Análise das Estimativas da Precipitação Diária do Produto GPM-IMERG na Bacia Hidrográfica do Rio Sapucaí, Região Sudeste do Brasil

Tendo em vista que as estimativas de precipitação (PP) por satélite são importantes fontes de informações para modelos hidrológicos, o objetivo deste estudo é avaliar os acumulados diários de PP do produto Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) - Early Run do Global Precipitation Measurement (GPM) na Bacia Hidrográfica do Rio Sapucaí (BHRS) que se encontra localizada no sudeste do Brasil. Para realizar essa avaliação foram utilizadas métricas estatísticas de performance e de contingência. Os dados utilizados na validação foram os acumulados diários de PP das estações pluviométricas da Agência Nacional de Águas (ANA). O período analisado no estudo compreende os verões dos anos de 2015 a 2019. No geral, os resultados indicaram que o IMERG subestima em média 27% a PP diária sobre a bacia, sendo que o RMSE é da ordem de 12,9 a 28,5 mm/dia. Além disso, foi observado também que os valores do coeficiente de correlação de Pearson na maioria dos pontos de grade analisados ficaram abaixo de 0,7. Isso indica que não existe uma boa correlação entre os dados do IMERG com os dados das estações pluviométricas. As métricas estatísticas de contingência mostraram que o IMERG - Early Run possui baixa capacidade para descrever os eventos de chuva na BHRS. Portanto, pode-se inferir que o produto Early Run do GPM-IMERG possui dificuldades em estimar a PP diária na BHRS durante os meses de verão

THE INFLUENCE OF THE REMOTELY SENSED RAINFALL PRODUCTS’ SPATIAL RESOLUTION TO UNMASK EXTREME EVENTS IN NORTHEAST BRAZIL

This work presents the influence of the spatial resolution on precipitation samples to understand extreme events in the Agreste region of Pernambuco, northeast of Brazil. Among the materials used, the following sources of precipitation data (1998 to 2019) can be cited: The Tropical Rainfall Measuring Mission (TRMM), the Climatic Research Unit (CRU), and weather stations. In the process of validating the precipitation time series with the weather stations, the TRMM data showed a strong Pearson correlation (0.86 - 0.90) and the CRU data a moderate one (0.71 - 0.76). The relative bias (RB) and the standard deviation of observation ratio (RSR) were also calculated to identify the data’s trend, which showed an overestimation for both sources. The extreme events were identified through the calculation of the Standardized Precipitation Index (SPI), where the TRMM with strong correlation (0.80 - 0.91) obtained a better performance than the CRU data. The TRMM data were selected to understand the extreme drought events in the study area, where the cities with altitudes above 500m obtained maximum values of probability of occurrence with 19%. Conversely, for extreme humidity events, the maximum was 14% for those with altitudes below 200m

Evaluating the latest IMERG products in a subtropical climate : the case of Paraná state, Brazil

The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis

Improving an Extreme Rainfall Detection System with GPM IMERG data

Many studies have shown a growing trend in terms of frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a key point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG products proved to be the most appropriate source of information for precipitation retrievement by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall events for varying time aggregation intervals. This is performed by comparing the IMERG data with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h aggregation interval ensures a probability of detection (defined as the number of hits divided by the total number of observed events) greater than 80%. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall events using a threshold methodology based on the mean annual precipitation

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0–0.5 mm/day) and heavy (>25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications

High-resolution gridded climate dataset for data-scarce region

The knowledge of spatiotemporal distribution of climate variables is essential for most of hydro-climatic studies. However, scarcity or sparsity of long-term observations is one of the major obstacles for such studies. The main objective of this study is to develop a methodological framework for the generation of high-resolution gridded historical and future climate projection data for a data-scarce region. Egypt and its densely populated central north region (CNE) were considered as the study area. First, several existing gridded datasets were evaluated in reproducing the historical climate. The performances of five high-resolution satellite-based daily precipitation products were evaluated against gauges records using continuous and categorical metrics and selected intensity categories. In addition, two intelligent algorithms, symmetrical uncertainty (SU) and random forest (RF) are proposed for the evaluation of gridded monthly climate datasets. Second, a new framework is proposed to develop high-resolution daily maximum and minimum temperatures (Tmx and Tmn) datasets by using the robust kernel density distribution mapping method to correct the bias in interpolated observation estimates and WorldClim v.2 temperature climatology to adjust the spatial variability in temperature. Third, a new framework is proposed for the selection of Global Climate Models (GCMs) based on their ability to reproduce the spatial pattern for different climate variables. The Kling-Gupta efficiency (KGE) was used to assess GCMs in simulating the annual spatial patterns of Tmx, Tmn, and rainfall. The mean and standard deviation of KGEs were incorporated in a multi-criteria decision-making approach known as a global performance indicator for the ranking of GCMs. Fourth, several bias-correction methods were evaluated to identify the most suitable method for downscaling of the selected GCM simulations for the projection of high-resolution gridded climate data. The results revealed relatively better performance of GSMaP compared to other satellite-based rainfall products. The SU and RF were found as efficient methods for evaluating gridded monthly climate datasets and avoid the contradictory results often obtained by conventional statistics. Application of SU and RF revealed that GPCC rainfall and UDel temperature datasets as the best products for Egypt. The validation of the 0.05°×0.05° CNE datasets showed remarkable improvement in replicating the spatiotemporal variability in observed temperature. The new approached proposed for the selection of GCMs revealed that MRI-CGCM3 gives the best performance and followed by FGOALS-g2, GFDL-ESM2G, GFDL-CM3 and lastly MPI-ESM-MR over Egypt. The selected GCMs projected an increase in Tmx and Tmn in the range of 2.42 to 4.20°C and 2.34 to 4.43°C respectively for different scenarios by the end of the century. Winter temperature is projected to increase higher than summer temperature. For rainfall, a 62% reduction over the northern coastline is projected where rain is currently most abundant with an increase of rainfall over the dry southern zones. Linear and variance scaling methods were found suitable for developing bias-free high-resolution projections of rainfall and temperatures, respectively. As for the CNE, the high-resolution projections showed a rise in maximum (1.80 to 3.48°C) and minimum (1.88 to 3.49°C) temperature and change in rainfall depth (-96.04 to 36.51%) by the end of the century, which could have severe implications for this highly populated region