Time Series Station Research Articles

Interannual changes in nutrient and phytoplankton dynamics in the Eastern Mediterranean Sea (EMS) predict the consequences of climate change; results from the Sdot-Yam Time-series station 2018–2022

Global climate change is predicted to reduce nutrient fluxes into the photic zone, particularly in tropical and subtropical ocean gyres, while the occasional major storms will result in increased nutrient pulses. In this study the nutrient and phytoplankton dynamics have been determined at a new time-series station in the southeastern Levantine basin of the Eastern Mediterranean Sea (EMS) over 4.5 years (2017–2022). In 2018 and 2019, there was a moderate concentration of residual nitrate and nitrite (N + N) in the photic zone (280–410 nM) in winter, resulting in phytoplankton dynamics dominated by cyanobacteria with relatively few picoeukaryotes (280 ± 90 μgC m−2). Winter storm driven mixing was much reduced in 2020 and particularly in 2021, resulting in a lower concentration of N + N in the photic zone, which decreased during summer stratification, such that by August 2021, the N + N was highly depleted (<60 nM) resulting in an integrated phytoplankton biomass of 23 μgC m−2. A major storm in December 2021 (Storm Carmel) injected high N + N (750 nM; max = 1090 nM) in the upper 100 m, which stimulated pico and nanophytoplankton biomass (∼2400 μgC m−2) and according to our inference increased eukaryotes (diatoms). The pattern of measured silica reinforced our conclusion that we sampled 3 different nutrient and ecosystem states. Phosphate was always at or close to limit of detection (LoD) because of rapid uptake by cyanobacteria into their periplasm. These results predict that climate change in the EMS will result in periods of nutrient and phytoplankton depletion (Famine) interrupted by short periods of Mesotrophy (Feast) caused by major storms.

Short-Term Water Demand Forecasting from Univariate Time Series of Water Reservoir Stations

Short-Term Water Demand Forecasting from Univariate Time Series of Water Reservoir Stations

An improved Gaussian process for filling the missing data in GNSS position time series considering the influence of adjacent stations

Due to various unavoidable reasons or gross error elimination, missing data inevitably exist in global navigation satellite system (GNSS) position time series, which may result in many analysis methods not being applicable. Typically, interpolating the missing data is a crucial preprocessing step before analyzing the time series. The conventional methods for filling missing data do not consider the influence of adjacent stations. In this work, an improved Gaussian process (GP) approach is developed to fill the missing data of GNSS time series, in which the time series of adjacent stations are applied to construct impact factors, together with a comparison of the conventional GP and the commonly used cubic spline methods. For the simulation experiments, the root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are adopted to evaluate the performance of the improved GP. The results show that the filled missing data of the improved GP are closer to the true values than those of the conventional GP and cubic spline methods, regardless of the missing percentages ranging from 5 to 30%, with an interval of 5%. Specifically, the mean relative RMSE and MAE improvements for the improved GP with respect to the conventional GP are 21.2%, 21.3% and 8.3% and 12.7%, 16.2% and 11.01% for the North (N), East (E) and Up (U) components, respectively. In the real experiment, eight GNSS stations are analyzed using improved GP, together with conventional GP and a cubic spline. The results indicate that the first three principal components (PCs) of the improved GP can perverse 98.3%, 99.8% and 77.0% of the total variance for the N, E and U components, respectively. This value is obviously higher than those of the conventional GP and cubic spline. Therefore, we can conclude that the improved GP can better fill in the missing data in GNSS position time series than the conventional GP and cubic spline because of the impacts of adjacent stations.

Deep metagenomic sequencing unveils novel SAR202 lineages and their vertical adaptation in the ocean

SAR202 bacteria in the Chloroflexota phylum are abundant and widely distributed in the ocean. Their genome coding capacities indicate their potential roles in degrading complex and recalcitrant organic compounds in the ocean. However, our understanding of their genomic diversity, vertical distribution, and depth-related metabolisms is still limited by the number of assembled SAR202 genomes. In this study, we apply deep metagenomic sequencing (180 Gb per sample) to investigate microbial communities collected from six representative depths at the Bermuda Atlantic Time Series (BATS) station. We obtain 173 SAR202 metagenome-assembled genomes (MAGs). Intriguingly, 154 new species and 104 new genera are found based on these 173 SAR202 genomes. We add 12 new subgroups to the current SAR202 lineages. The vertical distribution of 20 SAR202 subgroups shows their niche partitioning in the euphotic, mesopelagic, and bathypelagic oceans, respectively. Deep-ocean SAR202 bacteria contain more genes and exhibit more metabolic potential for degrading complex organic substrates than those from the euphotic zone. With deep metagenomic sequencing, we uncover many new lineages of SAR202 bacteria and their potential functions which greatly deepen our understanding of their diversity, vertical profile, and contribution to the ocean’s carbon cycling, especially in the deep ocean.

The 2019 Marine Heatwave at Ocean Station Papa: A Multi‐Disciplinary Assessment of Ocean Conditions and Impacts on Marine Ecosystems

AbstractIn the past decade, two large marine heatwaves (MHWs) formed in the northeast Pacific near Ocean Station Papa (OSP), one of the oldest oceanic time series stations. Physical, biogeochemical, and biological parameters observed at OSP from 2013 to 2020 are used to assess ocean response and potential impacts on marine life from the 2019 northeast Pacific MHW. The 2019 MHW reached peak surface and subsurface temperature anomalies in the summertime and had both coastal, impacting fisheries, and offshore consequences that could potentially affect multiple trophic levels in the Gulf of Alaska. In the Gulf of Alaska, the 2019 MHW was preceded by calm and stratified upper ocean conditions, which preconditioned the enhanced surface warming in late spring and early summer. The MHW coincided with lower dissolved inorganic carbon and higher pH of surface waters relative to the 2013–2020 period. A spike in the summertime chlorophyll followed by a decrease in surface macronutrients suggests increased productivity in the well‐lit stratified upper ocean during summer 2019. More blue whale calls were recorded at OSP in 2019 compared to the prior year. This study shows how the utility of long‐term, continuous oceanographic data sets and analysis with an interdisciplinary lens is necessary to understand the potential impact of MHWs on marine ecosystems.

Hydraulic River Models From ICESat‐2 Elevation and Water Surface Slope

AbstractForecasting flood and drought events requires accurate modeling tools. Hydraulic river models are based on estimates of riverbed geometry which are traditionally collected in situ. The novel Ice, Cloud and Land Elevation Satellite 2 [ICESat‐2] lidar altimetry mission with 6 simultaneous high‐resolution laser beams provides the opportunity to define river cross‐section geometries as well as observe water surface elevation [WSE] and water surface slope spatially resolved along the river chainage. This paper describes a method to utilize terrain altimetry and water surface slope estimates to define complete river geometries from ICESat‐2 data products, using the diffusive wave approximation to calculate depth in the submerged section not penetrated by the lidar. Exemplifying the method, cross‐sections are defined for a stretch of the Mekong River. Hydrodynamic model results of the stretch are compared with ICESat‐2 WSE estimates and in situ gauging station time series. Insights in river characteristics from satellite imagery and the ICESat‐2 slope estimates allow for fine‐tuning of the cross‐sections using spatially varying Manning numbers. The final model achieves a root mean square error against the ICESat‐2 WSE of 0.676 m and average Kling‐Gupta Efficiency against gauging station time series of 0.880. The method is limited by the diffusive wave approximation resulting in inaccurate cross‐section estimates in sections with supercritical flow or significant acceleration. Errors can be identified from ICESat‐2 WSE estimates and reduced with additional cross‐sections. Combined with hydrological models, the method will allow for cross‐section definition without in situ data.

Effect of coastal hypoxia on bacterial diversity as elucidated through 16S rRNA amplicon sequencing

The formation of oxygen-depleted zones in the bottom waters is one of the most widespread phenomena in coastal areas. Upwelling episodes along India’s west coast due to the southwest monsoon increase biological productivity, further lowering the dissolved oxygen in the upwelled waters, which intensifies annually between June and October. Here, we have determined the changes in the microbial community in response to the varying oxygen levels and other physicochemical parameters at the Candolim Time Series Station using high-throughput sequencing. Amplicon Sequence Variants across all the samples collected in different seasons were mostly affiliated to the phyla Proteobacteria, Actinobacteria, Bacteroidetes, Verrucomicrobia, Chloroflexi, Firmicutes and Planctomycetes, with the most dominant being Proteobacteria (21-41%). Statistical analysis revealed that microbial diversity differed significantly with changing DO, ammonia, nitrate and nitrite concentrations during different seasons. The microbial community shift due to seasonal hypoxia results in the differential biogeochemical cycling of essential nutrients, with certain years seeing redox conditions up to sulphate reduction while certain years seeing only nitrogen loss. Future global warming scenarios will serve as a big challenge for understanding the role of microbial diversity and its implications in the cycling of natural elements.

Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations

The variation of solid Earth's hydrologic loading could cause the elastic vertical deformation of the crust, and the Global Navigation Satellite System (GNSS) could effectively monitor the vertical displacement of surface loads. However, the widely used Green's function method does not work well in areas where GNSS sites are sparse. Here, the vertical displacement time series of GNSS stations and the Slepian basis function method have been applied to convert displacement signals into spatial spectrum signals. The elastic mass load theory is used to study the changes in terrestrial water storage on the Northeastern Tibetan Plateau (NETP). The temporal and spatial characteristics of seasonal water changes are well-represented by the GNSS, the Gravity Recovery and Climate Experiment (GRACE), and the Global Land Data Assimilation System (GLDAS). Several data points suggest that the change in water storage shows a gradual increase from the northeast to the southwest. The greatest annual amplitude of water storage retrieved by GNSS is ∼159 mm, which is greater than the ∼47 mm and ∼44 mm obtained by GRACE and GLDAS. These results demonstrate that GNSS is capable of capturing small-scale hydrological changes in this region, whereas GRACE and GLDAS data tend to underestimate seasonal variations in water storage. We also used GNSS to describe the hydrological drought conditions in NETP, showing that GNSS could be used as an independent method to characterize hydrological drought events. The findings suggest it could observe water storage with high spatial and temporal resolution and aid in comprehending regional hydrological trends with a sparse GNSS station network.

Remote Sensing-Based Extension of GRDC Discharge Time Series - A Monthly Product with Uncertainty Estimates

The Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km3/month.

Trends and drivers of CO2 parameters, from 2006 to 2021, at a time-series station in the Eastern Tropical Atlantic (6°S, 10°W)

The seawater fugacity of CO2 (fCO2) has been monitored hourly at an instrumented mooring at 6°S, 10°W since 2006. The mooring is located in the South Equatorial Current and is affected by the equatorial Atlantic cold tongue. This site is characterized by large seasonal sea surface temperature variations (&amp;gt;4°C). The fCO2 is measured by a spectrophotometric sensor deployed at about 1.5 meters deep. Measurements of seawater fCO2, sea surface temperature (SST) and sea surface salinity (SSS) are used to calculate total dissolved inorganic carbon (TCO2) and pH. Total alkalinity (TA) is calculated using an empirical relationship with SSS determined for this region. Satellite chlorophyll-a concentrations at 6°S, 10°W are low (&amp;lt;0.2 mg m-3) but some peaks over 0.8 mg m-3 are sometimes detected in August. Nevertheless, the site is a permanent source of CO2 to the atmosphere, averaging 4.7 ± 2.4 mmol m-2d-1 over 2006-2021. Despite the weakening of the wind, the CO2 flux increases significantly by 0.20 ± 0.05 mmol m-2d-1 yr-1. This suggests that the source of CO2 is increasing in this region. This is explained by seawater fCO2 increasing faster than the atmospheric increase during 2006-2021. Most of the seawater fCO2 increase is driven by the increase of TCO2, followed by SST. The fCO2 increase leads to a pH decrease of -0.0030 ± 0.0004 yr-1. The SST anomalies (SSTA) at 6°S, 10°W are correlated to the Tropical Southern Atlantic (TSA) index and to the Atlantic 3 region (ATL3) index with a correlation coefficient higher than 0.75. The strong positive phase of both ATL3 and TSA, observed towards the end of the time-series, is likely contributing to the strong increase of seawater fCO2.

Drivers of Air‐Sea CO2 Flux in the Subantarctic Zone Revealed by Time Series Observations

AbstractThe subantarctic zone is an important region in the Southern Ocean with respect to its influence on air‐sea CO2 exchange and the global ocean carbon cycle. However, understanding of the magnitude and drivers of the flux are still being refined. Using observations from the Southern Ocean Time Series (SOTS) station (∼47°S, 142°E) and auxiliary data, we developed a multiple linear regression model to compute the sea surface partial pressure of CO2 (pCO2) over the past two decades. The mean amplitude of the pCO2 seasonal cycle between 2004 and 2021 was 44 μatm (range 30–54 μatm). Summer minima ranged from 310 to 370 μatm and winter maxima were near equilibrium with the atmosphere. The non‐thermal (i.e., biological processes and mixing) contribution to the seasonal variability in pCO2 was several times larger than the thermal contribution. The SOTS region acted as a net carbon sink at annual time scales, with mean magnitude of 6.0 mmol m−2 d−1. The positive phase of the Southern Annular Mode (SAM) increased ocean carbon uptake primarily through an increase in wind speed at zero time lag. Increased surface pCO2 was correlated with a positive SAM with a lag of 4 months, mainly due to reduced biological uptake and increased mixing. During the autotrophic season, pCO2 was predominantly impacted by primary productivity, whereas water mass movement, inferred by temperature and salinity anomalies, had a larger impact on the heterotrophic season. In general, mesoscale processes such as eddies and frontal movement impact the local biogeochemical features more than the SAM.

Optical properties and molecular differences in dissolved organic matter at the Bermuda Atlantic and Hawai'i ALOHA time-series stations

Fundamental differences in DOM composition at the Bermuda Atlantic time series (BATS) station versus the Hawai’i time series station ALOHA.

Seasonal changes of community structure and vertical distribution/migration for mesopelagic fish in the western subarctic Pacific

Abstract Although mesopelagic fish dominate the oceanic ecosystem, they remain one of the least investigated components. From the vertically stratified sample collections, we investigated community structure, vertical distribution and diel/ontogenetic vertical migration covering four seasons for larval and juvenile/adult mesopelagic fish at the time-series station K2 in the Western Subarctic Gyre of the North Pacific. We collected 10 and 20 species of larval and juvenile/adult fish, respectively. Among the larval fish community, Leuroglossus schmidti was the most abundant; total abundances except L. schmidti were low throughout the year; species richness of myctophids were low. Among the 20 species of juvenile/adult fish, larvae of only six species were collected; thus, most mesopelagic fish species do not use the Western Subarctic Gyre as their nursery. In the juvenile/adult fish community, Stenobrachius leucopsarus and Stenobrachius nannochir were abundant. Species diversity and total abundance in the warm seasons (summer and autumn) were higher than in the cold seasons (winter and spring). The decreasing of species diversity and total abundance during the cold seasons was probably affected by horizontal migrations of fish for reproduction toward the southern transition or subarctic slope areas. Stenobrachius leucopsarus was distributed at shallow depths with opportunistic diel vertical migration (DVM); in contrast Stenobrachius nannochir occurred at greater depths, without DVM. The distribution depths of S. leucopsarus during day and those of S. nannochir changed seasonally and synchronously; shallowest in autumn, deepest in spring.

Understanding seasonal variability of mesozooplankton biomass in the upwelling system of central-southern Chile: A modelling approach

Understanding the mechanisms that influence variability in zooplankton biomass in eastern boundary upwelling systems (EBUS) is a critical step in estimating secondary production in the ocean, particularly in the context of biogeochemical modelling and assessing ecosystem productivity. In this study, we utilised a physical-biogeochemical hindcast simulation (2002–2008) to investigate the sources of seasonal and interannual variability of mesozooplankton biomass within the upwelling zone of central-southern Chile. Monthly observations were obtained from an oceanographic time series conducted at Station 18 (36°30′S) off the coast of Concepción, Chile. The first step was to assess the coherence between model outputs and field observations of physical and biogeochemical variables, including the biomass of mesozooplankton. The factors accounting for the variation in biomass at Station 18 were then assessed. In the model, advective flows along the shore and across the shelf control the majority of biomass. The advection effect appears to be more pronounced during the upwelling season (austral spring-summer) than in winter, although it still significantly contributes to biomass variation during the latter. Our physical-biogeochemical model ROMS-PISCES applied on this EBUS showed that alongshore and cross-shelf advection play a crucial role in driving spatial and temporal variability in zooplankton biomass within the upwelling zone. Therefore, it is essential to consider these physical processes when interpreting observational data from fixed time series stations.

Determination of horizontal deformation of the Earth`s crust on the territory of Ukraine based on GNSS measurements

The purpose of research is to identify horizontal deformation of the Ukraine territory, using only proven and suitable for geodynamic interpretation GNSS stations. The initial data are observations from 30 GNSS stations for 2017 to 2020. Methodology. The methodology includes the analysis of modern Earth's crust deformations of Ukraine. As a result, for the first time the impact of the coordinates time series created by two different methods: Precise Point Positioning (PPP) and the classical differential method, on determining deformation processes was analysed. It was established that nowadays for the tasks of monitoring, including geodynamic, it is necessary to use the Precise Point Positioning (PPP) method, because the accuracy of determined velocities of the GNSS stations by this method was higher than in the classical differential method. Results. A map of horizontal Earth's crust deformations on the territory of Ukraine was created according to the coordinates time series of GNSS stations. The extension areas of Shepetivka-Starokostiantyniv Khmelnytsky region, Boryspil- Pryluky-Pereyaslav-Khmelnitsky Kyiv and Chernihiv region, as well as a compression area of the Earth's crust in Nizhyn - Stepovi Khutory - Kozelets of Chernihiv region was identified. Additionally, a map of horizontal displacements of the GNSS-stations was created, where the diverse of these displacements was observed, which is likely to be caused by the presence of modern subvertical and sub-horizontal faults and fault areas. For better interpretation of the obtained results, it is necessary to involve geological and geophysical data of tectonic activity of the Ukraine territory.

Linking extreme seasonality and gene expression in Arctic marine protists

At high latitudes, strong seasonal differences in light availability affect marine organisms and regulate the timing of ecosystem processes. Marine protists are key players in Arctic aquatic ecosystems, yet little is known about their ecological roles over yearly cycles. This is especially true for the dark polar night period, which up until recently was assumed to be devoid of biological activity. A 12 million transcripts catalogue was built from 0.45 to 10 μm protist assemblages sampled over 13 months in a time series station in an Arctic fjord in Svalbard. Community gene expression was correlated with seasonality, with light as the main driving factor. Transcript diversity and evenness were higher during polar night compared to polar day. Light-dependent functions had higher relative expression during polar day, except phototransduction. 64% of the most expressed genes could not be functionally annotated, yet up to 78% were identified in Arctic samples from Tara Oceans, suggesting that Arctic marine assemblages are distinct from those from other oceans. Our study increases understanding of the links between extreme seasonality and biological processes in pico- and nanoplanktonic protists. Our results set the ground for future monitoring studies investigating the seasonal impact of climate change on the communities of microbial eukaryotes in the High Arctic.

A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation.

Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system.

Wind Resource Assessment over the Hellenic Seas Using Dynamical Downscaling Techniques and Meteorological Station Observations

The current work focuses on establishing the parameters that influence the wind’s behavior over the Aegean and Ionian Seas and estimating the wind potential in the region based on long-term historic climate data. Combining a downscaling technique performed with the well-founded WRF-ARW computational algorithm and a number of simultaneous meteorological station time series, an attempt is made to investigate how regional changes may affect low-altitude wind speed distribution at hub height (100 m a.s.l.). The provided time-series coastal data span the entire region of interest from north to south. WRF-ARW v.3.9 is utilized to associate the geostrophic wind distribution obtained from long-term Copernicus ERA5 wind data with the localized wind potential over lower altitudes. Evaluation and correlation of the observational data to the predicted wind climate are performed, and the statistical differences that arise are investigated. High-accuracy wind resource potential maps are thus obtained in the region. Also, a few distinctive flow patterns are identified, such as wind speed cut-off regions and very high wind speed distributions, which are presented in specific southern regions of the Aegean Sea.

The Seasonal Variations Analysis of Permanent GNSS Station Time Series in the Central-East of Europe

Observations from permanent GNSS stations are actively used for the research and monitoring of geodynamic processes. Today, with the use of modern scientific programs and IGS products, it is possible to determine GNSS station coordinates and velocities at the level of a few millimeters. However, the scientific community constantly faces the question of increasing the accuracy of coordinate definitions to obtain more reliable data in the study of geodynamic phenomena. One of the main sources of errors is systematic measurement errors. To date, the procedure for their removal is still incomplete and imperfect. Also, during the processing of long-term GNSS measurements, it was found that the coordinate time series, after the removal of trend effects, are also characterized by seasonal variations, mainly of annual and semi-annual periods. We estimated the daily coordinate time series of 10 permanent GNSS stations in the central-eastern part of Europe from 2001 to 2019 and calculated the seasonal variation coefficients for these stations. The average value of the coefficients for the annual cycle for the N, E, and H components is −0.7, −0.2, and −0.7 mm, and for the semi-annual cycle the average value is 0.3, 0.4, and −0.5 mm. The obtained coefficients are less than 1 mm, which is why it can be argued that there is no seasonal component in the coordinate time series or that it is so small that it is a problematic task to calculate it. This practical absence of a seasonal component in long-term time series of GNSS coordinates, in our opinion, is partly compensated by the use of modern models of mapping functions (such as VMF3) for zenith tropospheric delays instead of the empirical GMF. To test the obtained results, we calculated the coefficients of seasonal variations for the sub-network of GNSS stations included in the category of the best EPN stations—C0 and C1. The values of the coefficients for the stations of this network are also less than 1 mm, which confirms the previous statement about the absence of a seasonal component in the long-term time series of coordinates. We also checked the presence of seasonal changes in the time series using the well-known decomposition procedure, which showed that the seasonal component is not observed because the content does not exceed 10% for additive decomposition and 20% for multiplicative decomposition.

A MATLAB toolbox for computation of velocity and strain rate field from GNSS coordinate time series

We propose a MATLAB toolbox for the computation of the strain rate field from the coordinate time series of some continuous GNSS stations. It consists of several functions, also compatible with GNU Octave, implementing the following steps: (i) time series download from a data repository (e.g., the Nevada Geodetic Laboratory database); (ii) calculation of velocities of the selected stations by means of the Maximum Likelihood Estimation (MLE) method implemented in the external package Hector, including modeling of offsets, outliers, noise and periodic components; (iii) (optional) filtering of Common Mode Errors; (iv) calculation of the strain rate field with the modified least squares method, in which a scale factor can be introduced to define the locality of the deformation analysis and, besides uncertainty estimation, a geometric evaluation of the significance of the results is provided; (v) visualization of the results for immediate use and easy interpretation for scientific purposes. The toolbox is divided into two components: the first one, named StaVel, performs the steps (i)-(iii) and the second component, GridStrain, performs the steps (iv) and (v). The potential of the toolbox is demonstrated on a real dataset. Time series from several continuous GNSS stations in South-Eastern Sicily (Southern Italy) are processed by means of StaVel and GridStrain in order to provide the strain rate field.