The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

Dataset

This dataset was created by Amber Song

Released under CC0: Public Domain

Contents

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.

This is the official Live Well San Diego database with the Top 10 Indicators and Expanded Indicators. Baseline data begin in 2012 where data are available and continue through current day. Data are collected on an annual basis.

For definitions and sourcing, please use the Live Well San Diego Data Dictionary: https://data.sandiegocounty.gov/Live-Well-San-Diego/Live-Well-San-Diego-Data-Dictionary/37vr-nftn/about_data

Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services Division, Community Health Statistics Unit.

Baseline data used for the analysis of interobserver reliability.

This project aims to enhance survivability of a multi-mode point absorber. Included in this submission are content models providing a system definition and baseline LCOE calculations.

Abstract

The Hunger Safety Net Programme (HSNP) is a social protection project being conducted in the Arid and Semi-Arid Lands (ASALs) of northern Kenya. The pilot phase has now concluded and the HSNP is beginning to scale up under Phase 2. The ASALs are extremely food-insecure areas highly prone to drought, which have experienced recurrent food crises and food aid responses for decades. The HSNP is intended to reduce dependency on emergency food aid by sustainably strengthening livelihoods through cash transfers.

Oxford Policy Management was responsible for the monitoring and evaluation (M&E) of the programme under the pilot phase, with the intention of informing programme scale-up as well as the government’s social protection strategy more generally. The M&E involved a large-scale rigorous community-randomised controlled impact evaluation household survey, assessment of targeting performance of three alternative targeting mechanisms (Social Pension; Dependency Ratio; Community-based Targeting), qualitative research (interviews and focus group discussions) to assess targeting and impact issues less easily captured in the quantitative survey, and on-going operational and payments monitoring to ensure the smooth implementation of the programme. Findings were communicated to the HSNP Secretariat, Government of Kenya and the Department for International Development (DFID) on a regular basis to inform and advise on policy revisions and development. The M&E component used the data it produced to advise the design of HSNP Phase 2, including micro-simulations of different programme targeting scenarios and review of the phase 2 targeting approach which combines proxy means testing with community-based targeting.

The impact evaluation study compares the situation of HSNP and control households at the time of their selection into the programme (baseline), with their situation 12 months (year 1 follow-up) and 24 months later (year 2 follow-up). Over this 24-month period most of the HSNP households covered by the evaluation had received 11 or 12 bi-monthly transfers (initially KES 2,150, increased to KES 3,500 by the end of the evaluation period).

Fieldwork for the baseline survey was conducted in 48 sub-locations, stratified by greater district (Mandera, Marsabit, Turkana, Wajir), by HSNP status (treatment and control), and by targeting mechanism (Social Pension; Dependency Ratio; Community-based Targeting). The survey covered 5,108 households and 245 communities.

The baseline data collection was completed in November 2010, the first round of follow-up data collection finished in November 2011, and the final round of fieldwork - in November 2012.

Geographic coverage

Counties of Turkana, Marsabit, Wajir, and Mandera.

Analysis unit

• individuals,
• households,
• community.

Universe

All persons living within "secure" sub-locations across all counties at the time of sampling (2008; due to sporadic insecurity across the four counties, a small portion of sub-locations were deemed to be insecure when the sample was drawn and so excluded from the sample frame).

Kind of data

Sample survey data [ssd]

Sampling procedure

Quantitative and qualitative fieldwork for the HSNP M&E baseline survey was conducted in 48 sub-locations, stratified by greater district (Mandera, Marsabit, Turkana, Wajir), by HSNP status (treatment and control), and by targeting mechanism (CBT, DR, SP). The survey covered 5,108 households and 245 communities.

The evaluation sub-locations were selected from a sample frame of all secure sub-locations in each district. In each district 12 sub-locations were selected with PPS (Probability Proportional to Size) with implicit stratification by population density such that there is an even number of selected sub-locations per new district.

The evaluation sub-locations were sorted within districts by population density and paired up, with one of the pair being control and one being treatment. The sampling strategy for the quantitative survey was designed in order to enable a comparison of the relative targeting performance of three different targeting mechanisms. These are:

• Community-based targeting (CBT): The community collectively selected households they consider most in need of transfers, up to a quota of 50% of all households in the community;
• Dependency ratio targeting (DR): Households were selected if individuals under 18 years old, over 55 years old, disabled or chronically ill made up more than a specified proportion of all household members;
• Social pension (SP): All individuals aged 55 or older were selected.

For both the treatment and control sub-locations there are an equal number of CBT, SP and DR sub-locations. Assignment of targeting mechanisms to sub-locations was done randomly across the same pairs that were defined to assign treatment and control status. In all the evaluation sub-locations, the HSNP Admin component implemented the targeting process. In half the sub-locations the selected recipients started receiving the transfer as soon as they were enrolled on the programme - these are referred to as the treatment sub-locations. In the other half of the evaluation sub-locations, the selected recipients were not to receive the transfer for the first two years after enrolment - these are referred to as the control sub-locations.

The households in the treatment sub-locations that are selected for the programme are referred to as the treatment group. These households are beneficiaries of the programme. In control sub-locations the households that are selected for the programme are referred to as the control group. These households are also beneficiaries of the programme but only begin to receive payments two years after registration. The targeting process was identical in the treatment and control sub-locations. The following population groups can thus be identified and sampled: - Group A: Households in the treatment sub-locations selected for inclusion in the programme; - Group B: Households in control sub-locations selected for inclusion in the programme but with delayed payments; - Group C: Households in treatment sub-locations that were not selected for inclusion in the programme; - Group D: Households in control sub-locations that were not selected for inclusion in the programme.

Because targeting was conducted in both treatment and control areas, households were sampled in the same way across treatment and control areas. Selected households (groups A and B) were sampled from HSNP administrative records. Sixty six beneficiary households were sampled using simple random sampling (SRS) in each sub-location (in two of sub-locations this was not possible due to insufficient numbers of beneficiaries in the programme records). In cases of household non-response replacements were randomly drawn from the remaining list of non-sampled households. This process was strictly controlled by the District Team Leaders.

Non-selected households (groups C and D) were sampled from household listings undertaken in a sample of three settlements within each sub-location. These settlements were randomly sampled. The settlement sample was stratified by settlement type, with one settlement of each type being sampled. Settlements were stratified into three different types: 1. Main settlement (the main settlement was defined as the main permanent settlement in the sub-location, often known as the sub-location centre and usually where the sub-location chief was based. As there was always one main settlement by definition, the main settlement was thereby always selected with certainty). 2. Permanent settlements (permanent settlement is defined as a collection of dwellings where at least some households are always resident, and/or there is at least one permanent structure). 3. Non-permanent settlements.

As concern community level data, community questionnaires were conducted in every community for which at least one household interview was attached. A community was defined as a settlement or a sub-section of a settlement if that settlement had been segmented due to its size. Due to missing data, a small proportion of households are not linked to any community data.

The above explanation is taken from "Kenya HSNP Monitoring and Evaluation Component: Impact Evaluation Final Report 2009 to 2012". For more details please refer to this report in Related Materials section.

Mode of data collection

Computer Assisted Personal Interview [capi]

Poster presented at SNL 2018.Baseline correction plays an important role in past and current methodological debates in ERP research (e.g. the Tanner v. Maess debate in Journal of Neuroscience Methods), serving as a potential alternative to strong high-pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, making it statistically unnecessary and undesirable. In particular, the assumption that the electrophysiological activity of the baseline interval does not differ systematically between conditions implies by definition that the baseline interval is essentially a by-channel noisy reference. The noise from the baseline interval is then projected into the target interval, thereby reducing power. Moreover, as a reference, the baseline interval can bias topographies, especially if the no-systematic-difference assumption is violated. This reference nonetheless serves to address non-experimental recording factors (electrode drift, differences in environmental electrical noise), but there are better methods for controlling for theses environmental issues. Instead of assuming a fixed baseline correction, whether trial-by-trial or at the level of single-subject averages, we can instead include the baseline interval as a statistical predictor, similar to other GLM-based deconvolution approaches (e.g. removal of eye-artifacts, Dimigen et al. 2011; rERP, Smith & Kutas 2014). The baseline interval can then interact with, i.e. allow its influence to be weighted by topographical and experimental factors. This controls for topographical biases, addresses electrode drift in block designs, does not require the no-systematic-difference assumption, and allows the data to determine how much baseline correction is actually needed. Additionally, both full traditional baseline correction and no baseline correction are included as special cases. The lack of the no-systematic-difference assumption also allows for this method to be applied more naturalistic settings, which have recently begun to gain ground in M/EEG research (cf. Alday et al. 2017, Broderick et al. 2017, Brodbeck et al. 2018). In addition to this theoretical argument, we show the effectiveness of this method by reanalysis of previous ERP studies on language. We find that the empirically determined baseline correction is often much less than the traditional correction. Using semi-parametric simulations from mixed-effects models fit to these data, we further show that the trade-off in power between additional model complexity and the noisiness of the dependent variable is worth the improved fit to the data.

Baseline data collection.

Landscape conservation, and management of protected areas in particular, needs leadership, knowledge, practical skills, science, innovation, creativity and collaboration.Available onlineCall Number: [EL]Physical Description: 58 p

Abstract

As part of the Adaptive Safety Net Project, the Government of Niger (with support from the World Bank and the Sahel Adaptive Social Protection Program) launched the implementation of productive inclusion measures to foster more productive livelihoods and improve resilience of cash transfer beneficiary households. This dataset covers three rounds of household surveys from the impact evaluation of these productive inclusion measures among cash transfer beneficiary households. It is published along with the related paper: Bossuroy, Thomas; Goldstein, Markus; Karimou, Bassirou; Karlan, Dean; Kazianga, Harounan; Pariente, William; Premand, Patrick; Thomas, Catherine; Udry, Christopher; Vaillant, Julia; Wright, Kelsey. 2022. "Tackling Psychosocial and Capital Constraints Opens Pathways out of Poverty".

Geographic coverage

The study focuses on a sub-sample of communes in all five regions chosen for the second phase of the Niger Adaptive Safety Net project (Dosso, Maradi, Tahoua, Tillabery, and Zinder). 17 communes were selected for the study, covering 322 villages across the 5 regions where cash transfer beneficiaries were eligible to receive complementary productive inclusion measures. In each sample village, approximately 14 households (maximum 15) were interviewed at baseline.

Analysis unit

Households as well as individuals within households.

Universe

Only households that are beneficiaries of the national cash transfer, located in communes and villages mentioned above

Kind of data

Sample survey data [ssd]

Sampling procedure

Cash transfer beneficiary households were chosen by either proxy means testing, community-based targeting, and a formula to proxy temporary food insecurity (as described in Premand and Schnitzer, 2021). 22,507 cash transfer beneficiary households were later assigned to either a control group or 3 productive inclusion treatment arms (Bossuroy et al., 2022). All three treatment arms include a core package of group savings promotion, coaching, and entrepreneurship training, in addition to the regular cash transfers from the national program. The first variant also includes a lump-sum cash grant (“capital” package). The second variant substitutes the cash grant with psychosocial interventions (“psychosocial” package). The third variant includes the cash grant and the psychosocial interventions (“full” package). The control group only receives the regular cash transfers from the national program. 4,712 households were drawn into a sample for data collection (1206 households in control, 1191 households in capital, 1112 households in psychosocial and 1203 households in full). Before the study, we conducted power calculations assuming an ICC of 0.10 (based on data from Ghana and a Niger national household survey) and equal sized arms. To maximize power, we sampled all villages in this phase. Sampling 15 households per village allowed for minimum detectable sizes of 0.057 SD between arms, before adjusting for baseline outcomes or strata.

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

Household surveys were collected in 3 survey rounds as described above.

The questionnaires included the following sections:

I. Beneficiary section Roster Health Beneficiary activity Household business Time use Finance Housing Food security Cash transfers Relationships Mental health Treatment measures II. Household head section Food consumption Head of household activities Relationships Agriculture Livestock and Fish Assets Education and Health spending Non food consumption Other programs Household transfers Shocks

Questions are generally consistent across rounds.

The data includes process variables, see attachment for variable definitions and Bossuroy et al. (2022) for details.

Cleaning operations

Survey data are labelled, deduplicated and cleaned. It includes constructed variables. The data is documented in three files. A household panel dataset shows data from the baseline, midline, and end-line surveys where observations missing at in the baseline survey are replaced with strata means. Households are observed in two periods. A household-level file shows select variables from the baseline survey. Finally, a food-level file shows median food prices per food unit.

Variables were constructed according to a pre-analysis plan, registered at https://www.socialscienceregistry.org/versions/52534/docs/version/document, and are further described in Bossuroy, et al (2022).

Response rate

The original sample included 4712 households. The baseline, endline, and end-line samples include 4608, 4476, and 4303 households, respectively, and thus completion rates of 97.8%, 95.0%, and 91.3%.

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from databases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these databases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of the AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

During the Paris Conference in 2015, nations of the world strengthened the United Nations Framework Convention on Climate Change by agreeing to holding 'the increase in the global average temperature to well below 2C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5C'. However, 'pre-industrial' was not defined. Here we investigate the implications of different choices of the pre-industrial baseline on the likelihood of exceeding these two temperature thresholds. We find that for the strongest mitigation scenario RCP2.6 and a medium scenario RCP4.5, the probability of exceeding the thresholds and timing of exceedance is highly dependent on the pre-industrial baseline; for example, the probability of crossing 1.5C by the end of the century under RCP2.6 varies from 61% to 88% depending on how the baseline is defined. In contrast, in the scenario with no mitigation, RCP8.5, both thresholds will almost certainly be exceeded by the middle of the century with the definition of the pre-industrial baseline of less importance. Allowable carbon emissions for threshold stabilization are similarly highly dependent on the pre-industrial baseline. For stabilization at 2C, allowable emissions decrease by as much as 40% when earlier than nineteenth-century climates are considered as a baseline. # Funding # Data produced as part of the ERC funded project (EC-320691), Transition into the Anthopocene (TITAN) at the School of GeoSciences.

Competition Overview

This dataset is associated with the WWW 2025 SmartMem Competition. The competition task is to predict whether a memory module (DIMM) will experience a failure in the future based on its historical logs. For more details, please visit the competition homepage or the promotion homepage.

Dataset Background

Considering the large scale of the competition dataset and the long processing time required to run the full baseline for feature generation, we have released a feature dataset produced by the baseline. This dataset contains high-level features (referred to as "new features") extracted from the raw data of all DIMMs. Each DIMM's new feature set consists of 100 columns. Participants can leverage this dataset to explore memory failure prediction strategies—for example, by incorporating additional features to enhance model performance, using data augmentation or sampling methods to address class imbalance, or testing different models to tackle challenges from distribution drift in event sequence data.

Terminology Definitions

• DIMM: Refers to a memory module, identified by its serial_number (abbreviated as SN). The memory failure prediction task is to forecast whether a given DIMM will fail in the future.
• CE (Correctable Error): Each DIMM generates several log entries:
  • Read CE: Occurs when a memory fault leads to data errors during data exchanges in business processes.
  • SCRUB CE: Detected during memory inspections conducted by the Intel CPU while the server is running.
• RdErrLogParity: A 32-bit binary number that records the 8-bit data transmitted in each cycle across 4 data buses (DQ) of an x4 granularity DDR4 memory during CPU-memory data exchanges; a bit value of 1 is considered an error.
• Deduplication Rule: If the same DIMM records the identical RetryRdErrLogParity error on the same cell within a single observation window, only the earliest CE is retained.
• Failure Mode:
  • Multiple failures in lower-level modules within a higher-level module are treated as a failure mode of the higher-level module. For example, if a Device (which contains multiple Banks) shows multiple Bank failures within an observation window, the Device failure mode is set to 1.
  • Module Hierarchy: Others > Device > Bank > Row/Column > Cell.
  • Only the failure mode of the highest-level module is recorded in each observation window.

New Feature Generation Process

• Features are generated every 15 minutes, with the generation timestamp denoted as T.
• For each generation at time T, data from the preceding 15 minutes, 1 hour, and 6 hours are used to compute features.
• Each DIMM's new feature set is presented as tabular data, comprising:
  • 1 column: LogTime (the feature generation time T, considered as the timestamp of the last CE used).
  • 99 columns: 33 features for each of the three time windows (15 minutes, 1 hour, and 6 hours).

Feature Category Descriptions

Temporal Features

• {read/scrub/all}_ce_log_num_{window_size}: The total number of de-duplicated CE log entries (read, scrub, or all) within the window.
• {read/scrub/all}_ce_count_{window_size}: The total count of CE entries (read, scrub, or all) before deduplication within the window.
• log_happen_frequency_{window_size}: The log frequency, defined as the observation window duration divided by the total number of CEs.
• ce_storm_count_{window_size}: The number of CE storms (for details, see the baseline method _calculate_ce_storm_count).

Macro-level Spatial Features

• fault_mode_{others/device/bank/row/column/cell}_{window_size}: Indicates whether a failure mode occurred at the corresponding module level (for details, see _get_spatio_features).
• fault_{row/column}_num_{window_size}: The number of columns experiencing simultaneous row failures or the number of rows experiencing simultaneous column failures.

Micro-level Spatial Features

• error_{bit/dq/burst}_count_{window_size}: Total count of errors (bit, dq, or burst) within the window.
• max_{dq/burst}_interval_{window_size}: The maximum interval between parity errors (dq or burst) within the window.
• dq_count={1/2/3/4}_{window_size}: The total number of occurrences where the dq error count equals n (with n ∈ {1, 2, 3, 4}).
• burst_count={1/2/3/4/5/6/7/8}_{window_size}: The total number of occurrences where the burst error count equals n (with n ∈ {1, 2, 3, 4, 5, 6, 7, 8}).

How to use?

In the baseline program (baseline_en.py), place the feature files in the directory specified by Config.feature_path. Then, remove the call to ...

Southeast Florida witnesses an enormous seasonal influx of upper trophic level marine predators each year as massive aggregations of migrating blacktip sharks (Carcharhinus limbatus) overwinter in nearshore waters. The narrow shelf and close proximity of the Gulf Stream current to the Palm Beach County shoreline drive tens of thousands of sharks to the shallow, coastal environment. This natural bottleneck provides a unique opportunity to estimate relative abundance. Over a four year period from 2011–2014, an aerial survey was flown approximately biweekly along the length of Palm Beach County. A high definition video camera and digital still camera mounted out of the airplane window provided a continuous record of the belt transect which extended 200 m seaward from the shoreline between Boca Raton Inlet and Jupiter Inlet. The number of sharks within the survey transect was directly counted from the video. Shark abundance peaked in the winter (January-March) with a maximum in 2011 of 12,128 individuals counted within the 75.6 km-2 belt transect. This resulted in a maximum density of 803.2 sharks km-2. By the late spring (April-May), shark abundance had sharply declined to 1.1% of its peak, where it remained until spiking again in January of the following year. Shark abundance was inversely correlated with water temperature and large numbers of sharks were found only when water temperatures were less than 25°C. Shark abundance was also correlated with day of the year but not with barometric pressure. Although shark abundance was not correlated with photoperiod, the departure of the sharks from southeast Florida occurred around the vernal equinox. The shark migration along the United States eastern seaboard corresponds spatially and temporally with the spawning aggregations of various baitfish species. These baseline abundance data can be compared to future studies to determine if shark population size is changing and if sharks are restricting their southward migration as global water temperatures increase.

It represents the baseline for the definition of the set-back areas as required by Article 8 of the Protocol for the "integrated management of coastal zones". The baseline corresponds to the upper winter high water level, calculated as a manual interpolation of data relating to the 'storm surge vulnerability' cartography, carried out as part of the Cadsealand and Micore projects. For this purpose it was assumed to use the vulnerability data referable to 10-year return periods, considered, on a historical basis, good indicators of the maximum winter sea level.

Environment related legislation reviewAvailable onlineCall Number: [EL]Physical Description: 20 p. ; 29 cm

New Zealand Territorial SeasThe territorial sea is the belt of sea adjacent to the coast out to a distance of 12 nautical miles from prescribed baselines over which New Zealand, as the coastal state, has the same rights of sovereignty that it exercises over its land territory subject to the right of innocent passage (and transit passage through any straits used for international navigation) of ships of other states. 'Innocent passage' excludes fishing activities.The term Territorial Sea Baseline refers to the line from which the seaward limits of New Zealand's maritime zones are measured. The breadth of the territorial sea, the seaward limits of the contiguous zone, the exclusive economic zone and, in some cases, the continental shelf, are measured from the Territorial Sea Baseline.References: Territorial Sea, Contiguous Zone, and Exclusive Economic Zone Act 1977 More information on the definition of Territorial Sea (12nm limit): Maritime boundary definitions | Marine information Guidance (linz.govt.nz)More information on the definition of Territorial Sea Baseline: Maritime boundary definitions | Marine information Guidance (linz.govt.nz)Downloaded from LINZ data service as of 25/07/2023

Data category and features used to analyse data, and their definitions.

Environment related legislation reviewAvailable onlineCall Number: [EL]Physical Description: 20 p. ; 29 cm