Abstract
This study analyzed the behavior of daily rainfall in the State of Paraiba using the data from five meteorological stations distributed across the mesoregions of this state. We used the three-state Markov Chain model, in which states are defined as dry, wet and rainy. We calculated transition probabilities among states, probabilities of equilibrium of states, and expected lengths of the defined states for all stations and seasons to investigate spatial/seasonal variability. Results showed that for the entire region and for all seasons, the probability of dry days is greater than the probability of rainy days; expected values of rainy spells are low, indicating that the rainfall regime in Paraiba is characterized by high rainfall intensity distributed over short rainy periods. The dry-dry transition probability presents the highest values for all seasons and stations, as well as the corresponding expected dry spell length, indicating that this region is subjected to prolonged dry periods. The transition probabilities that lead to dry condition are higher in the interior of the State, while probabilities that lead to rainy condition are higher in the coastal region as well as the probability of rainy days, which is greater in fall, during the rainy season.
Highlights
Depletion of water resources in different parts of the world is one of the renowned environmental problems of this century
This study aimed to provide further insight into the pattern of rainfall distribution in the Brazilian northeast, the State of Paraíba with a large part located in the so called ‘semiarid polygon’, making it extremely vulnerable to rainfall seasonal and interannual variability (Silva, Costa, Campos, & Dantas, 2009)
The results of goodness-to-fit test indicate that Markov chain is an appropriate model for rainfall dynamics in Paraíba
Summary
Depletion of water resources in different parts of the world is one of the renowned environmental problems of this century. An essential aspect of water resources planning is the analysis of long-term records of hydro−meteorological variables. As a primary input to the hydrological cycle, rainfall represents the potential availability of water resources of an area (Maruyama, Kawachi, & Singh, 2005). The knowledge about the daily rainfall distribution is fairly crucial for water use practices and future planning in agriculture (planting, irrigation and drainage), civil defense (risk of landslide, forest fires or floods), and hydrology (river flow estimation, sediment transport) (Cull, Hearn, & Smith, 1981; Ingram, Roncoli, & Kirshen, 2002; Seeger et al, 2004; Pereira, Trigo, Camara, Pereira, & Leite, 2005; Collischonn, Haas, Andreolli, & Tucci, 2005; Minder, Roe, & Montgomery, 2009; Guhathakurta, Sreejith, & Menon, 2011). The first order two state (‘rain’ and ‘no rain’) Markov model was applied on Tel Aviv daily rainfall data by Gabriel and Neuman (1962) and since Markov chain models including multistate (Haan, Allen, & Street, 1976; Pegram, 2009) higher order (Lana & Burgueño, 1998; Deni, Jemain, & Ibrahim, 2009), hidden Markov model (Hughes, Guttorp, & Charles, 1999; Robertson, Kirshner, & Smyth, 2004) and non-homogeneous Markov model (Rajagopalan, Lall, & Tarboton, 1996) were widely used for modeling daily occurrence of precipitation
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