Geostatistical Analysis of Pattern of Rainfall Distribution and Prediction in Taraba State, North-east Nigeria

Acquisition of up-to-date and reliable data has been a great obstacle for researchers in the fields of climatology and climate change. The manual instruments earlier used for acquisition of climatic data were prone to errors and inconsistencies ranging from inadequate skill, negligence, mechanical problems, inaccessibility due to hazards or related occurrences and insurgency. Automatic whether instruments were latter designed to solve or at least minimize the problems faced with measurements. However, many institutions especially in developing countries found it difficult to procure due to the high cost. In recent years, on-line climatic data were made available, many of which are free for research and related purposes. These on-line data are still not yet known by many in the developing countries, while the problem of acquisition and use as well as the authenticity are still being studied by most people. Therefore, in this study, Diva-GIS which is one of the most popular on-line climatic data was used to generate, map and predict the mean annual rainfall distribution in Taraba State. The mean annual rainfalls (1950-2000) of Eighty (80) settlements in the state were obtained from Diva-GIS which were used to map the mean annual rainfall distribution in the state. Geostatistical analysis was also used to assess the randomness of the rainfall distribution in the State which was found to tend towards clustering and which means that rainfall distribution in the State is not random but influenced by some factors. The study revealed that the distribution is mostly influenced by altitudes and latitudes, since high altitude areas such as Mambilla Mountains and the lower latitudes of southern parts of the State receive more rainfall than the plains and the higher latitudes of the northern parts respectively. It was recommended that if Taraba State Government and all other stakeholders imbibe this technique, most of the climatic data and maps that are not available either as data or information will not only be made available but will also be highly accessible with minimal cost.