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Characterizing the dynamic relationship between rainfall and runoff is a highly interesting modeling problem in hydrology. This study develops a deterministic linearized recurrent neural network (denoted as DLRNN) that deals with the system’s nonlinearity by recalibration at each time interval, and relates the weights of DLRNN to unit hydrographs in order to describe the transition of the rainfall–runoff processes. Case studies of 38 events, from 1966 to 1997, are implemented in the Wu-Tu watershed of Taiwan, where the runoff path-lines are short and steep. A comparison between the DLRNN and a feed-forward neural network demonstrates the advantage of DLRNN as a dynamic system model. It is concluded that DLRNN shows superiority in the performance of rainfall–runoff simulations and the ability to recognize transitions in hydrological processes.

Rainfall-runoff processes are dynamic systems that are better described by a dynamic model. In this paper, a specific dynamic neural network, called state space neural network (SSNN), is modified to perform short term rainfall-runoff forecasts. The lead time is extended to 3 h. To improve the link between the weights of the network and physical concepts that most neural networks lack for, a method of the unit hydrograph representation is proposed to reproduce the unit hydrographs based on the weights of the network. Hence, a transition of rainfall-runoff systems can be observed via the changes of the unit hydrograph hour by hour. Furthermore, a new learning method developed from the interchange of the roles of the network states and the weight matrix is applied to train the SSNN and helps the network to evolve into a time-variant model while forecasting the rainfall-runoff process. A study case has been implemented in Taiwan's Wu-Tu watershed, where the runoff path-lines are short and steep. Forty-seven events from 1966 to 1997 are forecasted via the SSNN, and the results are validated via four criteria. The convergence of the new learning algorithm is shown during the model training process. Performance of the SSNN for short term rainfall-runoff forecasting reveals that the specific dynamic recurrent neural network is appropriate for hydrological forecasts.

This study focuses mainly on observing urban development in Taiwan's Wu-Tu watershed from the perspective of urban hydrological theory. An approach is proposed for developing a method for incorporating available meteorological data to define the degree of change in a runoff hydrograph for urbanizing basins. The mean rainfall was estimated using the Kriging method. For calibration, two methods of calculating the effective rainfall (the Φ-index method and the non-linear-programming (NLP) method) were used as model inputs, and the optimal global parameters of the linear reservoir model were then obtained from the shuffled complex evolution (SCE) algorithm. Twenty-six (1966–1991) and eight (1994–1997) rainfall–runoff events were used for calibration and verification, respectively. The NLP method yielded better results than the Φ-index method, especially for multipeak rainfall–runoff events. The regression equation determined the relationship between the parameters of the model and impervious areas. A comparison based on the results of the instantaneous unit hydrograph of the study area revealed that three decades of urbanization had increased the peak flow by 27%, and the time to peak was decreased by 4 h. The study simply describes the results of the impact of imperviousness on hydrological modelling. Copyright © 2002 John Wiley & Sons, Ltd.

The main purpose of this paper is to introduce a semi-distributed parallel surface rainfall-runoff conceptual model. In this paper, a general solution of the instantaneous unit hydrograph (IUH) has been derived successfully for N linearly connected reservoirs, each having a different storage constant. The solution is a function of geomorphologic parameters, meteorologic factors and roughness coefficients. The model also takes into account the hydrologic response which is influenced by outflow downstream of a reservoir. For calibration, the shuffled complex evolution (SCE) algorithm is used to search for the global optimal parameters of the model. Because of the parallel structure, the mean roughness parameter of the channel becomes a “conceptual” parameter without a real physical meaning. To evaluate the adaptability of the model adopted, three watersheds around the city of Taipei in Taiwan were chosen to test the effectiveness of the model. The study provides an appropriate rainfall-runoff model for planning flood mitigation in Taiwan. Copyright © 1999 John Wiley & Sons, Ltd.

The main purpose of the colored noise effect in parameter estimation for a linear model is to make the estimators tend to the true values as closely as possible. The differential hydrological grey model with dual series is adopted in this study. The least squares estimation of model parameters with the colored noise effect is applied to improve the parameter identification. By using the shuffled complex evolution (SCE) algorithm, the parameter derived from the least squares estimation with the colored noise effect is optimized. Then, the degree of the colored noise effect affecting the parameter estimation of a linear model is investigated. The applicability of the proposed procedure to two watersheds in Taiwan is verified and the simulated results with and without the colored noise effect are compared. From the theoretical investigation and discussion, the superiority of the parameter estimation with the colored noise effect is proved. That colored noise effect plays an important role not only in parameter estimation, but also in simulation of the rainfall-runoff process, is also verified

A preliminary method for coding random self-similar river networks and the corresponding distance calculations are proposed in a companion paper. The coding method is applied to generate random self-similar river networks, and the corresponding algorithm for calculating the geometric distances of the links is employed to determine the width function of the river networks, and thus evaluates the adaptability of the process. The width function-based geomorphological instantaneous unit hydrograph (WF-GIUH) model is then applied to estimate the runoff of the Po-bridge watershed in northern Taiwan. The results imply that the separately random self-similar generating algorithm can be used to simulate river networks during the rainfall–runoff process. It can also help analyse the variations of the river network when rainfall locations change and study the influence on hydrological responses (IUH) when the shape of river network changes.