Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature

Abstract

Coastal nuclear power plants discharge large amounts of warm cooling water, which may have environmental impacts. This study proposes a method for determining the long-term impact area based on the average distribution of sea surface temperate (SST) increases. Taking the Daya Bay Nuclear Power Plant as a case study, 101 TM/ETM+ images acquired from 2000 to 2013 were used to obtain SST products. Cross-validation with NR_2P products showed that the accuracy of the SST products, in terms of the systematic error, root-mean-square error, and mean absolute error of 1,000 randomly selected verification points, was all <0.3°C, while Willmott’s index of agreement values was all >0.7. An annual SST cycle harmonic model was established. The mean difference between the modeled and observed SSTs was −2.1 to 2.5°C with a standard deviation range of 0–1°C. The long-term impact area was extracted by the harmonic analysis method and multi-year average method for comparison. The following conclusions can be drawn: 1) with sufficient SST samples, the temperature distributions of the two methods are similar, with the multi-year average method giving less noise and clearer boundaries. 2) When SST data are lacking for some months, the mean and standard deviation of the percentage of pixels belonging to areas of different temperature rise were calculated. The standard deviations of the two methods were both <0.04 in the temperature-rise classes of 1–2, 2–3, 3–4, and 4–5°C, while in the 0–1°C class, the standard deviation of the multi-year average method was 0.461, which is much higher than that of the multi-year average method (0.098). Performing statistical analysis on all pixels of >0°C, the multi-year average method had a standard deviation of 0.506, while the harmonic analysis method had a value of 0.128. Overall, the harmonic analysis method makes it possible to obtain and evaluate the long-term stability impact area of the thermal discharge over a period of time comprehensively and quantitatively. Even though it introduces a small amount of noise, it has less dependence on the input SST products and could improve the stability and reliability of thermal discharge monitoring, providing technical support for precise pollution control.