Mapeamento Da Precipitação Pluviométrica No Bioma Pantanal Do Estado Do Mato Grosso

Marcuzzo, Francisco Fernando Noronha; Rocha, Hudson Moraes; Melo, Denise Christina de Rezende

doi:10.5216/geoambie.v0i16.26021

Cited by 5 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data analysis also failed to detect significant trends in annual precipitation in any particular region. However, two studies analyzing monthly trends in precipitation from 1977−2006 using 12 rain gauge stations in the Pantanal found a small decrease in precipitation associated with pronounced inter-annual variability [48,49]. The resulting variation might stem from differences in the time lapse considered as well as from limits related to the spatial data resolution.…”

Section: Discussionmentioning

confidence: 99%

Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature

et al. 2020

View full text Add to dashboard Cite

Climate change poses a critical threat to the Pantanal, the largest wetland in the world. Models indicate an increase in the frequency of extreme precipitation events and extended periods of drought. These changes can amplify consequences for Pantanal's ecological functioning, which has already experienced intensive human modification of its hydrological system and environmental health. The present study analyzed the spatial and temporal dynamics of rainfall and resulting extremes in the Brazilian area of the Upper Paraguay River Basin (UPRB) along with a co-evaluation of the global Sea Surface Temperature data (SST). The predicted results indicate that wet extreme precipitation events will become more frequent in the highlands, while severe and prolonged droughts triggered by warming SSTs in the Northern Hemisphere (North Atlantic and North Pacific oceans) will affect the Pantanal. The linear relations between precipitation with SST of very specific oceanic regions and even from specific oceanic indexes obtained in the present study significantly improve the forecasting capacity, mainly from a resulting reduction to two months of the lead-time between SST warming to concomitant precipitation impacts, and by explaining 80% of Pantanal´s precipitation variation from major oceanic indexes (e.g., ENSO, PDO, NAO, ATL3). Current SST trends will result in inter-and intra-annual flooding dynamic alterations, drastically affecting the Pantanal ecosystem functioning, with consequences for wildlife diversity and distribution. Regarding the foreseeable global climate and land use change scenarios, the results from the present study provide solid evidence that can be used at different decision-making levels (from local to global) for identifying the most appropriate management practices and effectively achieving sustainability of the anthropic activity occurring in the Pantanal.

show abstract

Section: Discussionmentioning

confidence: 99%

Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature

et al. 2020

View full text Add to dashboard Cite

show abstract

“…These studies with a low density of pluviographic stations were only possible due to the implementation of regression equations in pluviometric stations of locations with correlated Rc coefficients, as performed by Oliveira et al (2012), Waltrick et al (2015) and Almeida and Casaroli (2016). However, although Mato Grosso State has extensive agricultural activities concentrated between October and March, when 85% of the annual precipitation occurs (Marcuzzo et al, 2011a;2011b), no research has yet provided the spatial variation of R and spatiotemporal variation of EI30 in this region.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of erosivity in Mato Grosso, Brazil

Raimo¹,

Amorim²,

Couto³

et al. 2018

Rev. ambiente água

View full text Add to dashboard Cite

The impact of rainfall on surfaces lacking vegetal cover can dissociate soil particles, thereby initiating the erosion process. This is known as rainfall erosivity and is expressed by the R factor in the Universal Soil Loss Equation. Agricultural areas often show seasonally erosion susceptibility throughout the year due to oscillations of the soil exposure rate and the vegetation change. Considering that approximately 30 million ha of the Mato Grosso State in Brazil is used for agriculture, this study aimed to predict and map the spatial and temporal variability of its territory. We evaluated the monthly (EI30) and annual (R) erosivity for 158 rain gauge stations and spatialized the values of EI30 and R by the Kriging method. It was observed that R values ranked as very high in the north, and high and medium-high in the south of Mato Grosso state. The mean value is 8835 MJ mm ha-1 h-1 year-1, considered high. Ninety-one percent of the annual erosivity was concentrated in the period between October and April, corresponding to the rainy season. The highest R factor values were found in the macro-regions of the northwest, north, west and medium-north of Mato Grosso State.

show abstract

“…Another study in Mato Grosso State, but covering only the Pantanal, mapped the distribution of rainfall using data from 5 weather stations covering the 30 years from 1977 to 2006, and isohyetal maps were elaborated via the Topo to Raster method, which uses the simple multi-grid method to minimize the equation for better resolutions (MARCUZZO et al, 2011b).…”

Section: Introductionmentioning

confidence: 99%

“…Using a GIS (Geographic Information System) for the spatial representation of climatic variables has been crucial as it represents an approximation to the reality of particular variables in areas of interest (Marcuzzo et al, 2011b). As rainfall is monitored at collection points which provide specific information, spatial interpolation methods are necessary to create maps (MELLO et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Modeling of the Ten-Day Rainfall in Mato Grosso State

et al. 2018

View full text Add to dashboard Cite

Mapping the spatial-temporal distribution of rainfall allows the analysis of tendencies and changes on local and regional scale, which is crucial for the development of environmental and agricultural projects. The objective of this paper is to adjust and choose semivariographic mathematical models to analyze the spatial variability of the 75 percentile rainfall in Mato Grosso State, covering October to March, to subsequently represent it on maps using Kriging techniques. Data from 155 weather stations in Mato Grosso S and around were used, with data for over ten years provided by the National Water Agency. The semivariograms were adjusted using the least squares method and chosen among spherical, exponential, and Gaussian models. The exponential model adjusted best to the experimental semivariograms using the criterion of standard deviation in reduced errors obtained from cross validation and level of dependence. The spatial variability of the rainfall was then mapped using Kriging techniques

show abstract

Mapeamento Da Precipitação Pluviométrica No Bioma Pantanal Do Estado Do Mato Grosso

Cited by 5 publications

References 1 publication

Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature

Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature

Spatio-temporal variability of erosivity in Mato Grosso, Brazil

Geostatistical Modeling of the Ten-Day Rainfall in Mato Grosso State

Contact Info

Product

Resources

About