Temporal Variability Research Articles

When to Measure Accessibility? Temporal Segmentation and Aggregation in Location-Based Public Transit Accessibility

Accessibility analyses are important for public transit (PT) planning, as they reveal possible deficits in PT services. Since such accessibility analyses are highly time-dependent, the Modifiable Temporal Unit Problem (MTUP) should be considered. In this context, one approach is to calculate accessibility continuously for an entire day and aggregate it appropriately. However, this approach is complex and computationally intensive and is therefore rarely, if ever, applied. Instead, practitioners and researchers rely on simplified methods without considering temporal effects in detail. This paper bridges this gap by developing a simple yet representative method to account for the temporal variability of PT services. For this purpose, we calculate and compare PT accessibility for different time windows and through different aggregation methods for Germany. The results show that PT analyses between 9–11 a.m. were most representative. Alternatively, the time windows between 7–9 a.m. and between 1–3 p.m. adequately reflected accessibility. The median was suitable for aggregating individual time intervals into a representative value for the PT service throughout a day, while the maximum or mean value distorted the results. For practical planning purposes, we therefore recommend using the 9–11 a.m. time window.

Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona

BackgroundAir pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects. Aim: We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO2, PM2.5, black carbon (BC), and PM2.5-constituents (Fe, Cu, Zn) in Barcelona. MethodsTwo monitoring campaigns were conducted. In the first, NO2 concentrations were measured twice at 984 home addresses and in the second, NO2, PM2.5, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO2 levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots. ResultsModels showed moderate to good performance. LUR exhibited R2LOOCV of 0.62 (NO2), 0.45 (PM2.5), 0.83 (BC), and 0.85 to 0.89 (PM2.5-constituents). DM model comparison showed R2 values of 0.39 (NO2), 0.26 (PM2.5), and 0.65 (BC). HM models had higher R210-CV 0.64 (NO2), 0.66 (PM2.5), 0.86 (BC), and 0.44 to 0.70 (PM2.5-constituents). Validation for NO2 showed R2 values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from −0.38 to 0.92 for long-term exposure, and − 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO2 and BC. ConclusionsOur models varied substantially, with some models performing better in validation samples (NO2 and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.

Temporal changes in streamflow can predict parasitism levels in freshwater prawns better than host traits

Abstract Understanding how changes in the hydrological regime drive parasite loads and dynamics remains a challenging issue in ecological parasitology. Temporal changes in streamflow and rainfall are key hydrological factors that could alter interactions between the parasite and host and affect parasitism levels. However, to investigate the effect of streamflow, rainfall and its mechanisms, it is important to control host traits that can also influence parasitism levels in freshwater systems. Here, we used a cymothoid‐palaemonid prawn model to test the combined effects of streamflow and rainfall accounting for host traits to predict variations in parasite loads in stream ecosystems. We collected the palaemonid prawns monthly for 2 years and measured the variation of rainfall and streamflow (i.e. stream discharge) at the time of prawn sampling. Our best model showed that streamflow can predict parasitism levels in prawns better than host traits. We found a higher prevalence and abundance of parasites in reduced streamflow compared to increased ones. We also found higher ectoparasite loads in females rather than males and occurring in autumn and winter rather than in spring and summer. Our results also showed that ectoparasite loads also suffer an effect of host body size, sex and moult stage, but not host age. Our findings show that reduced streamflow can facilitate host finding that favours parasite transmission in dry seasons and increase parasitism levels in stream systems. Temporal variability in streamflow can have a strong influence on parasite loads and dynamics compared to host traits in stream ecosystems. Identifying how interactions between aquatic invertebrates and parasites respond to variability in the hydrological regime can help to better understand and predict disease outbreaks as habitat reduction and disturbance continue in stream ecosystems.

Flow prediction via adaptive dynamic graph with spatio-temporal correlations

Flow prediction has shown its significance in optimizing and smoothing resource allocation. However, it still faces challenges due to inherent temporal variability and dynamic spatial correlations of flow data. Although many methods have been introduced to solve the problem, they often rely on predefined graphs to capture shared patterns, which is likely to overlook the critical spatiotemporal dependencies that are necessary for mid-term and long-term prediction tasks. To address the issue, a novel flow prediction model, termed Dynamic Graph Multi-Step Prediction via Long Short-Term Memory Network and Adaptive Learning (DMLSA), is proposed. DMLSA employs Node Adaptive Learning (NAL) to learn node-specific patterns without relying on predefined static adjacency matrices. Moreover, DMLSA utilizes Graph Adaptive Generation (GAG) to extract dynamic features from node attributes and automatically infer interdependencies among flow sequences, thus enhancing the ability to predict complex flow dynamics. Additionally, DMLSA integrates multi-head self-attention mechanisms with gated recurrent units, effectively capturing both long-term and short-term node dependencies, therefore allowing for more precise identification of spatiotemporal patterns at the individual node level. Extensive experiments on five datasets demonstrate that DMLSA outperformed existing baseline models in terms of accuracy and stability for multi-step prediction tasks.

Composition, divergence and variability: A comprehensive analysis of fish trait responses to connectivity

Connectivity, a fundamental concept in ecology, refers to the extent to which different habitats or ecosystems are interconnected through the movement of organisms, nutrients, and energy. Lateral hydrological connectivity (LHC) plays an especially critical role in shaping aquatic community organization in river-floodplain systems. However, a comprehensive understanding of various trait responses to LHC remains elusive. We characterized how attributes of fish community traits, specifically composition, divergence, and temporal variability respond to LHC in the Austrian-Hungarian floodplains of the Danube River using environmental DNA (eDNA) metabarcoding. Trait composition was quantified by community-level weighted means (CWM) as the degree of changes in trait responses along the LHC gradient from isolated oxbows to the main river. Divergence was measured using Rao’s quadratic functional diversity index and a null model approach to calculate standardized effect sizes (SES), with larger SES values indicating greater divergence and smaller values indicating convergence. Temporal variability, representing the degree of instability in community traits over time, was calculated using a functional beta diversity measure for multiple communities. Our findings revealed apparent compositional changes for many trait variables, highlighting the significance of LHC in shaping community functional diversity. Divergence patterns indicated that isolated habitats foster trait convergence presumably due to habitat filtering, whereas more connected areas promote trait divergence due to higher species richness and habitat availability. Temporal variability of traits associated with flow preference exhibited a hump-shaped relationship with LHC, suggesting intermediate connectivity zones are hotspots of ecological dynamism. The study suggests that examining composition, divergence, and temporal variability together provides a more complete understanding of trait responses to connectivity, stressing the importance of maintaining diverse connectivity levels in river-floodplain systems for effective conservation and ecosystem management.

Temporal variability of spatial patterns of correlations between summer rainfall and the Oceanic Niño Index in the Pampean region

The Argentinean Pampean region is essential for global food security, known for its extensive production of soybeans, corn, and wheat. The November to January (NDJ) trimester is critical for rainfed summer crops, as precipitation during this period directly affects soil moisture and crop yields. The El Niño-Southern Oscillation (ENSO), particularly its Oceanic Niño Index (ONI), plays a crucial role in influencing precipitation patterns in this region. This study investigates the spatial correlation between the ONI and NDJ precipitation from 1990 to 2021 sing a 20-year sliding window approach. We conducted Pearson correlation and cluster analyses to identify regions with consistent ONI-precipitation relationships. Our findings reveal notable temporal variability, with a general decrease in correlation strength since the window 1995–2014. This decline is likely driven by changes in ENSO dynamics and the influence of other climate variability modes, highlighting the need to consider factors modulating the ONI-precipitation relationship. Notably, the addition of the strong 2015 El Niño, which exhibited inconsistent precipitation behaviour compared to previous strong El Niño events such as 1997, contributed to this weakening. While the 1997 El Niño brought widespread positive rainfall anomalies, the 2015 event was marked by weaker and even negative precipitation anomalies in part of the region. This difference mirrors similar patterns reported in other parts of South America, such as Ecuador and Coastal Peru, and may be linked to a poleward shift in the jet streams during the 2015/2016 El Niño. The results underscore the complexity of ENSO's impact on regional climate and highlight the need for adaptive agricultural planning. By enhancing the understanding of ONI-precipitation dynamics, this study aims to improve long-term climate predictions and support sustainable agricultural practices in the Pampean region.

Alternations voxel-wise interhemispheric and intrahemipheric functional connectivity dynamics in internet gaming disorder

BackgroundCurrently, numerous studies have indicated that individuals with internet gaming disorder (IGDs) have aberrant functional connection patterns between multiple brain regions and networks. However, temporal variability in the intra- and interhemispheric dynamic functional connectivity in IGDs remains unknown. MethodsThis study investigated resting-state functional magnetic resonance data from 55 IGDs and 50 demographically matched healthy controls (HCs). Functional connectivity density (FCD) combined with sliding window analysis is employed to calculate the temporal variability of global functional connectivity. The temporal variability of dynamic functional connectivity further quantified utilizing the standard deviations of global, intra-, and interhemispheric FCD. Finally, correlation analyses were performed between dynamic FCD varience (dFCD) in differential brain regions and clinical behaviors. ResultIGDs showed decreased intra- and interhemispheric dFCD variance in the visual attention network (precuneus and calcarine) and also demonstrated hemispheric-level dFCD variance abnormalities in the posterior cingulate cortex (PCC) compared to HCs. Moreover, abnormal global dFCD variability of the calcarine and ipsilateral dFCD variability of the PCC were negatively correlated with the severity of IGDs in the IGD group. ConclusionOur results demonstrate abberant intra- and interhemispheric dynamic functional connectivity in the visual attention network, which emphasizes the neurobiological basis for impaired concentration in IGDs.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

A New Cyclo‐Synchrotron Model for Lunar‐Based Observations of Earth's Radiation Belts

AbstractThe energetic electrons trapped in the terrestrial radiation belts spontaneously emit radiation due to magnetic deflection. The observation of this radiation can offer insights into the space and energy distribution of the electrons, as well as their temporal variability. Given the energy range of the electrons in the Earth's radiation belts (0.1 keV to 10 MeV), the emission corresponds to cyclo‐synchrotron radiation, an intermediate radiation regime between the low‐energy cyclotron radiation and the extremely relativistic synchrotron emission. Terrestrial cyclo‐synchrotron radiation cannot be observed from Earth as it is emitted at frequencies below the ionospheric cutoff frequency (f < 10 MHz). However, a lunar near‐side observation could provide real time measurements of the dynamics of the Earth radiation belts. In this study, we present the development of a full cyclo‐synchrotron model for the electron radiation belt emissions, building upon a preliminary synchrotron model. While previous simulations focused on >1 MeV electrons, our new model is able to simulate the radiation of >100 keV electrons which have much larger fluxes than MeV electrons. This new model accurately simulates the cyclo‐synchrotron radiation emitted by Earth's radiation belts as observed from the lunar surface, which would help the design of lunar‐based radio interferometers and would offer a new perspective on radiation belts research and monitoring.

CO2 and CH4 Concentrations in Headwater Wetlands Influenced by Morphology and Changing Hydro-Biogeochemical Conditions

AbstractHeadwater wetlands are important sites for carbon storage and emissions. While local- and landscape-scale factors are known to influence wetland carbon biogeochemistry, the spatial and temporal heterogeneity of these factors limits our predictive understanding of wetland carbon dynamics. To address this issue, we examined relationships between carbon dioxide (CO2) and methane (CH4) concentrations with wetland hydrogeomorphology, water level, and biogeochemical conditions. We sampled water chemistry and dissolved gases (CO2 and CH4) and monitored continuous water level at 20 wetlands and co-located upland wells in the Delmarva Peninsula, Maryland, every 1–3 months for 2 years. We also obtained wetland hydrogeomorphologic metrics at maximum inundation (area, perimeter, and volume). Wetlands in our study were supersaturated with CO2 (mean = 315 μM) and CH4 (mean = 15 μM), highlighting their potential role as carbon sources to the atmosphere. Spatial and temporal variability in CO2 and CH4 concentrations was high, particularly for CH4, and both gases were more spatially variable than temporally. We found that groundwater is a potential source of CO2 in wetlands and CO2 decreases with increased water level. In contrast, CH4 concentrations appear to be related to substrate and nutrient availability and to drying patterns over a longer temporal scale. At the landscape scale, wetlands with higher perimeter:area ratios and wetlands with higher height above the nearest drainage had higher CO2 and CH4 concentrations. Understanding the variability of CO2 and CH4 in wetlands, and how these might change with changing environmental conditions and across different wetland types, is critical to understanding the current and future role of wetlands in the global carbon cycle.

Global Estimates of Particulate Organic Carbon Concentration From the Surface Ocean to the Base of the Mesopelagic

AbstractThe gravitational settling of organic particles from the surface to the deep ocean is an important export pathway and one of the largest components of the ocean carbon pump. The strength and efficiency of the gravitational pump are often measured using metrics reliant on reference depths and empirical formulations that parameterize the relationship between depth and the flux or concentration of particulate organic carbon (POC). Here, BGC‐Argo profiles were used to identify the isolume where POC concentration, [POC], starts to decline, revealing attenuation trends below this isolume that are remarkably consistent across the global ocean. We developed a simple empirical approach that uses observations from the first optical depth to predict [POC] from the surface ocean to the base of the mesopelagic (1,000 m), allowing assessments of spatial and temporal variability in gravitational pump efficiencies. We find that rates of [POC] attenuation are high in areas of high biomass and low in areas of low biomass, supporting the view that bloom events sometimes result in a relatively weak deep biological pump that is characterized by low transfer efficiency to the base of the mesopelagic. Our isolume‐based attenuation model was applied to satellite data to yield the first remote sensing‐based estimate of integrated global POC stock of 3.02 Pg C over the top 1,000 m, with an uncertainty of 0.69 Pg C. Of this total stock, approximately 1.02 Pg was located above the reference isolume where [POC] begins to attenuate.

-temporal variability in fire events due to crop residue burning and their impact on atmospheric variables using ground and remote sensingdata in Punjab

We attempted monitoring and mapping of the active fire events in Punjab, during the wheat and rice harvesting period of 2017-2021 from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m aboard. The analysis showed that the highest fire counts were observed in the central region, followed by the south-west and thelowest in the north-east region of Punjab during all the years. Moreover, during the wheat season, highest fire counts were observed in 2019, being 2535, 11062 and 6212 in north-east, central and south-west regions, respectively. However, during rice, highest fire counts were observed in 2020 in the north-east being 2857 and in 2021 the central and south-west regions being 40960 and 30351 respectively. In line with the number of fire counts, the highest concentration of gases and particulate matter obtained from the Central Pollution Control Board (CPCB) was also observed in the central zone. During the wheat harvesting season, central zone experienced the highest concentration of PM2.5 and PM10 in May 2018 and that of SO2 and O3 in May 2019. Similarly, during the rice harvesting season, central zone also experienced the highest concentration of PM2.5 and PM10 during November 2017 and that of SO2 in 2018. However, the highest concentration of NO2 was observed in October 2018 and that of O3 in October 2020 in the central region. Analysis of the concentration of NO2 and SO2 obtained from the Soumi-NPP satellite also had similar results to CPCB. Such a high concentration of gases and particulate matter might be attributed to crop residue burning a significant positive correlation was observed between fire counts and concentration of particulate matter (PM2.5 and PM10). In view of the alarming deterioration of air quality, there is a dire need to check this practice in the region by providing incentives and viable alternatives to the farmers.

Trends of N2O production during decentralized wastewater treatment: a critical review

Nitrous oxide (N2O) emissions have become a significant concern due to their potential high contribution to the carbon footprint of wastewater treatment plants. While their production pathways in centralized systems have been already deeply investigated, an analysis of N2O production trends from decentralized wastewater treatment plants, with a detailed description of the operating conditions affecting N2O generation in each technology, is missing. In this work, research on N2O emission from several decentralized technologies, including septic tanks and leach fields, soil infiltration systems, waste stabilization ponds, onsite activated sludge systems, Johkasou systems, onsite advanced technologies and constructed wetlands is reviewed. N2O emissions were found to depend on operating parameters, among which aeration regime, light exposure, influent carbon-to-nitrogen ratio, environmental temperature, nitrite concentration, plant species, and pH. It was not always possible to clearly understand the influence of a specific parameter on N2O emissions, given the complexity of the producing pathways, the temporal and spatial N2O variability, and the limited data. Research gaps, such as the lack of monitoring nitrification and denitrification intermediates, and the lack of long-term N2O monitoring campaigns, have also been identified. Considering the widespread diffusion of decentralized wastewater technologies and their need to meet more stringent requirements on sanitation, it will be more and more important in the future to understand their specific contribution to wastewater treatment carbon footprint. Standardized monitoring techniques, established estimation methods for emission factors, and a wider application of mathematical models could help in achieving a greater understanding of the complex N2O generation pathways.

Temporal and spatial variability in availability bias has consequences for marine bird abundance estimates during the non‐breeding season

Abstract To effectively monitor how marine ecosystems are being reshaped by anthropogenic pressures, we require understanding of species abundances and distributions. Due to their socio‐economic and ecological value, predatory species are often at the forefront of survey efforts. However, survey data are only valuable if they can reliably be converted into estimates of underlying distributions. We consider at‐sea surveys of marine predators that often inform ecological impact assessments of offshore windfarms. These surveys are subject to a form of detection bias called ‘availability bias’ whereby individuals which are submerged below the surface are consequently ‘unavailable’ for detection. Although correction factors are commonly used in these surveys, they are currently based on limited data that may not be species‐, time‐, or area‐specific. Here, we use time‐depth‐recorder data to investigate variation in marine bird availability bias. We found that the proportion of diving marine birds submerged below the sea surface during daylight hours, and therefore unavailable to be counted during surveys, varied by species, month, and area. For three of our focal species wintering around northwest Europe (Atlantic puffin, common guillemot, razorbill), our results were different to comparable values previously used to correct for the availability bias, whereas no correction factors are regularly used for the fourth species (red‐throated diver). We now present availability bias correction factors that are species‐ and month‐specific to the areas the study populations use during their non‐breeding seasons: the North Sea, the north and west coasts of the UK, the Baltic Sea, and Icelandic coastal waters. Practical implication: Variation in the proportion of daylight hours that marine birds spent submerged lead to differences in availability bias correction factors, thereby impacting estimations of their abundances. We encourage use of correction factors that use data from the species, marine area, and month during which surveys are conducted to provide more accurate abundance estimates. Using more relevant correction factors will result in increasingly accurate abundance and distribution estimates of diving marine birds, with relevance for a range of applications including planning for offshore windfarm developments, the designation and monitoring of protected areas, and understanding environmental change.

Multi-site comparison and source apportionment of equivalent Black Carbon mass concentrations (eBC) in United States: Southern California Basin and Rochester, New York,

Spatial and temporal variability of equivalent black carbon (eBC) mass concentrations were studied using Aethalometers (AE33 and AE21) at 10 sites, including 5 urban and 5 near-road across the south coast of California and New York-Rochester. Statistical methods were applied to perform intra-urban and multi-site comparisons. Given that nominal eBC values provided by the Aethalometer were significantly overestimated, eBC concentrations were corrected using an appropriate multiple-scattering enhancement correction factor (C0) to accurately calculate light absorption. Annual and seasonal variations highlighted the significant contributions of traffic emissions to eBC mass concentrations at all sites. Source apportionment using the Aethalometer approach demonstrated that fuel combustion—primarily from gasoline and diesel vehicles— accounted for up to 80% of eBC emissions. Emission sources were found to be largely region specific. Our findings suggest that the implementation of restrictive regulations aimed at reducing gasoline and diesel vehicle emissions, such as California's Tier 3 (SULEV, 2015) and New York (2017), has led to a higher proportion of cleaner, emission-controlled vehicles in the South Coast Air Basin compared to Rochester. However, the effects of these measures may require more time to be fully observed in traffic-polluted areas across the US.

Deep-water ambient sound over the Atlantis II seamounts in the Northwest Atlantica).

Ambient sound was continuously recorded for 52 days by three synchronized, single-hydrophone, near-bottom receivers. The receivers were moored at depths of 2573, 2994, and 4443 m on flanks and in a trough between the edifices of the Atlantis II seamounts. The data reveal the power spectra and intermittency of the ambient sound intensity in a 13-octave frequency band from 0.5 to 4000 Hz. Statistical distribution of sound intensity exhibits much heavier tails than in the expected exponential intensity distribution throughout the frequency band of observations. It is established with high statistical significance that the data are incompatible with the common assumption of normally distributed ambient noise in deep water. Spatial variability of the observed ambient sound appears to be controlled by the seafloor properties, bathymetric shadowing, and nonuniform distribution of the noise sources on the sea surface. Temporal variability of ambient sound is dominated by changes in the wind speed and the position of the Gulf Stream relative to the experiment site. Ambient sound intensity increases by 4-10 dB when the Gulf Stream axis is within 25 km from the receivers. The sound intensification is attributed to the effect of the Gulf Stream current on surface wave breaking.

Measurements of temporal variability of acoustic scattering from the seafloor in shallow-water sandy sites.

In the ocean, the performance of active sonar systems depends on the acoustic properties of the seafloor. Daily to monthly variations in near-bottom hydrodynamics and benthic biological activity may affect seafloor properties which then influence the acoustic response of the seafloor. The dependence of seafloor scatter on evolving environmental parameters was investigated using high-frequency active acoustic systems. Seafloor scattering measurements were analyzed in a series of experiments (two weeks to five months in duration) from downward-looking sonars oriented at 20° grazing angle with respect to the seafloor. Data were obtained in two shallow water locations near Portsmouth, New Hampshire, USA: a wave-dominated site and a site dominated by tidal currents. The bottom type for both sites was gravelly sand. The experimental set-up consisted of a tripod placed on the seafloor equipped with three transducers operating at 38, 70, and 200 kHz, a wave-sensing CTD, and underwater cameras. Scattering strength time series were obtained taking into account the local seafloor slope. Results show that there is variability in scattering strength (both in mean levels and distributions). Large variations often coincided with storm events, suggesting that this variability may be driven by changes in bottom roughness caused by storm-related hydrodynamics.

Models of the Sea Surface Height Expression of the Internal-Wave Continuum

Abstract Several models are presented for the sea surface height (SSH) signature of the interior-ocean internal-wave continuum. Most are based on the Garrett–Munk internal-wave model. One is derived from the frequency spectrum of dynamic height from mooring observations. The different models are all plausibly consistent with accepted dynamical and semiempirical spectral descriptions of the climatological interval-wave field in the interior ocean, but they result in different proportionalities between interior and SSH spectral energy levels. The differences arise in part from differences in the treatment of near-surface stratification, and a major source of uncertainty for all the models comes from inadequately constrained assumptions about the energy in the low-vertical-mode internal-wave field. Most of these models suggest that the SSH signature of the internal-wave continuum will be visible in SSH measurements from the Surface Water and Ocean Topography (SWOT) wide-swath satellite altimeter. Temporal variability of internal-wave energy levels and the internal-wave directional spectrum are less well characterized but will also be consequential for the observability of internal-wave signals in SWOT data.

Ambulatory assessment of suicidal ambivalence: The temporal variability of the wish to live and the wish to die and their relevance in the concurrent and prospective prediction of suicidal desire.

Suicidal ambivalence is increasingly understood as the result of the interplay of two conflicting motivational orientations, the wish to die (WTD) and wish to live (WTL). However, research is scarce regarding the nature of their relationship, their temporal structure, and their relevance in predicting suicidal desire. Fifty psychosomatic/psychiatric inpatients (17 reporting suicide attempts) took part in an intensive longitudinal design with 10 signal-contingent prompts per day over 10 days; assessing WTD, WTL, and suicidal desire. We calculated mean values and variability of WTD, WTL, suicidal ambivalence, and suicidal desire and tested mixed-effects models of suicidal desire. The mean number of answered prompts was 40.98 (SD = 21.68). Repeated-measures correlation of WTD and WTL was r = -0.60. Patients with a history of suicide attempts reported higher WTD, lower WTL, more suicidal ambivalence, stronger suicidal desire, and showed higher variability in all constructs. At the same assessment, WTD was the strongest statistical predictor of suicidal desire. Only WTL and the interaction of WTD and WTL predicted suicidal desire prospectively. Wish to die and WTL are likely two distinct (although correlated) constructs that should not be reduced to a single ambivalence score. A stronger focus on WTL holds potential for suicide research and practice.

Rainfall And Drought Characteristics For Crop Planning In Cuttack District Of Odishastate, India

The increasing frequency of agricultural droughts and variability in rainfall patterns over the past three decades prompted this study, which analyzed 34 years of block-wise daily rainfall data (1988-2021) from Cuttack district, Odisha. The aim was to determine the long-term average and temporal variability of rainfall on weekly, monthly, seasonal, and annual scales, with a focus on identifying strategies to mitigate production risks in the region. The average annual rainfall in Cuttack was 1597.6 mm, with August receiving the highest amount of rainfall (476 mm), followed by July (407 mm) during the monsoon season. A trend analysis on rainfall over the past 34 years showed an increasing pattern of an average 6.03 mm and 3.6 mm per year in annual and kharif season rainfall, respectively. Agricultural droughts were frequently observed during the early (22-28 Standard Meteorological Week) and late (36-42 SMW) stages of kharif crops. The study also revealed that the station is prone to mild-moderate meteorological droughts of different intensities, including mild,moderate, and severe. To minimize production risks, short duration, low water-requiring, high-value crops such as maize, pulses, oilseeds, and some vegetables are recommended for this region.