Depth Time Series Research Articles

Coral reef aerosol emissions in response to irradiance stress in the Great Barrier Reef, Australia.

We investigate the correlation between stress-related compounds produced by corals of the Great Barrier Reef (GBR) and local atmospheric properties-an issue that goes to the core of the coral ecosystem's ability to survive climate change. We relate the variability in a satellite decadal time series of fine-mode aerosol optical depth (AOD) to a coral stress metric, formulated as a function of irradiance, water clarity, and tide, at Heron Island in the southern GBR. We found that AOD was correlated with the coral stress metric, and the correlation increased at low wind speeds, when horizontal advection of air masses was low and the production of non-biogenic aerosols was minimal. We posit that coral reefs may be able to protect themselves from irradiance stress during calm weather by affecting the optical properties of the atmosphere and local incident solar radiation.

The efficiency of urban combined sewer systems during wet weather

The intensive development of urban areas results in the sealing of increasingly large areas. In such conditions the existing sewer systems are quite often unable to simultaneously collect sewage along with the additional volume of rainwater. These systems require control of the hydraulic parameters in order to recognize the hydraulic conditions that occur in different operational states. Nowadays, such control may be exercised through the use of models that are capable of prediction as a result of the process of learning from a database of historical events. The study presents the possibilities of using Artificial Neural Networks (ANNs) for the analyses of the time series of waste-water depth and flows in a combined sewer system. The measurement campaign organized in Wrocław (Poland) enabled obtaining data on the hydraulic parameters of the flow of sewage in the sewer systems, and rainfall of various characteristics. The test results demonstrate that algorithms of the MLP (Multi-Layered Perceptron) Artificial Neural Network may be implemented to predict the flow rate in the system. The method presented in the paper may be applied to the daily operation of sewer systems to predict transient flows. The obtained results demonstrate a good and very good accuracy of prediction model.

Analysis of snowpack dynamics during the spring melt season for a sub‐alpine site using point measurements and numerical modeling

AbstractSnowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr−1, an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm−3. Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr−1, snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm−3. Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr−1, stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm−3. Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr−1, a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.

Volcanic stratospheric sulfur injections and aerosol optical depth from 500 BCE to 1900 CE

Abstract. The injection of sulfur into the stratosphere by explosive volcanic eruptions is the cause of significant climate variability. Based on sulfate records from a suite of ice cores from Greenland and Antarctica, the eVolv2k database includes estimates of the magnitudes and approximate source latitudes of major volcanic stratospheric sulfur injection (VSSI) events from 500 BCE to 1900 CE, constituting an update of prior reconstructions and an extension of the record by 1000 years. The database incorporates improvements to the ice core records (in terms of synchronisation and dating) and refinements to the methods used to estimate VSSI from ice core records, and it includes first estimates of the random uncertainties in VSSI values. VSSI estimates for many of the largest eruptions, including Samalas (1257), Tambora (1815), and Laki (1783), are within 10 % of prior estimates. A number of strong events are included in eVolv2k which are largely underestimated or not included in earlier VSSI reconstructions, including events in 540, 574, 682, and 1108 CE. The long-term annual mean VSSI from major volcanic eruptions is estimated to be ∼ 0.5 Tg [S] yr−1, ∼ 50 % greater than a prior reconstruction due to the identification of more events and an increase in the magnitude of many intermediate events. A long-term latitudinally and monthly resolved stratospheric aerosol optical depth (SAOD) time series is reconstructed from the eVolv2k VSSI estimates, and the resulting global mean SAOD is found to be similar (within 33 %) to a prior reconstruction for most of the largest eruptions. The long-term (500 BCE–1900 CE) average global mean SAOD estimated from the eVolv2k VSSI estimates including a constant background injection of stratospheric sulfur is ∼ 0.014, 30 % greater than a prior reconstruction. These new long-term reconstructions of past VSSI and SAOD variability give context to recent volcanic forcing, suggesting that the 20th century was a period of somewhat weaker than average volcanic forcing, with current best estimates of 20th century mean VSSI and SAOD values being 25 and 14 % less, respectively, than the mean of the 500 BCE to 1900 CE period. The reconstructed VSSI and SAOD data are available at https://doi.org/10.1594/WDCC/eVolv2k_v2.

The Aerosol Index and Land Cover Class Based Atmospheric Correction Aerosol Optical Depth Time Series 1982–2014 for the SMAC Algorithm

Atmospheric effects, especially aerosols, are a significant source of uncertainty for optical remote sensing of surface parameters, such as albedo. Also to achieve a homogeneous surface albedo time series, the atmospheric correction has to be homogeneous. However, a global homogeneous aerosol optical depth (AOD) time series covering several decades did not previously exist. Therefore, we have constructed an AOD time series 1982–2014 using aerosol index (AI) data from the satellite measurements of the Total Ozone Mapping Spectrometer (TOMS) and the Ozone Monitoring Instrument (OMI), together with the Solar zenith angle and land use classification data. It is used as input for the Simplified Method for Atmospheric Correction (SMAC) algorithm when processing the surface albedo time series CLARA-A2 SAL (the Surface ALbedo from the Satellite Application Facility on Climate Monitoring project cLoud, Albedo and RAdiation data record, the second release). The surface reflectance simulations using the SMAC algorithm for different sets of satellite-based AOD data show that the aerosol-effect correction using the constructed TOMS/OMI based AOD data is comparable to using other satellite-based AOD data available for a shorter time range. Moreover, using the constructed TOMS/OMI based AOD as input for the atmospheric correction typically produces surface reflectance [-20]values closer to those obtained using in situ AOD values than when using other satellite-based AOD data.

Seasonal Forecasting of Rainfall and Runoff Volumes in Riyadh Region, KSA

Analyses of Total Quarterly Rainfall (TQR) depth time series (1964–2014) at four gauges surrounding one potential rainwater harvesting site in this region show strong temporal and spatial variability. Using Box-Jenkins Methodology, three best-fit seasonal ARIMA models for 10-year predictions of TQR were selected; among more than 20 models, based on criteria of minimum values of variance, AIC and SBC. Comparisons between the forecasted and the actual TQR show good agreement. Data analyses show that spring records have the heaviest rainfall. Accuracy assessment results of the models indicate that for the most favorable model 58% of the residuals are within ± 10 mm, and 74% are within ± 20 mm. Making use of these results, it was possible to obtain seasonal predictions of runoff volumes for water harvesting at the potential site. The results indicate that the largest runoff volumes are expected to occur during the springs of 2016, 2019 and 2022 with an estimate of approximately 3.764 Mm3. Such results are useful for the design and maintenance of the storage facilities of the harvested water.

Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns

BackgroundMovement pattern variations are reflective of behavioural switches, likely associated with different life history traits in response to the animals’ abiotic and biotic environment. Detecting these can provide rich information on the underlying processes driving animal movement patterns. However, extracting these signals from movement time series, requires tools that objectively extract, describe and quantify these behaviours. The inference of behavioural modes from movement patterns has been mainly addressed through hidden Markov models. Until now, the metrics implemented in these models did not allow to characterize cyclic patterns directly from the raw time series. To address these challenges, we developed an approach to i) extract new metrics of cyclic behaviours and activity levels from a time-frequency analysis of movement time series, ii) implement the spectral signatures of these cyclic patterns and activity levels into a HMM framework to identify and classify latent behavioural states.ResultsTo illustrate our approach, we applied it to 40 high-resolution European sea bass depth time series. Our results showed that the fish had different activity regimes, which were also associated (or not) with the spectral signature of different environmental cycles. Tidal rhythms were observed when animals tended to be less active and dived shallower. Conversely, animals exhibited a diurnal behaviour when more active and deeper in the water column. The different behaviours were well defined and occurred at similar periods throughout the annual cycle amongst individuals, suggesting these behaviours are likely related to seasonal functional behaviours (e.g. feeding, migrating and spawning).ConclusionsThe innovative aspects of our method lie within the combined use of powerful, but generic, mathematical tools (spectral analysis and hidden Markov Models) to extract complex behaviours from 1-D movement time series. It is fully automated which makes it suitable for analyzing large datasets. HMMs also offer the flexibility to include any additional variable in the segmentation process (e.g. environmental features, location coordinates). Thus, our method could be widely applied in the bio-logging community and contribute to prime issues in movement ecology (e.g. habitat requirements and selection, site fidelity and dispersal) that are crucial to inform mitigation, management and conservation strategies.

Variability in snow depth time series in the Adige catchment

Study regionThe Upper and Middle Adige catchment, Trentino-South Tyrol, Italy. Study focusWe provide evidence of changes in mean seasonal snow depth and snow cover duration in the region occurred in the period from 1980 to 2009. New hydrological insights for the regionStations located above and below 1650 m a.s.l. show different dynamics, with the latter experiencing in the last decades a larger reduction of average snow depth and snow cover duration, than the former. Wavelet analyses show that snow dynamics change with elevation and correlate differently with climatic indices at multiple temporal scales. We also observe that starting from the late 1980s snow cover duration and mean seasonal snow depth are below the average in the study area. We also identify an elevation dependent correlation with the temperature. Moreover, correlation with the Mediterranean Oscillation Index and with the North Atlantic Oscillation Index is identified.

Statistical models for short- and long-term forecasts of snow depth

ABSTRACTForecasting of future snow depths is useful for many applications like road safety, winter sport activities, avalanche risk assessment and hydrology. Motivated by the lack of statistical forecasts models for snow depth, in this paper we present a set of models to fill this gap. First, we present a model to do short-term forecasts when we assume that reliable weather forecasts of air temperature and precipitation are available. The covariates are included nonlinearly into the model following basic physical principles of snowfall, snow aging and melting. Due to the large set of observations with snow depth equal to zero, we use a zero-inflated gamma regression model, which is commonly used to similar applications like precipitation. We also do long-term forecasts of snow depth and much further than traditional weather forecasts for temperature and precipitation. The long-term forecasts are based on fitting models to historic time series of precipitation, temperature and snow depth. We fit the models to data from six locations in Norway with different climatic and vegetation properties. Forecasting five days into the future, the results showed that, given reliable weather forecasts of temperature and precipitation, the forecast errors in absolute value was between 3 and 7 cm for different locations in Norway. Forecasting three weeks into the future, the forecast errors were between 7 and 16 cm.

Statistical Comparison and Combination of GPS, GLONASS, and Multi-GNSS Multipath Reflectometry Applied to Snow Depth Retrieval

Global navigation satellite system (GNSS) multipath reflectometry (MR) has emerged as a new technique that uses signals of opportunity broadcast by GNSS satellites and tracked by ground-based receivers to retrieve environmental variables such as snow depth. The technique is based on the simultaneous reception of direct or line-of-sight (LOS) transmissions and corresponding coherent surface reflections (non-LOS). Until recently, snow depth retrieval algorithms only used legacy and modernized GPS signals. Using multiple GNSS constellations for reflectometry would improve GNSS-MR applications by providing more observations from more satellites and independent signals (carrier frequencies and code modulations). We assess GPS and GLONASS for combined multi-GNSS-MR using simulations as well as field measurements. Synthetic observations for different signals indicated a lack of detectable interfrequency and intercode biases in GNSS-MR snow depth retrievals. Received signals from a GNSS station continuously operating in France for a two-winter period are used for experimental snow depth retrieval. We perform an internal validation of various GNSS signals against the proven GPS-L2-C signal, which was validated externally against in situ snow depth in previous studies. GLONASS observations required a more complex handling to account for topography because of its particular ground track repeatability. Signal intercomparison show an average correlation of 0.922 between different GPS snow depths and GPS-L2-CL, while GLONASS snow depth retrievals have an average correlation that exceeds 0.981. In terms of precision and accuracy, legacy GPS signals are worse, while GLONASS signals and modernized GPS signals are of comparable quality. Finally, we show how an optimal multi-GNSS combined daily snow depth time series can be formed employing variance factors with a ~59%–90% precision improvement compared to individual signal snow depth retrievals, resulting in snow depth retrieval with uncertainty of 1.3 cm. The developed combination strategy can also be applied for the European Galileo and the Chines BeiDou navigation systems.

Reconstruction of historical aerosol optical depth time series over Romania during summertime

ABSTRACTGround‐based meteorological observations are used here as proxy data, as well as aerosol reanalysis estimates for training, to reconstruct aerosol optical depth (AOD) time series over Romania during a multidecadal period (1961–2015). The approach uses sunshine duration fraction (SDF) data computed for cloudless summer days (identified from 6‐hourly cloud observations) and AOD data from the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) reanalysis. MERRA‐2 AOD data were validated first by comparing them to high‐quality AErosol RObotic NETwork (AERONET) ground observations at eight stations located in Central and Eastern Europe. The comparison of MERRA‐2 AOD data with AERONET ground observations showed that the reanalysis estimates are reasonably accurate, but tend to underestimate AOD in the region of interest. Spaceborne AOD observations from Moderate Resolution Imaging Spectroradiometer (MODIS) were also considered, but were found too biased and not complete enough for this task.A significant linear relationship between MERRA‐2's AOD and SDF data at the 57 of the 86 available stations was found, allowing the use of both a linear and bivariate regression models to back‐calculate the historical AOD time series from SDF observations. This reconstruction is restricted to clear summer days here because of the limited temporal resolution of the available cloud observations. The long‐term trends of the computed AOD time series correctly delineate the expected dimming and brightening periods that have been widely reported over Europe. The proposed method is suitable over areas where very long and concomitant time series of daily sunshine and hourly cloud observations are available, but also has limitations that are discussed.

Assessing Vegetation Dynamics Over Mainland Australia With Metop ASCAT

Recently, the slope and curvature estimation of the backscatter–incidence angle relationship within the TU Wien retrieval algorithm has been improved. Where previously only climatologies of the slope and curvature parameters were available, i.e., one value for every day of year, slope and curvature are now calculated for every day. This enables the retrieval of time series of vegetation optical depth ( $\tau _a$ ) from backscatter observations. This study demonstrates the ability to detect interannual variability in vegetation dynamics using $\tau _a$ derived from backscatter provided by the advanced scatterometer on-board Metop-A. $\tau _a$ time series over Australia for the period 2007–2014 are compared to leaf area index (LAI) from SPOT-VEGETATION by calculating the rank correlation coefficient ( $r_s$ ) for original time series and anomalies. High values for $r_s$ are found over bare soil and sparse vegetation in central Australia with median $r_s$ values of 0.78 and 0.58, respectively. Forests and ephemeral lakes and rivers impact the retrieval of $\tau _a$ , and the negative values for $r_s$ are found in these areas. Looking at the annual averages of $\tau _a$ , LAI, and surface soil moisture, significantly high values are found for the anomalously wet years 2010 and 2011. Patterns in the increased $\tau _a$ correspond to regions with increased soil moisture and LAI. Values for $\tau _a$ and LAI are anomalous especially in sparsely vegetated regions, where the flush of grasses increases $\tau _a$ and LAI. Regions with enough precipitation and higher woody vegetation component show a smaller increase in 2010 and 2011. This study demonstrates the skill of $\tau _a$ , and subsequently of scatterometers, to monitor the vegetation dynamics thanks to the multiincidence angle observation capability.

Habitat use, vertical and horizontal behaviour of Atlantic bluefin tuna (Thunnus thynnus) in the Northwestern Mediterranean Sea in relation to oceanographic conditions

We investigated the habitat utilization, vertical and horizontal behaviour of Atlantic bluefin tuna Thunnus thynnus (ABFT) in relation to oceanographic conditions in the northwestern Mediterranean Sea, based on 36 pop-up archival tags and different environmental data sets. Tags were deployed on early mature ABFT (127–255cm) between July and November in 2007-2014, on the shelf area off Marseille, France. The data obtained from these tags provided 1643 daily summaries of ABFT vertical behaviour over 8years of tag deployment. Based on a hierarchical clustering of this data, we could identify four principle daily vertical behaviour types, representing surface (≦10m) and subsurface (10–100m) orientation, moderate (50–200m) and deep (≧200m) diving behaviour. These vertical behaviour types showed seasonal variations with partly opposing trends in their frequencies. Accordingly, ABFT were more surface orientated during summer, while moderate diving behaviour was more common during winter. Depth time series data further revealed inverted day-night patterns for both of these periods. Tagged ABFT frequented the surface waters more regularly during daytime and deeper waters during the night in summer, while the opposite pattern was found in winter. Seasonal changes in the vertical behaviour of ABFT were accompanied by simultaneous changes in environmental conditions (SST, chla, thermal stratification). Accordingly, surface orientation and moderate diving behaviour appeared to be triggered by the thermal stratification of the water column, though less pronounced than previously reported for ABFT in the North Atlantic, probably indicating adaptive vertical behaviour related to the availability of epipelagic food resources (anchovies and sardines). Deep diving behaviour was particularly frequent during months of high biological productivity (February-May), although one recovered tag showed periodic and unusual long spike dives during summer-autumn, in relation to thermal fronts. Regional effects on the vertical behaviour of ABFT were identified through GAMs, with surface orientation being particularly pronounced in the Gulf of Lions, highlighting its suitability for an ongoing annual aerial survey program to estimate ABFT abundance in this region. In addition, increased levels of mesoscale activity/productivity (e.g. related to oceanic fronts) were detected in an area regularly utilized by ABFT, south of the Gulf of Lions, underlining its attractiveness as foraging ground. Kernel densities of geolocation estimates showed a seasonal shift in the horizontal distribution of ABFT from this “high-use” area towards the Gulf of Lions during summer, probably linked to the enhanced availability of epipelagic food resources at this time.

Snow depth from ICESat laser altimetry — A test study in southern Norway

Direct snow depth measurements are sparse, especially in remote areas. In this study, we assess the potential of ICESat laser altimetry for providing snow depths for its operational period 2003–2009 on the example of the Scandinavian Mountains in southern Norway. Snow cover during ICESat campaigns typically results in positive elevation differences (dh) between ICESat GLAH14 elevations and reference elevations from Digital Elevation Models (DEMs). Three DEMs are used: the Norwegian national DEM for the entire study area, and the SRTM DEM and a high-resolution airborne lidar DEM for a spatial subset on the Hardangervidda mountain plateau. To account for uncertainty in elevation data, ICESat samples are grouped into spatial subsets, elevation bands, and over time (e.g. all winter campaigns together). We find that ICESat has the potential to provide regional-scale snow depths for the years 2003–2009 for its winter (March) and late spring (June) campaigns. ICESat-derived snow depth time series for different elevation bands agree well with measured (RMSE 0.47m) and modelled (RMSE 0.61m) snow depths in the study area. Annual differences in snow amounts and the increase of snow depths with elevation and coastal proximity over the study area are correctly reproduced. Uncertainties in reference elevations exceed ICESat elevation uncertainty and good control over errors and biases in reference DEMs turn out essential. Spatially varying vertical offsets between ICESat and the reference DEMs make it necessary to bias-correct March/June snow depths with autumn dh per spatial unit or elevation band. Best results are achieved when samples are summarised per season over the entire observation period. After correction of local DEM biases, the spatial pattern of ICESat 2003–2009 March dh matches spatially distributed modelled snow depths in southern Norway with decimeter-scale accuracy. In the western part of Hardangervidda, ICESat-based March snow depths agree better with measurements (RMSE ≤0.15m for all DEMs) than modelled snow depths do (RMSE 0.61m). In eastern Hardangervidda, the coarse resolution SRTM DEM (RMSE 0.41m) performs better than the 10m Norwegian DEM (RMSE 0.64m) which is based on a less consistent mosaic of elevation data. Using the high-resolution lidar DEM, even single footprints show good agreement (R2 0.59, RMSE 0.94m) with measured snow depths from the same year. Snow depth estimates could be further improved by using full waveform ICESat data or elevation measurements from ICESat-2 once this satellite is operational. Good quality reference DEMs may still be acquired in the future even in areas where no such data exists today.

Performance of the Standard Normal Homogeneity Test for the homogenization of mean seasonal snow depth time series

ABSTRACTTime series of snow depth bear relevant information for environmental studies in Alpine areas. In addition, due to its sensitivity to air temperature, snow depth dynamic is a robust indicator of climate change effects in mountainous areas. Unfortunately, collecting accurate snow depth data is a difficult task and often time series are obtained by merging data from different sources. Especially in the past, observations were not standardized, such that changes in the operator and in the equipment were sources of inhomogeneities in the time series. To overcome this problem and make the most efficient use of the available information, homogenization techniques may be used. However, a standardized approach for homogenizing snow depth data is currently lacking, despite its importance in climatic and hydrological studies. We evaluate the performance of the Standard Normal Homogeneity Test (SNHT) to homogenize mean seasonal snow depth data collected in the Province of Trento, Northeastern Italy. The proposed algorithm showed good performance in both detecting breakpoints and identifying homogeneous time series. Breakpoints have been detected in about 20% of the analysed time series. A homogeneity analysis on mean seasonal snow depth datasets is hence recommended before performing climatological studies.

Two’s company, three’s a crowd: fine-scale habitat partitioning by depth among sympatric species of marine mesopredator

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 561:173-187 (2016) - DOI: https://doi.org/10.3354/meps11937 Two’s company, three’s a crowd: fine-scale habitat partitioning by depth among sympatric species of marine mesopredator Nicolas E. Humphries1,*, Samantha J. Simpson1,2, Victoria J. Wearmouth1, David W. Sims1,2,3 1Marine Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth PL1 2PB, UK 2Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Waterfront Campus, European Way, Southampton SO14 3ZH, UK 3Centre for Biological Sciences, Building 85, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK *Corresponding author: nicmph@mba.ac.uk ABSTRACT: A sympatric assemblage of morphologically similar predators is expected to exhibit fine-scale habitat segregation, or resource partitioning, to reduce the effects of direct competition. This principle has been well studied for predators in terrestrial ecosystems. In the marine environment, the fine-scale spatial segregation of sympatric species of large predators is poorly understood because detailed movement and behavioural data are often not available across multiple species within the same timeframe. The ways in which co-occurring congeneric predators separate spatially is even less well understood. Medium-sized species of skates (genus Raja) co-occur in temperate habitats of the north-east Atlantic Ocean, share similar morphologies and have distributional ranges that overlap significantly in the western English Channel ecosystem. In the present study, detailed depth time series retrieved from 89 electronic data storage tags attached to 4 species of skate were analysed to determine preferred depth ranges. The 4 species were found to segregate spatially into 2 groups, with one group having a significantly shallower core annual depth range than the other. To our knowledge, fine-scale segregation by depth has not been observed previously. Interestingly, the members of each species group appeared complementary, each group comprising species with different dietary preferences and with a larger and smaller body size. An understanding of how core depth ranges differ and how these species utilise vertical habitat could potentially enable geographic ranges around the coast to be predicted, with important implications for how these species interact with fisheries and Marine Protected Areas. KEY WORDS: Segregation · Resources · Marine · Raja · Ray · Skate · Niche Full text in pdf format Supplementary material PreviousNextCite this article as: Humphries NE, Simpson SJ, Wearmouth VJ, Sims DW (2016) Two’s company, three’s a crowd: fine-scale habitat partitioning by depth among sympatric species of marine mesopredator. Mar Ecol Prog Ser 561:173-187. https://doi.org/10.3354/meps11937 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 561. Online publication date: December 15, 2016 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2016 Inter-Research.

Aplicação do modelo Hartt no estudo das oscilações dos níveis freáticos do Sistema Aquífero Bauru (SAB) sob vegetação de cerrado

O monitoramento da relação entre a oferta e demanda de água em regiões vulneráveis e de interesse estratégico, como o Sistema Aquífero Bauru (SAB), é necessário para que sejam equilibrados os interesses ecológicos e econômicos das atividades desenvolvidas nessas áreas. O objetivo desse trabalho é explicar a partir do modelo autorregressivo HARTT (Hydrograph Analysis: Rainfall and Time Trend) as oscilações dos níveis freáticos do Sistema Aquífero Bauru na Estação Ecológica de Santa Bárbara, onde há vegetação nativa (Cerrado) e eventualmente invasões de Pinus, a fim de entender como os níveis freáticos respondem aos diferentes estímulos dos eventos de precipitação e o manejo da vegetação. No modelo HARTT são utilizados como dados de entrada séries de precipitação e de níveis freáticos monitorados em cinco piezômetros. O modelo apresentou similaridades com os dados de entrada, com calibrações alcançando valores de R2 superiores a 80%. O tempo de resposta de todos os poços analisados em relação a precipitação foi de um mês, ou seja, houve um evento de precipitação, e os níveis dos poços aumentaram após essa defasagem temporal. Esses resultados são necessários para gerar o conhecimento técnico para a gestão desse recurso. O uso do modelo de séries temporais permitiu detectar alterações no ciclo hidrológico e qual foi o padrão dessas alterações, decorrentes principalmente da precipitação.

Estimation of Snow Depth Using L1 GPS Signal-to-Noise Ratio Data

Accurate measurements of snowpack properties are needed by scientists to better understand effects of climate variability on water resource availability. Satellite measurements currently assess snow cover rather than snow depth. Many in situ snow sensors/networks lack the necessary spatial and temporal sensitivity needed for such studies. Existing GPS networks are a potential source of new snow data for climate and hydrology studies, but current operational analyses only use signal-to-noise ratio (SNR) data from the new GPS signal centered at 1.2 GHz (L2C). These data are often unavailable in GPS archives. A snow depth algorithm that used the older (less precise) GPS signal centered at 1.5 GHz (L1) would provide longer snow depth time series that are needed by climate scientists. Here, an algorithm is developed to use the L1 SNR data. Snow depth estimates are derived for 23 sites for 5 years. These data are compared with existing snow depth time series derived from the L2C signal. They show an average bias of 1 cm and correlation of 0.95. Some of this disagreement is due to differences in the azimuthal coverage of the two datasets. The L1 snow depth solutions are also compared with in situ measurements, yielding a bias of $- {4}\;\text{cm}$ , comparable to the $- {6}\;\text{cm}$ bias found in a previous study of the L2C retrieval algorithm.

Experimental and numerical study of wave-induced backfilling beneath submarine pipelines

This paper presents results of complementary experimental and numerical studies involving wave-induced backfilling of current-generated scour holes beneath submarine pipelines. The laboratory experiments are conducted in a wave-plus-current flume, utilizing Laser Doppler Anemometry to measure velocities, synchronized flow visualizations using digital image technology, along with live-bed scour and backfilling measurements. Each experiment is based on a two-stage process: (1) initial scour induced by a pure current, followed by: (2) backfilling induced by pure waves (either regular or irregular). The time series of scour depths are closely monitored through video recordings. Systematic analysis of these has resulted in a closed form expression for the backfilling time scale, which is demonstrated to be a full order of magnitude greater than the well-known time scale of scour (with both governed primarily by the Shields parameter). The developed expression is strictly valid for the current-to-wave backfilling scenarios considered, while likely serving as an upper limit for more general wave-induced backfilling circumstances. The experiments are complemented by similar backfilling simulations utilizing a fully-coupled hydrodynamic and morphodynamic CFD model. The numerical simulations demonstrate the ability of the model to predict backfilling towards expected equilibrium scour depths based on the new wave climate, with time scales reasonably in line with experimental expectations.

A new methodology for planning snow drift fences in alpine terrain

The planning of snow fence constructions in alpine terrain is site-specific and lacks published guidelines. A methodology of snow drift analysis for the evaluation of snow drift constructions is presented. The background to the methodology are recent findings of time series of spatial snow depth maps obtained by laser scanning over a period of several years, where snow accumulation patterns over a whole winter season were similar in different years of observation. It could be concluded that the knowledge of spatial snow distribution at annual peak accumulation provides a reliable basis for the planning of drift constructions. It was hypothesized that snow accumulation patterns at a new project site could be assessed using a combination of snow mapping via terrestrial laser scanning and high resolution numerical wind field modelling. Modelling results localized areas of large vertical and horizontal wind speeds, which coincide with extrema of wind stress on the snowpack surface. TLS surface maps provided the spatial snow depth distribution, and erosional and depositional zones were apparent. Insufficiencies in fence functioning at the study site were determined and adaptations to fence constructions using existing recommendations could be applied. The functionality of the fences was significantly improved using the presented methodology, so it is recommended that future construction projects consider a similar site evaluation.