Monica Cristina Meschiatti

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of Sao Paulo, Brazil (1890-2012). As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.

Increasing the regional availability of the Standardized Precipitation Index: an operational approach

The need to use a length of rainfall records of at least 30 years to calculate the Standardized Precipitation Index (SPI) limits its application in several Drought Early Warning Systems of developing countries. Therefore, in order to increase the number of weather stations in which the SPI may be applied, this study quantified the difference among SPI values derived from calibration periods (CP) smaller than 30 years in respect to those computed from the 30-year period of 1985 – 2014 in the State of Sao Paulo, Brazil (time scales ranging from 1 to 12 months were considered). The correlation, agreement and consistency of SPI values derived from CP ranging from the last 30 to 21 years have been evaluated. The Kolmogorov-Smirnov/Lilliefors test indicated, for all CP, that the 2-parameter gamma distribution may be used to calculate the SPI in the State of Sao Paulo. The normality test indicated that, even for the period of 1985 – 2014, the normally assumption of the SPI series is not always met. However, it was observed no remarkable difference in the rejection rates of the normality assumption obtained from the different CP. Finally, both absolute mean error and the modified index of agreement indicated a high consistence among SPI values derived from the calibration period of 1991 – 2014 (24 years) in respect to those derived from the 30-year period. Accordingly, it is possible to use weather stations with rainfall records starting in 1991 (or earlier) to calculate, in operational mode, the SPI in the State of Sao Paulo.

Distribuições de Tendências Sazonais de Temperatura Média e Precipitação nos Biomas Brasileiros

The aim of this work was to identify climate trends in time series of seasonal rainfall indexes and average air temperatures, registered in 243 Brazilian locations. Based on data from National Institute of Meteorology (INMET) Mann-Kendall and Pettitt statistical tests were used. The average temperature was the variable where the most quantity of positive trends were observed, mainly during 1990 decade, with highlight on Amazon and Cerrado biomes and negative trends were observed mainly at the Caatinga and Cerrado biomes during Fall, Winter and Spring. For the rainfall it was observed negative trends on all four year seasons on 1980, 1990 and 2000 decades and positive trends were observed on 2000 decade during Fall and Winter, mainly at Amazon and Atlantic Forest.

On the performance of three indices of agreement: an easy-to-use r-code for calculating the Willmott indices

A key step for any modeling study is to compare modelproduced estimates with observed/reliable data. The original index of agreement (also known as original Willmott index) has been widely used to measure how well model-produced estimates simulate observed data. However, in its original version such index may lead the user to erroneously select a predicting model. Therefore, this study compared the sensibility of the original index of agreement with its two newer versions (modified and refined) and provided an easy-to-use R-code capable of calculating these three indices. First, the sensibility of the indices was evaluated through Monte Carlo Experiments. These controlled simulations considered different sorts of errors (systematic, random and systematic + random) and errors magnitude. By using the R-code, we also carried out a case of study in which the indices are expected to indicate that the AGROMETEOROLOGY Article On the performance of three indices of agreement: an easy-to-use r-code for calculating the Willmott indices Heloisa Ramos Pereira, Monica Cristina Meschiatti, Regina Célia de Matos Pires, Gabriel Constantino Blain* Instituto Agronômico Centro de Ecofisiologia e Biofísica Campinas (SP), Brazil. *Corresponding author: gabriel@iac.sp.gov.br Received: Feb. 15, 2017 – Accepted: May 29, 2017 empirical Thornthwaite’s model produces poor estimates of daily reference evapotranspiration in respect to the standard method Penman-Monteith (FAO56). Our findings indicate that the original index of agreement may indeed erroneously select a predicting model performing poorly. Our results also indicate that the newer versions of this index overcome such problem, producing more rigorous evaluations. Although the refined Willmott index presents the broadest range of possible values, it does not inform the user if a predicting model overestimate or underestimate the simulated data, resulting in no extra information regarding those already provided by the modified version. None of the indices represents the error as linear functions of its magnitude in respect to the observed process.