The Luangwa Rift active fault database and fault reactivation along the southwestern branch of the East African Rift

Seismic hazard assessment in slow straining regions is challenging because earthquake catalogues only record events from approximately the last 100 years, whereas earthquake recurrence times on individual faults can exceed 1,000 years. Systematic mapping of active faults allows fault sources to be used within probabilistic seismic hazard assessment, which overcomes the problems of short-term earthquake records. We use Shuttle Radar Topography Mission (SRTM) data to analyse surface deformation in the Luangwa Rift in Zambia and develop the Luangwa Rift Active Fault Database (LRAFD). The LRAFD is an open-source geospatial database containing active fault traces and their attributes and is freely available at: https://doi.org/10.5281/zenodo.6513691. We identified 18 faults that display evidence for Quaternary activity and empirical relationships suggest that these faults could cause earthquakes up to Mw 8.1, which would exceed the magnitude of historically recorded events in southern Africa. On the four most prominent faults, the median height of Late Quaternary fault scarps varies between 12.9 ± 0.4 and 19.2 ± 0.9 m, which suggests they were formed by multiple earthquakes. Deformation is focused on the edges of the Luangwa Rift: the most prominent Late Quaternary fault scarps occur along the 207 km long Chipola and 142 km long Molaza faults, which are the rift border faults and the longest faults in the region. We associate the scarp on the Molaza Fault with possible surface ruptures from two 20th Century earthquakes. Thus, the LRAFD reveals new insights into active faulting in southern Africa and presents a framework for evaluating future seismic hazard

Investigating groundwater and surface water interactions using remote sensing, hydrochemistry, and stable isotopes in the Barotse Floodplain, Zambia

ABSTRACTGroundwater and surface water are inseparably linked, thus the understanding of their interactions is indispensable toward developing effective conjunctive water resource management strategies, particularly for ecosystems, such as wetlands, which provide numerous ecosystem services. This study aimed to identify potential areas of Groundwater-surface water (GW-SW) interactions and mechanisms that facilitate them using remote sensing, hydrochemistry, and stable isotopes. The results of remote sensing analysis using Normalised Difference Vegetation Index (NDVI) show that the vegetation is sensitive to the dynamics of groundwater level, with shallower levels (10 m) in the upper catchment. The study postulates that the near surface groundwater level, maybe due to the near proximity river system (Zambezi River) losing water to recharge the aquifer system, compared to upstream which may be discharging to floodplains (gaining system). Using Hierarchical Cluster Analysis (HCA) of the hydrochemical data, surface, and groundwater showed three similarity clusters of hydrochemistry consisting of Ca-Mg-HCO3, Na-HCO3, and Na-Cl. These zones are further investigated and likely represent geological variability, aquifer confinement, and the degree of GW–SW interactions. The outcomes of this research provide critical input for integrated protection and conservation of ecosystem services in floodplain and aquifer systems

Variations in Canopy Cover and Its Relationship with Canopy Water and Temperature in the Miombo Woodland Based on Satellite Data

Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R2) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009–2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 °C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII)

Modeling land use/land cover changes using quad hybrid machine learning model in Bangweulu wetland and surrounding areas, Zambia

Wetlands are among the most productive natural ecosystems globally, providing crucial ecosystem services to people. Regrettably, a substantial 64 % –71 % of wetlands have been lost worldwide since 1900, mainly due to changes in land use and land cover (LULC). This issue is not unique to Zambia's Bangweulu Wetland System (BWS), which faces similar challenges. However, there is limited information about the LULC changes in BWS. Furthermore, finding accurate and cost-effective methods to understand LULC dynamics is complicated by the multitude of available techniques for LULC classification. Non-parametric methods like Machine Learning (ML) offer greater accuracy, but different ML models come with distinct strengths and weaknesses. Combining multiple models has the potential to create a more precise LULC classification model. Open-source software like QGIS and spatial data like Landsat also play a significant role in this endeavour. The primary objective of this study was to enhance the accuracy of modeling LULC changes in wetland areas. Six ML models: Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Artificial Neural Network (ANN), Random Forest (RF), and K-Nearest Neighbour (KNN) were used for LULC image classification of Landsat 8 (2020 image) and Landsat 5 (1990, 2000, and 2010 images) in QGIS. Four models: SVM, NB, DT, and KNN, performed better than the other models. Consequently, The Quad (4) hybrid model was created by fusing the maps from these four models with the highest performance. Results revealed that the fusion of the four classified maps of the SVM, NB, DT, and KNN (Quad hybrid model) showcased superior performance compared to the individual models with Kappa Index scores of 0.87, 0.72, 0.84 and 0.87 for the years 1990, 2000, 2010 and 2020, respectively. The analysis of the LULC changes from 1990 to 2020 showed a yearly decline of -1.17 %, -1.01 %, and -0.12 % in forest, grassland, and water body coverage, respectively. In contrast, built-up areas and cropland increased at rates of 1.70 % and 2.70 %, respectively. This study underscores the consistent growth of cropland and built-up areas from 1990 to 2020, alongside the reduction of forest cover and grassland. Although the water body experienced a gradual decrease over this period, the decline was minimal. Long-term monitoring will be essential for evaluating the success of interventions, guiding conservation efforts, mitigating negative impacts on the wetland ecosystem, and determining whether the reduction in water bodies is a sustained trend or a short-term phenomenon