Spatial Variance Research Articles

Early warning signals of grassland ecosystem degradation: A case study from the northeast Qinghai-Tibetan Plateau

As a typical vulnerable ecosystem, alpine steppes can be subject to nonlinear accelerations in degradation, once tipping points are reached or exceeded, and thus far more effort and investment will be needed, to reverse the degraded status of such ecosystems, if effective preventive measures or interventions are not adopted sufficiently early. Thus, detecting early warning signals of tipping events is extremely important for ecosystem protection and recovery, but remains highly challenging. For alpine steppes, coverage or biomass loss and species invasion are both key transitions. Using multi-source data, including ground surveys and remote sensing images with different resolutions, we tried to screen and identify potential early-warning signals for alpine grassland degradation events and analyze their efficacy. The results showed that the degradation levels derived from spatial heterogeneity information combined with a vegetation index were reliable, as was verified by ground surveys, and could illustrate the degradation process of a grassland ecosystem. Six common early-warning signals (variance, skewness, and autocorrelation, from both the spatial and temporal domains) were tested according to degradation degree, and spatial autocorrelation and temporal autocorrelation had the best performances, as they could indicate different degradation levels, and the change trends of temporal autocorrelation could provide early-warning functions. A waved change of spatial variance could reflect a vegetation growth dynamic that might indicate species invasion. In this study, the threshold values of spatial and temporal autocorrelation were 0.7 and 0.2, respectively, and from these, the thresholds of vegetation coverage and biomass were then obtained. However, these signals and thresholds had some limitations during application. These findings provide insights into better understanding of the alpine grassland degradation process, and contribute to a better management approach for vulnerable ecosystems using early-warning methods.

Distribution and risk assessment of microplastic pollution in a rural river system near a wastewater treatment plant, hydro-dam, and river confluence

Rivers are the natural drainage system, transporting anthropogenic wastes and pollution, including microplastics (plastic < 5 mm). In a riverine system, microplastics can enter from different sources, and have spatial variance in concentration, physical and chemical properties, and imposed risk to the ecosystem. This pilot study presents an examination of microplastics in water and sediment samples using a single sample collection from the rural Raquette River, NY to evaluate a hypothesis that distinct locations of the river, such as downstream of a wastewater treatment plant, upstream of a hydro-dam, and river confluence, may be locations of higher microplastics concentration. In general, our results revealed the presence of high microplastic concentrations downstream of the wastewater treatment plant (in sediments), upstream of the hydro dam (both water and sediment), and in the river confluence (water sample), compared to other study sites. Moreover, the risk assessment indicates that even in a rural river with most of its drainage basin comprising forested and agricultural land, water, and sediment samples at all three locations are polluted with microplastics (pollution load index, PLI > 1; PLIzone = 1.87 and 1.68 for water and sediment samples respectively), with risk categories between Levels I and IV (“minor” to “danger”). Overall, the river stands in a “considerable” risk category (PRIzone = 134 and 113 for water and sediment samples respectively). The overall objective of this pilot study was to evaluate our hypothesis and advance our understanding of microplastic dynamics in rural river systems, elucidating their introduction from a point source (wastewater treatment plant), transit through an impediment (hydro-dam), and release into a vital transboundary river (confluence of Raquette-St. Lawrence Rivers).

Hydro-morphometry of a trans-Himalayan River basin: Spatial variance, inference and significance

Subansiri is the largest tributary of the Brahmaputra River basin originated in the Tibet Autonomous Region of China. After flowing about 160 km as Chayul Chu in Tibet, it runs as Subansiri in Arunachal Pradesh state of India for about 360 km before merging into the Brahmaputra River in Assam (India). This study was carried out to understand the hydro-morphometry of the entire Subansiri Basin comprising of three sub-basins: Chayul Chu, Upper Subansiri and Lower Subansiri. Geomorphometric parameters were extracted from Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model of 30 m spatial resolution using Google Earth Engine and GIS software. On comparison, significant difference and spatial variation have been noticed in various morphometric parameters of the sub-basins. Notable differences are observed in some key morphometric parameters such as drainage density, stream frequency, constant of channel maintenance, infiltration number, drainage ratio, drainage texture, dissection index, ruggedness number, form factor ratio, elongation ratio etc. Hypsometric analysis has highlighted further the difference in geomorphic fabric, terrain maturity and hydrologic characteristics of these different sub-basins. Hydro-morphometric parameters have direct bearing on the hydrology. Thus, outcome of this study will be useful in water resources management and in finding engineering solutions to hydro-hazards for this basin and other basins worldwide.

A general, resource‐based explanation for density dependence in populations of large herbivores

AbstractThe discipline of ecology seeks to understand how ecosystems, communities, and populations are regulated. A ubiquitous mechanism of population regulation of consumers is that capturing energy and nutrients in sufficient quantities for survival and reproduction becomes more difficult as population density increases. Extensive evidence has revealed that populations of large herbivores are often regulated by density dependence, defined as the reduction in the per‐capita population growth rate that occurs as populations grow large. Diminished body mass of individuals has been repeatedly observed in high‐density populations, implicating compromised nutrition as the primary cause of density dependence. However, there is no general explanation for why these nutritional deficiencies occur. Recent work demonstrated that reduced food intake rates resulting from the functional response of herbivores to depleted plant biomass does not provide a sensible explanation for density dependence because rates of food intake of herbivores are often insensitive to changes in plant biomass. A new model of feedbacks from plant biomass to herbivores shows how reduced nutrition of herbivores can result from increased dilution of nutrients in the plant tissue they consume as populations grow, even when their rate of consumption of plants remains constant. The model contains parameters that can be scaled to body mass, allowing unusually general predictions. The model shows that convex, concave, and linear relationships between the per‐capita growth rate and population density can arise from the effects of depletion of plant biomass by herbivore foraging. The model is the first to explicitly include spatial variance in the nutritional quality of plants as a general driver of herbivore population dynamics. I show how regulation of herbivore abundance by plant nutrients can occur, even when a large fraction of the consumable plant biomass remains uneaten, providing a simple, mechanistic explanation for bottom‐up control of population dynamics of primary consumers in a “green world.”

Intra-colony spatial variance of oxyregulation and hypoxic thresholds for key Acropora coral species.

Oxygen (O2) availability is essential for healthy coral reef functioning, yet how continued loss of dissolved O2 via ocean deoxygenation impacts performance of reef building corals remains unclear. Here, we examine how intra-colony spatial geometry of important Great Barrier Reef (GBR) coral species Acropora may influence variation in hypoxic thresholds for upregulation, to better understand capacity to tolerate future reductions in O2 availability. We first evaluate the application of more streamlined models used to parameterise Hypoxia Response Curve data, models that have been used historically to identify variable oxyregulatory capacity. Using closed-system respirometry to analyse O2 drawdown rate, we show that a two-parameter model returns similar outputs as previous 12th-order models for descriptive statistics such as the average oxyregulation capacity (Tpos) and the ambient O2 level at which the coral exerts maximum regulation effort (Pcmax), for diverse Acropora species. Following an experiment to evaluate whether stress induced by coral fragmentation for respirometry affected O2 drawdown rate, we subsequently identify differences in hypoxic response for the interior and exterior colony locations for the species Acropora abrotanoides, Acropora cf. microphthalma and Acropora elseyi. Average regulation capacity across species was greater (0.78-1.03 ± SE 0.08) at the colony interior compared with exterior (0.60-0.85 ± SE 0.08). Moreover, Pcmax occurred at relatively low pO2 of <30% (±1.24; SE) air saturation for all species, across the colony. When compared against ambient O2 availability, these factors corresponded to differences in mean intra-colony oxyregulation, suggesting that lower variation in dissolved O2 corresponds with higher capacity for oxyregulation. Collectively, our data show that intra-colony spatial variation affects coral oxyregulation hypoxic thresholds, potentially driving differences in Acropora oxyregulatory capacity.

Role of time variant and invariant terms on soil moisture spatio‐temporal variability in a subhumid coastal wetland

AbstractThe knowledge of soil moisture spatio‐temporal variability is highly relevant for water resources management. This paper reports an analysis of the spatial–temporal variability of soil moisture data for a small to medium‐scale soil‐sensors network in a coastal wetland of southwestern Spain. Measurements were taken from five sites located in the Doñana National Park over the time‐period of one hydrological year from September 2017 to September 2018. The total area of the soil‐sensors network shows an extension about 25 × 3 km. Soil moisture data was separated into time invariant (the temporal mean of the whole period at each site) and time‐variant terms (the deviations of soil moisture from the mean, or anomalies). The time‐invariant component was generally the main contributor to the total spatial variance of soil moisture and it was mostly controlled by the groundwater levels in the area. Nevertheless, the time variant terms have a huge effect on soil moisture variability in very dry states. Characteristic convex time‐dependent patterns for this field site were found between spatially averaged soil moisture and its variability. This information could be used for the up and downscaling of soil moisture from satellite data. Those patterns of relation between spatial mean and variability of soil moisture were only affected by heavy rainfalls giving rise to hysteretic behaviour. This study shows that even though groundwater level is a time‐variant variable, it significantly affects soil moisture's time‐variant but also time‐invariant terms due to the different average groundwater level depths at the different sites.

Dual contrastive attention-guided deformable convolutional network for single image super-resolution

With its powerful ability to model geometric transformations, the deformable convolutional network brings great improvements for single image super-resolution (SISR). Nevertheless, its location-variant sampling method leads to an escalation in spatial variance as the deformable convolutional layers are stacked, consequently resulting in limited performance. Hence, we propose a novel and effective approach called dual contrastive attention-guided deformable convolutional network (DCADCN) for SISR modeling. Specifically, we propose an attention-guided deformable convolutional module with joint inner and external attention mechanisms to fully exploit the correspondences between input and deformation features and preserve spatial characteristics to the extent possible. Additionally, we propose a dual mixed feature extractor consisting of two parallel sub-paths. This design allows for the learning of diverse and complementary spatial features. Furthermore, contrastive learning is applied to further amplify the role of key features and mitigate the interference of noisy features. Extensive experimental results demonstrate that DCADCN is capable of effectively handling classic SISR, SISR with blind noise, and real-world SISR tasks. Moreover, our method achieves comparable or even better performance with lower computational cost compared to state-of-the-art methods.

Stochastic reconstruction of geological reservoir models based on a concurrent multi-stage U-Net generative adversarial network

For complex geological reservoir modeling, some numerical-simulation-based methods, such as traditional multiple-point statistics (MPS), cannot extract nonstationary patterns of training images (TIs) effectively. CPU-intensive calculations require that variables information only be stored in RAM instead of other storage mediums to avoid huge time consumption if many simulations are performed successively. Generative Adversarial Network (GAN) modeling methods are able to overcome these issues, but when the training datasets are limited, mode collapse easily occurs. Meanwhile, fixed receptive fields might cause the problem that the corresponding reconstruction cannot take local features and global features of the TIs into account simultaneously. Therefore, we propose the Concurrent Multi-Stage U-Net GAN (Con-MSUGAN) based on a single TI. The pyramid structure is used for spatial pattern multi-scale representation in Con-MSUGAN, which can capture the corresponding feature information of TIs under different scales. Meanwhile, concurrent training patterns can make network parameters in adjacent stages correlated with each other to accelerate network convergence speed, thereby realizing stochastic multi-scale reconstructions. Stationary channel and nonstationary delta facies TIs were used to verify the performance of Con-MSUGAN. Results show that simulation of different datasets using this method are more similar in terms of spatial variance, connectivity degree and facies type frequency distribution to the original TIs. Meanwhile, muti-dimensional-scaling (MDS) plots show small discrepancies between simulation results and the corresponding TI. It indicates that Con-MSUGAN can overcome the problem that complex spatial patterns are hard to reproduce. In addition, saved network parameters can be reused. A variety of equiprobability simulation results can be obtained rapidly, thereby realizing stochastic reconstruction of geological reservoir models.

Dynamical evolution of spinodal decomposition in holographic superfluids

We study the nonlinear dynamical evolution of spinodal decomposition in a first-order superfluid phase transition using a simple holographic model in the probe limit. We first confirm the linear stability analysis based on quasinormal modes and verify the existence of a critical length scale related to a gradient instability — negative speed of sound squared — of the superfluid sound mode, which is a consequence of a negative thermodynamic charge susceptibility. We present a comparison between our case and the standard Cahn-Hilliard equation for spinodal instability, in which a critical length scale can be also derived based on a diffusive instability. We then perform several numerical tests which include the nonlinear time evolution directly from an unstable state and fast quenches from a stable to an unstable state in the spinodal region. Our numerical results provide a real time description of spinodal decomposition and phase separation in one and two spatial dimensions. We reveal the existence of four different stages in the dynamical evolution, and characterize their main properties. Finally, we investigate the strength of dynamical heterogeneity using the spatial variance of the local chemical potential and we correlate the latter to other features of the dynamical evolution.

Study on the Coordinated Development of Tourism Industry–Regional Economy–Ecological Environment in the Yili River Valley

This study aims to clarify the relationship among the three systems of tourism industry, regional economy, and ecological environment in the Yili River Valley, which is essential for the sustainable development of the region. We explore the spatio-temporal evolution characteristics and influencing factors of the tourism–economy–ecosystem in the Yili River Valley using the center of gravity model, spatial mismatch index model, spatial variance model, and obstacle degree model, based on panel data from 2010 to 2019. The study shows that (1) the comprehensive development indices of the tourism industry, regional economy, and ecological environment in the Yili River Valley exhibit varying degrees of growth trends. Smaller increases are observed in ecological environment indices, while larger increases are found in regional economy and tourism industry indices. Both the regional economy and tourism industry indices share a strong correlation and similar development trends. (2) At the macro-regional scale, during the study period, the centers of gravity of the tourism industry, regional economy, and ecological environment in the Yili River Valley have consistently been separated from the spatial geometric center of the region. The degree of misalignment of the centers of gravity of the three systems as a whole has gradually reduced, indicating that the level of coordinated development of the region has continuously improved. (3) At the micro-regional scale, during the study period, most of the spatial misalignment indices of the Yili River Valley regions showed positive and negative fluctuations, and the types of misalignment varied to different degrees. However, the gap in spatial misalignment indices between different regions gradually narrowed, and the divergent characteristics of “east-west concentration, north-south dispersion” were gradually broken, with the overall indices showing a balanced development trend. (4) The spatial dislocation index of each region in the Yili River Valley is affected by multiple systems and factors, and the overall spatial dislocation factors are mainly the comprehensive utilization rate of general solid waste, exhaust gas emission, dust removal amount, etc., which mostly originate from the ecological environment system.

Climate change signals of extreme precipitation return levels for Germany in a transient convection‐permitting simulation ensemble

AbstractThe increase in extreme precipitation with global warming (GW) and associated uncertainties are major challenges for climate adaptation. To project future extreme precipitation on different time and intensity scales (return periods [RPs] from 1 to 100 a and durations from 1 h to 3 days), we use a novel convection‐permitting (CP), multi‐global climate model ensemble of COSMO‐CLM regional simulations with a transient projection time (1971–2100) over Germany. We find an added value of the CP scale (2.8 km) with respect to the representation of hourly extreme precipitation intensities compared to the coarser scale with parametrized deep convection (7 km). In general, the return levels (RLs) calculated from the CP simulations are in better agreement with those of the conventional observation‐based risk products for the region for short event durations than for longer durations, where an overestimation by the simulation‐based results was found. A maximum climate change signal of 6–8.5% increase per degree of GW is projected within the CP ensemble, with the largest changes expected for short durations and long RPs. Analysis of the uncertainty in the climate change signal shows a substantial residual standard deviation of a linear approximation, highlighting the need for transient data sets instead of time‐slice experiments to increase confidence in the estimates. Furthermore, the ensemble spread is found to be smallest for intensities of short duration, where changes are expected to be based mainly on thermodynamic contributions. The ensemble spread is larger for long, multi‐day durations, where a stronger dependence on the dynamical component is ascribed. In addition, an increase in spatial variance of the RLs with GW implies a more variable future climate and points to an increasing importance of accounting for uncertainties.

Assessing the pulmonary vascular responsiveness to oxygen with proton MRI.

Ventilation-perfusion matching occurs passively and is also actively regulated through hypoxic pulmonary vasoconstriction (HPV). The extent of HPV activity in humans, particularly normal subjects, is uncertain. Current evaluation of HPV assesses changes in ventilation-perfusion relationships/pulmonary vascular resistance with hypoxia and is invasive, or unsuitable for patients because of safety concerns. We used a noninvasive imaging-based approach to quantify the pulmonary vascular response to oxygen as a metric of HPV by measuring perfusion changes between breathing 21% and 30%O2 using arterial spin labeling (ASL) MRI. We hypothesized that the differences between 21% and 30%O2 images reflecting HPV release would be 1) significantly greater than the differences without [Formula: see text] changes (e.g., 21-21% and 30-30%O2) and 2) negatively associated with ventilation-perfusion mismatch. Perfusion was quantified in the right lung in normoxia (baseline), after 15 min of 30% O2 breathing (hyperoxia) and 15 min normoxic recovery (recovery) in healthy subjects (7 M, 7 F; age = 41.4 ± 19.6 yr). Normalized, smoothed, and registered pairs of perfusion images were subtracted and the mean square difference (MSD) was calculated. Separately, regional alveolar ventilation and perfusion were quantified from specific ventilation, proton density, and ASL imaging; the spatial variance of ventilation-perfusion (σ2V̇a/Q̇) distributions was calculated. The O2-responsive MSD was reproducible (R2 = 0.94, P < 0.0001) and greater (0.16 ± 0.06, P < 0.0001) than that from subtracted images collected under the same [Formula: see text] (baseline = 0.09 ± 0.04, hyperoxia = 0.08 ± 0.04, recovery = 0.08 ± 0.03), which were not different from one another (P = 0.2). The O2-responsive MSD was correlated with σ2V̇a/Q̇ (R2 = 0.47, P = 0.007). These data suggest that active HPV optimizes ventilation-perfusion matching in normal subjects. This noninvasive approach could be applied to patients with different disease phenotypes to assess HPV and ventilation-perfusion mismatch.NEW & NOTEWORTHY We developed a new proton MRI method to noninvasively quantify the pulmonary vascular response to oxygen. Using a hyperoxic stimulus to release HPV, we quantified the resulting redistribution of perfusion. The differences between normoxic and hyperoxic images were greater than those between images without [Formula: see text] changes and negatively correlated with ventilation-perfusion mismatch. This suggests that active HPV optimizes ventilation-perfusion matching in normal subjects. This approach is suitable for assessing patients with different disease phenotypes.

Spatial and temporal variance in public perception of electric vehicles: A comparative analysis of adoption pioneers and laggards using twitter data

Promotional strategies for electric vehicle (EV) adoption need to be tailored to the public's perception of these vehicles. However, there is a notable gap in understanding how public perception of EVs differs between adoption pioneers and those slower to adopt (“adoption laggards”) in terms of the percentage of registered EVs. This understanding is vital for the development of effective promotional strategies, especially for adoption laggards. To address this research gap, this study compares various public perception of EVs among adoption pioneers and laggards in U.S. states by analyzing historical Twitter discussions using Natural Language Processing (NLP) techniques, including topic and sentiment analysis. The findings reveal distinct patterns in online EV discussions between the two groups, with adoption laggards being concerned more about affordability and gas prices, and adoption pioneers consistently discussing various topics covering EV and battery manufacturing, charging infrastructure, government EV initiatives, and the EV market. Adoption pioneers exhibit stable, positive attitudes, while adoption laggards display frequent attitude fluctuations, including more negative ones recently. These differences emphasize the importance of EV affordability for adoption laggards and indicate a possible widening gap in EV adoption due to ongoing debates among adoption laggards surrounding the value of transportation electrification. By addressing adoption laggards' concerns and promoting the benefits of EVs, policymakers and stakeholders can enhance perceptions toward EVs, bridging the gap between adoption pioneers and laggards, and promoting widespread adoption of cleaner transportation options.

Exploring the relationship between land use/land cover and apparent temperature in China (1996–2020): implications for urban planning

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China's rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it's observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), Wa_ratio=0.263(±0.0269)-0.437(±0.0417)×Ar_ratio\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{W}}_{{\ ext{a}}} \\_{\ ext{ratio}} = {0}{{.263(}} \\pm {0}{{.0269)}} - {0}{{.437(}} \\pm {0}{{.0417)}} \ imes {\ ext{Ar}}\\_{\ ext{ratio}}$$\\end{document}. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

An empirical study of the influences of gustiness on vertical mixing at the air-sea boundary

The exchange of momentum across the air-sea interface is a key driver of the earth system and its accurate parameterization is essential for precise weather and climate forecasting. However, our understanding of gustiness as an independent factor that can contribute to the momentum flux is limited. Using data collected from the R/P FLIP, as part of the Couple Air-Sea Processes and Electromagnetic ducting Research (CASPER) experiment, we explored the mechanisms by which gustiness contributes to the total interfacial momentum flux. We investigate how gustiness affects both the temporal and spatial (vertical) variance of turbulence in the atmospheric surface layer and show that high gustiness was associated with strong anisotropic turbulence at each measurement height. This was found to increase vertical wind fluctuations and inject additional momentum across the air-sea interface at lower wind speeds. Increased gustiness was also associated with the breakdown of the constant flux layer, which is generally assumed to exist over the ocean. This study has implications for both momentum flux parameterization and the use of similarity theory to model the flux-gradient relationship in the gusty atmospheric surface layer, thereby influencing the forecasting of climate and weather.

Regional Low Flow Hydrology: Model Development and Evaluation

AbstractLow flows result from the interplay of climatic variability and catchment storage dynamics, but it is unclear which of these variables is more relevant for explaining low flow spatial patterns. Here, we develop a new conceptual model that integrates process‐based hydrological knowledge with statistics and test it for 1,400 Brazilian catchments. Through comparative hydrology, we isolate the low flow generating mechanisms and estimate their components using linear model trees. The model explains 58% of the spatial variance in 7‐day minimum annual flows (Qmin) based on climate and catchment characteristics. The primary Qmin controls depend on the spatial scale of analysis. Catchment characteristics govern Qmin up to the continental scale (107 km2), where their relative importance matches that of climate. At subcontinental scales, catchment characteristics are twice as important as climate in predicting Qmin, suggesting that low flows are governed by the catchment's capacity to attenuate the climatic variability through water storage. Geological properties are the most important catchment characteristics, particularly bedrock type, lithology and topographic slope, determining streamflow recession rates in the dry season. Soil properties, primarily soil class and depth, are half as important as geology. Climate impacts Qmin mainly through mean annual rainfall minus evaporation, representing the potential groundwater recharge, while dry‐season length has the lowest impact. These results hold mainly for highly seasonal and snow‐free climates. Low flow hydrology that combines statistics with process understanding offers a promising framework for understanding regional low flow generating mechanisms and could support other estimation models than that presented here.

The proposed model for analyzing off-street parking Dynamics: A case study of Taipei City

To reduce traffic congestion, air pollution, and lost revenue due to wasted driving around looking for parking spaces, effective parking management has become a crucial issue in urban areas. Previous studies have shown that parking demand is related to the location of parking spaces/lots, parking fees, time spent looking for spaces, and various driver and environmental characteristics. However, there have been more studies of on-street rather than off-street parking. Also, the spatio-temporal variance between variables and turnover rate (such as the hours of operation for various types of Points of Interest (POIs) as well as the causes of bias estimators has been largely overlooked.To solve the above problems, the Geographical and Temporal Weighted Regression (GTWR) model was utilized in this study to predict the hourly parking turnover rate in off-street parking lots. The flexible coefficient setting of this model can be used to capture the spatial and temporal variance. In addition to this model, the Geographically Weighted Regression (GWR) and the Ordinary Least Squares (OLS) models were used as a basis for comparison. The study included 346 public off-street parking lots in Taipei and 10 types of nearby POIs. The study period was for 24 h on August 31, 2021. Due to the dissimilar trend of the turnover rate, the researchers separated all parking lots into three groups for regression analysis: those that experience a daytime decrease, remain stable, and those with an increase during the day.Our results show the following: (1) For the types of parking lots that experienced increases and decreases, the GTWR model performed best for the prediction of hourly turnover rate, while all models performed similarly for the prediction of the stable type parking lot. (2) With regard to related factors (the type 1 parking lot for example), parking lots in areas with more traditional markets, MRT stations, and with fewer schools, supermarkets, hotel bus stops, and lower parking fees tended to have higher turnover rates. (3) Incorporating a more precise POI dataset (the number and types of POIs as well as their business hours) and applying a suitable spatial temporal model improved the parking demand model’s performance. However, the GTWR model’s limitations may hamper its performance, especially with regard to analysis of the time-stable parking lots or variables. Future studies might include other datasets, such as nearby private parking lots or holiday data to improve the model fit.

One- and Three-Dimensional Hydrodynamic, Water Temperature, and Dissolved Oxygen Modeling Comparison

Understanding and modeling water quality in a lake/reservoir is important to the effective management of aquatic ecosystems. The advantages and disadvantages of different water quality models make it challenging to choose the most suitable model; however, direct comparison of 1-D and 3-D models for lake water quality modeling can reveal their relative performance and enable modelers and lake managers to make informed decisions. In this study, we compared the 1-D model MINLAKE and the 3-D model EFDC+ for water temperature, ice cover, and dissolved oxygen (DO) simulation in three Minnesota lakes (50-m Carlos Lake, 23.5-m Trout Lake, and 5.6-m Pearl Lake). EFDC+ performed well for water temperature and DO simulation in the open water seasons with an average root mean square error (RMSE) of 1.32 °C and 1.48 mg/L, respectively. After analyzing the ice thickness with relevant data, it was found that EFDC+ calculates a shorter ice cover period and smaller ice thickness. EFDC+ does not consider snowfall for ice thickness simulation. The results also revealed that EFDC+ considers spatial variance and allows the user to select inflow/outflow locations precisely. This is important for large lakes with complex bathymetry or lakes having multiple inlets and outlets. MINLAKE is computationally less intensive than EFDC+, allowing rapid simulation of water quality parameters over many years under a variety of climate scenarios.

Characterizing the assembly of dark matter haloes with protohalo size histories: I. Redshift evolution, relation to descendant haloes, and halo assembly bias

ABSTRACT We propose a novel method to quantify the assembly histories of dark matter haloes with the redshift evolution of the mass-weighted spatial variance of their progenitor haloes, that is, the protohalo size history. We find that the protohalo size history for each individual halo at z ∼ 0 can be described by a double power-law function. The amplitude of the fitting function strongly correlates to the central-to-total stellar mass ratios of descendant haloes. The variation of the amplitude of the protohalo size history can induce a strong halo assembly bias effect for massive haloes. This effect is detectable in observation using the central-to-total stellar mass ratio as a proxy of the protohalo size. The correlation to the descendant central-to-total stellar mass ratio and the halo assembly bias effect seen in the protohalo size are much stronger than that seen in the commonly adopted half-mass formation time derived from the mass accretion history. This indicates that the information loss caused by the compression of halo merger trees to mass accretion histories can be captured by the protohalo size history. Protohalo size thus provides a useful quantity to connect protoclusters across cosmic time and to link protoclusters with their descendant clusters in observations.

Vegetation change analysis using Normalized Difference Vegetation Index (NDVI) in Sumedang Regency

Vegetation is a crucial element of livable and healthy cities and has been linked to a number of advantages, such as enhanced human health, habitat provision, and natural system regulation. Planning sustainable cities requires an understanding of and documentation of changes in urban vegetation. The Normalized Difference Vegetation Index (NDVI)'s spatial variance and driving force are useful for managing natural resources and protecting ecological environments. Using Sumedang Regency as the research area, the normalized vegetation index (NDVI) was computed using Landsat-7 ETM and Landsat 8 OLI/TIRS data from 2003 to 2023. The findings show that the high greenness index first declined and subsequently increased between 2003 and 2023. Sumedang Regency's high greenness index shrank in area between 2003 and 2018. In 2003, 144793.17 ha was categorised as high greenness index, but in 2018 the high greenness index was only 122392.08 ha. Furthermore, the index with non-vegetated land increases every year. This shows that Sumedang Regency continues to experience land use change into non-vegetated areas, such as settlements and bare land. This research can provide assistance for the development of a sustainable natural environment in Sumedang Regency.