Abstract
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.
Highlights
Water quality is a crucial factor for both coastal ecosystems and human societies
This paper aims to set up the framework for an operational water quality ensemble forecasting system, even though the proposed method is only applied in a hindcasting case study and further steps are required to reach the operational stage
The above-presented ensemble method was tested for hindcasting the Chlorophyll-a concentration in the southern North Sea for different hydrodynamic years, first for year 2009 and for year 2007
Summary
Water quality is a crucial factor for both coastal ecosystems and human societies. Accurate real-time phytoplankton concentration prediction is required for ecosystem and economic benefits. Information regarding water quality allows for early warning and adequate response such as mitigation measures and targeted monitoring. Existing hybrid ecosystem models are powerful tools for modelling water quality; their reliability depends highly on the uncertainty stemming from different sources. Considering the high level of uncertainty in the coastal water quality forecasting process, it is assumed that a single-valued deterministic forecast may not be sufficiently reliable for decision making. A strong need arises for an ensemble forecasting system that could potentially account for the uncertainty associated with the driving forces, the model simplifications or the parameterization
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