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Sci Data
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Scientific Data
2052-4463
Nature Publishing Group UK London

39251662
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10.1038/s41597-024-03764-5
Data Descriptor
Century-scale dataset of bathymetry and shoreline position for Tagus ebb-tidal delta, Portugal
http://orcid.org/0000-0002-9947-6724
Ponte Lira Cristina fclira@fc.ul.pt

12
Taborda Rui 12
1 grid.9983.b 0000 0001 2181 4263 Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016 Portugal
2 https://ror.org/01c27hj86 grid.9983.b 0000 0001 2181 4263 Geology Department, Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016 Portugal
9 9 2024
9 9 2024
2024
11 98313 11 2023
8 8 2024
© The Author(s) 2024
2024
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Understanding long-term coastal changes requires comprehensive records of morphological data spanning extended periods, ideally over multiple decades. Nevertheless, significant challenges arise from the restricted availability and accessibility of reliable data, especially in the case of complex coastal systems dominated by wave-current dynamics, such as ebb-tidal deltas (ETDs). This study introduces a new dataset for Tagus ETD in central Portugal, from 1845 to 1985 based on historic nautical charts available for the area. The data includes: (1) high-resolution bathymetric surfaces of ETD configuration for years 1845, 1879, 1893, 1929, 1939, 1960 and 1985; (2) shoreline position of three different indicators (Low-Water Line, High-Water Line and foredune dune seaward limit) at the adjacent coast for the same years; (3) rates-of-change for shoreline position of the High-Water Line indicator. Data also includes an uncertainty assessment for hydrographical and shoreline position information estimated from historic nautical charts. These data provide a valuable resource for describing, understanding, and modelling the long-term behaviour of ETDs not covered by other currently available datasets.

Subject terms

Geodynamics
Geomorphology
Palaeoceanography
https://doi.org/10.13039/501100011656 University of Lisbon | Instituto Dom Luiz, Universidade de Lisboa (Instituto Dom Luiz) UIDB/50019/2020 - ID UIDB/50019/2020 - ID Ponte Lira Cristina Taborda Rui issue-copyright-statement© Springer Nature Limited 2024
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pmcBackground & Summary

Ebb-tidal deltas (ETDs) are sand bodies that develop on the seaward side of inlets and result from complex interactions between hydrodynamic and morphological processes1. ETDs have a profound impact on coastal-sediment budgets, exerting a substantial influence on the morphodynamics and evolution of adjacent coastlines2–4.

Morphological changes on ETDs can be related to cycles of erosion/accretion5 or to long-term trends6,7 but observational timeframes may hinder the understanding of current stages of change8. Consequently, investigating the morphological evolution of ETDs over time is essential to (1) characterize past changes; (2) understand the observed changes and how they relate with the system present state, and (3) support future behaviour predictions and promote a sustainable management of these coastal environments9,10.

Despite the importance of understanding ETDs evolution at long-term time scales, detailed (single or multi-beam) bathymetric surveys are only available for the recent past, allowing to go back in time only a couple of decades. Moreover, existing data is fragmented and can only render partially or critically incomplete information on the ETDs. The use of historical maritime maps11–13 to reconstruct past bathymetry can provide pictures of coastal state from past decades and even centuries9,10,14–20. Such analysis may support the much-needed view of ETD systems at longer timescales, including their evolution in close relation with the adjacent coastlines and also be useful for modelling future adjustments21.

The Tagus ebb-tidal delta is an example of a complex and environmental vulnerable coastal system22 that has experienced critical changes in the last century. Around the 1950s, the area experienced an intense and rapid reconfiguration. The first striking change is the complete disappearance of a 2 km spit located on the south entrance of the inlet, and consequent coastline retreat of the same order of magnitude23. The second striking change is the migration and subsequent disappearance of Bugio sand island, which at some point had vegetated dunes, and is now reduced to a swash bar. The Bugio island exhibited a pattern where long stabilization periods alternated with sudden reconfigurations until its complete disappearance in the early 21st century24. It remains unclear if this dynamic is natural, anthropogenic or a combination of the two25.

Several studies have characterized the area and evaluated some of the main drivers of change at different spatial and timescales: hydrodynamic conditions26–33, longshore drift34–36, sediment transport and morphological evolution25,35–37. However, none have offered a comprehensive spatiotemporal perspective of the system across extended timeframes (multidecadal), encompassing quantitative estimations of both the ebb-tidal delta’s morphology and shoreline positioning.

Taking advantage of the fact that Tagus outer limit and inlet are well represented in historic cartography25,38, due to its importance as a navigation port, this study compiles an unprecedentedly long and high-resolution historical dataset of Tagus ETD. This dataset, spanning from mid- XIX century to the present, can be used to assess the delta’s morphological configuration at different time-snaps in close relation with the adjacent coastline.

This paper describes the procedures and datasets produced using historic cartography available for the study site since the mid-XIX century (Fig. 1). The dataset is accessible via fishare39. The repository includes the bathymetric surfaces and shorelines extracted from the historic cartography, a shoreline change analysis and the estimated uncertainty associated with each dataset.Fig. 1 Summary of the workflow used to generate bathymetric and shoreline datasets form historical nautical charts.

Methods

Site description

Tagus ETD develops at the seaward end of one of the largest estuaries in Europe and faces the North Atlantic. The morphology of the Tagus ETD is typical of an ebb-tidal delta with tide-dominated characteristics (Fig. 2). Main characteristics are:Two well developed yet highly asymmetric ebb-lobes. A north one, elongated and smaller, called Cachopo Norte, whose south-end incurvate’ towards the main channel (Bico do Pato). A south one, wider and larger, called Cachopo Sul, which includes in some representation’s supratidal domains, of which, Bugio island was the utmost expression24.

A deep main ebb-channel that correspond to the main navigational channel and estuary entrance - called Barra Grande.

Two smaller, marginal and shallower ebb-channels, one more stable at the north (called Barra Pequena) and another at the south (called Golada). Both channels have changed positions over the years. Golada channel, separates the south-lobe from Cova do Vapor/Caparica beaches, and has been recognized as being present in all historic representations of the inlet (Taborda & Andrade, 2017 in40). Most representations indicate that Golada channel can be used by small vessels (shallow draft), but its position and depth changes between different charts.

Fig. 2 Location of the Tagus ebb-tidal delta. Elevation and contour lines are referred to meters Mean Sea Level and represent present morphometric state of the system. Era5 point (38.5°N, 10°W).

The adjacent coastlines exhibit a notable geographic contrast. The north side features primarily rocky shores, with small pocket beaches (the largest spanning about 1.2 km). On the south side, a low-lying sandy coastline is generally backed by dune fields and extends for about 23 km. Near the inlet entrance, the south shore presents a shore-perpendicular set of groins, coupled with a seawall that protects more urbanized beaches areas.

The ETD’s clear asymmetric configuration is shaped by a combination of factors including seafloor, the distinct adjacent shorelines and the incident wave obliquity25, which is largely influenced by the sheltering effect of Cape Raso.

To characterize the offshore wave climate at the study area an hourly hindcast wave data from ERA5 available from Copernicus Climate Change Service (C3S; https://climate.copernicus.eu/) was used41. Wave data were derived for a location west of the study area and beyond the continental shelf (38.5°N, 10°W - Fig. 2), where hourly data for the variables significant wave height of combined wind waves and swell, mean and peak wave period, and mean wave direction were obtained for the interval 1940–2022.

The offshore climate for the time in analysis presents a maximum and mean Hs of 6.0 and 2.2 m; a peak and mean period of 10.8 and 8.7 s, respectively; the mean direction is 316°N. This means that the area develops sheltered from the incident offshore wave regime.

To gain a qualitative understanding on wave sheltering within the ETD, the nearshore climate was evaluated by42 at 2 points near the shore, at the 10 m bathymetric contour line using SWAN and a 56 years wave reanalysis43: one point (TM1) near the northern adjacent coastline and another (CC1) at the southern coastline (Fig. 2). The complete methodology used to characterize the nearshore climate is described in detailed in42. The ETD northern coastline is less energetic and more sheltered from the dominant incident wave regime than the southern coastline, although exposed to SW storms. TM1 point presents a maximum and mean Hs of 4.8 and 0.7 m; a mean peak period of 10 s and mean direction of 209°N. CC1 point is characterized by a maximum and mean Hs of 4.6 and 1.2 m, Tp of 9.6 m and mean direction of 236°N.

Tides are semi-diurnal, ranging from 0.55 to 3.86 m in the open sea (Cascais tidal gauge), but resonance significantly amplifies the semi-diurnal tidal constituents within the estuary27,44. At Cascais, the main harmonic constituents is M2 with amplitudes of ~1 m45. Tagus has a mean tidal prism of ~60 × 107m3 and the river inflow per tidal cycle is 8.2 × 106 m3 45,46.

Datasets

The representation of the Tagus’ River entrance is documented since the 16th century due to the presence of the Lisbon harbour (located inside the Tagus estuary in the North margin) and their approach channels from off-the-coast. Maps and nautical charts describing the location and depth of the main channels and shoals date back to the 17th century but lack sufficient detail and confidence to map the inlet configuration for supporting quantitative morphological studies. Nevertheless, these maps are to able show the main channel(s) and shoals, illustrating that the key ebb-tidal domain features have not changed significantly (Taborda & Andrade, 2017 in40). From the mid-XIX century onwards, cartography in Portugal underwent a modernization of its techniques (e.g. establishment of triangulation network), with new rules and standards created to produce detailed and high-quality hydrographic charts.

In this study, we have used the hydrographic charts available between 1845 and 1985 (Table 2). More recent digital maps (2018–2020) derived from multibeam surveys, made available by COSMO coastal monitoring project at https://cosmo.apambiente.pt/47, were used for technical validation.

Chart georeferencing

The nautical charts were provided in digital format without georeferentiation, so a thorough georeferencing procedure was done to ensure that all were co-registered to the same target dataset (2018 orthoimages and coordinates of geodetic landmarks). The georeferencing procedure consists of the identification of a series of notable points (ground-control points – GCPs) linking the locations of know objects in the raster dataset with locations in the spatial referenced target dataset. In this study the georeferenced procedure used GCPs that corresponded to common elements in both datasets (geodetic landmarks with known positions were used when present and other notable common elements visible on the 2018 orthoimages48). The georeferencing process was based on a spline adjustment method (a piecewise polynomial that maintains continuity and smoothness between adjacent polynomials), accounting for possible sources of uneven distortions of the charts, such as paper deformation, over time.

Exception to this procedure was made for the charts of years 1893 and 1939, which were provided in WMS server format by the Instituto Hidrográfico (IH) institution (https://geomar.hidrografico.pt/) with an expedite geolocation. When comparing the geo-position of these maps with the target dataset used in this study, some inconsistencies were detected. As georeferencing a WMS server dataset is not possible; after the vectorizing information procedure (see next session) the isobath and soundings information were co-registered to the target dataset using a transformation matrix (Rubbersheet method). The transformation matrix was calculated using the misalignment of geodetic landmarks present in the charts, thus representing the chart’s geolocation mismatches.

Vectorizing information

To reconstruct the delta’s geomorphic configuration for each timeframe, different elements representing depth and shoreline position information were used.

The different elements representing depth, isobaths and soundings, were manually vectorized in ArcGIS Pro. The isobaths were digitized as lines and the soundings as vector point files. At each point and line location, the information about the depth was manually added to the vector file attribute table. The information was added using the charts depth units, and later harmonized by providing the necessary conversion into meters and a common vertical datum. The oldest chart datum (1845) presented depth information in fathoms and used the formula conversion of 1 fathom = 1.83 m.

In a similar way, shoreline position was digitized as vector lines using the information presented on the charts, where two lines are clearly represented as shorelines and position of dune fields are represented as round elements (Fig. 3) (see next session for more details).Fig. 3 Example of the information represented on historical maritime charts. (A,B) details of the Hydrographic Plan of the Bar and Port of Lisbon chart – 1878 and 1879 (publicly available at https://purl.pt/16767), where in A the LWL is represented as 0 m and B the legend of the map in Portuguese and (English). (C) The 1879 nautical chart used in this study (available at https://purl.pt/16765/2/) and the interpreted and digitized shoreline elements: green line – foredune foot (BDUN); blue line – High-Water Line (HWL); light-blue line – Low-Water Line (LWL). The 1879 chart also represents the LWL for the 1842/3/5 surveys.

Shoreline position

This study considered the most seaward line symbolized on the charts as representative of the low-water line – LWL; the foreshore line as representative of the high-water line- HWL; and the seaward limit of the dune field as the foredune toe line (BDUN). LWL is clearly identified as chart datum (0 m line – lowest tides) on the Hydrographic Plan of the Bar and Port of Lisbon map – 1878 and 1879 (publicly available at https://purl.pt/16767). This map represents the same shore limits as the 1879 nautical chart used in this study. Combining the information from the two maps, the HWL was interpreted as the landward line represented on the maps, aided also by the representation of dune fields inward of this limit on the 1879 nautical chart. Also, other authors refer that historical nautical chart commonly represent either the Low-Water Line or the High-Water Line as shore limits49,50. Furthermore, current hydrographic survey standards51 also refer that coastline/shoreline must be represented by the high-water mark (or line of mean sea level where no appreciable tide or change in water level exists, which is not the case), so it is with confidence that this study uses the landward limit as indicative of HWL position.

LWL limit z-heights (elevation) for each date were considered the same as chart’s datum values, as this limit represents the lowest-low tide, in the case of the oldest dates, and charts’ zero value from 1929 onwards.

The z-heights for the other shorelines were estimated by assuming a constant beach profile throughout the centuries (profile readjusts in horizontal and vertical position but maintains the same shape)52,53 and whose position is only influenced by sea level changes. The temporal scale between the represented shorelines was considered sufficiently long to absorb any short-term morphological responses to extreme oceanic responses.

Z-heights for the HWL were estimated using the Maximum-High Water value of 2.38 m (MSL) registered between years 2014 and 2023. Mean z-heights derived from beach topographical surveys42 of the study area were used to estimate the BDUN limit position, yielding a value of 4.80 m MSL. Corrections for z-heights values of both HWL and BDUN limits were done accounting for a sea level rise at a rate of 2.5 mm/year54 from the year 1938 onwards, yielding the values represented on Table 1. This information was then manually added to the attribute table of each shoreline vector file. BDUN limits could only be represented for years 1879, 1893, 1929 and 1939, as no shoreline limit for this indicator could be clearly identified on the other maps.Table 1 Estimated z-height for different shorelines indicators.

Date	Shoreline Indicator	
LWL	HWL	BDUN	
m (MSL)	
1845	−2.00	2.18	—	
1879	−2.00	2.18	4.80	
1893	−1.95*	2.08*	4.80	
1929	−2.20	2.18	4.80	
1939	−2.20	2.18	4.80	
1960	−2.20	2.24	—	
1985	−2.00	2.30	—	
*LWL and HWL values clearly stated on the chart; - no shoreline limit clearly symbolized on the chart.

Shoreline change

Rates of change (R) were calculated in ArcGIS environment using the open-source ArcGIS application Digital Shoreline Analysis System (DSAS)55, version 5.0. DSAS application is frequently used for these type of analysis (e.g.56). DSAS uses the shoreline position lines and the associated errors (see Technical Validation section for quantification of this errors) and allows to automatically analyse change and calculate change rates along transects defined by the user and different statistical methods (see the installation and users guide in57). The shoreline change analysis exemplified in this study is based on the HWL, as this indicator is represented in all datasets. Transects were defined every 50 m, roughly perpendicular to the coastline trend. Rate-of-change was computed between each successive dates using the end point rate (EPR) method. EPR is determined by dividing shoreline movement by the elapsed time between the oldest and the newer shoreline57. Results for each pair of years were summarized using statistical measures: maximum (Max(R)), mean (R®) and minimum (Min(R)) rates-of change and corresponding uncertainties. Based on the information of the uncertainty associated with each shoreline position, DSAS automatically calculates the change rate uncertainty (UR) for each transect and statistical measure (see Table 11 and Technical Validation session).

Also, for the entire time frame of available dates (1845–1985) the analysis using the linear-regression rate-of-change (LRR), which is determined by fitting a least-squares regression line to all shoreline points, and the regression line is placed to minimize the sum of the squared residuals57. LRR was also used to calculate rates-of change for the interval of 1845–1939, before the disappearance of Cova do Vapor spit, and for the 1939 – 1985 timeframe, incorporating major changes in the spit dynamics. Supplemental statistics were also computed57: (1) LR2 – coefficient of determination, representing the percentage of variance explained by the regression; (2) LSE – standard error of estimates, which measures the accuracy of the predicted values; (3) LCI95 - standard error of the rate with 95% confidence interval.

Data harmonization

To create a set of comparable bathymetric surfaces, it was essential to ensure that depths were referred to a common datum. As the charts used in the bathymetric reconstructions were referred to different plans of reference (Table 2), a data harmonization procedure process was necessary to ensure consistency. The official mainland Portugal vertical reference system refers to the mean sea level at Cascais until the last day of 1938, and is called Cascais Helmert 38. The fundamental mark for definition of this official altimetric datum is located at the Cascais tidal gauge. The nautical charts mention this mark, whose position is well documented in nautical charts from 1893 onwards. It is, therefore, with confidence that we can re-calculate charts depth information to Mean Sea Level – MSL (Cascais Helmert 38).Table 2 Hydrographic charts with information about the Tagus ebb-tidal delta and adjacent coastlines.

Map Title	Map Scale	Map edition	Years of surveys	Chart information	Source	
Plano Hydrographico da Barra do Porto de Lisboa	1:20000	1857	1842, 1843 and 1845	Soundings in fantoms and referred to the Lowest Low Water plan	BVD	
Plano Hydrographico da Barra do Porto de Lisboa	1:20000	1857	1842, 1843, 1845; the south margin surveyed again in 1879	Soundings in meters and referred to the Lowest Low Water plan	BNP	
Plano Hydrographico da Barra do Porto de Lisboa	1:20000	1893	1842, 1843, 1845; the north margin between Belém and Port Salvo and the south margin surveyed again in September and December 1893	Soundings in meters and referred to the Lowest Low Water plan of 1.95 m	IH	
Plano Hidrografico da barra do Porto de Lisboa	1:25000	1929	1929	Soundings in meters and referred to a plan 2.20 m below MSL. State of navigational channels and Golada referred to November 1929	BPN	
Plano Hidrografico da barra do Porto de Lisboa	1:25000	1939	1929, updated in 1939	Soundings in meters and referred to a plan 2.20 m below MSL State of navigational channels referred to September 1939	IH	
Levantamento Hidrográfico da Zona Circundante do Bugio	1:5000	1974	July and August 1974	Soundings in meters and referred to a plan 2.08 m below MSL. The chart also represents information of isobaths (−5, −10, −15, −20 m) for years 1939, 1954, 1966 and 1982. The chart represented information limited to the Bugio Area.	APL	
Plano Hidrografico da barra do Porto de Lisboa	1:25000	1962	1929, updated in 1960	Soundings in meters and referred to a plan 2.20 m below MSL	C	
Série Costeira Cabo da Roca ao Cabo Espichel	1:75000	1987	1979, 1985 (inlet area)	Soundings in meters and referred to a plan 2.00 m below MSL	C	
The sources of the maps are: C – Copies of Nautical Charts issued by the Instituto Hidrográfico; BVD – Biblioteca Virtual de Defesa, available at https://bibliotecavirtual.defensa.gob.es/BVMDefensa/es/consulta/resultados_ocr.do?id=45940&tipoResultados=BIB&posicion=10&forma=ficha; BNP – Biblioteca Nacional Portuguesa, available at https://purl.pt/16765/2/ (1845) and https://purl.pt/22480 (1879); IH – Instituto Hidrográfico; APL – Port of Lisbon Authority.

Exception is made for the charts of 1845 and 1879 surveys, where the information about chart datum refers to the lowest low waters. Considering a stable sea level between 1845 and 193858, this study used the reference plan of 2.00 m MSL for the 1845 and 1879 charts. This refence plan is also similar to the hydrographic zero (ZH) adopted for Portugal. The hydrographic zero (ZH) is a standardized reference level based on the Lowest Astronomical Tide. In the study area, ZH is 2.00 m below MSL.

Table 3 presents the chart’s datum position in relation to MSL for each surveyed year. Therefore, the harmonization procedure ensured the reduction to the common Cascais Helmert 38 vertical datum using the values stated in Table 3.Table 3 Chart’s datum position in relation to MSL (Cascais 38 – ESPG:5780) for each surveyed year.

Survey dates	Chart’s datum below MSL (m)	
1845	2.00	
1879	2.00	
1893	1.95	
1929	2.20	
1939	2.20	
1960	2.20	
1974*	2.08	
1985	2.00	
*Chart only available for the Bugio area.

Reconstruction of bathymetric surfaces

In the morphological reconstruction process all available elements containing elevation information were used (isobaths, soundings and shorelines), thus enabling the creation of a seamless morphological surface from the nearshore up to the landward end of the upper-beach (shore domain). The reconstruction of the surface was conducted with interpolation procedure of Triangular Irregular Networks (TIN) generation and Natural Neighbours’ interpolator, allowing the direct use of both points and lines information. This procedure also ensured that all generated surfaces were aligned to a common bathymetric grid of 5 × 5 m that covered the entire area of the elements used in the interpolation and the result had the same resolution of this common grid.

After the generation of each bathymetric surface, the Digital Elevation Models (DEMs) were cut using two area shaped polygons (Fig. 4):Common Area - representing the shared overlapped area between the historical surfaces and the ones available for more recent years (COSMO bathymetric surfaces47).

Bugio Area - representing the Bugio area that is common for all surveys, including the historical chart of 1974.

Fig. 4 Areas for bathymetric and validation analysis.

This process ensured that the areas covered by all dates were represented in all surfaces, facilitating further analysis.

Finally, two multidimensional rasters were built in ArcGIS Pro using all available dates for each surface for the Common Area and Bugio Area and exported to NetCDF format. Final bathymetric surfaces were named Bat_c_all for the Common Area and Bat_c_Bugio for the Bugio Area of analysis.

Data Records

The archived datasets presented in this study, spanning from 1845 to 1985, can be accessed at figshare39.

Table 4 documents the repository folder, data format and metadata for the bathymetric surfaces dataset. DEM files representing the bathymetric surfaces are provided at a regular grid of 5 × 5 m and a surface coverage of 123.5 km2 for the Common Area of analysis and 16.3 km2 for the Bugio Area of analysis (cf. Fig. 4). The DEMs for each analysis area are stored in a single NetCDF (.nc) format file, simplifying the analysis format in either GIS and coding environments. DEM surfaces present a blind spot (no data) corresponding to the location of Bugio lighthouse (Fig. 2). Together with the DEM is: (1) a detailed metadata text file (.txt), summarizing the uncertainty associated with it; (2) an image imprint of each bathymetric surface in Portable Network Graphics (.png) format; (3) an example of Python code in Jupyter Notebook (.ipynb) format to read, plot and analyse bathymetric surfaces; read and plot shoreline position and evolution.Table 4 Bathymetric surfaces dataset.

BATHYMETRIC SURFACES	
Folder	Data Files	Format	Time-series	Area	Domain (km2)	
DEM	Bat_c_all.nc	NetCDF	1845–1985	Common Area	69.0	
Bat_c_all.png	PNG	
Bat_c_metadata.txt	TXT	
Bat_c_Bugio.nc	NetCDF	1845–1985 – including year 1974	Bugio Area	10.4	
Bat_c_Bugio.png	PNG	
Bat_c_Bugio_metadata.txt	TXT	
Bathymetry_Analysis.ipynb	Jupyter Notebook	—	—	—	
Horizontal and vertical coordinates are relative to the Portuguese mainland coordinate system ETRS89 TM06 and national elevation datum Cascais 1938, respectively.

Table 5 documents the repository folder, data format and metadata for the shorelines dataset. Three vector files in GeoJSON (.geojson) format representing each indicator of shoreline position (LWL, HWL and BDUN) extracted from the charts are provided. A detailed metadata text file (SL_GeoJSON_Foles_metadata.txt) explains the elements of the attribute tables, including the z-height estimated for the elements (Height_MSL); date of the elements (Date), the indicator used (Shoreline_Ind) and the uncertainty estimated for the elements position (Up). Regarding the analysis of change and evolution:rates of change calculated using EPR method for each analysed transect and pair of shoreline data of type HWL is available in text format (EPR_HWL.txt). The transects used in the analysis are also provided (Transects.geojson).

a table in text format (Statistics_EPR_HWL.txt) is available with the statistical measures of mean (Mean(R)), maximum (Max(R)), minimum (Min(R)) rate of change, number of transects used in the analysis (transects_num) and the respective uncertainty measures (Mean_Unc(UR), Max_Unc(UR) and Min_Unc(UR)) assessed in the technical validation session.

rates of change calculated for all the HWL shorelines (1845–1985 timeframe) using the method LRR is available in text format (LRR_HWL.txt), with supplemental statistics LR2, LSE and LCI95 (see methods session for further detail).

image imprints of EPR plots (e.g. Fig. 5) for all two consecutive dates and LRR plots for 1845 to 1985, 1845 to 1939 and for 1939 to 1985 in Portable Network Graphics (.png) format.

Table 5 Shoreline dataset.

SHORELINES DATASET	
Folder	Data Files	Format	Time-series	Domain	
Shorelines	LWL.geojson	GeoJSON	1845–1985	South margin of the inlet - Caparica area	
HWL.geojson	1845–1985	
BDUN.geojson		
Transects.geojson	1879–1985	
EPR_HWL.txt	TXT	1845–1879	
1879–1893	
1893–1929	
1929–1939	
1939–1960	
1960–1985	
Statistics_EPR_HWL.txt	1845–1985	
LRR_HWL.txt	1845–1939	
LRR_HWL_1845_1939.txt	1845–1939	
LRR_HWL_1939_1985.txt	1939–1985	
SL_GeoJSON_Files_metadata.txt	1939–1985	
EPR_HWL.png	PNG	1845–1879	
1879–1893	
1893–1929	
1929–1939	
1939–1960	
1960–1985	
LRR_HWL_1845_1985.png	1845–1985	
LRR_HWL_1845_1939.png	1845–1939	
LRR_HWL_1939_1985.png	1939–1985	
Horizontal and vertical coordinates of vector files are relative to the Portuguese mainland coordinate system ETRS89 TM06 (EPSG:3760) and national elevation datum Cascais 1938 (EPSG:5780), respectively.

Fig. 5 Example of the long-term shoreline analysis made with the High-Water Line shoreline products. Differences for the rate of change using the Linear Regression Rate method can be seen for four distinct sectors of the southern adjacent shoreline of Tagus ETD. The top sector is inlet-adjacent and located inside the estuary. Further south, the sector between Cova do Vapor and S. João beach is represented - this sector is the one that experienced the most change. From S. João to Costa da Caparica, the sector where a groin field and seawall presently protects the coast is represented. The southern sector represents the beaches south of the groin field, which are less influenced by anthropic action.

Technical Validation

Interpolation of bathymetric surfaces

In this study two different interpolation approaches were tested: (1) Topo to Raster tool (TR), available at ArcGIS Pro and based on the ANUDEM (Australian National University Digital Elevation Model) Hutchinson (1989) model20,59. TR interpolates values while imposing constrains to assure a connected drainage structure and correct representation of ridges and streams from input contour data. TR was also tested with two options of dominant elevation data type of the input feature data: TR-C - The dominant type of input data is elevation contours; and TR-S - The dominant type of input is points.

(2) TIN generation and natural neighbours’ interpolation (T-NN), similarly to the work of17. TIN is a triangulated irregular network, whose edges from contiguous, non-overlapping triangular facets and can be used to capture the position of linear features that play an important role in a surface, such as a ridge line or stream course. To convert a TIN to a raster surface another interpolation method is used, in this case the Natural Neighbours method, that uses an area-based weighting design on the closest TIN nodes found in all directions around each output cell centre.

Both methodologies have the advantage of allowing the usage of both points (soundings) and lines (isobaths) data types, the two types of information available in nautical charts as input data for modelling the interpolated bathymetric surface.

Besides the input data of soundings (points) and isobaths (lines), the interpolation procedure used a common area polygon (Fig. 4) to perform the interpolation to a common raster surface with 5 m resolution, thus ensuring the cell-alignment between different surfaces.

Validation - interpolation approaches

To assess the uncertainty associated with various interpolation methods and the diverse spatial distribution of soundings, several approaches were developed using the high-resolution multibeam bathymetric survey from 2018 (BS_2018) as ground-truth set. The 2018 bathymetric surface is freely available at https://cosmo.apambiente.pt47 and has 0.30 m resolution and 0.015 m precision.

Interpolation methods were tested using a synthetic set of points (500), irregularly spaced between 100 and 200 m, representing the historical surveyed schema. Also, a set of synthetic isobaths at 2, 0, 2, −5, −10, −20 and −30 m, were extracted from the ground-truth surface (Fig. 6), thus mimicking the isobath information represented on the charts. Synthetic points and lines were then used to generate three surface bathymetric models using the chosen TR - C/S and T-NN interpolation methods. Results were compared with the ground-truth data using a DEM of Differences (DoD) approach and the mean, median, standard deviation and R2 measures. Table 6 presents the results of this assessment. Based on the results, T-NN resulted in the lowest standard deviation (0.82 m).Fig. 6 Synthetic set of points and lines derived from the BS_2018 survey and used for the validation of interpolation approaches.

Table 6 Uncertainty assessment of using different interpolation methods: TR-C – Topo to Raster with contours as dominant type of input data; TR-S - Topo to Raster with points as dominant type of input data; T-NN- TIN generation and natural neighbours’ interpolation. BS_20018 – bathymetric survey for year 2018 – ground-truth and IM – Interpolation Method.

Interpolation method (IM)	DEM of Difference (BS_20018 – IM)	
Mean (m)	Median (m)	Standard deviation (m)	R2	
TR-	C	0.0087	0.0040	0.9038	0.9799	
S	0.0280	0.0170	0.9467	0.9785	
T-NN	−0.0333	0.0020	0.8178	0.9848	
Due to similarity between values, statistical measures were represented to the ten thousandths.

Using the T-NN interpolation approach, a test was also conducted on the impact of using different sets of points in the interpolation procedure.

To evaluate the impact of the spatial distribution of soundings on result accuracy, the spatial distribution statistics of raw data points and lines for each chart/date was analysed (see Table 7). Point density and the nearest neighbour ratio (R) were computed to characterize sounding density and assess the spatial distribution nature (clustered: R ≪ 1, random: R ≅ 1, or dispersed: R ≫ 1), as these factors are known to influence interpolation results.Table 7 Information about the spread of raw data points (soundings - S) and lines (isobaths - I) used in the bathymetric surface reconstruction process, and the test set used to evaluate the impact of the spatial distribution of soundings on result accuracy.

Chart	Raw data – Distances between features	Test set – Test Domian (53.8 km2)	
Type	Min (m)	Mean (m)	Max (m)	STD (m)	Count	Bias (m)	STD (m)	R	Count	D Points/ km2	
1845	S	81	234	600	79	434	−0.09	1.33	1.22	356	6.62	
I	26	153	422	146	12	—	
1879	S	1	244	552	79	610	−0.05	0.85	1.15	521	9.68	
I	20	115	613	187	17	—	
1893	S	42	162	443	60	851	−0.01	1.00	1.51	714	13.27	
I	21	48	124	43	5	—	
1929	S	45	187	378	64	1272	−0.02	0.84	1.63	1093	20.32	
I	7	132	1260	305	16	—	
1939	S	62	205	386	60	1099	0.03	0.91	1.66	944	17.55	
l	0	134	1174	313	17	−	
1960	S	62	242	373	50	855	0.03	0.99	1.73	713	13.25	
I	0	91	1054	217	24	—	
1974*	S	16	36	71	10	529	—	
I	0	19	88	19	124	
1985	S	311	673	1410	259	74	−0.10	2.73	1.43	61	1.13	
l	0	87	361	84	31	—	
(-) not analysed.

Depth information for each survey point set was extracted from the BS_2018 surface data. Each new set of points was then interpolated to a bathymetric surface using the T-NN method and compared to the high-resolution BS_2018 surface (within a Test Area domain marginally smaller than the Common Area – 53.8 km2 to exclude boundary issues). Results, summarized in Table 7, show that the bias and standard deviation are generally low (bias consistently below 0.1 m and standard deviation ranging from 0.80 to 1.33 m, with moderate influence from point density. The larger standard deviation, estimated for the 1985 map (2.73 m), is attributed to the very low number of soundings (61). Despite this small sample size, the bias remains low (-0.10 m). Additionally, the typically dispersed nature of the point distribution (R ≫ 1) has minimal impact on the results.

DEM quality assessment

Validating old bathymetric surveys in ETDs can be a challenging, task because these are dynamic environments in constant evolution. A conventional approach involves comparing the seabed morphology in areas with minimal changes, such as rocky bottoms60–62. The test for the quality of estimated bathymetries was conducted on a rocky area located at Tagus main ebb-channel (Fig. 4). This area yielded a difference of 0.015 m between three high-resolution multibeam surveyed bathymetries (from years 2018, 2019 and 202047), confirming the stability of this geomorphological area that is also represented as rocky in all historical charts. All final bathymetric surfaces derived from historical charts were compared with the ground-truth surface of 2018 (DoD), and evaluated regarding accuracy/bias, here reported has mean error, and survey precision, reported as the standard deviation error62. The surface that presents the largest bias is the 1893 bathymetry and the one presenting the best accuracy is the 1879. Regarding surveys’ precision, results are more consistent, with less variation between each interpolated surface, and a mean vertical error of 1.42 m was estimated for all bathymetric surfaces (Table 8). Regarding the estimated mean vertical error, it should be noted that: (1) due to the characteristics of the domain used for quality analysis (located on the main ebb-channel with large ebb-currents and at depths between 19 and 30 m MSL), the estimated error might be exacerbated; (2) the rocky nature of the bottom, with larger roughness and depth variability than the smooth sandy bottom that dominates ETD domain; and (3) the mean vertical error already includes the uncertainties related to the interpolation procedures (0.8178 m - Table 6). Furthermore, Jakobbson et al.63 refer the 1960 Hydrographic Manual indicates that depth measurements should have a maximum error of ±0.4571 m in shallow waters (which includes errors related to tide control measures). The vertical error solely related to survey uncertainty) is ±1.16 m (excluding the interpolation uncertainty of 0. 82 m). Considering the observed morphological changes across ETD domain in and the timeframe in analysis, the error can be considered as acceptable.Table 8 Quality assessment of bathymetric surfaces using a DEM of Differences (DoD) approach.

DoD	Mean (m)	STD (m)	
2018–1845	0.81	1.21	
2018–1879	0.15	1.71	
2018–1893	1.46	1.56	
2018–1929	−0.34	1.26	
2018–1939	−0.26	1.31	
2018–1960	−0.88	1.43	
2018–1985	−0.31	1.48	
Mean (m)	0.09	1.42	
Statistical measures of mean represent accuracy or bias, and Standard Deviation (STD) represents the survey precision.

Shoreline position and change

From shoreline definition to shoreline extraction, various sources of error need to be accounted for to ensure that shoreline change results are valid and significant56,64. Therefore, sources of uncertainty regarding shoreline position for each date were estimated accounting for georeferencing (Er), digitizing (Ed), shoreline position (Ei) and image resolution (Eir) errors of the nautical charts used in the study (Table 10). A complete explanation of this procedure can be found in64.

Er was estimated using a different set of GCPs that the ones used for the georeferenced process (Table 9). This independent set of GCPs was mostly located in the nautical chart periphery area (as preference in the georeferenced procedure was made for common points located near/on the ebb-tidal delta area). As nautical charts tend to give less importance to inland features, the location of this validation set might have exacerbated the Er error. Nonetheless, the mean standard deviation for all surveys (15.06 m) is below the acceptable positional error values for the surveys (1 mm at the survey scale)63.Table 9 Positional error assessment using an independent set of GCPs.

Positional error assessment	Hydrographic chart	
1845	1879	1893	1929	1939	1960	1985	
N° of points	9	6	7	10	5	8	8	
RMSD (m)	28.18	13.61	20.18	22.11	28.88	20.5	35.4	
STD (m)	20.53	7.31	13.77	12.07	16.54	10.38	24.81	

Table 10 Sources of uncertainty used to estimate shoreline position uncertainty (Up_date).

Source of uncertainty	Hydrographic chart	
1845	1879	1893	1929	1939	1960	1985	
Georeferencing (Eg) (m)	28.18	13.61	20.18	22.11	28.88	20.5	35.48	
Digitizing of LWL and HWL limit (Ed) (m)	2.00	2.00	2.00	2.00	2.00	2.00	2.00	
Digitizing of BDUN limit (Ed) (m)	10.00	10.00	10.00	10.00	10.00	10.00	10.00	
Image resolution (Eir) (m)	2.55	1.00	1.75	2.20	2.10	6.50	5.50	
Shoreline indicator (Ei)* (m)	27.33	27.33	27.33	27.33	27.33	27.33	27.33	
*The position uncertainty of indicators HWL and LWL was estimated considering a vertical uncertainty of 0.82 m and a beach slope of 0.03.

Ed was estimated as 2.00 m for the LWL and HWL limits and as 10.00 m for the BDUN limits as the latter was not symbolized on the charts with a line and had to be interpreted from chart symbology. Er error for the BDUN case also incorporates the subjectivity regarding representing the dune seaward limit.

Ei was determined by considering a vertical displacement error of 0.82 m (similar to the estimated value for the interpolation procedure) and a beach slope (tanβ) of 0.0342, which amounts to a total of ~27 m.

The final shoreline position uncertainty (Up_date) for each year was estimated (Table 11) using Eq. 1 and these uncertainty values used in the shoreline change and evolution procedure (see Methods session). The change rate uncertainty (UR) for each transect is calculated using Eq. 2 and the uncertainty of a mean rate (UR®) using Eq. 3.1 Up_date=±Er2+Ed2+Ei2+Eir2

2 UR=Update older2+Update newer2t,where t is the time frame

3 UR®=∑i=1nUR,i2n,where n is the total number of transects(i)

Table 11 Uncertainty of shoreline position (Up_date), change rates (UR) and mean rates (UR®) for each year and pair of HWL shorelines used for change analysis.

Uncertainty	Hydrographic chart	
1845	1879	1893	1929	1939	1960	1985	
Shoreline position uncertainty BDUN (m) (Up date)	—	32.15	35.46	36.62	41.06	—	—	
Shoreline position uncertainty HWL and LWL (m) (Up date)	39.39	30.62	34.08	35.28	39.87	34.84	45.17	
**UR_1845,1879 (m/year)	1.47						
**UR_1879,1893 (m/year)		3.27					
**UR_1893,1929 (m/year)			1.36				
**UR_1929,1939 (m/year)				5.32			
**UR_1939,1960 (m/year)					2.52		
**UR_1960,1985 (m/year)						2.28	
**UR®_1845,1879 (m/year)	0.13						
**UR®_1879,1893 (m/year)		0.27					
**UR®_1893,1929 (m/year)			0.11				
**UR®_1929,1939 (m/year)				0.39			
**UR®_1939,1960 (m/year)					0.18		
**UR®_1960,1985 (m/year)						0.22	
(-) No data available for these dates. **Estimation done using the HWL limit.

Usage Notes

Bathymetric surfaces

DEMs of the bathymetric surfaces provide an unprecedent description of Tagus ETD morphology for the past two centuries. To allow the comparison between different data sources, data were harmonized with other and more recent sources of hydrographic information, making it possible to analyse the newly proposed data together with already freely available datasets.

Different analyses can be made with the dataset to characterize Tagus ETD, including a morphological evolution assessment, surface anomaly calculations10 and sediment budgets approaches10,15,16. Nonetheless, careful must be taken when analysing these estimations, as the uncertainty related to these datasets has to be accounted for65 and is considered high due to the inherit uncertainties. Error estimates provided together with shoreline position are directly usable in uncertainty-based geomorphic change analysis. Furthermore, the dataset can be used to compare Tagus ETD evolution with other similar ETDs.

Shoreline datasets

Shorelines also provide a detailed position of the shoreline for low-lying-sandy southern-adjacent shore; and an already comprehensive assessment of change for the High-Water Line indicator is also given. Shorelines can directly be used for other change analysis together with other complementary datasets, and uncertainty values assure that standard methods of shoreline assessment such as DSAS57 can directly use the lines for the analysis.

The provided shoreline assessment can already provide an insight into the development of the shoreline on the area for the last two centuries, providing rates-of-change between the different morphological snapshot.

Complementary datasets

The datasets of this study can be used together with the topo-hydrographic data from COSMO monitoring programme and available at https://cosmo.apambiente.pt/data. Beware that this study provides bathymetric surfaces referred to the adopted mean sea level (Datum Cascais 1938, EPSG:5780) and bathymetric data from COSMO are referred to Hydrographic Zero (which in open sea is 2 m below the adopted mean sea level).

Acknowledgements

Nautical charts from years 1893 and 1939 were provided in WMS format by Instituto Hidrográfico: https://www.hidrografico.pt/. Bathimetric survey in map format from 1974 of the Bugio area was made available by Porto de Lisboa: https://www.portodelisboa.pt/. Nautical chart from 1879 and 1929 were provided in digital format by Biblioteca Nacional de Portugal: https://www.bnportugal.gov.pt/. Recent datasets used for data harmonization and technical validation were obtained under the Monitoring Programme of the Coastal Strip of Mainland Portugal (COSMO), of the Portuguese Environment Agency, co-financed by the Operational Programme for Sustainability and Efficiency in the Use of Resources (POSEUR). Available at: https://cosmo.apambiente.pt. The research was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national PIDDAC funds - UIDB/50019/2020: 10.54499/UIDB/50019/2020, UIDP/50019/2020: 10.54499/UIDP/50019/2020 and LA/P/0068/2020: 10.54499/LA/P/0068/2020; C.P.L. was funded by DL57/2016/CP1479/CT0079: 10.54499/DL57/2016/CP1479/CT0079.

Author contributions

C.P.L. conceived the public release and publication of Tagus ETD dataset, searched for existing nautical charts from different sources; georeferenced, digitized and processed all data. R.T. contributed with insightful ideas for data harmonization, uncertainty calculations and validation procedures. C.P.L. led the writing of the manuscript with writing and editing contributions from R.T.

Code availability

A Python code Jupyter Notebook example is provided together with the dataset exemplifying: (1) the representation of bathymetric surfaces for each of the estimated years and for the Common Area and Bugio Area of analysis; (2) surface anomaly analysis for each bathymetric surfaces; (3) Shoreline and rate-of-change plots.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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