Olavo Pedrollo

Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting

This study reports on the performance of two medium-range streamflow forecast models: (1) a multilayer feed-forward artificial neural network; and (2) a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models. The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km(2), with lead times up to 12 days, at daily intervals. The Nash-Sutcliffe efficiency index, the root-mean-square error, the mean absolute error, and the mean relative error were used to assess the relative performance of the models. Results showed that the performance of streamflow forecasts was strongly dependent on the quality of quantitative precipitation forecasts used. The artificial neural network (ANN) method seemed to be less sensitive to precipitation forecast error relative to the distributed hydrological model. Hence, the latter presented a better skill in flow forecasting when using the more accurate perfect precipitation forecast. The conceptual hydrological model also demonstrates better forecast skill than ANN models for longer lead times, when the representation of the rainfall-runoff process and of the water storage in the watershed becomes more important than the flow routing along the drainage network. In addition, results obtained by incorporating a quantitative precipitation forecast in both models performed better than the current streamflow obtained by the Brazilian national electric system operator using statistical models which do not utilize information on precipitation, whether observed or forecast. (Less)

Simplifying artificial neural network models of river basin behaviour by an automated procedure for input variable selection

The objective of the present work is to present a simplified and automated method for identifying and excluding unnecessary input variables, with a consequent reduction in dimensionality of ANN-based hydrological models. The proposed method is iterative and computationally efficient: it consists of perturbing the input variables, recording the change in model performance, establishing an index showing the contribution of each variable to the ANN (the relative contribution index, RCI) and excluding the least-influential variables that fall below a threshold. The method was used to simulate mean daily flow for a 20-year period 1989-2009 from four drainage basins nested at different scales ranging from 19.4km? to 9426km?, in the Southern Brazil. The main result of this method of simplifying ANN-based hydrological models was to increase the Nash-Sutcliffe (NS) coefficient and to reduce RMSE in all the simulations undertaken. The potential of ANN models was therefore improved by eliminating unnecessary and/or redundant variables. Simulating the intermediate basin with area 5414km? (Santo i?ngelo), for example, the initial performance (12 inputs; NS=0.894) improved when a simpler and more parsimonious model was used (4 inputs; NS=0.944). To validate the simplification procedure, a comparison was made between the proposed method (RCI) and the well-known methods of Overall Connection Weights (OCW) and Forward Stepwise Addition (FSA). For the comparison between RCI and OCW methods, in most cases, the ordering of selected variables was similar, confirming that the two procedures satisfactorily identify the more important variables, although the RCI is computationally more efficient giving a small advantage in the resulting model performance. In the FSA method, although the performance of the obtained models has also been satisfactory, the computational effort was much greater than with the other two methods because of the excessive number of the neural network training performed (117 training procedures in Combination 2, against only six for the RCI method, for example). An excessive number of input variables can reduce the efficiency of ANN simulation.A criterion was established by which input variables could be selected for exclusion.The proposed method satisfactorily identifies the most important variables.Simplification of the ANN improved performance of hydrological simulations.

AS INCERTEZAS ASSOCIADAS ÀS CONDIÇÕES CLIMÁTICAS OBTIDAS PELO MODELO ETA CPTEC/HADCM3: AVALIAÇÃO COMPARATIVA ENTRE OS DADOS SIMULADOS E OBSERVADOS DE PRECIPITAÇÃO, EVAPOTRANSPIRAÇÃO E VAZÃO NA BACIA HIDROGRÁFICA DO RIO IJUÍ, BRASIL.

This study aims to evaluate the climate scenarios simulated by the Eta CPTEC/HadCM3 model, conducted by four members of the HadCM3 global climate model (CNTRL, LOW, MID and HIGH), at the Ijui River Basin, Brazil. The used control period was from 1961 to 1975, looking for the assessment of climate scenarios and river flow during the 1976 and 1990 period. The task was divided into five stages: spatial interpolation of climate data, correction of simulated climatic variables series (bias correction), calculation of reference evapotranspiration, hydrological simulation of monthly river flow, comparison between simulated and observed conditions of precipitation, evapotranspiration and river flow. Although the correction methods used to eliminate the simulated climate series biases have originated very different scenarios, neither methods presented a better performance in all examined criteria. Sometimes, the differences between the simulated values based on the Eta Model and the observed values were higher than 20%, both in both rainfall and river flow resulting from the hydrologic modeling processes. Therefore, one must consider that these uncertainties will propagate to future scenarios, when analyzing the effects of climate change on water availability.

Key factors in vertical mixing processes in a reservoir bordering the Pantanal floodplain, Brazil

Abstract The aim of this study was to understand seasonal variations in the vertical structure of the water column, and to quantify the importance of the physical forces (solar radiation, wind and hydraulic retention time) that control mixing processes in a reservoir bordering the Pantanal floodplain. Samples were taken every three months in the reservoir centre, at four depths, for the measurement of nine physical and chemical water quality parameters. The reservoir presented a long stratification period with complete mixing in winter. The vertical structure showed that, during the stratification period, the upper layers of the reservoir are homogeneous and the physical and chemical composition only changes at greater depths. The wind acting over an extended period is the only factor that significantly influences the vertical structure in the reservoir, giving rise to mixing processes. Moreover, the position of the draw-off point in the upper layer of the reservoir, together with the reservoir depth, enhances vertical stability.

Estimation of soil water content in watershed using artificial neural networks

ABSTRACT Soil water content (SWC) is an important factor in transfer processes between soil and air, contributing to water and energy balances, and quantifying it remains a challenge. This study uses artificial neural networks (ANNs) to analyse spatial and temporal variation of SWC in a Brazilian watershed, based on climate information, soil physical properties and topographic variables. Thirty eight input variables were tested in 200 models. The outputs were compared with 650 gravimetric moisture measurements collected at 26 points (25 field studies). The results show that it is possible to estimate SWC efficiently (Nash-Sutcliffe statistic, NS = 0.77) using topographic data, soil physical properties and rainfall. If only climate information is considered, modelling is less efficient (NS = 0.28). Using many variables does not necessarily improve performance. Alternatively, SWC can be estimated by simplified models using rainfall and topographic maps information, although the performance is less good (NS = 0.65).