Light adsorption of phytoplankton and parameters of its biomass and cell biovolumes in the Black Sea in 1998-2000

Bio-optical characteristics of phytoplankton have been observed during two-year monitoring in the western Black Sea. High variability in light absorption coefficient of phytoplankton was due to change of pigment concentration and chlorophyll a specific absorption coefficient. A relationships between light absorption coefficients and chlorophyll a concentration have been found: for the blue maximum (a_ph(440) = 0.0413x**0.628; R**2 = 0.63) and for the red maximum (?_ph(678) = 0.0190x**0.843; R**2 = 0.83). Chlorophyll a specific absorption coefficients decreased while pigment concentration in the Sea increased. Observed variability in chlorophyll a specific absorption coefficient at chlorophyll a concentrations <1.0 mg/m**3 had seasonal features and was related with seasonal change of intracellular pigment concentration. Ratio between the blue and red maxima decreased with increasing chlorophyll a concentration (? = 2.14 x**-0.20; R**2 = 0.41). Variability of spectrally averaged absorption coefficient of phytoplankton (a'_ph ) on 95% depended on absorption coefficient at the blue maximum (y = 0.421x; R**2 = 0.95). Relation of a_ph with chlorophyll a concentration was described by a power function (y = 0.0173x**0.0709; R**2 = 0.65). Change of spectra shape was generally effected by seasonal dynamics of intracellular pigment concentration, and partly effected by taxonomic and cell-size structure of phytoplankton.

EPANET Input Files of New York tunnels and Pacific City used in a metamodel-based optimization study

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.