Paper Sample on Climate Change and Rainfall in Sub-Saharan Countries: Challenges and Solutions

Introduction

Rainfall plays a significant role in the developing countries of sub-Saharan because these countries mainly depend on rain for agricultural production (Mahuni, 2016). Also, since agriculture is the backbone for most West African countries and Africa at large, there have been many concerns about the global climate change trends (Nouaceur and Murarescu, 2020). These changes lead to extreme rainfall conditions where some parts receive too much rainfall that cause floods, while others experience very little that leads to drought and famine (Mahuni, 2016). Ghana, as a country in West Africa, is not an exception to these changes.

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According to Mircea Grecu and William S. Olson, precipitation retrievals from satellite combined radar and radiometer observations approaches of estimation are faced with various challenges. The two approaches are based on optimal estimation theory. Regardless of the procedure used to address the optimization problem, other challenges, such as the mismatch between the two methods of estimation and mathematical errors, need to be addressed. In their article, the two researchers used Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) algorithm approaches to mitigate the challenges. In addition to that, they availed suggestions and potential solutions to the identified problems. The main difficulty in obtaining Path Integration Attenuation estimates derived from satellite radiometer observations is that low-frequency radiometer stations are higher than space-born radars. Regardless of the inconsistencies between the frequencies, the estimation of rainfall information from satellite combined radar and radiometer observations is a topic that has attracted researches in recent years because of the associated benefits that the combined approaches bring on the table for the advances of satellite radiometer rainfall estimation algorithms. The information obtained from this article introduces challenges that various methods of estimation are faced with. Knowledge of these challenges will help in the development of the research on TAMSAT estimates.

Mountain areas

In areas such as mountain areas where rain gauges are sparse, high-resolution precipitation products are essential for evaluating the areas' water cycles according to Gao and Liu (2020). Gao and Liu set out to assess high-resolution satellite precipitation products using rain gauge observation in the Tibetan Plateau in northwestern China. Their research was spread out through a period of 6 years (Gao and Liu, 2013). The four main data sets used included, Tropical Rainfall Measuring Mission, Climate Prediction Center Morphing Technique, Multi-satellite Precipitation Analysis 3B42 version 6 as well as Precipitation Estimation. Among the data sets, Multi-satellite Precipitation Analysis 3B42 version 6 was the least biased factor brought about by the correction method. The main observations from their research are that the four data sets showed increased similarity with rain gauge measurements in humid regions than arid areas. The linear regression model was used to investigate the bias relationship with the topography of the areas. Besides, the three precipitation factors (moisture supply, frontal position and unstable atmosphere) were observed to overestimate the light shower. On the other hand, they underestimated moderate as well as heavy rainfall. Their study also showed the importance of rain gauge correction on the accuracy of estimating rainfall. From the research, rainfall gauge measurements variations are introduced. This will be the basis of comparing rain gauge estimates with satellite obtained estimates.

African countries vulnerable in cases of rainfall variations and fluctuation during different seasons. Therefore, real-time monitoring of rain is crucial (Maidment et al., 2012). The information obtained can be used in warning the farmers of any probable crop shortfall in areas that occasionally experience drought. This was an observation made by Maidment, Grimes, and Rojas while investigating satellite-based and model re-analysis rainfall estimates for Uganda (Maidment et al. 2012). The countries are, however, faced with the challenge of depending on ground-based observations, which are faced with various challenges that make the data obtained unreliable. Yet, this gap can be filled through data collected from satellite-based methods such as numerical models and algorithms. The plans will, however, require critical analysis to come up with applicable and usable data. In their studies, they used three satellite products, two numerical models, and a network of twenty-seven rain gauges. Rainy season from February to June was considered for a period of four years from 2001 to 2005. The three satellite products used exhibited comparable traits that can be used in rainfall estimates.

Evaluation of water resources

Evaluation of water resources is challenging in most regions of Africa that are data-sparse since estimation availability of water needs a thoughtful consideration of the spatial data and temporary rainfall variations (Asadullah et al., 2008). Therefore, it is necessary for a country to have reliable data sources that will be used to analyse various seasons. A study was conducted by Anita Asadullah, Neil Mcintyre, and Max Kigobe to evaluate five satellite products (TRMM, CMORPH (Climate Prediction Center MORPHing technique), TAMSAT, RFE (Recursive Feature Elimination) and PERSIANN) for the approximation of rainfall in Uganda in East Africa. The research made use of five satellite-based algorithms for the estimation of rainfall alongside historical rainfall data that was obtained from rain gauges in four regions of Uganda. The focus of the research was to analyse the differences between the products as well as the accuracy in estimating rainfall.

All the agricultural activities, including crop production, farming methods, and the growing seasons, depend on the country's climatic condition. Therefore, a country needs to have the right information about their weather to advise the citizens on the best course of action. This is highlighted in research by Peter Kwebenah Acheampong, who was investigating the water balance analysis in Ghana (Acheampong 1990). In his study, he compared the mean monthly statistics of potential evaporation and the rainfall in the country. The quantity of the water that evaporates from the soil, open water sources, water that transpires from plants, and the water stored in the ground was known during the research. The two factors, evaporation rate, and rainfall, are treated as two independent climatic variables since their annual course rarely relates. There are periods when the country experiences water shortages while other times, it undergoes a surplus of water. In different months of the year, various regions in Ghana experience different climatic regions. There are also extreme circumstances in which almost all the months of the year, someplace in the country potential evaporation exceeds rainfall and vice versa (Poméon et al., 2018).

Conclusion

Satellite obtained estimates help to provide consistent statistics that can be used to estimate rainfall conditions to improve the preparedness to appreciate the different rainfall variations. In a study conducted by Dinku, Grover, and Connor, extensive changes of 10 different satellite rainfall products were used from network-based stations from a complex topography (Sadeghi et al., 2019). One of the main techniques that were used includes Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates. This technique improved the confidence of the results obtained from a vigorous evaluation. The study was spread out through 10 days of activities. TAMSAT technique was based on the assumption that cold cloud-top temperature of the tropical storms recognises clouds associated with rain. The temperatures are achieved from Meteosat thermal-infrared images (Dinku et al., 2007). TAMSAT will improve the sparse nature of rainfall data since it shows a good comparison with the rain gauge measurements, especially at accumulations of rain that are below 100 mm. There is also no significant bias for accumulations above 100 mm. The technique also seems to overestimate high rainfall values. TAMSAT algorithm also shows less scatter as compared to other estimation algorithms. Besides, the algorithm has statistics that have higher efficiency.