Daily pan evaporation modelling using multi‐layer perceptrons and radial basis neural networks

This paper reports on investigations of the abilities of three different artificial neural network (ANN) techniques, multi‐layer perceptrons (MLP), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) to estimate daily pan evaporation. Different MLP models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity were developed to evaluate the effect of each of these variables on pan evaporation. The MLP estimates are compared with those of the RBNN and GRNN techniques. The Stephens‐Stewart (SS) method is also considered for the comparison. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R²) statistics. Based on the comparisons, it was found that the MLP and RBNN computing techniques could be employed successfully to model the evaporation process using the available climatic data. The GRNN was found to perform better than the SS method. Copyright © 2008 John Wiley & Sons, Ltd.     

Climate Regime Shift and Phytoplankton Phenology in a Macrotidal Estuary: Long-Term Surveys in Gyeonggi Bay,Korea

Phytoplankton are finely tuned to the seasonality of their environment, and shifts in the timing of phytoplankton phenology provide some of the most compelling evidence that species and ecosystems are being influenced by global climate change. Evaluation of a 50-year dataset of climatic parameters, a 12-year dataset of nutrients, and a 15-year dataset of phytoplankton biomass and composition in Gyeonggi Bay of the Yellow Sea revealed that the climate has shifted from a cold to a warm phase in the last few decades and that recent warm climatic and eutrophication trends are affecting phytoplankton biomass, phenology, and structure. In Gyeonggi Bay, climatic and ecological regime shifts were detected during the 1990s and 2000s, respectively. The asymmetric relationship between climate and ecological regime shift probably depends on macrotidal system configurations that are more resistant to environmental perturbation. The spring diatom blooms observed in the 1990s have moved forward to winter blooms in the 2000s because early winter warming has been induced by higher light and precipitation, which has removed prior light limitation and control of diatom blooms. Summer blooms are triggered by enhanced nutrients, which leads to frequent and recurring dominance of dinoflagellates and diatoms, supporting the hypothesis that summer phenology might be brought about by local processes such as eutrophication, as well as by climate change. Overall, differences in phenological trends can be brought about by differences in the underlying drivers of seasonality. Based on the results of this study, perspectives are drawn regarding the utility of phenology as an organizing principle for analysis of pelagic ecosystems.     

Multiple linear regression,multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

ABSTRACT

Nowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

Seasonal variation in the community and size structure of nano- and microzooplankton in Gyeonggi Bay,Yellow Sea

To investigate the seasonal variation and community structure of nano- and microzooplankton in Gyeonggi Bay of the Yellow Sea, the abundance and carbon biomass of nano- and microzooplankton were evaluated at 10-day intervals from January 1997 to December 1999. Four major groups of nano- and microzooplankton communities were classified: heterotrophic ciliates, heterotrophic dinoflagellates (HDF), heterotrophic nanoflagellates (HNF), and copepod nauplii. The total carbon biomass of nano- and microzooplankton ranged from 10.2 to 168.8 μg C L⁻¹ and was highest during or after phytoplankton blooms. Nano- and microzooplankton communities were composed of heterotrophic ciliates (7.4–81.4%; average 41.7% of total biomass), HDF (0.1–70.3%; average 26.1% of total biomass), copepod nauplii (1.6–70.6%; average 20.7% of total biomass), and HNF (0.8–59.5%; average 11.5% of total biomass). The relative contribution of individual components in the nano- and microzooplankton communities appeared to differ by seasons. Ciliates accounted for the most major component of nano- and microzooplankton communities, except during summer and phytoplankton blooming seasons, whereas HDF were more dominant during the phytoplankton blooming seasons. The abundance and biomass of nano- and microzooplankton generally followed the seasonal dynamics of phytoplankton. The size and community distribution of nano- and microzooplankton was positively correlated with size-fractionated phytoplankton. The carbon requirement of microzooplankton ranged from 60 to 83% of daily primary production, and was relatively high when phytoplankton biomass was high. Therefore, our result suggests that the seasonal variation in the community and size composition of nano- and microzooplankton appears to be primarily governed by phytoplankton size and concentration as a food source, and their abundance may greatly affect trophic dynamics by controlling the seasonal abundance of phytoplankton.     

Microbial diversity in polluted harbor sediments II: Sulfate-reducing bacterial community assessment using terminal restriction fragment length polymorphism and clone library of dsrAB gene

Sulfate-reducing bacteria (SRB) are important regulators of a variety of processes in coastal marine sediments regarding organic matter turnover, biodegradation of pollutants, and sulfur and carbon cycles. Yet their community compositions have not been investigated in polluted harbor sediments. This study described the diversity and spatial variation of SRB communities in surface sediments in Victoria Harbor, Hong Kong. The spatial variation of SRB communities was described by terminal restriction fragment length polymorphism (T-RFLP). The results showed that the most diversified terminal restriction fragments were found at polluted sites. In addition, cluster analysis indicated that although the SRB communities were different at the two polluted sites, they were still more similar to each other than to the two more distant reference sites. Based on a dsrAB clone library constructed at a polluted site, diversified SRB were found, represented by 30 Operational Taxonomic Units (OTUs). Upon comparisons among the SRB sequences detected from this study and those in the GenBank, five clades of SRB were found. Three clades belonged to the known families Desulfobacteraceae, Desulfobulbaceae, and Syntrophobacteriaceae. The majority of sequenced clones, which distantly related to sequences in the GenBank, constituted the remaining two unclassified groups, suggesting unique SRB members related to the polluted harbor environment. Statistical analyses indicated that estimated SRB richness correlated with environment factors such as sulfur content, acid volatile sulfate, and redox potential.     

Multiple‐indicator study of groundwater flow and chemistry and the impacts of river and paddy water on groundwater in the alluvial fan of the Tedori River,Japan

To investigate the source, flow paths, and chemistry of rich resources of high‐quality, shallow groundwater in the alluvial fan between the Tedori and Sai rivers in central Japan, we analysed stable isotope ratios of H, O, and Sr and concentrations of major dissolved ions and trace elements in groundwater, river water, and paddy water. The ⁸⁷Sr/⁸⁶Sr ratios of the groundwater are related to near‐surface geology: groundwater in sediment from the Tedori River has high ⁸⁷Sr/⁸⁶Sr ratios (>0.711), whereas that from the Sai River in the north of the fan has low ⁸⁷Sr/⁸⁶Sr ratios (<0.711). δ²H and δ¹⁸O values and ⁸⁷Sr/⁸⁶Sr ratios indicate that groundwater in the central and southern fans is recharged by the Tedori River, whereas recharge in the north is from the Sai River. Mg²⁺, Ca²⁺, Sr²⁺, HCO₃^?, and SO₄^2? concentrations and δ²H and δ¹⁸O values in the groundwater are high in the central fan and, except for the northern area, tend to increase with distance from the Tedori River. There are linear relationships between ⁸⁷Sr/⁸⁶Sr ratio and the reciprocal concentrations of Sr²⁺, Mg²⁺, and Ca²⁺. These geochemical characteristics suggest that as groundwater recharged from the Tedori River flows towards the central fan, it mixes with waters derived from precipitation and paddy water that have become enriched in these components during downward infiltration. These results are consistent with our hydrological analysis and numerical simulation of groundwater flow, thus verifying the validity of the model we used in our simulation of groundwater flow. Copyright © 2016 John Wiley & Sons, Ltd.     

Estimation of Design Wave Height for the Waters around the Korean Peninsula

Long term wave climate of both extreme wave and operational wave height is essential for planning and designing coastal structures. Since the field wave data for the waters around Korean peninsula is not enough to provide reliable wave statistics, the wave climate information has been generated by means of long-term wave hindcasting using available meteorological data. Basic data base of hindcasted wave parameters such as significant wave height, peak period and direction has been established continuously for the period of 25 years starting from 1979 and for major 106 typhoons for the past 53 years since 1951 for each grid point of the North East Asia Regional Seas with grid size of 18 km. Wind field reanalyzed by European Center for Midrange Weather Forecasts (ECMWF) was used for the simulation of waves for the extratropical storms, while wind field calculated by typhoon wind model with typhoon parameters carefully analyzed using most of the available data was used for the simulation of typhoon waves. Design wave heights for the return period of 10, 20, 30, 50 and 100 years for 16 directions at each grid point have been estimated by means of extreme wave analysis using the wave simulation data. As in conventional methodsi of design criteria estimation, it is assumed that the climate is stationary and the statistics and extreme analysis using the long-term hindcasting data are used in the statistical prediction for the future. The method of extreme statistical analysis in handling the extreme events like typhoon Maemi in 2003 was evaluated for more stable results of design wave height estimation for the return periods of 30–50 years for the cost effective construction of coastal structures.