Weather forecasting for Eastern Amazon with OLAM model

O modelo OLAM tem como característica a vantagem de representar simultaneamente os fenômenos meteorológicos de escala global e regional através de um esquema de refinamento de grades. Durante o projeto REMAM, o modelo foi aplicado para alguns estudos de caso com objetivo de avaliar o desempenho do modelo na previsão numérica de tempo para a região leste da Amazônia. Estudos de caso foram feitos para os doze meses do ano de 2009. Os resultados do modelo para estes casos foram comparados com dados observados na região de estudo. A análise dos dados de precipitação mostrou que o modelo consegue representar a distribuição média da precipitação acumulada e os aspectos da sazonalidade da ocorrência dos eventos, mas não consegue prever individualmente a acumulação de precipitação local. No entanto, avaliação individual de alguns casos mostrou que o modelo OLAM conseguiu representar dinamicamente e prever, com alguns dias de antecedência, o desenvolvimento de fenômenos meteorológicos costeiros como as linhas de instabilidade, que são um dos mais importantes sistemas precipitantes da Amazônia.

Progressive recovery of a tropical deforested stream community after a flash flood

AIM: In this study, we evaluated and compared community attributes from a tropical deforested stream, located in a pasture area, in a period before (PRED I) and three times after (POSD I, II, and III) a flash flood, in order to investigate the existence of temporal modifications in community structure that suggests return to conditions previous to the flash flood. METHODS: Biota samples included algae, macrophytes, macroinvertebrates, and fish assemblages. Changes in stream physical structure we also evaluated. Similarity of the aquatic biota between pre and post-disturbance periods was examined by exploratory ordination, known as Non-Metric Multidimensional Scaling associated with Cluster Analysis, using quantitative and presence/absence Bray-Curtis similarity coefficients. Presence and absence data were used for multivariate correlation analysis (Relate Analysis) in order to investigate taxonomic composition similarity of biota between pre and post-disturbance periods. RESULTS: Our results evidenced channel simplification and an expressive decrease in richness and abundance of all taxa right after the flood, followed by subsequent increases of these parameters in the next three samples, indicating trends towards stream community recovery. Bray-Curtis similarity coefficients evidenced a greater community structure disparity among the period right after the flood and the subsequent ones. Multivariate correlation analysis evidenced a greater correlation between macroinvertebrates and algae/macrophytes, demonstrating the narrow relation between their recolonization dynamics. CONCLUSIONS: Despite overall community structure tended to return to previous conditions, recolonization after the flood was much slower than that reported in literature. Finally, the remarkably high flood impact along with the slow recolonization could be a result of the historical presence of anthropic impacts in the region, such as siltation, riparian forest complete depletion, and habitat simplification, which magnified the effects of a natural disturbance.

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Determining spatial resolution in spatial load forecasting using a grid-based model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Volcanological aspects of the northwest region of Parana continental flood basalts (Brazil)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Chemical weathering rate, denudation rate, and atmospheric and soil CO2 consumption of Parana flood basalts in Sao Paulo State, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The October 1998 Flood of the Upper Guadalupe River System, Central Texas

The October 1998 flood on the upper Guadalupe River system was produced by a 24-hour precipitation amount of 483 mm at one station, over 380 mm at several other stations, and up to 590 mm over five days, precipitation amounts greater than the 100-year storm as prescribed in Weather Bureau Technical Papers 40 (1961) and 49 (1964). This study uses slope-area discharge estimates and published discharge and precipitation data to analyze flow characteristics of the three major branches of the Guadalupe River on the Edwards Plateau. The main channel of the Guadalupe has a single large flood-control structure at Canyon Dam and five flood dams on the tributary Comal River. On the upper San Marcos River there are five detention dams that regulate 80% of its drainage. The Blanco River, which has no structural controls, generated a peak discharge of 2,970 m3/s from a 1,067 km2 basin. Downstream of Canyon Dam, the Guadalupe River generated a peak discharge greater than 3,000 m3/s from an area of 223 km2. The event exceeded the capacity of both the Comal River and San Marcos flood-control projects and produced spills that inundated areas greater than the 100-year floodplain defined by the Federal Emergency Management Agency.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Re-Os isotope and highly siderophile element systematics of the Parana continental flood basalts (Brazil)

Basalts of the Parana continental flood basalt (PCFB) province erupted through dominantly Proterozoic continental crust during the Cretaceous. In order to examine the mantle source(s) of this major flood basalt province, we studied Os, Sr, Nd, and Pb isotope systematics, and highly siderophile element (HSE) abundances in tholeiitic basalts that were carefully chosen to show the minimal effects of crustal contamination. These basalts define a precise Re-Os isochron with an age of 131.6 +/- 2.3 Ma and an initial Os-187/Os-188 of 0.1295 +/- 0.0018 (gamma Os-187 = +2.7 +/- 1.4). This initial Os isotopic composition is considerably more radiogenic than estimates of the contemporary Depleted Mantle (DM). The fact that the Re-Os data define a well constrained isochron with an age similar to Ar-40/Ar-39 age determinations, despite generally low Os concentrations, is consistent with closed-system behavior for the HSE. Neodymium, Sr, and Pb isotopic data suggest that the mantle source of the basalts had been variably hybridized by melts derived from enriched mantle components. To account for the combined Os, Nd, Sr, and Pb isotopic characteristics of these rocks, we propose that the primary melts formed from metasomatized asthenospheric mantle (represented by arc-mantle peridotite) that underwent mixing with two enriched components, EM-I and EM-II. The different enriched components are reflected in minor isotopic differences between basalts from southern and northern portions of the province. The Tristan da Cunha hotspot has been previously suggested to be the cause of the Parana continental flood basalt magmatism. However, present-day Tristan da Cunha lavas have much higher Os-187/Os-188 isotopic compositions than the source of the PCFB. These data, together with other isotopic and elemental data, preclude making a definitive linkage between the Tristan plume and the PCFB. (C) 2012 Elsevier B.V. All rights reserved.

Forecasting the need for medical specialists in Spain: application of a system dynamics model

[EN] Background: Spain has gone from a surplus to a shortage of medical doctors in very few years. Medium and long-term planning for health professionals has become a high priority for health authorities. Methods: We created a supply and demand/need simulation model for 43 medical specialties using system dynamics. The model includes demographic, education and labour market variables. Several scenarios were defined. Variables controllable by health planners can be set as parameters to simulate different scenarios. The model calculates the supply and the deficit or surplus. Experts set the ratio of specialists needed per 1000 inhabitants with a Delphi method. Results: In the scenario of the baseline model with moderate population growth, the deficit of medical specialists will grow from 2% at present (2800 specialists) to 14.3% in 2025 (almost 21 000). The specialties with the greatest medium-term shortages are Anesthesiology, Orthopedic and Traumatic Surgery, Pediatric Surgery, Plastic Aesthetic and Reparatory Surgery, Family and Community Medicine, Pediatrics, Radiology, and Urology. Conclusions: The model suggests the need to increase the number of students admitted to medical school. Training itineraries should be redesigned to facilitate mobility among specialties. In the meantime, the need to make more flexible the supply in the short term is being filled by the immigration of physicians from new members of the European Union and from Latin America.