Spatiotemporal Scales Research Articles

Hour-Long, Kilohertz Sampling Rate Three-Dimensional Single-Virus Tracking in Live Cells Enabled by StayGold Fluorescent Protein Fusions.

A viral infection process covers a large range of spatiotemporal scales. Tracking the viral infection process with fluorescent labels over long durations while maintaining a fast sampling rate requires bright and highly photostable labels. StayGold is a recently identified green fluorescent protein that has a greater photostability and higher signal intensity under identical illumination conditions compared to existing fluorescence protein variants. Here, StayGold protein fusions were used to generate virus-like particles (StayGold-VLPs) to achieve hour-long 3D single-virus tracking (SVT) with 1000 localizations per second (kHz sampling rate) in live cells. The expanded photon budget from StayGold protein fusions prolonged the tracking duration, facilitating a comprehensive study of viral trafficking dynamics with high temporal resolution over long time scales. The development of StayGold-VLPs presents a simple and general VLP labeling strategy for better performance in SVT, enabling exponentially more information to be collected from single trajectories and allowing for the future possibility of observing the entire life cycle of a single virus.

Monitoring Long-Term Land Cover Change in Central Yakutia Using Sparse Time Series Landsat Data

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Electricity mix from renewable energies can avoid further fragmentation of African rivers

In Africa, mitigating climate change in a context of a growing human population and developing economies requires a bold transition to renewable energy (RE) resources. Declining costs for solar photovoltaics (by 90% between 2009 and 2023) and wind turbines (by 57% between 2010 and 2023) fuelled their construction, and hybrid forms such as floating photovoltaics (FPV) on existing hydropower reservoirs are increasingly being explored. Nevertheless, 65% of the proposed RE capacity in Africa remains hydropower, despite confirmed ecological, socioeconomic, and political ramifications on different spatiotemporal scales. The 673 proposed hydropower plants (HPPs) would increasingly affect river systems and threaten their biodiversity. While there is clear evidence that a transition to RE in Africa is technically feasible, there is a lack of spatially explicit studies on how this transition could be implemented. Hence, the aim of the present study is to explore options for an RE mix that avoids additional hydropower construction and, therefore, further river fragmentation. Attribute data of the open-accessible Renewable Power Plant Database (RePP Africa) were analysed to assess the amount of lost capacity due to operation stops. Geospatial analyses of solar irradiation and existing reservoir data were used to derive the potential for FPV. The degree of possible replacement of future hydropower was assessed under consideration of economically feasible wind and solar photovoltaic (PV) potential. To enhance electricity generation from existing HPPs, efficient and sustainable renewable power plant planning must integrate the risk of failure, as it has diminished the available capacity in the past up to 24%. Our findings further reveal that 25 African countries could replace the proposed hydropower development by FPV covering less than 25% of the surface area of their existing hydropower reservoirs. All 36 African countries could replace proposed hydroelectricity generation by fully exploiting feasible onshore wind and solar PV potential with a mean surplus of 371 TWh per year. In summary, our findings provide scientific evidence to support policy discussions on the potential electricity gains from (1) minimizing plant failure, (2) installing FPV as a co-use option, and (3) exploiting wind and solar resources. This study provides quantitative, data-based, and spatially explicit scenarios on the implementation of an RE mix that could relieve the dam building pressure on African rivers.

Lattice Boltzmann simulation of breaking waves

Water wave breaking at the ocean’s surface is a turbulent phenomenon and encompasses a wide range of physical processes such as generation of bubbles/drops with different spatio-temporal scales. In the present study, breaking of periodic waves in deep water regime has been simulated using phase-field lattice Boltzmann method (LBM). Compared to direct Navier–Stokes equation based numerical models, the present LB model is Poisson-free and explicit in time integration. Both the flow field and interface are solved using LBM with two sets of distribution functions. The normalized pressure-velocity formulation is used for the flow field, whereas the conservative Allen–Cahn equation is used to capture the interface. An impulsively started wave approach with Stokes third order wave profile has been used to initiate the breaking process. Through systematic variation of Weber number and initial steepness, the effect of surface tension on the kinematic, geometric, and energy dissipation characteristics has been studied for three breaker types, viz., spilling, weak plunging, and plunging breakers. The results are in good agreement with the past experimental observations and direct numerical simulation. In particular, the obtained breaking strength for different Weber numbers agrees well with the semi-empirical relation used in a wind-wave forecasting models.

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.

Scale effects and implications of the stochastic structure of customer water demands

ABSTRACT The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 m) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth to embrace a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance

AbstractThe advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.

Individual foraging site fidelity persists within and across stopover seasons in a migratory shorebird, Numenius phaeopus (Whimbrel)

Abstract Site fidelity—returning repeatedly to the same site—can help many migratory species reduce uncertainty in their environment, especially when migratory stopover periods leave little time to explore and evaluate new habitat. Avian taxa, though, have shown wide variation in their levels of site fidelity during migration, and few studies have been able to examine individual-level fidelity at fine spatiotemporal scales. We used a high-resolution GPS tracking dataset of Numenius phaeopus (Whimbrel), a long-distance migratory shorebird, to assess levels of fidelity to specific foraging and roosting sites during migration, both within and between stopover seasons. We found that individuals are almost exclusively faithful to one shared roost site at night, but disperse to individual foraging territories during the day that overlap with each other by <20%. We show that individuals remain faithful to these distinct territories over time, on average, shifting the center of their daily home ranges by <1.5 km within a single season, and overlapping with their previous season’s home range by 70% when they return during subsequent stopovers. Our findings reveal for the first time that a shorebird species exhibits fine-scale, individual foraging site fidelity during and between migratory stopovers—an important insight to inform effective conservation and management action.

Spatial transition tensor of single cells.

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

Assessing the spatiotemporal variability of dissolved organic matter fluorescence composition in the Lake George, NY watershed

AbstractLake George (LG) is a temperate, oligotrophic, medium-sized lake (114 km2) located in northeastern New York State (U.S.). Lakes are highly understudied environments where extensive dissolved organic matter (DOM) processing occurs. With this study we establish the foundation for researching the organic biogeochemistry of the LG watershed, in particular, the numerous tributaries flowing into the lake. Collected were 213 samples from 64 tributaries and 12 lake locations. Some of the tributaries had unique wastewater, agricultural, or wetland influences. We employed fluorescence spectroscopy, a common biogeochemical technique, to characterize the fluorescent DOM (FDOM) component. We developed a parallel factor analysis (PARAFAC) model for the deconvolution of FDOM data allowing to depict six underlying FDOM constituents, which varied in source and biogeochemical reactivity on spatiotemporal scales. Tributary DOM, in comparison to lake DOM, was much more aromatic, of larger molecular weight, more humic, and contained less protein-like material. The distribution of humic and protein-like PARAFAC components was impacted by land-use and wastewater influences. Supporting characterization of the chromophoric DOM (CDOM) and total DOM (on dissolved organic carbon basis) allowed differentiating the influence of wetlands, which could not be depicted by spatiotemporally assessing the variability of PARAFAC components. Temporal assessment revealed minor variabilities in tributary DOM quantity and quality except in cases of point sources such as wastewater treatment facilities. Overall, this primer study establishes baseline understanding of the baseflow levels of DOM constituents in the LG watershed, and more broadly, presents a PARAFAC model for the deconvolution of fluorescence spectra of DOM from temperate and oligotrophic lake watersheds such as LG.

Fluvial biogeomorphological feedbacks from plant traits to the landscape: Lessons from selected French rivers in line with A.M. Gurnell's influential contribution

AbstractResearch in fluvial biogeomorphology largely aims to promote our understanding of the interactions between riparian vegetation and fluvial morphodynamics within riverine ecosystems. Starting at the end of the last century, Angela M. Gurnell has made a major contribution to fluvial geomorphology by considering water flow and sediment transport in combinaton with riparian and later also aquatic vegetation and thus significantly promoting the fluvial biogeomorphological approach from its early days until today. The objective of the present paper is to present a set of studies and results obtained over the last 20 years mainly by the authors and many collaborators, including Angela M. Gurnell, on a panel of French rivers: the Tech, Garonne, Isère and Allier. In particular, feedback mechanisms between fluvial morphodynamics and riparian vegetation dynamics were investigated directly in the field and by using high‐resolution remote sensor systems at the scale of individual plants, populations, communities and landscapes as well as during semi‐controlled ex‐situ experiments at the scale of individual plants. Collectively, the authors' research conducted over the past 20 years has helped to elucidate some key aspects of the feedback dynamics between the lowest and highest levels of the riparian ecosystem organization. This article reviews and discusses those key aspects. The gradually obtained results have contributed to a better understanding and quantification of feedback between river morphodynamics and vegetation at nested spatiotemporal scales, from plant species traits to the riverine landscape. Furthermore, the biogeomorphological approach advocated for more than 20 years now has clearly facilitated the extension of the discipline of geomorphology to ecology in general and evolutionary ecology in particular and thus contributed to a more integrative vision of earth surface processes.

Large-scale overlap and fine-scale avoidance: assessing interactions between coyotes, bobcats, and cougars at multiple scales

Abstract In multi-carnivore systems individuals must forage and reproduce while also competing with other carnivores and avoiding intraguild predation. These interactions may vary by strength and scales across different ecosystems. We used occupancy analyses and attraction–avoidance indices to assess large- and fine-scale interactions, respectively, between Cougars (Puma concolor), Bobcats (Lynx rufus), and Coyotes (Canis latrans) in northeast Oregon based on data from camera traps set during the summer and fall of 2016 and 2017. To determine the importance of habitat preferences and spatial overlap, we compared occupancy (probability of use) models that included habitat covariates with and without species co-occurrence terms. Detection was different for all species and was influenced by roads, game trails, and canopy cover. Terrain ruggedness was important for all species; Bobcats showed the strongest preference for ruggedness, Cougars showed a weak preference, and Coyotes significantly but weakly avoided rugged areas. The top probability of use model showed that Bobcats and Coyotes were 1.88 times more likely to spatially overlap on roads. We also found evidence that interactions among carnivores were scale-dependent. At larger scales, habitat preference was more important than interactions with other carnivores, whereas at finer scales, most carnivores avoided each other. At fine spatiotemporal scales, our attraction–avoidance analyses showed that Bobcats avoided Coyotes and Coyotes avoided both Bobcats and Cougars. These findings suggest that carnivores in our study system were adept at minimizing conflict through fine-scale avoidance in space and time, that the human footprint can influence carnivore interactions, and that studying carnivore interactions at multiple scales is important for understanding the effects of spatial overlap and potential competition.

All-optical subcycle microscopy on atomic length scales.

Bringing optical microscopy to the shortest possible length and time scales has been a long-soughtgoal, connecting nanoscopic elementary dynamics with the macroscopic functionalities of condensed matter. Super-resolution microscopy has circumvented the far-field diffraction limit by harnessing optical nonlinearities1. By exploiting linear interaction with tip-confined evanescent light fields2, near-field microscopy3,4 has reached even higher resolution, prompting a vibrant research field by exploring the nanocosm in motion5-19. Yet the finite radius of the nanometre-sized tip apex has prevented access to atomic resolution20. Here we leverage extreme atomic nonlinearities within tip-confined evanescent fields to push all-optical microscopy to picometric spatial and femtosecond temporal resolution. On these scales, we discover an unprecedented and efficient non-classical near-field response, in phase with the vector potential of light and strictly confined to atomic dimensions. This ultrafast signal is characterized by an optical phase delay of approximately π/2 and facilitates direct monitoring of tunnelling dynamics. We showcase the power of our optical concept by imaging nanometre-sized defects hidden to atomic force microscopy and by subcycle sampling of current transients on a semiconducting van der Waals material. Our results facilitate access to quantum light-matter interaction and electronic dynamics at ultimately short spatio-temporal scales in both conductive and insulating quantum materials.

Disaggregating National Statistical Data to Assess the Sub-National SDG 6.4.2 Water Stress Indicator by Integrating Satellite Observations and Model Data

Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator 6.4.2 is currently based on statistical data at the national scale, i.e., one value for one country, which cannot reflect spatial variability in water stress in a country. The lack of data at sub-national scales limits the assessment of water stress in sub-national regions. This study developed a method of disaggregating national statistical renewable water resources (TRWR) and freshwater withdrawals (TFWW) to estimate the SDG 6.4.2 water stress indicator at a sub-national scale by combining satellite remote sensing data and model simulated data. Remote sensing (RS)-based precipitation (P); the difference between precipitation and evapotranspiration (P-ET); and the difference between precipitation, evapotranspiration, terrestrial water storage change (P-ET-dS), and model-simulated naturized runoff and withdrawal water use were used as spatial and temporal surrogates to disaggregate the national-scale statistics of TRWR and TFWW to the grid scale. Gridded TRWR and TFWW can be used to calculate the water stress of any interest regions. Disaggregated TRWR, TFWW, and water stress estimation were validated at three different spatial scales, from major river basins and provinces to prefectures in China, by comparing the corresponding statistical data. The results show that the disaggregation for TRWR is generally better than for TFWW, and the overall accuracy for water stress estimation can reach up to 91%. The temporal evolution of disaggregated variables also showed good consistency with statistical time series data. The RS-based P-ET and P-ET-dS have great potential for disaggregating TRWR at different spatiotemporal scales, with no obvious differences with the results using the model simulation as a surrogate for the disaggregation of SDG indicator 6.4.2. The disaggregation accuracy can be further improved when the sub-regional statistical data of TRWR and TFWW are applied to the disaggregation approach.

Scale-invariant breathing oscillations and transition of the electron energization mechanism in magnetized discharges

Scale-invariant breathing oscillations are observed in similar magnetized discharges at different spatiotemporal scales via fully kinetic particle-in-cell simulations. With an increase in the similarity invariant B/p, i.e., the ratio of magnetic field to pressure, breathing oscillations are triggered, leading to an appreciable time-averaged potential fall outside the sheath. With the onset and development of breathing oscillations, the electron energization mechanism shifts from sheath energization to direct Ohmic heating in the ionization region due to the change in the potential fall inside and outside the cathode sheath. Based on the scale invariance of the Boltzmann equation and its collision term, the characteristics of breathing oscillations and the transition of the electron energization mechanism are confirmed to be scale-invariant under similar discharge conditions.

Advp Measurements Of Flow Over Low-Angle Bedforms In A Laboratory Flume Setup

In many coastal and estuarine environments, bedforms develop due to the complex interactions between hydrodynamics, sediment transport and morphology which affect hydro-morphodynamic processes at various spatio-temporal scales. Furthermore, bedforms are considered main drivers of flow resistance via turbulence and affect near bottom processes such as bed roughness, bottom shear stress and turbulent structures. A detailed study and characterisation of flow over bedforms is therefore significant to many fundamental and engineering applications such as sediment transport calculation and prediction, channel managements, or burial of submarine cables. Asymmetric dunes with an angle-of-repose (30°) lee side have been well studied (Best, 2005; Venditti, 2013). They are representative of dunes commonly found in laboratory flumes and small rivers. Over such bedforms, flow is characterised by a permanent flow separation zone and intense turbulence over their lee side. Recently, it was shown that bedforms in large rivers are mainly low- to intermediate-angle dunes (mean lee side ca. 5-20°) having their steepest slope located close to the trough (Cisneros et al., 2020). Over such low- and intermediate-angle river dunes, flow separation is inexistent or intermittent and only little turbulence is generated (e.g. Kwoll et al., 2016; Lefebvre and Cisneros, 2023). Estuarine bedforms also possess mostly low- to intermediate-angle mean lee slopes between 5° and 20° (Dalrymple and Rhodes, 1995; Lefebvre et al., 2021). However, contrary to river bedforms, estuarine bedforms usually have a sharp pointed crest with the steepest slope situated near the crest and a relatively flat trough (Aliotta and Perillo, 1987; Lefebvre et al., 2021). It is not clear yet how much flow properties vary between high-angle flat-crested dunes and low-angle sharp-crested dunes. In particular, it is unknown whether a permanent or an intermittent flow separation can be observed over some segments of the lee side. Furthermore, the relation between the reversing tidal flow and natural estuarine morphology is not well understood yet. In this study, laboratory flume experiments will be conducted to measure the mean flow and turbulence over estuarine bedforms. Previous experiments performed by Carstensen and Holzwarth (2023) demonstrated the potential of high-resolution measurements over a large-scale estuarine dune in a laboratory flume. Building on this, experiments will be carried out over a fixed concrete low- and intermediate- angle estuarine bedform field. The results from this study can be used to characterise in detail the flow dynamics over estuarine bedforms.

A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)

A significant and unprecedented increase of temperature has been recorded worldwide during the last decades, leading to the occurrence of numerous extreme events. Coastal areas, with their high population density, interconnected economic activities, fragile ecosystems, are particularly vulnerable to climate change impacts. These impacts can be intensified by the interactions of multiple hazards which operate at different spatio-temporal scales and affect exposure and vulnerability patterns. An integrated approach is here proposed to assess the relationship between risk factors and to evaluate the multiplicity of impacts that may affect the coastline. A new path to tackle these multi-risk events is offered by ML algorithms to effectively handle vast amounts of heterogenous data, and model complex non-linear relationships between multiple factors and feedback mechanisms. To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a ML approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive during the 2009–2019 timeframe, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The MLP algorithm showed satisfactory performances (weighted-F1-score of 0.94) to estimate the relative importance of input features. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process.

Anomalous dynamics of a passive droplet in active turbulence

Motion of a passive deformable object in an active environment serves as a representative of both in-vivo systems such as intracellular particle motion in Acanthamoeba castellanii, or in-vitro systems such as suspension of beads inside dense swarms of Escherichia coli. Theoretical modeling of such systems is challenging due to the requirement of well resolved hydrodynamics which can explore the spatiotemporal correlations around the suspended passive object in the active fluid. We address this critical lack of understanding using coupled hydrodynamic equations for nematic liquid crystals with finite active stress to model the active bath, and a suspended nematic droplet with zero activity. The droplet undergoes deformation fluctuations and its movement shows periods of “runs” and “stays”. At relatively low interfacial tension, the droplet begins to break and mix with the outer active bath. We establish that the motion of the droplet is influenced by the interplay of spatial correlations of the flow and the size of the droplet. The mean square displacement shows a transition from ballistic to normal diffusion which depends on the droplet size. We discuss this transition in relation to spatiotemporal scales associated with velocity correlations of the active bath and the droplet.

Multi-scale analysis of satellite, reanalysis and muti-source precipitation estimates over the Tibetan Plateau

Due to the important hydrological and meteorological significance, the performance of different kinds of precipitation products for the Tibetan Plateau (TP) has been evaluated in previous studies. However, the performance of the products on a high spatial-temporal scale has not been clearly shown. Based on the Chinese Meteorological Administration (CMA) ground rain gauge observations during 2017–2018 over the TP, this paper analyzes the multi-scale performance of four satellite-based datasets (IMERG_Uncal, IMERG_Cal, GSMaP_MVK, and GSMaP_Gauge), two reanalysis datasets (ERA5 and ERA5-L), and one multi-source merged dataset (CMFD) for different precipitation intensities with a special attention to the characterization of spatiotemporal variability of heavy precipitation. Firstly, this study examines how the quality of precipitation products varies with time scale and precipitation intensity. CMFD generally shows the best quality and has the most stable quality at all temporal scales. Satellite products perform better in summer while reanalysis products show better results in cold seasons. Another significant issue is the impact of gauge-based calibration. GSMaP_Gauge with daily-scale gauge calibration is often considered to outperform other products. However, our study finds that GSMaP_Gauge is problematic at smaller spatiotemporal scales, especially in capturing heavy precipitation. The long-term performance of multi spatiotemporal scale comparison of different precipitation products is the focus of this study, which can help with their application and improvement.

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India.

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms-Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( ) algorithm-to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.