Resultados de la Estimación del Modelo con Ajuste

There are lot of research papers published in this area. However, it is not intended to review all research approaches in this area such as neural networks, genetic algorithm (GA), etc. The review is based on few selected research papers in several interested topics, and they are summarised below.

Georgakakos and Smith (1990) applied the method of Kalman filters to improve hydrologic forecasting. This forecasting experiment implemented an extended Kalman filter that updated the system states based on discharges at the basin outlet observed up to the forecast time. The dominant sources of forecast uncertainty were assumed to be the errors in estimates of model parameters and the errors in observations of model inputs. Using these assumptions, a shortcoming of the earlier hydrologic Kalman filters (i.e. lacking a procedure for estimating the time-varying covariance matrix of errors) was improved. The results confirmed improvements both in forecasts and the model’s operational viability.

Smith and Eli (1995) investigated the use of neural networks for runoff response from different rainfall patterns considering spatial and temporal distribution by a very simple 5 × 5 grid cell synthetic watershed. A backpropagation neural network was trained to calculate the

peak discharge and its corresponding time from a single rainfall pattern. The neural network was also trained to map a time series of three rainfall patterns into a range of discharges over future time by replacing the entire outflow hydrograph using a discrete Fourier series fit. Imrie et al. (2000), Chang and Chen (2001) and Sivakumar et al. (2002) did similar research in the application of neural networks. Sivakumar et al. (2002) compared the performance of artificial neural networks (ANN) approach with performance of phase-space reconstruction (PSR) approach by comparing the forecast river flows from both approaches. The daily time step was used and 1-day and 7-day ahead forecasts of daily river flows were conducted. The methods were applied to Nakhon Sawan station in Chao Phraya River basin in Thailand. The results showed that the performance of the PSR approach was consistently better than that of ANN.

Wang (1991) introduced the genetic algorithm (GA) for calibrating the conceptual rainfall- runoff model. Liong et al. (1995) also used a GA to determine the optimal values of catchment calibration parameters. The catchment model was developed using the widely used conceptual rainfall-runoff model, the storm water management model (SWMM) (Huber et al., 1982). The method was applied to a catchment in Singapore of 6.11 km2 _{in size. Six storms} were used in the study, and three of them used for calibration while the remaining three were used for validation. The results showed that GA required only a small number of catchment- model simulations and produced relatively high peak-flow prediction accuracy with a range of the prediction errors from 0.045% to 7.265%. Kumar and Douglas (1997) in their discussion of the work of Liong et al. (1995) suggested using another objective function, which is minimizing the cumulative runoff volume, instead of minimizing percentage of peak discharges used by Liong et al.

Garrote and Becchi (1997) presented a software environment for real-time flood forecasting using distributed models. The software architecture was discussed based on its database design (data file format), object organization, model inference and user interface. The software namely Real-Time Interactive Basin Simulator - RIBS included two distributed rainfall-runoff models based its object–oriented design. These two models were incorporated in the system to demonstrate the real-time flood forecasting. They were: a simple conceptual model with a light computational load – HYDBL (Becchi et al., 1989) and a complex, physically based model with more realistic representations of hydrologic processes – DBSIM (Garrote and Bras, 1995). The system included the functions of process organization and data

handling; user interaction and results visualization; and access to model structure, hydrologic process and model inference. However, the design of data flow was not described in the article and the influence of the file structure of database needed further investigation.

Jinwon et al. (1998) presented a numerical study of precipitation and river flow in two California basins, Hopland and Sierra Nevada in USA to investigate the hydro-climate, snow budget, and streamflow at different elevations. The coupled atmosphere, land surface, and hydrology in the developed Regional Climate System Model (RCSM) were used for hydroclimate simulation. The large-scale weather information was downscaled to the catchment scale using a mesoscale model in RCSM and fine-resolution geographic information data. The semi-distributed TOPMODEL (Beven and Kirkby, 1979) and two versions of the lumped Sacramento model (Burnash et al., 1973) were used to simulate streamflow. Simulated precipitation from the mesoscale model and streamflow at the Hopland basin were in good agreement with the observed data. Both Sacramento models predicted a similar response of river outflows from this basin, while TOPMODEL predicted a faster recession of river flow with less base flow after precipitation stopped.

A fuzzy conceptual rainfall-runoff (CRR) framework was introduced by Özelkan and Duckstein (2001) to determine parameter uncertainties of conceptual rainfall-runoff models. The conceptual rainfall-runoff system was first fuzzified and then different operational modes were formulated using fuzzy rules. Next, the parameter identification aspect was verified using fuzzy regression techniques. For linear conceptual rainfall-runoff models, the bi-objective and tri-objective fuzzy regression models were used, while a fuzzy least squares regression framework was used to get the model parameters in the non-linear models. Three models, linear conceptual rainfall-runoff model, an experimental two-parameter model and a simplified version of the Sacramento soil moisture accounting model, were used. The results showed that the sensitivity and uncertainty stemming from the elements of the CRR model could be obtained from the fuzzy logic framework.

Jasper et al. (2002) studied coupling of atmospheric and hydrological models for flood forecasting in complex mountain watersheds. The grid-based hydrological catchment model WaSiM-ETH was used to simulate the continuous runoff hydrographs. Two different sets of meteorological input data were used: (1) surface observation data from station measurements and from weather radar, and (2) forecast data from five different high-resolution numerical weather prediction (NWP) models with grid cell sizes between 2 and 14 km. The simulated

flood runoffs were compared. The results of sensitivity studies pointed out the limitations of high-resolution flood runoff predictions in complex mountain terrain.

There are many other research works using various approaches. Some examples are listed below. Madsen et al. (2002) studied three different automated methods for calibration of rainfall-runoff models. Niehoff et al. (2002) studied the impact of land use changes on runoff generation. Szymkiewicz (2002) reported an alternative Instantaneous Unit Hydrograph approach for both the Muskingum model and the linear reservoir model. Zhang et al. (2002) developed a runoff routing model with a linear routing structure. Schumann et al. (2000) presented three semi-distributed models to show how statistical descriptions of distributed catchment characteristics could be used to consider spatial heterogeneity within conceptual models. All these works demonstrate that the rainfall runoff process can be described using an algorithm or a numerical model or an approach with reasonable accuracy for decision making in flood warning.