Assessing the Impacts of Climate Change on Climatic Extremes and Hydrological Regimes in the Congo River Basin Using Statistical Analysis and Hydrological Modelling

Abstract

The Congo River Basin (CRB) in central Africa holds tremendous environmental importance due to its rich biodiversity, serving as a haven for countless unique plant and animal species. It stands as one of the world's most biodiverse regions, and safeguarding this diversity is critical for global conservation efforts. The basin provides a crucial source of freshwater for millions of people in Central Africa, supporting their livelihoods, agriculture, and industrial needs. The CRB encompasses a vast drainage area that crosses nine political boundaries, including countries like Angola, Burundi, Central African Republic, Democratic Republic of Congo, Cameroon, Republic of Congo, Rwanda, Tanzania, and Zambia. Within this basin lies the world's second-largest rainforest, which plays a pivotal role in the global carbon cycle. Similar to other rainforests, its structure and distribution are likely to be affected by shifts in regional rainfall patterns linked to global warming. This basin, along with the western Pacific Ocean and the Amazon basin, serves as a significant source of major storms, while also experiencing periods of drought. Therefore, a comprehensive study of climatic changes in the region is imperative to provide valuable insights for mitigation and adaptation planning. Despite its significance, the basin has received limited attention in hydrological and climate research, leaving it largely understudied. Extreme events in the Congo basin, which are tied to rainfall variability, may intensify due to climate change, posing challenges to the well-being of local communities. Hence, it is of utmost importance to examine the present and future trends of extreme events like precipitation and drought frequency and intensity for the preservation of local infrastructure and sustainable livelihoods. This thesis contributes to better water resources management in the CRB by a) assessing the impacts of climate change on climatic extremes in the Congo basin from the present day to the year 2100 and b) developing, calibrating, and validating a Soil and Water Assessment Tool (SWAT) model to assess the impacts of climate change on hydrological regimes in the CRB and c) developing an efficient method to generate flood maps in a data-scarce environment. The first part of the thesis deals with the challenge of evaluating the anticipated shifts in selected hydroclimatic extremes, some of which are proxies for rainfall-triggered flash floods and drought. These indicators encompass total annual precipitation (PCPTOT), the count of days with rainfall exceeding 20 mm (PCP20), the standardized precipitation index (SPI), and the standardized precipitation-evapotranspiration index (SPEI). To perform this analysis, the selected indices were computed using the statistically downscaled data from eleven Regional Climate Models (RCMs) participating in the Coordinated Downscaling Experiment (CORDEX-AFRICA). This was done under two distinct Representative Concentration Pathways: RCP 8.5, a high emissions scenario, and RCP 4.5, a moderate emissions scenario. The second part of the thesis utilizes the results from the climate study to examine future modifications of the hydrological regime of the CRB and discuss the potential impacts of these projected changes. To achieve this, a SWAT model for the CRB was calibrated and validated. Subsequently, the calibrated model was utilized to assess variations in discharge throughout all segments of the basin. The third part of the thesis deals with the development of an innovative and computationally efficient approach to mapping floods in the Oubangui River section of the Congo Basin, as an alternative to the resource-intensive 2D hydrodynamic modeling. The methodology involves extracting surface water data from Sentinel-1 Synthetic Aperture Radar (SAR) imagery and establishing a relationship between pixels and water discharge values. This information enables the creation of a spatial pixel probability distribution, facilitating accurate mapping of flooded and dry areas. By harnessing the capabilities of Sentinel-1 SAR imagery, this novel approach aims to provide a more accurate and timely flood assessment for the Oubangui River region within the Congo Basin. The fusion of remote sensing data and advanced analytical methods will contribute to more effective flood monitoring and mitigation strategies.