Celso Augusto Guimarães Santos

Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models

Abstract New wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. Editor D. Koutsoyiannis; Associate editor L. See Citation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324.

Erosivity, surface runoff, and soil erosion estimation using GIS-coupled runoff–erosion model in the Mamuaba catchment, Brazil

This study evaluates erosivity, surface runoff generation, and soil erosion rates for Mamuaba catchment, sub-catchment of Gramame River basin (Brazil) by using the ArcView Soil and Water Assessment Tool (AvSWAT) model. Calibration and validation of the model was performed on monthly basis, and it could simulate surface runoff and soil erosion to a good level of accuracy. Daily rainfall data between 1969 and 1989 from six rain gauges were used, and the monthly rainfall erosivity of each station was computed for all the studied years. In order to evaluate the calibration and validation of the model, monthly runoff data between January 1978 and April 1982 from one runoff gauge were used as well. The estimated soil loss rates were also realistic when compared to what can be observed in the field and to results from previous studies around of catchment. The long-term average soil loss was estimated at 9.4 t ha(-1) year(-1); most of the area of the catchment (60%) was predicted to suffer from a low- to moderate-erosion risk (<6 t ha(-1) year(-1)) and, in 20% of the catchment, the soil erosion was estimated to exceed > 12 t ha(-1) year(-1). Expectedly, estimated soil loss was significantly correlated with measured rainfall and simulated surface runoff. Based on the estimated soil loss rates, the catchment was divided into four priority categories (low, moderate, high and very high) for conservation intervention. The study demonstrates that the AvSWAT model provides a useful tool for soil erosion assessment from catchments and facilitates the planning for a sustainable land management in northeastern Brazil.

Influência do tipo da cobertura vegetal sobre a erosão no semi-árido Paraibano

The type of vegetation cover present in an area, greatly influences the surface runoff as well as the sediment yield. The objective of this paper is to establish a relationship between the type of vegetal cover and erosion by means of an empirical equation for soil loss. The proposed equation was calibrated using synthetic data obtained from a physically-based runoff-erosion model in which the erosion parameter values are representative of a cleared bare-land surface in the semiarid area of Paraiba State. A comparison between the values obtained from the equation and the observed data collected from several erosion plots in the Sume Experimental Watershed with different conditions of vegetal cover and slope is presented as an evaluation of the influence of the vegetation cover on soil erosion.

Identification of precipitation zones within São Francisco River basin (Brazil) by global wavelet power spectra

Abstract The definition of rainfall behaviour at the regional level is of great importance in planning policy for the rational use of water resources for both agricultural and urban uses. It allows the delimitation of areas of homogeneous rainfall features and shows the system dynamics in the area, so providing more comprehensive knowledge about the rainfall. Precipitation zones were identified within the São Francisco River basin, Northeast Brazil, by analysing the rainfall frequencies by means of global wavelet power spectra. Data from 200 raingauges were analysed and the results of the overall power spectra showed a high annual frequency throughout the basin; however, other frequencies are present with minor significance that represent changes in the rainfall regime. Although, the computed global wavelet power spectra presented an annual frequency, they showed peculiar patterns (denoted A and B) that could be used to characterize the region. Thus, three sub-regions with homogeneous rainfall patterns were identified as: Region A (south part of the basin) and Region B (north part of the basin), with frequency patterns A and B, respectively, and a Transition Zone in the central part that shows both frequency patterns. Citation Santos, C.A.G. and Morais, B.S., 2013. Identification of precipitation zones within São Francisco River basin (Brazil) by global wavelet power spectra. Hydrological Sciences Journal, 58 (4), 789–796.

Análise da variabilidade espaço-temporal e identificação do padrão da precipitação na bacia do Rio Tapacurá, Pernambuco

The study aimed to evaluate the space-time variability of rainfall for the period 1970-2000, in Tapacura river basin, located in Pernambuco State. Many statistical parameters were used to analyze the monthly and annual rainfall, such as mean standard deviation, normalized deviation, fitted equation and coefficient of variability. The study showed a major concentration of rainfall between March and July, when 60% of annual rainfall occurred, and that the dry season occurred between August to February. The annual variability showed a significant decrease of the rainfall in order of -20% compared to the mean rainfall. About rainfall spatial variability, it was verified that it decreases at the direction East-West, and that the periods with low rainfall depths have higher coefficient of variation.

Multispectral image unsupervised segmentation using watershed transformation and cross-entropy minimization in different land use

A general-purpose unsupervised segmentation algorithm based on cross-entropy minimization by pixel was developed; this algorithm, known as the SCEMA (Segmentation Cross-Entropy Minimization Algorithm), starts from an initial segmentation and iteratively searches the best statistical model, estimating the probability density of the image to reduce the cross-entropy with respect to the previous iteration. The SCEMA was tested using satellite images from the Landsat Thematic Mapper sensor of Landsat 5 for the Amazon region (12 images for testing and 15 for validation). Theme classes identified in the image were (1) water, (2) vegetation, and (3) agriculture. Using the Kappa index and other statistics parameters, the comparison of classifications is made with the following segmentation methods: (1) cross-entropy minimization by pixel, (2) cross-entropy minimization by region, (3) K-means, and (4) maximum likelihood. The results indicate that cross-entropy minimization by pixel results in a consistent segmenta...A general-purpose unsupervised segmentation algorithm based on cross-entropy minimization by pixel was developed; this algorithm, known as the SCEMA (Segmentation Cross-Entropy Minimization Algorithm), starts from an initial segmentation and iteratively searches the best statistical model, estimating the probability density of the image to reduce the cross-entropy with respect to the previous iteration. The SCEMA was tested using satellite images from the Landsat Thematic Mapper sensor of Landsat 5 for the Amazon region (12 images for testing and 15 for validation). Theme classes identified in the image were (1) water, (2) vegetation, and (3) agriculture. Using the Kappa index and other statistics parameters, the comparison of classifications is made with the following segmentation methods: (1) cross-entropy minimization by pixel, (2) cross-entropy minimization by region, (3) K-means, and (4) maximum likelihood. The results indicate that cross-entropy minimization by pixel results in a consistent segmentation of images. The algorithm also compares favorably to other well-known image segmentation methods, and the numerical test results illustrate the efficiency of our approach for image segmentation.

Application of a simulated annealing optimization to a physically based erosion model

A major risk concerning the calibration of physically based erosion models has been partly attributable to the lack of robust optimization tools. This paper presents the essential concepts and application to optimize the erosion parameters of an erosion model using data collected in an experimental basin, with a global optimization method known as simulated annealing (SA) which is suitable for solving optimization problems of large scales. The physically based erosion model that was chosen to be optimized here is the Watershed Erosion Simulation Program (WESP), which was developed for small basins to generate the hydrograph and the respective sedigraph. The field data were collected in an experimental basin located in a semiarid region of Brazil. On the basis of these results, the following erosion parameters were optimized: the soil moisture-tension parameter (N(s)) that depends also on the initial moisture content, the channel erosion parameter (a), the soil detachability factor (K(R)), and the sediment entrainment parameter by rainfall impact (K(I)), whose values could serve as initial estimates for semiarid regions within northeastern Brazil.

Hepatitis G virus / GB virus C in Brazil. Preliminary report

Hepatitis G virus/GB virus C is a novel flavivirus recently detected in hepatitis non A-E cases. In this study, the presence of this virus in chronic non-B, non-C hepatitis patients was evaluated using GBV-C specific PCR and this virus was detected in one out of thirteen patients. This patient has presented a severe liver failure, has lived for a long time in the Western Amazon basin and no other cause for this clinical picture was reported. The impact of the discovery of this new agent is still under evaluation throughout the world. The study of the prevalence of this virus among chronic hepatitis patients and healthy individuals (as blood donors) will furnish subside to evaluate its real pathogenicity.

Drought assessment using a TRMM-derived standardized precipitation index for the upper São Francisco River basin, Brazil

In this work, the use of Tropical Rainfall Measuring Mission (TRMM) rainfall data and the Standardized Precipitation Index (SPI) for monitoring spatial and temporal drought variabilities in the Upper São Francisco River basin is investigated. Thus, the spatiotemporal behavior of droughts and cluster regions with similar behaviors is identified. As a result, the joint analysis of clusters, dendrograms, and the spatial distribution of SPI values proved to be a powerful tool in identifying homogeneous regions. The results showed that the northeast region of the basin has the lowest rainfall indices and the southwest region has the highest rainfall depths, and that the region has well-defined dry and rainy seasons from June to August and November to January, respectively. An analysis of the drought and rain conditions showed that the studied region was homogeneous and well-distributed; however, the quantity of extreme and severe drought events in short-, medium- and long-term analysis was higher than that expected in regions with high rainfall depths, particularly in the south/southwest and southeast areas. Thus, an alternative classification is proposed to characterize the drought, which spatially categorizes the drought type (short-, medium-, and long-term) according to the analyzed drought event type (extreme, severe, moderate, and mild).

Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy

Abstract Rainfall forecasting and approximation of its magnitude have a huge and imperative role in water management and runoff forecasting. The main objective of this paper is to obtain the relationship between rainfall time series achieved from wavelet transform (WT) and moving average (MA) in Klang River basin, Malaysia. For this purpose, the Haar and Dmey WTs were applied to decompose the rainfall time series into 7, 10 different resolution levels, respectively. Several preprocessing case studies based on 2-, 3-, 5-, 10-, 15-, 20-, 25-, and 30-month MAs were carried out to discover a longer-term trend compared to a shorter-term MA. The information and data were gathered from Klang Gates Dam, Malaysia, from 1997 to 2008. Regarding the behavior, the time series of 10-, 15-, 20-, and 30-day rainfall are decomposed into approximation and details coefficient with different kind of WT. Correlation coefficient R2 and root-mean-square error criteria are applied to examine the performance of the models. The results show that there are some similarities between MA filters and wavelet approximation sub-series filters due to noise elimination. Moreover, the results obtained that the high correlation with MAs can be achieved via Dmey WT compared to Haar wavelet for rainfall data. Moreover, clean signals could be used as model inputs to improve the model performance. Therefore, signal decomposition techniques for the purpose of data preprocessing could be favorable and could be appropriate for elimination of the errors.