scholarly journals Natural hazards in the mountainous region of Rio de Janeiro state, Brazil: rainfall erosivity as an early warning index

2021 ◽  
Author(s):  
Geovane Alves ◽  
Carlos de Mello ◽  
Li Guo ◽  
Michael Thebaldi
Keyword(s):  
Spatial Distribution ◽  
Natural Hazards ◽  
Rio De Janeiro ◽  
Daily Rainfall ◽  
Water Erosion ◽  
Rainfall Erosivity ◽  
Mountainous Region ◽  
Seasonal Model ◽  
Maximum Daily Rainfall ◽  
Janeiro State

Rainfall erosivity is defined as the potential of rain to cause erosion. It has great potential for application in studies related to landslides and floods, in addition to water erosion. The objectives of this study were: i) to model the Rday using a seasonal model for the Mountainous Region of the State of Rio de Janeiro (MRRJ); ii) to adjust thresholds of the Rday index based on catastrophic events which occurred in the last two decades; and iii) to map the maximum daily rainfall erosivity (Rmaxday) to assess the region’s susceptibility to rainfall hazards according to the established Rday limits. The fitted Rday model presented a satisfactory result, thereby enabling its application as an estimator of the daily rainfall erosivity in MRRJ. Events that resulted in Rday > 1,500 MJ.ha-1.mm.h-1.day-1 were those with the highest number of fatalities. The spatial distribution of Rmaxday showed that the entire MRRJ has presented values that can cause major rainfall. The Rday index proved to be a promising indicator of rainfall hazards, which is more effective than those normally used that are only based on quantity (mm) and/or intensity (mm.h-1) of the rain.

scholarly journals High Uranium Concentrations in the Groundwater of the Rio de Janeiro State, Brazil, Mountainous Region

2018 ◽  
Author(s):  
José Godoy ◽  
Paulo Ferreira ◽  
Elder de Souza ◽  
Larisse da Silva ◽  
Isabela Bittencourt ◽  
...  
Keyword(s):  
Rio De Janeiro ◽  
Mountainous Region ◽  
Janeiro State

scholarly journals Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 834
Author(s):  
Priscila da Cunha Luz Barcellos ◽  
Marcio Cataldi
Keyword(s):  
Natural Hazards ◽  
Land Surface ◽  
Rio De Janeiro ◽  
Flash Flood ◽  
Weather Prediction ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Atmospheric Conditions ◽  
Streamflow Forecast ◽  
Janeiro State

Flash floods and extreme rains are destructive phenomena and difficult to forecast. In 2011, the mountainous region of Rio de Janeiro state suffered one of the largest natural hazards in Brazil, affecting more than 300,000 people, leaving more than 900 dead. This article simulates this natural hazard through Quantitative Precipitation Forecasting (QPF) and streamflow forecast ensemble, using 18 combinations of parameterizations between cumulus, microphysics, surface layer, planetary boundary layer, land surface and lateral contour conditions of the Weather Research and Forecasting (WRF) Model, coupling to the Soil Moisture Accounting Procedure (SMAP) hydrological model, seeking to find the best set of parametrizations for the forecasting of extreme events in the region. The results showed rainfall and streamflow forecast were underestimated by the models, reaching an error of 57.4% to QPF and 24.6% error to streamflow, and part of these errors are related to the lack of skill of the atmospheric model in predicting the intensity and the spatial-temporal distribution of rainfall. These results bring to light the limitations of numerical weather prediction, possibly due to the lack of initiatives involving the adaptation of empirical constants, intrinsic in the parametrization models, to the specific atmospheric conditions of each region of the country.

scholarly journals Crustaceans composition in sandy beaches of Sepetiba Bay, Rio de Janeiro, Brazil

Check List ◽  
2011 ◽  
Vol 7 (6) ◽  
pp. 778 ◽  
Author(s):  
Ricardo S. Cardoso ◽  
Felipe Meireis ◽  
Gustavo Mattos
Keyword(s):  
Spatial Distribution ◽  
Rio De Janeiro ◽  
Sandy Beaches ◽  
Southeastern Brazil ◽  
Sepetiba Bay ◽  
Main Species ◽  
Janeiro State ◽  
Made In

A crustacean survey was made in Sepetiba bay, Rio de Janeiro state, southeastern Brazil. Twelve sandy beaches were sampled on five islands in this embayment. A total of 3024 individuals were collected, belonging to 21 species, which are grouped in 16 families, seven infraorders, seven suborders, and four orders. Isopods, followed by amphipods and tanaids, showed the highest abundance, amounting to over 92% of the dominance of crustaceans. The main species were Excirolana armata, Excirolana braziliensis (isopods), Atlantorchestoidea brasiliensis (amphipod), and Monokalliapseudes schubarti (tanaid), which together accounted about 80% of crustaceans of the beaches studied. Excirolana braziliensis had the highest frequency. The majority of species found are typical of sandy beaches, with large spatial distribution.

Flash flood in the mountainous region of Rio de Janeiro state (Brazil) in 2011: part I—calibration watershed through hydrological SMAP model

2020 ◽  
Vol 102 (3) ◽  
pp. 1117-1134
Author(s):  
Marianna Rodrigues Gullo Cavalcante ◽  
Priscila da Cunha Luz Barcellos ◽  
Marcio Cataldi
Keyword(s):  
Rio De Janeiro ◽  
Flash Flood ◽  
Mountainous Region ◽  
Janeiro State

scholarly journals Geografia dos Votos Inválidos na Eleição de 1982 no Estado do Rio de Janeiro

1984 ◽  
Vol 8 ◽  
pp. 46-76
Author(s):  
Marina Sant'Anna ◽  
Ludy Freire
Keyword(s):  
Spatial Distribution ◽  
Distribution Pattern ◽  
Rio De Janeiro ◽  
Social Economic ◽  
Spatial Distribution Pattern ◽  
The Social ◽  
Janeiro State

Firstly this paper tries to analyze and explain the spatial distribution pattern of null and blank votes across the territorial administrative divisious (municipios) of Rio de Janeiro State in 1982 election. Secondly, it tries to identify and evaluate the correlation between void votes and the social-economic characteristcs of the population of these municipios.

scholarly journals MINHA CASA, MINHA VIDA PROGRAM AND ITS SPATIAL DISTRIBUTION IN RIO DE JANEIRO STATE

Geo UERJ ◽  
2020 ◽  
pp. e51548
Author(s):  
Gustavo Bezerra De Brito ◽  
José Silvan Borborema Araújo ◽  
Glaucio José Marafon
Keyword(s):  
Spatial Distribution ◽  
Rio De Janeiro ◽  
Janeiro State

O presente artigo traz como tema aspectos significativos da problemática habitacional brasileira. A falta de moradias, sua qualidade e distribuição espacial são questões relevantes na urbanização do país e, em especial, no Estado do Rio de Janeiro. Nesse sentido, a partir de 2009 um novo ponto de inflexão se faz presente, o “Programa Minha Casa, Minha Vida” (PMCMV) foi criado de forma a dar subsídios a solvência do déficit habitacional brasileiro, se destacando pelos grandes subsídios orçamentários, do grande volume de construções e avanços na perspectiva social: o Programa buscava atender as faixas de renda mais baixas. Assim, o objetivo deste trabalho é verificar através da análise dos dados quantitativos e cartográficos um possível aumento da segregação socioespacial no estado do Rio de Janeiro em função da renda, além da concentração de unidades habitacionais nas faixas de renda com maior poder aquisitivo. A metodologia inclui análise de dados quantitativos e espaciais oficiais do Governo Federal, revisão da literatura ligada à habitação no Brasil e elaboração e análise de mapas a partir de dados espaciais.

scholarly journals Distribution of the deaths from childhood and adolescent cancer across the regional health agencies from the Rio de Janeiro State, Brazil

2020 ◽  
pp. 890-896
Author(s):  
Aline Cerqueira Santos Santana da Silva ◽  
Bianka Queiroz da Silva ◽  
Rayssa Goulart Valente ◽  
Virginia Maria de Azevedo Oliveira Knupp ◽  
Leila Leontina do Couto Bárcia ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Rio De Janeiro ◽  
Ecological Study ◽  
Health Agency ◽  
Malignant Neoplasms ◽  
Regional Health ◽  
Adolescent Cancer ◽  
Central Nervous System Cancer ◽  
Health Agencies ◽  
Janeiro State

Objective: The study’s main purpose has been to analyze the spatial distribution of deaths from malignant neoplasms in patients aged up to 19 years old across the regional health agencies of Rio de Janeiro State. Methods: This ecological study analyzed the spatial distribution of deaths from January to December 2015 through data of the Sistema de Informações sobre Mortalidade (SIM) [Mortality Information System]. The data were tabulated in Tabnet and analyzed using the R statistical software. Results: Considering the 101 deaths observed, 24 (23.8%) were from central nervous system cancer. The Metropolitan I regional health agency had the highest death rates (63.3%), and Baixada Litorânea had the highest proportion of deaths from leukemia (27.9%). Conclusion: Identifying the most frequent deaths from malignant neoplasms makes it possible to formulate public policies aimed at prevention, diagnostics, and treatment consistent with the local reality.

scholarly journals Spatial Distribution and Temporal Trends of Rainfall Erosivity in Three Gorges Reservoir Area of China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Huiying Liu ◽  
Guanhua Zhang ◽  
Pingcang Zhang ◽  
Shengnan Zhu
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Three Gorges Reservoir ◽  
Temporal Trends ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Annual Rainfall ◽  
Three Gorges Reservoir Area ◽  
Rainfall Erosivity ◽  
Three Gorges

Rainfall erosivity is a key factor to predict soil erosion rate in universal soil loss equation (USLE) and revised USLE (RUSLE). Understanding rainfall erosivity characteristics, especially its spatial distribution and temporal trends, is essential for soil erosion risk assessment and soil conservation planning. In this study, the spatial-temporal variation of rainfall erosivity in the Three Gorges Reservoir Area (TGRA) of China during 1960–2010, at annual and seasonal scales, was explored based on daily rainfall data from 40 stations (26 meteorological stations and 14 hydrologic stations). The Mann–Kendall test and Co–kriging interpolation method were applied to detect the temporal trends and spatial patterns. The results showed that TGRA’s annual rainfall erosivity increased from west, south, and east to the north-central, ranging from 3647.0 to 10884.8 MJ·mm·ha−1·h−1 with an average value of 6108.1 MJ·mm·ha−1·h−1. The spatial distribution of summer and autumn rainfall erosivity was similar to the pattern of annual rainfall erosivity. Summer is the most erosive season among four seasons, accounting for 53% of the total annual rainfall erosivity, and winter is the least erosive season. July is the most erosive month with an average of 1327.3 MJ·mm·ha−1·h−1, and January is the least erosive month. Mean rainfall erosivity was 5969.2 MJ·mm·ha−1·h−1 during 1960–2010, with the lowest value of 3361.0 MJ·mm·ha−1·h−1 in 1966 and highest value of 8896.0 MJ·mm·ha−1·h−1 in 1982. Mann–Kendall test showed that the annual rainfall erosivity did not change significantly across TGRA. Seasonal rainfall erosivity showed a significant decrease in autumn and insignificant decrease in summer and winter. Monthly rainfall erosivity in TGRA showed insignificant increases from Jun to Jul and then underwent decreases from Aug to Nov. and from Dec to Feb and it rose again in Feb reaching a 0.01 level significance. The daily rainfall data of supplemental stations is very useful to interpolate rainfall erosivity map, which could help to find the credible maximum and minimum value of TGRA. In total, the findings could provide useful information both for soil erosion prediction, land management practices, and sediment control project of TGRA.

scholarly journals Forecasting Temporal and Spatial Climatological Influence for Land Suitability Evaluation in Bentota Sri Lanka

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Gayani Ranasinghe ◽  
Ranjana UK Piyadasa
Keyword(s):  
Spatial Distribution ◽  
Distribution Pattern ◽  
Daily Rainfall ◽  
Land Suitability ◽  
Monthly Rainfall ◽  
Spatial Distribution Pattern ◽  
Annual Rainfall ◽  
Suitability Evaluation ◽  
Land Suitability Evaluation ◽  
Maximum Daily Rainfall

Climate change has raised much concern regarding its impacts on future land use planning, varying by region, time, and socio-economic development path. The principle purpose of land suitability evaluation is to predict the potential and limitation of the land for crop production and other land uses. This study was carried out to predict the temperature and rainfall trends as one of the major factor for evaluating land suitability. Climatic data such as monthly mean temperature, total monthly rainfall, maximum daily rainfall and total annual rainfall during last 30 years of all weather stations located in Bentota River basin was collected and analyzed applying time series analysis, correlation analysis and Manna Kendall trend test methods. Spatial distribution of forecast rainfall values was illustrated applying Arc GIS software. The findings revealed that monthly mean temperature and maximum daily rainfall had a general increasing trend whereas, total monthly rainfall and total annual rainfall showed a general decreasing trend in  Bentota area. It was indicated relatively high rainfall situations during May and October while low rainfall situations during January and February by occurring flood situation in once per five year. During Yala season the area will be received comparatively more rainfall (331mm) than Maha season (300mm) in future. Community and the farmers in this area can be aware about the anticipated spatial distribution of total monthly rainfall during two major seasons and flood occurrence periods. Decision makers should evaluate land suitability of Bentota area by considering above climatological influences and its spatial distribution pattern that identified as major outcome of this research. The approach and the methodology adopted in this study will be useful for other researchers, agriculturalist and planners to identify the future climatological influences and its spatial distribution pattern for land suitability evaluations and other decision making purposes for other areas. 

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