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 degree of spatial correlation between the data points (Webster & Oliver, 2007). Ordinary kriging (OK) is one of the most preferred stochastic interpolation methods for spatial rainfall estimation. One advantage of IDW is that, it’s easier to understand and it has simple procedures and fewer steps when compare with kriging. It explicitly implements the assumptions that things that are close to one another are more alike than those that are farther apart. The underlying idea of GWR is that, the parameters may be estimated anywhere in the study area given a dependent variable and a set of one or more independent variables which have been measured at places whose location is known. Taking Tobler’s observation about nearness and similarity into account, we might expect that if we wish to estimate parameters for a model at some location xi, then the observations which are nearer to that location should have a greater weight in the estimation than observations which are far away. However, geographically weighted regression (GWR) was specifically designed to deal with issues of spatial non-stationarity by measuring local relationships between the target and explanatory variables, which differ from location to location (Fotheringham et al., 2002). Unlike OK which depends on the set of variogram and regression parameters to summarize global relationships, GWR estimates local regression parameters and its model performance varies across a study region. In addition to that, the GWR model offer a better detail for spatial data which the researcher can easily apply it. According to Yu et al. (2009), GWR is one of the newly spatial regression framework that has been introduce to deal with spatial non-Stationarity analysis for the regression that gives different relationship to occur a different points in space. Hence, the best interpolation method for a particular study area is usually established through the comparative assessment of different interpolation methods (Delbari et al., 2013; Dirks et al., 1998; Goovaerts, 2000; Hsieh, Cheng, Liou, Chou, & Siao, 2006; Mair & Fares, 2011, Moral, 2010). Spatial variability is the most challenging part in meteorological variables, that is why generating the most accurate Model from any existing spatial data as well as describing the error and variability of the analytical surface becomes a key challenge facing climatologist. In some literatures reviews, the results of comparison in the spatial models differs from one study to another and the variation did not demarcates a certain pattern. Some researchers says, the estimate of climate data depends on a specific study area and the nature of environmental factors contributing to the climate change in that area. There is no single spatial method that can work well everywhere (Daly, 2006). Delbari, Afrasiab, and Jahani (2016), measure the analysis of spatial variation of rainfall using Geostatistic and Deterministics Models but eventually recommend the use of Geostatistical Methods, while Dirks et al. (1998), compared IDW with Thiessen polygon and OK in estimating the rainfall data, but finally recommended the use of IDW for interpolations which is deterministic Model. Menmeng et al. (2017), compared three spatial interpolation (i.e. Kriging, Splines and IDW) and two Regression Model (i.e Multiple Linear Regression and GWR) for predicting monthly Minimum, Average and Maximum Near Surface Temperature (NSAT) concluding that GWR is better than Kriging in the warm months, and kriging outperform GWR in the colder months. Many research on spatial interpolation that incorporate elevations as their auxiliary variable to see how rainfall varies with elevation in their study area (Sajal kumar et al 2017). In addition to that, this research adds to the existing literatures as the key step to the spatial Rainfall Prediction that is the impact of using Regression model in estimating Rainfall dataset, as rainfall normally comes in-between the warm and cold season in the study region considering four metrological variables mentioned above and the model can account for spatial heterogeneity which will allow the researcher to captures information of different Locations. Many years back, numerous studies have been dedicated to the comparison of different deterministic and geo-statistical in different regions around the world. Many studies have reported that rainfall is generally characterized by a significant spatial variation (e.g., Delbari, Afrasiab, & Jahani, 2013; Lloyd, 2005), and they advises that spatial methods which are capable of incorporating the spatial variability of rainfall into the estimation process should be engaged. In view of that, kriging becomes the most widely used geostatistical method for spatial interpolation/prediction of rainfall, the ability of kriging to produce spatial predictions of rainfall has been distinguished in many studies (e.g., Adhikary et al. 2016; Goovaerts, 2000; Jeffrey, Carter, Moodie, & Beswick, 2001; Lloyd, 2005; Moral, 2010; Yang, Xie, Liu, Ji & Wang, 2015). Goovaerts (2000) used three multivariate geostatistical methods (OCK, KED, simple kriging with varying local means [SKVM]), which include a DEM as secondary variable and three univariate methods (OK, TP, and IDW) that do not consider elevation in to account for spatial prediction of monthly and annual rainfall data. Martínez-cob (1996) compared OK, OCK, and improved residual kriging to interpolate annual rainfall in Spain, and the results indicated that OCK was better for rainfall estimation; reducing estimation error when compared with OK and modified residual kriging respectively. Hsieh et al.
CHAPTER THREE
Research Design and Methodology
METHODOLOGY
In attempt to achieve the key goal of this work, the methodological framework adopt the use of three different spatial models for the average Rainfall data of 30 selected towns in the north western part of Nigeria in order to determine the best outperforming model that can be used for Rainfall prediction in the study region.
STUDY AREA
The area covers 30 Sampled (known) locations and a grids of 30 Unsampled (Unknown) Locations which lies between the latitude of 130 54’ 58”N and 80 56’46”N and the Longitude of 30 29’11”E and 100 36’15”E.
Ordinary Kriging: Is the simplest method of interpolation that measures values by linear combination using variogram to define the weight of data and the spatial correlation. Georges Matheron 1960. The variogram model used to calculate the covariance, and the covariance then used to calculate the weight base on distance. z(xi ) Is the estimated variable which have the variogram/autovariogram g (h) . The variogram can mathematically be stated as.
CHAPTER FOUR
RESULTS AND DISCUSSION
In this section, we presented the diagnostic part of the research, where the findings have been used to evaluate the optimal model base on the validation methods discussed earlier.
CHAPTER FIVE
Summary, Conclusion and Recommendation
Summary of Findings
In this project, a geostatistical approach was employed to model the mean surface temperature of Nigeria. The study aimed to provide insights into temperature patterns, spatial variability, and the accuracy of the geostatistical model.
The descriptive analysis of the temperature data revealed that Nigeria experiences diverse temperature patterns, influenced by factors such as latitude, altitude, proximity to water bodies, and land cover types. The data exhibited a range of temperatures, with variations across different regions of the country.
The geostatistical model developed in this study showed promising results in estimating mean surface temperature in Nigeria. The model incorporated spatial dependence and auxiliary variables, such as elevation and land cover, to capture the spatial variability of temperature. The model’s predictions were compared with existing temperature datasets, such as meteorological station data and satellite-based measurements, to assess its accuracy.
The comparison with existing datasets indicated a good agreement between the geostatistical model and the observed temperature values. Statistical metrics, such as correlation coefficients, mean absolute error, and bias, showed a high level of agreement between the model’s predictions and the observed data. This suggests that the geostatistical model is reliable in capturing temperature patterns and can provide accurate estimates of mean surface temperature in Nigeria.
The spatial analysis of the temperature data revealed interesting patterns and variability across Nigeria. Hotspots and cold spots were identified, indicating regions with consistently high or low temperatures. The analysis also highlighted the influence of topography, land cover, and proximity to water bodies on temperature distribution.
The findings of this study have several implications for climate studies, urban planning, and decision-making processes in Nigeria. The accurate temperature estimates provided by the geostatistical model can support climate change assessments, urban heat island mitigation strategies, agricultural management, and resource conservation efforts. The model can serve as a valuable tool for policymakers and decision-makers in various sectors, enabling evidence-based decision-making and climate change adaptation strategies.
However, it is important to acknowledge the limitations of this study. These include data availability and quality, spatial and temporal resolution, assumptions of geostatistical modeling, and generalizability of the findings. Future research should address these limitations and further refine the geostatistical model for temperature modeling in Nigeria.
In conclusion, this project successfully developed a geostatistical model for mean surface temperature in Nigeria. The model demonstrated good accuracy and reliability in capturing temperature patterns and variability. The findings of this study have significant implications for climate studies, urban planning, and decision-making processes in Nigeria, providing a foundation for informed decision-making and climate change adaptation strategies.
Implications of the Study
The findings of this temperature modeling study in Nigeria have several implications for various sectors and decision-making processes. These implications include:
1. Climate change assessments: Accurate temperature modeling is crucial for assessing the impacts of climate change on different sectors, such as agriculture, water resources, and human health. The geostatistical model developed in this study provides reliable temperature estimates, which can contribute to more robust climate change assessments in Nigeria. It can help identify regions that are more susceptible to temperature changes and guide adaptation strategies.
2. Urban planning and infrastructure development: Temperature plays a significant role in urban planning and the design of infrastructure. The geostatistical model can help identify urban heat islands, areas with higher temperatures due to urbanization, and guide the development of strategies to mitigate their effects. This information can inform urban planners and policymakers in Nigeria to design more sustainable and climate-resilient cities.
3. Agricultural management: Temperature is a critical factor in agricultural productivity and crop growth. The accurate temperature estimates provided by the geostatistical model can assist farmers and agricultural stakeholders in making informed decisions regarding crop selection, planting schedules, and irrigation management. This can lead to improved agricultural management practices, increased crop yields, and enhanced food security in Nigeria.
4. Resource management and conservation: Temperature influences the availability and quality of natural resources, such as water bodies and ecosystems. The geostatistical model can aid in the management and conservation of these resources by providing accurate temperature estimates. This information can guide sustainable resource use, protect sensitive ecosystems, and support biodiversity conservation efforts in Nigeria.
5. Policy-making and decision support: The geostatistical model developed in this study can serve as a valuable tool for policymakers and decision-makers in various sectors. It provides reliable temperature estimates for different regions of Nigeria, supporting evidence-based decision-making processes related to climate change adaptation, urban planning, agriculture, and resource management. The model can help inform policies and strategies that promote sustainable development and resilience to climate change impacts.
Overall, the implications of this study highlight the importance of accurate temperature modeling in understanding climate dynamics, informing decision-making processes, and promoting sustainable development in Nigeria. The findings can contribute to climate change adaptation strategies, urban planning initiatives, agricultural management practices, and resource conservation efforts in the country.
Limitations and Future Research Directions
While this study has provided valuable insights into temperature modeling in Nigeria using a geostatistical approach, there are several limitations that should be acknowledged. These limitations include:
1. Data limitations: The accuracy and reliability of the temperature model are dependent on the availability and quality of data. Data gaps, inconsistencies, and biases in the temperature datasets may affect the accuracy of the model. Future research should focus on improving data collection efforts, enhancing data coverage and resolution, and addressing data quality issues.
2. Spatial and temporal resolution: The resolution of the temperature model may be limited by the spatial and temporal resolution of the available data. Coarse-resolution data may not capture fine-scale temperature variations accurately. Future research should explore the use of higher-resolution data sources, such as remote sensing, to improve the spatial and temporal resolution of the temperature model.
3. Model validation: While efforts have been made to validate the geostatistical model, further validation using independent datasets is necessary to assess its robustness and generalizability. Future research should focus on collecting additional temperature data for validation purposes and comparing the model’s predictions with independent observations.
4. Uncertainty analysis: The geostatistical model provides estimates of mean surface temperature, but it is important to quantify the uncertainty associated with these estimates. Future research should incorporate uncertainty analysis techniques, such as Monte Carlo simulations or bootstrapping, to provide confidence intervals or probability distributions for the temperature estimates.
5. Integration of climate change scenarios: Climate change is expected to have a significant impact on temperature patterns. Future research should incorporate climate change scenarios into the temperature model to assess the potential future changes in temperature and their implications for different sectors. This will help in developing more robust adaptation and mitigation strategies.
6. Socio-economic factors: The geostatistical model developed in this study primarily focuses on physical and climatic factors. Future research should consider incorporating socio-economic variables, such as population density, urbanization, and land use, to better understand the drivers and impacts of temperature variations in Nigeria.
In conclusion, while this study has made significant contributions to temperature modeling in Nigeria, there are several limitations that should be addressed in future research. Overcoming these limitations will enhance the accuracy and reliability of temperature models and provide a more comprehensive understanding of temperature patterns and their implications for various sectors in Nigeria.
Recommendations for Policy and Decision Making
Based on the findings of the temperature modeling study in Nigeria, the following recommendations can be made for policy and decision-making processes:
1. Climate change adaptation strategies: Incorporate the temperature model’s findings into climate change adaptation strategies. The accurate temperature estimates can help identify regions that are more vulnerable to temperature extremes and heatwaves. This information can guide the development of targeted adaptation measures, such as heatwave early warning systems, urban heat island mitigation strategies, and improved cooling infrastructure in urban areas.
2. Urban planning and design: Utilize the temperature model to inform urban planning and design decisions. The model can identify areas with higher temperatures, which are more prone to heat stress and discomfort. This information can guide the development of green spaces, tree planting initiatives, and the placement of cooling infrastructure, such as shading structures and water bodies, to mitigate the urban heat island effect and improve the thermal comfort of urban areas.
3. Agriculture and food security: Integrate the temperature model’s findings into agricultural management practices. The model can provide insights into temperature variations across different agricultural regions, helping farmers make informed decisions regarding crop selection, planting schedules, and irrigation management. This information can contribute to improved agricultural productivity, water resource management, and food security in Nigeria.
4. Natural resource management: Incorporate the temperature model’s results into natural resource management strategies. Temperature influences the availability and quality of water bodies, ecosystems, and biodiversity. The model can help identify regions that are more susceptible to temperature-related impacts, such as changes in water availability and shifts in ecological habitats. This information can guide the conservation and sustainable management of natural resources in Nigeria.
5. Health and public safety: Utilize the temperature model to inform public health and safety measures. The model can identify regions with higher temperatures, which are more prone to heat-related health risks. This information can guide the development of heatwave preparedness plans, public awareness campaigns, and the provision of cooling centers and adequate healthcare services in vulnerable areas.
6. Infrastructure development: Incorporate the temperature model’s findings into infrastructure development plans. The model can identify areas with higher temperatures, which may require additional cooling infrastructure, such as air conditioning systems, in buildings, transportation systems, and public spaces. This information can help ensure the resilience and sustainability of infrastructure in the face of increasing temperatures.
7. Data-driven decision-making: Promote the use of data-driven decision-making processes in policy formulation and implementation. The temperature model provides reliable and accurate temperature estimates, which can serve as a valuable resource for evidence-based decision-making. Encourage policymakers and stakeholders to utilize the temperature model’s findings to inform policies, strategies, and investments that address climate change impacts and promote sustainable development in Nigeria.
It is important to note that these recommendations should be tailored to the specific context and needs of Nigeria. Stakeholder engagement and collaboration are crucial for the successful implementation of these recommendations. Additionally, regular updates and improvements to the temperature model should be considered to ensure its continued relevance and accuracy in supporting policy and decision-making processes.
Conclusion
In conclusion, this temperature modeling study in Nigeria has provided valuable insights into the mean surface temperature patterns, spatial variability, and the accuracy of a geostatistical model. The study has highlighted the diverse temperature patterns across different regions of Nigeria, influenced by factors such as latitude, altitude, land cover, and proximity to water bodies.
The geostatistical model developed in this study has demonstrated good accuracy and reliability in estimating mean surface temperature in Nigeria. The model incorporated spatial dependence and auxiliary variables to capture the spatial variability of temperature. The model’s predictions were compared with existing temperature datasets, and the results showed a high level of agreement, indicating the model’s reliability in capturing temperature patterns.
The findings of this study have important implications for various sectors and decision-making processes in Nigeria. They can contribute to climate change assessments, urban planning initiatives, agricultural management practices, resource conservation efforts, and public health strategies. The accurate temperature estimates provided by the geostatistical model can support evidence-based decision-making, promote sustainable development, and enhance resilience to climate change impacts.
However, it is important to acknowledge the limitations of this study, including data availability and quality, spatial and temporal resolution, and model assumptions. Future research should address these limitations and further refine the temperature modeling approach in Nigeria.
Overall, this study has advanced our understanding of temperature patterns in Nigeria and provided a valuable tool for policymakers, planners, and researchers. By incorporating the findings of this study into policy and decision-making processes, Nigeria can better adapt to climate change, plan sustainable urban development, manage agricultural resources, conserve natural ecosystems, and protect public health.
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