is lower than 3.8 m, NDVI increases sharply with groundwater depth. However, beyond this threshold, NDVI tends to stabilize and fluctuate. In the eastern coastal areas, NDVI exhibits a strong positive correlation with groundwater depth, influenced by the surface soil TDS controlled by groundwater depth. In the central regions, a strong negative correlation is observed, where NDVI is primarily impacted by soil moisture under the control of groundwater. In the west and south, a strong positive correlation exists, with NDVI primarily influenced by the intensity of groundwater exploitation. Thus, precipitation and groundwater are the primary driving forces behind the spatiotemporal variability of vegetation in the HB, while in contrast, the influence of temperature is uncertain. This study has elucidated the mechanism of vegetation response, providing a theoretical basis for mitigating adverse factors affecting vegetation growth and formulating rational water usage regulations in the NCP.

Evaluation of the light pollution in the nature reserves of China based on NPP/VIIRS nighttime light data

ABSTRACT Nature reserves play a key role in the conservation of habitats, species and ecosystems in China. However, the issue of light pollution within these reserves has not received well recognition. Whereas previous studies have investigated light pollution in protected areas by remote sensing, the influence of the atmospheric scattered light from neighborhoods was ignored by using only direct light emissions. This study mapped the nighttime all-sky brightness from NPP/VIIRS data to quantify light pollution over the national nature reserves in China from 2013 to 2022. Based on the remotely sensed sky brightness, the spatio-temporal variations of sky brightness were analyzed. The mean sky brightness across all nature reserves was 0.63 mcd/m2 and the mean trend was 0.0189 mcd/m2/a, indicating obvious and accelerating light pollution. Furthermore, the sky brightness around the nature reserves was estimated and compared with the reserves. In general, the surrounding regions showed higher sky brightness and brightening trends, suggesting a certain but insufficient protective effect on the sky quality of nature reserves. This study provides a comprehensive understanding of the light pollution conditions in the national nature reserves in China and serves as a valuable reference for future assessments of light pollution using remote sensing data.

Spatiotemporal variation and prediction of NPP in Beijing-Tianjin-Hebei region by coupling PLUS and CASA models

Vegetation productivity is crucial for human production and livelihoods. Understanding net primary productivity (NPP) in historical contexts and predicting its future fluctuations is imperative for assessing the environmental sustainability of a region. However, relatively few researches have been conducted on predicting NPP, requiring further development and refinement of NPP prediction methods and models. This study introduces a novel approach that discretely couples the PLUS and CASA models for NPP prediction, and it validates the applicability of this approach in the research area. The objective of our study is to analyze the spatiotemporal patterns of NPP change in the Beijing-Tianjin-Hebei (BTH) region from 2001 to 2020, predict NPP under three different climate scenarios (SSP 1-2.6, SSP 2-4.5, SSP 5-8.5) in 2030, and identify an appropriate path for the future development of this region. The results indicate:(1) From 2001 to 2020, NPP in the research area has shown a gradual improvement trend and maintained a certain spatial distribution pattern in general. (2) The study discovered a correlation coefficient of 0.83 and an RMSE of 102.86 between predicted and actual NPP for 2020. This suggests that the method introduced in our study is suitable for predicting NPP in the research area. (3) NPP in the study area is predicted to decline in 2030 compared with 2020 under all three scenarios. Moreover, the SSP 1-2.6 scenario, representing a low-emission scenario, is suitable for the BTH region compared with other climate scenarios. This research sheds light on NPP variations in the BTH region over the past 20 years and the next 10 years, offering a scientific basis for relevant departments to formulate future policies.

Spatio-Temporal Variation in the Exceedance of Enterococci in Lake Burley Griffin: An Analysis of 16 Years’ Recreational Water Quality Monitoring Data

Recreational waterbodies with high levels of faecal indicator bacteria (FIB) pose health risks and are an ongoing challenge for urban-lake managers. Lake Burley Griffin (LBG) in the Australian Capital city of Canberra is a popular site for water-based recreation, but analyses of seasonal and long-term patterns in enterococci that exceed alert levels (>200 CFU per 100 mL, leading to site closures) are lacking. This study analysed enterococci concentrations from seven recreational sites from 2001–2021 to examine spatial and temporal patterns in exceedances during the swimming season (October–April), when exposure is highest. The enterococci concentrations varied significantly across sites and in the summer months. The frequency of the exceedances was higher in the 2009–2015 period than in the 2001–2005 and 2015–2021 periods. The odds of alert-level concentrations were greater in November, December, and February compared to October. The odds of exceedance were higher at the Weston Park East site (swimming beach) and lower at the Ferry Terminal and Weston Park West site compared to the East Basin site. This preliminary examination highlights the need for site-specific assessments of environmental and management-related factors that may impact the public health risks of using the lake, such as inflows, turbidity, and climatic conditions. The insights from this study confirm the need for targeted monitoring efforts during high-risk months and at specific sites. The study also advocates for implementing measures to minimise faecal pollution at its sources.

Evaluation of long-term spatio-temporal soil moisture variation over the lower Mahanadi River Basin of Odisha, India: a case study

Evaluation of long-term spatio-temporal soil moisture variation over the lower Mahanadi River Basin of Odisha, India: a case study

Monitoring spatio-temporal variations of terrestrial water storage changes and their potential influencing factors in a humid subtropical climate region of Southeast China

Identifying the changes in terrestrial water storage is essential for a comprehensive understanding of the regional hydrological mass balance under global climate change. This study used a partial least square regression model to fill the observation gaps between GRACE and GRACE-FO and obtained a complete series of terrestrial water storage anomaly data from April 2002 to December 2020 from southeast China. We investigated the variations in terrestrial water storage anomalies in the region and the influencing factors. The study revealed that terrestrial water storage (TWS) anomalies have been increasing in the region, with an average increase of 0.33 cm/yr (p < 0.01). The intra-annual variation showed a positive anomaly from March to September and a negative anomaly in other months. Terrestrial water storage anomalies increased in most regions (especially in the central and northern parts), whereas they decreased in the southern parts. In terms of the components, the soil moisture storage (SMS) contributes 58.3 % and the surface water storage (SWS, especially reservoirs water storage) contributes 41.4 % to the TWS. The study also found that changes in the precipitation explain approximately 71.7 % of the terrestrial water storage variation, and reservoirs contributes to the remaining 28.3 %. These results are essential for understanding the changes in the hydrological cycle and developing strategies for water management in Southeast China.

Model-Based Reinforcement Learning With Isolated Imaginations.

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based reinforcement learning approach that has two main contributions. First, we optimize the inverse dynamics to encourage the world model to isolate controllable state transitions from the mixed spatiotemporal variations of the environment. Second, we perform policy optimization based on the decoupled latent imaginations, where we roll out noncontrollable states into the future and adaptively associate them with the current controllable state. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild, such as self-driving cars that can anticipate the movement of other vehicles, thereby avoiding potential risks. On top of our previous work (Pan et al. 2022), we further consider the sparse dependencies between controllable and noncontrollable states, address the training collapse problem of state decoupling, and validate our approach in transfer learning setups. Our empirical study demonstrates that Iso-Dream++ outperforms existing reinforcement learning models significantly on CARLA and DeepMind Control.

Spatio-temporal variations of habitat quality and its driving factors in the Yangtze River Delta region of China

Spatio-temporal variations of habitat quality and its driving factors in the Yangtze River Delta region of China

Differential responses of ecosystem stability to climatic and anthropogenic factors in connected and isolated lake basins on the Yangtze River

Maintaining optimal ecological security in the Yangtze River-connected and isolated lake basins is of great significance to national projects involving Yangtze River protection. Ecosystem stability and associated factors are important components of ecological security in these basins. However, few studies have focused on ecosystem stability and its driving factors over long periods in the Yangtze River Basin. In this study, a remote sensing index was used to analyze the spatiotemporal variation in the ecosystem stability of the Dongting Lake Basin (DTL), Poyang Lake Basin (PYL), and the isolated Chaohu Lake Basin (CHL) and Taihu Lake Basin (THL) in the Yangtze River over the period 2000–2022 to determine the potential affecting factors. The results showed fluctuations in the ecosystem stability of the DTL and PYL, while a V-shape was observed for the CHL and THL during the same period; the closer to the lake, the weaker the stability of the ecosystem, especially in the DTL and PYL. Moreover, the ecosystem stability was greater in the DTL and PYL than in the CHL and THL. The spillover effect of anthropogenic activities on the ecosystem stability of the four basins and the direct effect of temperature have the greatest effect on the ecosystem stability. Specifically, the ecosystem stability index for the area around the DTL and PYL decreased with increasing human interference, whereas the opposite was observed in the CHL and THL. The effect of temperature was negative for the ecosystem stability of DTL and PYL and significantly positive for CHL and THL, at a level of 0.01 %. The findings of this study provide significant information for targeted ecological restoration of the Yangtze River Basin.

The Northeast Water Polynya, Greenland: Climatology, Atmospheric Forcing and Ocean Response

AbstractThe Northeast Water Polynya is a significant annually recurring summertime Arctic polynya, located off the coast of Northeast Greenland. It is important for marine wildlife and affects local atmospheric and oceanic processes. In this study, over 40 years of observational and reanalysis products (ERA5 and ORAS5) are analyzed to characterize the polynya's climatology and ascertain forcing mechanisms. The Northeast Water Polynya has high spatiotemporal variability; its location, size and structure vary interannually, and the period for which it is open is changing. We show this variability is largely driven by atmospheric forcing. The polynya extent is determined by the direction of the near‐surface flow regime, and the relative locations of high and low sea‐level pressure centers over the region. The surface conditions also impact the oceanic water column, which has a strong seasonal cycle in potential temperature and salinity, the amplitude of which decreases with depth. The ocean reanalyses also show a significant warming trend at all depths and a freshening near the surface consistent with greater ice melt, but salinification at lower depths (∼200 m). As the Arctic region changes due to anthropogenic forcing, the sea‐ice edge is migrating northwards and the Northeast Water Polynya is generally opening earlier and closing later in the year. This could have significant implications for both the atmosphere and ocean in this complex and rapidly changing environment.

Lake ice break-up in Greenland: timing and spatiotemporal variability

Abstract. Synthetic aperture radar (SAR) data from the Sentinel-1 (S1) mission with its high temporal and spatial resolution allows for an automated detection of lake ice break-up timings from surface backscatter differences across south (S), southwest (SW), and northwest (NW) Greenland (&lt;71° N latitude) during the period 2017 to 2021. Median break-up dates of the 563 studied lakes range between 8 June and 10 July, with the earliest being in 2019 and the latest in 2018. There is a strong correlation between the break-up date and elevation, while a weak relationship with latitude and lake area could be observed. Lake-specific median break-up timings for 2017–2021 increase (i.e., are later) by 3 d per 100 m elevation gain. When assuming an earlier break-up timing of 8 d which corresponds to the observed median variability of ±8 d, the introduced excess energy due to a changing surface albedo from snow-covered ice surface to water translates to melting 0.4 ± 0.1 m thick ice at the melting point or heating up a water depth down to 35 ± 3 m by 1 K across the entire surface area of each respective lake. Upscaling the results to 100 486 lakes across the S, SW, and NW regions, which correspond to 64.5 % of all lakes or 62.1 % of the overall lake area in Greenland, yields an estimate of 1.8 × 106 TJ additional energy input. This translates to melting 5.8 Gt ice at the melting point or warming 432.3 Gt water by 1 K.