Search

The study of rainfall trends is crucial for food security and water availability in Alagoas state, Northeast of Brazil. In this work, monthly, seasonal and annual rainfall trends have been studied (1960–2016) for homogeneous rainfall regions over the eastern part of the Northeast Brazil (ENEB) and later related to climate variability. Cluster analysis was applied to identify homogeneous rainfall regions while the Mann–Kendall (MK), modified Mann–Kendall (MMK) and Pettitt tests were used in the analysis and identification of trends on a spatial and temporal scale. To relate rainfall and climate variability modes, Spearman's correlation was used in each homogeneous region. The rainfall series provided evidence of a general decrease in rainfall in the rainy period and an increase in the dry period, mainly over the driest region. The break points of time series occurred mostly in periods of great variations in values of modes of climate variability, especially the Monthly Niño3.4 Index and the Southern Oscillation Index (SOI), both having a robust influence across the region. Moreover, the probable rainfall in the time series with trends was different in most months before and after the breakpoint. After the breakpoint, probable rainfall was lower, influenced by the breakpoint year (size of the series before and after the breakpoint), which mainly occurred in the 1980s and 1990s and presented a warm phase and a greater number of El Niño events. The MK and MMK trend tests showed the ability to detect trends, although there is no established standard on which test or version to use due to self‐correlated, nonhomogeneous series with nonrandom or nonindependent data. Rainfall is an important variable for water and food security and in the monitoring of natural disasters. The changes detected in this study can be used as reference information for public policies on water resources and future studies for Alagoas and similar regions. [ABSTRACT FROM AUTHOR]

Diffuse solar irradiation (H D) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, H D observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (H D h) and global solar irradiation (H G h) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating K D h (= H D h / H G h) (r2 > 0.726, modified Willmott – d m > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating K D h for hourly atmospheric transmittance (K T h) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, d m > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, d m > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating K D h superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for H D estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation. • Hourly diffuse solar irradiation fraction (K D h) was estimated using atmospheric transmittance(K t h). • Empirical models and artificial intelligence (AI) methods were assessed. • Solarimetric and, or meteorological variables were considered on AI methods. • The empirical models and AI methods had similar overall performance. • The AI methos estimating better the complexity of K D h for K T h > 0.80 [ABSTRACT FROM AUTHOR]