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Copyright of Journal of Taiwan Fisheries Research is the property of Fisheries Research Institute, Council of Agriculture, Executive Yuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigated environmental effects on larval anchovy fluctuations (based on CPUE from 1980 to 2000) in the waters off southwestern Taiwan using advanced time series analyses, including the state-space approach to remove seasonality, wavelet analysis to investigate transient relationships, and stationary bootstrap to test correlation between time series. For large-scale environmental effects, we used the Southern Oscillation Index (SOI) to represent the El Niño Southern Oscillation (ENSO); for local hydrographic conditions, we used sea surface temperature (SST), river runoff, and mixing conditions. Whereas the anchovy catch consisted of a northern species ( Engraulis japonicus) and two southern species ( Encrasicholina heteroloba and Encrasicholina punctifer), the magnitude of the anchovy catch appeared to be mainly determined by the strength of Eng. japonicus (Japanese anchovy). The main factor that caused the interannual variation of anchovy CPUE might change through time. The CPUE showed a negative correlation with combination of water temperature and river runoff before 1987 and a positive correlation with river runoff after 1988. Whereas a significant negative correlation between CPUE and ENSOs existed, this correlation was driven completely by the low-frequency ENSO events and explained only 10% of the variance. Several previous studies on this population emphasized that the fluctuations of larval anchovy abundance were determined by local SST. Our analyses indicated that such a correlation was transient and simply reflected ENSO signals. Recent advances in physical oceanography around Taiwan showed that the ENSOs reduced the strength of the Asian monsoon and thus weakened the China Coastal Current toward Taiwan. The decline of larval anchovy during ENSO may be due to reduced China Coastal Current, which is important in facilitating the spawning migration of the Japanese anchovy. [ABSTRACT FROM AUTHOR]

Many novel techniques for reconstructing rainfall‐runoff processes require hydrometeorologic and geomorphologic information for modelling. However, certain information is not always measurable. In this paper, we employ a special recurrent neural network to reconstruct the rainfall‐runoff process by using collected rainfall data. In addition, we propose an indirect system identification to overcome the drawback of a traditional, time‐consuming trial‐and‐error search. The indirect system identification is an efficient method to recognize the structure of a recurrent neural network. The unit hydrograph can be derived directly from the weights of the network due to a state‐space form embedded in the recurrent neural network. This improves the link between the weights of the network and the physical concepts that most neural networks fail to connect.The case studies of 41 events from 1966 to 1997 have been implemented in Taiwan's Wu‐Tu watershed, where the runoff path‐lines are short and steep. Two recurrent neural networks and one state‐space model are utilized to simulate the rainfall‐runoff processes for comparison. The results are validated by four criteria: coefficient of efficiency; peak discharge error; time to peak arrival error; total discharge volume error. The resulting data from the recurrent neural network reveal that the neural network proposed herein is appropriate for hydrological systems. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]