1.1 Introduction
Drought is a natural hazard characterized by prolonged periods of deficient precipitation that result in significant hydrological, agricultural, and socio-economic impacts. According to the World Meteorological Organization (WMO), drought can be defined as “a prolonged absence or marked deficiency of precipitation, a deficiency of precipitation that results in water shortage for some activity or for some group, or a period of abnormally dry weather sufficiently prolonged for the lack of water to cause serious hydrologic imbalance in the affected area” (WMO, 2006).
This chapter will address the background information that motivated this study, the challenges that prompted it, its aim, and its objectives as a preface to subsequent sections of the study. Additional factors include the study's significance, scope, limitations, research questions and hypotheses, and the definition of technical terms.
1.2 Background of Study
Drought has long been recognized as one of the most devastating natural hazards due to its slow onset, prolonged duration, and widespread socio-economic impacts. Unlike other disasters such as floods or earthquakes, droughts evolve silently and are often detected only after significant damage has occurred to agricultural productivity, water resources, and livelihoods. The increasing frequency and intensity of droughts, exacerbated by climate change and land degradation, have made it necessary for nations and global stakeholders to adopt proactive measures for early detection and response (IPCC, 2021).
Traditionally, drought monitoring has relied heavily on in-situ meteorological observations, such as rainfall and temperature data collected from weather stations. However, many developing countries, particularly those in sub-Saharan Africa and parts of Asia, lack sufficient ground-based monitoring infrastructure, resulting in limited spatial and temporal data coverage (Wilhite & Pulwarty, 2017). In response to these limitations, the integration of geospatial technologies especially satellite remote sensing and Geographic Information Systems (GIS) has emerged as a vital alternative for effective drought monitoring.
In recent years, the frequency, intensity, and duration of drought events have increased due to climate variability and change, making the development of effective drought monitoring and early warning systems (EWS) more essential than ever. Traditional drought monitoring approaches often rely on ground-based observations and historical climatic data, which are limited by spatial coverage, reporting frequency, and infrastructure. Kogan (2000) reported that a Drought Monitoring and Early Warning System (DMEWS) using geospatial data involves the integration of various remotely sensed indicators such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), precipitation anomalies, and soil moisture data to assess drought conditions across diverse regions (Kogan, 2000).
FAO (2014) and NASA (2020) reported that several international organizations, including the Food and Agriculture Organization (FAO), the United Nations Convention to Combat Desertification (UNCCD), and NASA, have developed frameworks and tools that integrate geospatial data for drought assessment. The FAO’s Agricultural Stress Index System (ASIS) and NASA’s GRACE (Gravity Recovery and Climate Experiment) mission are examples of such efforts aimed at building capacity in drought-prone regions (FAO, 2014; NASA, 2020). The background for this study is grounded in the urgent need to improve drought resilience through the deployment of advanced technological tools. A geospatially enabled Drought Monitoring and Early Warning System (DMEWS) offers a scientifically sound, cost-effective, and scalable approach to addressing data scarcity, improving response time, and enhancing policy-making at local, national, and regional levels. As climate variability continues to alter hydrological cycles, the development and implementation of such systems become increasingly important for achieving sustainable environmental and socio-economic outcomes.
1.3 Statement of Problems
Investigation revealed that there is challenge with the interpretation and standardization of drought indicators derived from satellite data. Multiple indices are often used, but without proper calibration and validation against ground truth data, their accuracy and reliability are questioned. The absence of a universally accepted framework for combining multiple geospatial indicators into a single, actionable early warning system further complicates the effective use of these technologies (WMO, 2016). Geospatial data and technologies, including remote sensing and geographic information systems (GIS), offer a more efficient and scalable solution to these challenges. However, the adoption and implementation of such systems remain limited in many regions due to a lack of technical expertise, institutional coordination, and investment in data infrastructure. In some cases, where geospatial data is available, it is not effectively integrated into decision-making processes or communicated in a timely and user-friendly manner to those who need it most (Vogt et al., 2018).
Furthermore, the absence of integrated and accessible early warning systems limits the ability of stakeholders such as farmers, government agencies, and humanitarian organizations to take proactive steps in drought preparedness. Traditional approaches often fail to capture the spatial extent and severity of drought accurately, making response efforts reactive rather than preventive. Communities are frequently caught off guard, leading to food insecurity, water shortages, livestock loss, and widespread socio-economic disruption (UNDRR, 2015). Hence, it is against this backdrop that this study aims to address these issues by designing and implementing a drought monitoring and early warning system that uses geospatial data to provide timely, accurate, and location-specific information.
1.4 Aim and Objectives of Study
The aim of the study is to design and implement a Drought Monitoring and Early Warning System using geospatial data to enhance real-time drought detection, improve preparedness, and support timely response mechanisms in drought-prone regions. In achieving this aim, the following specific objectives were laid out as follows:
- To develop an integrated system that uses NDVI, LST, and precipitation anomalies to assess drought conditions.
- To evaluate the accuracy of geospatial drought indicators against historical drought records.
- To collect and analyze satellite-derived geospatial data relevant for drought monitoring.
- To establish a geospatial-based early warning framework for real-time drought alerts.
- To assess stakeholder accessibility and usability of the developed early warning system.
1.5 Research Questions
The study came up with research questions so as to be able to ascertain the above stated objectives. The specific research questions for the study are stated below as follows:
- How effective is geospatial data in detecting drought conditions in real time?
- What is the correlation between NDVI, LST, and precipitation anomalies in drought monitoring?
- How accurate are geospatial drought indicators compared to historical drought occurrences?
- How functional is the developed early warning framework in issuing timely alerts?
- How accessible and usable is the early warning system for local stakeholders?
1.6 Research Hypothesis
In order to pursue the objective of this study, the following generalized statements have been designed to guide and aids in obtaining the result for the experiment to be conducted. For this work, the null hypothesis will be represented with H0 while the alternative hypothesis will be represented with hypothesis H1.
- H0: Geospatial data does not significantly improve the timeliness and accuracy of drought monitoring and early warning systems.
- H1: Geospatial data significantly improves the timeliness and accuracy of drought monitoring and early warning systems.
1.7 Significance of Study
The outcome of this study will contribute to improving early detection of drought using modern geospatial tools. It will also support proactive planning and resource allocation by government agencies.
Furthermore, the research study will reduce the socio-economic impact of drought by providing timely alerts. In addition, the study will enhance data accessibility for researchers and policymakers.
Lastly, the study will serve as a reference model for other developing regions facing similar challenges. More so, researchers will access real-time geospatial data for environmental studies.
1.8 Scope of Study
This study focuses on designing a Drought Monitoring and Early Warning System using geospatial data using Nigerian Meteorological Agency (NiMet) as a case study. The project will analyze satellite-based indices for drought detection, validate them with historical data, and develop an alert framework that supports decision-making in the state.
1.9 Limitations of the Study
A study of this nature is bound to experience certain problems as such the constraints imposed on the research include:
- Time Constraints: A study of this nature needs relatively long time during which information for accurate or at least near accurate inference could be drawn. The period of the study was short, time posed as constraints to the research.
- Financial Constraints: The research would have extended the survey to other area at the empirical level, but limitation as included cost of transportation to the source of material and the cost of time setting of the already completed work.
- Lack of Cooperation: Many of the respondents are usually aggressive on issue that border cooperation among the respondents border.
1.10 Definition of Terms
Drought: A prolonged period of deficient rainfall that results in a shortage of water for agricultural, environmental, and human needs. It disrupts the balance of ecosystems and often leads to food insecurity (WMO, 2006).
Early Warning System (EWS): A system that provides timely and relevant information on potential hazards such as drought, enabling individuals and organizations to take action to reduce risk (UNDRR, 2015).
Geospatial Data: Information that is associated with a specific location, often collected through satellite remote sensing or GPS systems. It is used to analyze spatial patterns and trends related to drought conditions (Goodchild, 2007).
Remote Sensing: The use of satellite or aerial sensor technologies to detect and monitor changes on the Earth's surface, such as vegetation health and land temperature, which are critical in drought detection (Lillesand et al., 2015).
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