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Setting up a water quality ensemble forecast for coastal ecosystems : A case study of the southern North Sea. / Mészáros, Lőrinc; El Serafy, Ghada.

In: Journal of Hydroinformatics, Vol. 20, No. 4, 2018, p. 846-863.

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@article{f91c4e0a457a40b2a3fa161db74070a8,
title = "Setting up a water quality ensemble forecast for coastal ecosystems: A case study of the southern North Sea",
abstract = "Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.",
keywords = "coastal ecosystems, ensemble forecasting, environmental modelling, North Sea, uncertainty",
author = "Lőrinc M{\'e}sz{\'a}ros and {El Serafy}, Ghada",
year = "2018",
doi = "10.2166/hydro.2018.027",
language = "English",
volume = "20",
pages = "846--863",
journal = "Journal of Hydroinformatics",
issn = "1464-7141",
publisher = "International Water Association (IWA)",
number = "4",

}

RIS

TY - JOUR

T1 - Setting up a water quality ensemble forecast for coastal ecosystems

T2 - Journal of Hydroinformatics

AU - Mészáros, Lőrinc

AU - El Serafy, Ghada

PY - 2018

Y1 - 2018

N2 - Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.

AB - Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.

KW - coastal ecosystems

KW - ensemble forecasting

KW - environmental modelling

KW - North Sea

KW - uncertainty

U2 - 10.2166/hydro.2018.027

DO - 10.2166/hydro.2018.027

M3 - Article

VL - 20

SP - 846

EP - 863

JO - Journal of Hydroinformatics

JF - Journal of Hydroinformatics

SN - 1464-7141

IS - 4

ER -

ID: 45687949