1. Indicator name

Index of coastal eutrophication potential

2. Date of metadata update

2023-06-01 12:00:00 UTC

3. Goals and Targets addressed

3a. Goal


3a. Target

Target 7: Reduce pollution risks and the negative impact of pollution from all sources by 2030, to levels that are not harmful to biodiversity and ecosystem functions and services, considering cumulative effects, including: (a) by reducing excess nutrients lost to the environment by at least half, including through more efficient nutrient cycling and use; (b) by reducing the overall risk from pesticides and highly hazardous chemicals by at least half, including through integrated pest management, based on science, taking into account food security and livelihoods; and (c) by preventing, reducing, and working towards eliminating plastic pollution.

4. Rationale

Coastal areas are areas of high productivity where inputs from land, sea, air and people converge. With over 40 percent of the human population residing in coastal areas, ecosystem degradation in these areas can have disproportionate effects on society (IGOS, 2006). One of the largest pressures on coastal environments is eutrophication, resulting primarily from land-based nutrient input from agricultural runoff and domestic wastewater discharge. Coastal eutrophication can lead to serious damage to marine ecosystems, vital sea habitats, and can cause the spread of harmful algal blooms.

5. Definitions, concepts and classifications

5a. Definition

The indicator aims to measure the contribution to coastal eutrophication from countries and the state of coastal eutrophication. Therefore, two levels of indicators are recommended:

Level 1: Globally available data from earth observations and modelling

Level 2: National data which will be collected from countries (through the relevant Regional Seas Programme, where applicable (i.e. for countries that are a member of a Regional Seas Programme)

Monitoring parameters

Level 1

Level 2

Indicator for Coastal Eutrophication Potential (N and P loading)


Chlorophyll-a deviations (remote sensing)


Chlorophyll-a concentration (remote sensing and in situ)


National modelling of indicator for Coastal Eutrophication Potential (ICEP)


Total Nitrogen of DIN (dissolved inorganic nitrogen)


Total Phosphorus or DIP (dissolved inorganic phosphorus)


Total silica


Unit of measure

Chlorophyll-a deviations and Chlorophyll-a anomaly: Percent (%).

Indicator for Coastal Eutrophication Potential (ICEP): kilograms of carbon (from algae biomass) per square kilometre of river basin area per day (kg C km-2 day-1).

5b. Method of computation

Level 1: Indicator for coastal eutrophication potential

-This indicator is based on loads and ratios of nitrogen, phosphorous and silica delivered by rivers to coastal waters (Garnier et al. 2010), which contribute to the ICEP. The basis for these loads is collected from land-based assessments of land use including fertilizer use, population density, socioeconomic factors and other contributors to nutrient pollution runoff. Given the land-based nature of the indicator, it provides a modelled number indicating the risk of coastal eutrophication at a specific river mouth. The indicator can be further developed by incorporating in situ monitoring to evaluate the dispersion of concentrations of nitrogen, phosphorous and silica to ground-truth the index. The indicator assumes that excess concentrations of nitrogen or phosphorus relative to silica will result in increased growth of potentially harmful algae (ICEP>0). ICEP is expressed in kilograms of carbon (from algae biomass) per square kilometre of river basin area per day (kg C km-2 day-1).

The ICEP model is calculated using one of two equations depending on whether nitrogen or phosphorus is limiting. The equations (Billen and Garnier 2007) are:

where PFlx, NFlx and SiFlx are respectively the mean specific values of total nitrogen, total phosphorus and dissolved silica delivered at the mouth of the river basin, expressed in kg P km-2 day-1, in kg N km-2 day-1 and in kg Si km-2 day-1.

Level 1: Chlorophyll-A deviation modelling. Satellite-based assessments of ocean colour began in 1978 with the launch of the Coastal Zone Color Scanner (CZCS) aboard the NASA Nimbus 7 satellite. Following a decade long break in observations, there has been continuous satellite ocean colour since 1997 with SeaWiFS, followed by MERIS, MODIS (Terra, Aqua), VIIRS (NPP, N20) and now OLCI (S-3A, S-3B). Data gaps from individual sensors are common due to revisit cycles, cloud cover, and spurious retrievals resulting from a host of confounding atmospheric and aquatic conditions. This issue has been addressed by combining data from multiple sensors and creating a consistent, merged ocean colour product (e.g., chlorophyll-a). The ESA Ocean Colour CCI (OC-CCI) project, led by the Plymouth Marine Laboratory (PML), has produced a consistent, merged chlorophyll-a product from SeaWiFS, MODIS, MERIS and VIIRS, spanning 1997 to 2018 (Sathyendranath et al., 2018).

A merged multi-sensor product will be updated in both time and with data from additional sensors (e.g., OLCI) under a forthcoming EUMETSAT initiative that will continue the time series on an operational basis.

For this indicator, Chlorophyll-a (4 km resolution, monthly products) will be derived from the OC-CCI project and generated for each individual pixel within a country’s territorial sea and EEZ. For generation of a climatological baseline, results are averaged by month over the time period of 2000 – 2004. Pixels with differences from the baseline that are in the 90th percentile of values >0 across the cumulative global EEZ and territorial sea. The percentage of pixels in a country’s EEZ and territorial sea that are identified as deviating from the baseline (falling in the 90th percentile) will be calculated for each national EEZ and territorial sea by month. The annual average of these monthly values is then calculated.

Level 1: Intra-annual EEZ chlorophyll-a anomalies:

This sub-indicator evaluates the intra-annual changes in chlorophyll-a concentration anomalies in each Exclusive Economic Zone (EEZ) and territorial sea using the NOAA VIIRS chlorophyll-a ratio anomaly product produced daily for the globe at 2 km spatial resolution. The daily global VIIRS chlorophyll-a concentrations are produced from the NOAA Multi-Sensor Level 1 to Level 2 (MSL12) processing of the VIIRS sensor on the Suomi SNPP satellite. [Wang et al., 2017; Wang et al.,2014]

This anomaly product is defined as the daily chlorophyll-a concentration subtracted from a rolling 61-day mean baseline with a 15-day lag (based on Stumpf et al., 2003), then normalized to the rolling 61 day mean to create the proportional difference anomaly.

Level 2: In situ monitoring of nutrients

Where national capacity to do so exists, national level measurements of Chlorophyll-a and other parameters (including nitrogen, phosphate and silica) (in situ or from remote sensing), should be used to complement and ground truth global remote sensing and modelled data and enable a more detailed assessment of eutrophication. Monitoring of supplementary eutrophication parameters is advisable to determine whether an increase in.

Level 2: National ICEP modelling

Existing ICEP modelling at the national level is limited but could be further developed following the model of a current study analysing basin level data in Chinese rivers (Strokal et al 2016). The study utilises Global NEWS – 2 (Nutrient Export from WaterSheds) and Nutrient flows in Food chains, Environment and Resources use (NUFER) as models. The Global NEWS-2 model is basin-scale and quantifies river export of various nutrients (nitrogen, phosphorus, carbon and silica) in multiple forms (dissolved inorganic, dissolved organic and particulate) as functions of human activities on land and basin characteristics (Strokal et al 2016). Furthermore, the model shows past and future trends.

A full methodology for this indicator is available in the document entitled, “Global Manual on Ocean Statistics for Measuring SDG 14.1.1, 14.2.1 and 14.5.1”.

5c. Data collection method

National data are collected through the Regional Seas Programmes to reduce the reporting burden on countries. For countries that are not included in the Regional Seas Programme, UNEP contacts countries directly. For globally derived data, UNEP has established a partnership with NOAA and GEO Blue Planet, the Global Nutrient Management System (GNMS) and with the Scientific Advisory Committee of the Ad hoc and Open Ended Expert Group on Marine Litter. This facilitates the production of global data products.

5d. Accessibility of methodology

The methodology for this indicator is published under the following link: https://wedocs.unep.org/bitstream/handle/20.500.11822/35086/USO.pdf?sequence=3&isAllowed=y

The data for this indicator is also available on the UN SDG Global database: https://unstats.un.org/sdgs/UNSDG/IndDatabasePage

5e. Data sources

For Level 1 indicators:

- Satellite data.

- Global models, which are based on official data from national governments as collected from UN organizations.

For Level 2 indicators:

- Data provided by national governments

5f. Availability and release calendar

For Level 1 indicators: Chlorophyll-a: the first reporting cycle was in 2020 and then every two years.

For Level 2 indicators: The first UNEP data collection is planned in 2023. After that, data collection will be synchronised with the Regional Seas data collection calendar.

5g. Time series

For Level 1 indicators:

  • Chlorophyll-a deviations: 2000-2022; global, regional and national scales.
  • Chlorophyll-a anomaly: 2018-2022; global, regional and national scales.
  • ICEP: 1900-2015; Global LMEs and River Basins.

For Level 2 indicators: The first UNEP data collection is planned in 2023.

5h. Data providers

For Level 1 data: Geo Blue Planet, NOAA, Esri, IOC-UNESCO.

For Level 2 data: National governments, through the Regional Seas, or directly to UNEP.

5i. Data compilers

United Nations Environment Programme (UNEP), in collaboration with partners mentioned in the other sections of this metadata.

5j. Gaps in data coverage

Level 2 Data for ICEP is not yet available.

5k. Treatment of missing values

For Level 1 data: Not applicable.

For Level 2 data: The United Nations Environment Programme (UNEP) and the Regional Seas do not make any estimation or imputation for missing values, so the number of data points provided are actual country data.

6. Scale

6a. Scale of use

Scale of application: Global, Regional, National

Scale of data disaggregation/aggregation: Regional and national level. *Sub-national level can also be derived upon request.

Global/ regional scale indicator can be disaggregated to national level

National data is collated to form global indicator:

Additional Information: It is a Global LMEs and River Basins scale indicator

6b. National/regional indicator production

The methodology for global (Level 1) and national (Level 2) indicators (Global Manual on Ocean Statistics for Measuring SDG 14.1.1, 14.2.1 and 14.5.1) is available by following link https://wedocs.unep.org/handle/20.500.11822/35086

6c. Sources of differences between global and national figures

For Level 1 indicators satellite data and global models are used. For Level 2 indicators national data is used.

6d. Regional and global estimates & data collection for global monitoring

6d.1 Description of the methodology

The methodology for global (Level 1) and national (Level 2) indicators (Global Manual on Ocean Statistics for Measuring SDG 14.1.1, 14.2.1 and 14.5.1) is available by following link https://wedocs.unep.org/handle/20.500.11822/35086

6d.2 Additional methodological details

6d.3 Description of the mechanism for collecting data from countries

National data collection through the Regional Seas already exists for many Regional Seas, this data will be compiled for SDG reporting in 2023.

7. Other MEAs, processes and organisations

7a. Other MEA and processes

SDGs: indicator 14.1.1 (a) Index of coastal eutrophication; and (b) plastic debris density

7b. Biodiversity Indicator Partnership


8. Disaggregation

A geospatial disaggregation of the state of pollution is proposed. For the ICEP loading indicators, this disaggregation should be at the sub-basin level

9. Related indicators


10. Data reporter

10a. Organisation

United Nations Environment Programme (UNEP)

10b. Contact person(s)

Dany Ghafari, dany.ghafari@un.org

11. References

Regional Seas website: https://www.unenvironment.org/explore-topics/oceans-seas/what-wedo/working-regional-seas

UN Environment (2018). Global Manual on Ocean Statistics. Towards a definition of indicator methodologies. Nairobi (Kenya): UN Environment. 46 pp. plus four appendices.

Wang, M., X. Liu, L. Jiang and S. Son (2017), The VIIRS Ocean Color Product Algorithm Theoretical Basis Document, National Oceanic and Atmospheric Administration, National Environmental Satellite and Data Information Service, 68 pp., doi: TBD.

Wang, M., X. Liu, L. Jiang, S. Son, J. Sun, W. Shi, L. Tan, P. Naik, K. Mikelsons, X. Wang and V. Lance (2014), Evaluation of VIIRS Ocean Color Products, Proc. SPIE 9261, 92610E, doi: 10.1117/12.2069251.

Stumpf, Richard P., Holderied, Kristine, Sinclair, Mark, (2003), Determination of water depth with high-resolution satellite imagery over variable bottom types, Limnology and Oceanography, 1, part, 2, doi: 10.4319/lo.2003.48.1_part_2.0547

Garnier, J., Beusen, A., Thieu, V., Billen, G. and Bouwman, L. (2010) N:P:Si nutrient export ratios and ecological consequences in coastal seas evaluated by the ICEP approach

Billen, G. and Garnier, J.(2007) River basin nutrient delivery to the coastal sea: Assessing its potential to sustain new production of non-siliceous algae Marine Chemistry 106(1-2):148-160

Sathyendranath S., Grant M., Brewin R.J.W., Brockmann C., Brotas V., Chuprin A., Doerffer R., Dowell M., Farman A., Groom S., et al. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1 Data. Centre for Environmental Data Analysis; Harwell, UK: 2018. Technical Report.

Strokal, M., Kroeze, C., Wang, M., and Ma, L. (2016) Reducing future river export of nutrients to coastal waters of China in optimistic scenarios Science of the Total Environment 579.

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