Modeling Mean Surface Temperature of Nigeria Using Geostatistical Approach Chapter One Objectives

Modeling Mean Surface Temperature of Nigeria Using Geostatistical Approach

Chapter One

Objectives

The main objective of this project is to model the mean surface temperature of Nigeria using a geostatistical approach. To achieve this overarching goal, the following specific objectives will be pursued:

1. Collect and compile temperature data: Gather temperature data from various sources, including meteorological stations, remote sensing, and climate reanalysis datasets. Ensure data quality and consistency by addressing data gaps, inconsistencies, and biases.

2. Preprocess and analyze temperature data: Clean and preprocess the collected temperature data, including data quality control, outlier detection, and spatial interpolation. Conduct exploratory data analysis to identify spatial and temporal patterns in the temperature data.

3. Develop a geostatistical model: Apply geostatistical techniques, such as kriging, to develop a spatially explicit model for mean surface temperature in Nigeria. Incorporate relevant covariates, such as elevation, land cover, and proximity to water bodies, to improve the model’s accuracy and capture spatial heterogeneity.

4. Validate and evaluate the model: Validate the developed geostatistical model using independent temperature observations or cross-validation techniques. Assess the model’s performance by comparing predicted temperatures with observed temperatures and evaluating statistical metrics, such as root mean square error (RMSE) and coefficient of determination (R-squared).

5. Analyze spatial patterns and variability: Analyze the spatial patterns and variability of mean surface temperature across Nigeria. Identify regions with high or low temperature values, hotspots, and areas experiencing significant temperature changes over time. Explore the influence of topography, land cover, and other factors on temperature variations.

6. Compare with existing temperature datasets: Compare the results of the geostatistical model with existing temperature datasets, such as gridded datasets or satellite-derived estimates. Assess the accuracy and reliability of the geostatistical model by evaluating its agreement with these reference datasets.

7. Provide recommendations and implications: Summarize the findings of the temperature modeling study and discuss their implications for climate studies, urban planning, and decision-making processes in Nigeria. Provide recommendations for utilizing the temperature model in various sectors, such as agriculture, water resource management, and climate change adaptation strategies.

By accomplishing these objectives, this project aims to contribute to the understanding of mean surface temperature patterns in Nigeria and provide a reliable geostatistical model that can be used for climate research, planning, and policy-making in the country.

CHAPTER TWO

Literature

The research focus on rainfall prediction and some meteorological variables such as Temperature, Pressure, Humidity and Wind-Speed that contributes towards the annual precipitation in the north western part of Nigeria. Rainfall is the major climate resources that can be used as an index of climate change (Adhikary et al. 2016). Rainfall by definition is a liquid in the form of droplets that has a condensed from the atmospheric water vapor and then became heavy enough to fall under gravity. The region under study was blessed with a fertile land, and if there is enough Rainfall and other supportive agricultural factors are okay, then there will be a bumper harvest. Rainfall is the most essential aspect in a farming system as it determines the accessibility of soil needed for maximum yield (Niles et al. 2015). Ismail, and Oke (2012) Crops Animals and Humans derived their water resources mainly from it and Irrigation scheduling depends on the correct estimation of the spatial distribution of rainfall and it also determines the time in which some crops types can be cultivated and the appropriate farming system for optimum yields. In this research, we compared the performance of Ordinary Kriging (OK), Geographically Weighted Regression (GWR) and Inverse Distance Weight (IDW) as the models are vital in spatial analysis. The major advantage of kriging is that, it takes into the account of spatial correlation between the data points and provides unbiased estimates with a minimum variance. The spatial variability in Kriging is quantified by using variogram that defines the 

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