This study aimed to estimate levels of return of extreme daily precipitation events, associating them with natural disasters in Northeast Brazil (NEB), a region characterized by different climatic conditions and low rates of social and economic development. For this, generalized Pareto distribution (GPD) models were adjusted to the daily extreme precipitation data estimated by the Tropical Rainfall Measuring Mission (TRMM) 3B42 product of the multisatellite precipitation analysis for a period of 16 years (2000-2015). In addition, the estimates of the GPD model were compared using two data sources, TRMM and pluviometer. The investigation showed that the results of the GPD model estimated by means of the extreme data from the rain gauge and the TRMM were statistically the same, with 95% confidence. Thus, using the data referring to the 2,082 grid points of the TRMM, it was possible to map the spatial distribution of the estimates of the levels of return of extreme precipitation to the return periods of 2, 5 and 10 years, per seasonal period. In general, the results indicated that the intensity of expected extreme precipitation depends on the seasonal period and the place of occurrence of precipitation. The eastern NEB stood out as the region where the highest intensities of extreme precipitation are expected. Extreme precipitation values of up to 178 mm are expected in 2 years. The areas where natural disasters occurred in the years 2016, 2017 and 2018 are similar to those in which the highest rainfall intensities are expected. The results of this study can allow the evaluation of the spatial distribution of risks related to extreme precipitation events, and therefore, support the planning of regional public policies and environmental management for the prevention of natural disasters in NEB. K E Y W O R D S diaries precipitation, generalized Pareto distribution, natural disaster, TRMM 1 | INTRODUCTION Extreme weather events are one of the main topics studied related to climate change. Future projections released by the Intergovernmental Panel on Climate Change (IPCC) show that these events will become increasingly frequent and intense in several regions of the world, including in areas of Northeast Brazil (NEB; Du et al., 2019). Extreme weather events can cause waves of heat and cold, floods, landslides, droughts and more.
In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes.
The objective of this study was to analyze the influence of large-scale atmospheric–oceanic mechanisms (El Niño–Southern Oscillation—ENSO and the inter-hemispheric thermal gradient of the Tropical Atlantic) on the spatial–temporal variability of soy yield in MATOPIBA. The following, available in the literature, were used: (i) daily meteorological data from 1980 to 2013 (Xavier et al., 2016); (ii) (chemical, physical, and hydric) properties of the predominant soil class in the area of interest, available at the World Inventory of Soil Emission Potentials platform; (iii) genetic coefficients of soybean cultivar with Relative Maturity Group adapted to the conditions of the region. The simulations were performed using the CROPGRO-Soybean culture model of the Decision Support System for Agrotechnology Transfer (DSSAT) system, considering sowing dates between the months of October and December of 33 agricultural years, as well as for three meteorological scenarios (climatology, favorable-wet, and unfavorable-dry). Results showed that the different climate scenarios can alter the spatial patterns of agricultural risk. In the favorable-wet scenario, there was a greater probability of an increase in yield and a greater favorable window for sowing soybean, while in the unfavorable-dry scenario these values were lower. However, considering the unfavorable-dry scenario, in some areas the reduction in yield losses will depend on the chosen planting date.
The São Francisco River basin is one of the largest in the Brazilian territory. This basin has enormous economic, social and cultural importance for the country. Its water is used for human and animal supply, irrigation and energy production. This basin is located in an area with different climatic characteristics (humid and semiarid) and studies related to precipitation are very important in this region. In this scenario, the objective of this investigation is to present an assessment of rainfall estimated through the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) product compared with rain gauges over the São Francisco river basin in Brazil. For that, a period from of 20 years and 18 surface weather stations were used to evaluate the product. Based on different evaluation techniques, the study found that the IMERG is appropriate to represent precipitation over the basin. According to the results, the performance of the IMERG product depends on the location where the rain occurs. The bias ranged from −1.67 to 0.34 mm, the RMSE ranged from 5.36 to 10.36 mm and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from 0.28 to 0.61. The results obtained by Student t-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. The exception is the eastern portion of the basin, where the product, on average, underestimates the precipitation (p-value < 0.05) and presents the worst statistical metrics.
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