Search Results (13)

The Okavango River is a transboundary waterway that flows through Angola, Namibia, and Botswana, forming a significant alluvial fan in northwestern Botswana. This fan creates a Delta that plays a vital role in the country’s GDP through tourism. While research has primarily focused on the Delta, the river’s catchment area in the Angolan highlands—its main water source and critical for downstream flow—has been largely overlooked. The basin is under pressure from development, water abstraction, and population growth in the surrounding areas, which negatively affect the environment. These challenges are intensified by climate change, leading to increased water scarcity that necessitates improved management strategies. Currently, there is a lack of published research on the basin’s hydrology, leaving many hydrological parameters related to streamflow in the catchments inadequately understood. Most existing studies have employed single-site calibration methods, which fail to capture the diverse characteristics of the basin’s catchments. To address this, a SWAT model has been developed to simulate the hydrologic behaviour of the basin using sequential multisite calibration with data from five gauging stations, including the main river systems: Cubango and Cuito. The SUFI2 program was used for sensitivity analysis, calibration, and validation. The initial sensitivity analysis identified several key parameters: the Soil Evaporation Compensation Factor (ESCO), the SCS curve number under moisture condition II (CN2), Saturated Hydraulic Conductivity (SOL_K), and Moist Bulk Density (SOL_BD) as the most influential. The calibration and validation results were generally satisfactory, with a coefficient of determination ranging from 0.47 to 0.72. Analysis of the water balance and parameter sensitivities revealed the varied hydrologic responses of different sub-watersheds with distinct soil profiles. Average annual precipitation varies from 1116 mm upstream to 369 mm downstream, with an evapotranspiration-to-precipitation ratio ranging from 0.47 to 0.95 and a water yield ratio between 0.51 and 0.03, thereby revealing their spatial gradients, notably increasing evapotranspiration and decreasing water yield downstream. The SWAT model’s water balance components provided promising results, with soil moisture data aligned with the TerraClimate dataset, achieving a coefficient of determination of 0.63. Additionally, the model captured the influence of the El Niño–Southern Oscillation (ENSO) on local hydrology. However, limitations were noted in simulating peak and low flows due to sparse gauge coverage, data gaps (e.g., groundwater abstraction, point sources), and the use of coarse-resolution climate inputs. Full article

Polymetallic nodules (nodules) are a predominant deep-sea mineral resource due to theirenrichment with critical metals, such as Co, Ni, and Cu, and rare earth elements (REEs). The loose and porous nature of nodules contributes to their adsorption and enrichment in trace metals from seawater and pore water. Consequently, the specific surface area (SSA) of nodules is a key factor requiring further study. However, controls on the SSA of nodules with various genetic types remain poorly characterized. This study aimed to investigate controls on nodule SSA by analyzing the transition metals, REEs, mineralogy, and SSA of nodules recovered from basins in the Atlantic, Indian, and Pacific oceans, including the Northwest Pacific Basin (NPB), Bauer Basin (BB), Tiki Basin (TB), Wharton Basin (WB), Central Indian Basin (CIB), and Angola Basin (AB). Nodule SSAs were compared among the various basins by calculating the BET SSA (based on the equation proposed by Brunauer, Emmett, and Teller, 1938). The results suggest thatnodules from the PNB, WB, CIB, and AB are mainly hydrogenetic, and those nodules have a relatively high SSA, high Co, low Ni and Cu, positive Ce anomalies, and low X-ray diffraction (XRD) intensities at ~10 Å. The nodules from the BB and TB are mainly diagenetic in origin, characterized by a relatively low SSA, low Co, high Ni and Cu, negative Ce anomalies, and high XRD intensities at ~10 Å. The SSAs of nodules were significantly positively correlated with Co, δCe, and light REEs (LREEs), and negatively correlated with the XRD intensity at ~10 Å, Ni, and Cu. The SSAs of nodules from the NPB ranged from 329.440 m²/g to 418.711 m²/g, comparable to the SSAs of Co-rich crusts on seamounts. This study proposes that nodule SAA is regulated by nodule genesis and that hydrogenetic nodules have a higher SSA. Full article

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of Convolutional Neural Networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one chromatographic oil images from different worldwide basins (Brazil, the USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations. Subsequently, the recurring features are grouped into common feature groups. The training result obtained an accuracy (CA) of 96.7% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 99.7%. In turn, the test result obtained a 97.6% CA and a 99.7% AUC. This work suggests that the processing of petroleum chromatographic images through CNN can become a new tool for the study of petroleum geochemistry since the chromatograms can be loaded, read, grouped, and classified more efficiently and quickly than the evaluations applied in classical methods. Full article

Here, we describe the first pterosaur remains from Angola, an assemblage of fourteen bones from the Lower Maastrichtian marine deposits of Bentiaba, Namibe Province. One new species is introduced, Epapatelo otyikokolo, gen. et sp. nov., which comprises an articulated partial left humerus and ulna as well as an articulated left ulna and radius (from a second individual). Phylogenetic analysis confirms a non-nyctosaurid pteranodontian attribution for this new taxon and supports a new apomorphy-based clade, Aponyctosauria, which is here defined. Late Cretaceous pteranodontians are rare in Sub-Saharan Africa and throughout the Southern Hemisphere. Preliminary histological analysis also reveals a likely sub-adult age for one of the specimens. This fossil assemblage provides a first glimpse of Angolan pterosaur paleobiodiversity providing further insight into the Gondwanan ecosystems of the Upper Cretaceous. Full article

The rare-earth element (REE) geochemistry of sedimentary deposits has been used in provenance investigations despite the transformation that this group of elements may suffer during a depositional cycle. In the present investigation, we used the geochemistry and XRD mineralogy of a set of sand and mud fluvial deposits to evaluate the ability of REE parameters in provenance tracing, and the changes in REE geochemistry associated with weathering and sorting. The analyzed deposits were generated in a subtropical drainage basin where mafic and felsic units are evenly represented, and these crystalline rocks are covered by sedimentary successions in a wide portion of the basin. A few element ratios appear to hold robust information about primary sources (Eu/Y, Eu/Eu*, La_N/Yb_N, La_N/Sm_N, and Gd_N/Yb_N), and the provenance signal is best preserved in sand than in mud deposits. Sediment cycles, however, change the REE geochemistry, affecting mud and sand deposits differently. They are responsible for significant REE depletion through quartz dilution in sands and may promote discernible changes in REE patterns in muds (e.g., increase in Ce content and some light REE depletion relative to heavy REE). Full article

Numerous ephemeral rivers and thousands of natural pans characterize the transboundary Iishana-System of the Cuvelai Basin between Namibia and Angola. After the rainy season, surface water stored in pans is often the only affordable water source for many people in rural areas. High inter- and intra-annual rainfall variations in this semiarid environment provoke years of extreme flood events and long periods of droughts. Thus, the issue of water availability is playing an increasingly important role in one of the most densely populated and fastest growing regions in southwestern Africa. Currently, there is no transnational approach to quantifying the potential storage and supply functions of the Iishana-System. To bridge these knowledge gaps and to increase the resilience of the local people’s livelihood, suitable pans for expansion as intermediate storage were identified and their metrics determined. Therefore, a modified Blue Spot Analysis was performed, based on the high-resolution TanDEM-X digital elevation model. Further, surface area–volume ratio calculations were accomplished for finding suitable augmentation sites in a first step. The potential water storage volume of more than 190,000 pans was calculated at 1.9 km³. Over 2200 pans were identified for potential expansion to facilitate increased water supply and flood protection in the future. Full article

The organic geochemistry of six oil samples from the offshore Block 17 (Lower Congo Basin, northwestern Angola) was studied by a combination of classical biomarker and extended diamondoid analyses to elucidate source rock facies, the extent of biodegradation, and thermal maturity. Based on molecular data, oils are interpreted as depicting a mixture of two pulses of hydrocarbon generation probably from the Bucomazi and Malembo formations. Geochemical results also gave evidence of mixing of a lacustrine siliciclastic-sourced oil charge and a second more terrestrially derived oil type in the samples analyzed. A single genetic oil family was identified through hierarchical cluster analysis; however, two groups of oils were identified on the basis of their biodegradation levels using the Peters/Moldowan scale. Lower and upper Malembo oils have a slight depletion and a notable absence of n-alkanes, suggesting PM levels of 1 and 2, respectively. Most molecular maturity parameters of the oil samples suggest a maturity level equivalent to the onset of the peak of the oil generative window. Full article

The Okavango River Basin is a hotspot of bat diversity that requires urgent and adequate protection. To advise future conservation strategies, we investigated the relative importance of a range of potential environmental drivers of bat species richness and functional community composition in the Okavango River Basin. During annual canoe transects along the major rivers, originating in the central Angolan highlands, we recorded more than 25,000 bat echolocation calls from 2015 to 2018. We corrected for possible biases in sampling design and effort. Firstly, we conducted rarefaction analyses of each survey year and sampling appeared to be complete, apart from 2016. Secondly, we used total activity as a measure of sample effort in mixed models of species richness. Species richness was highest in the Angola Miombo Woodlands and at lower elevations, with higher minimum temperatures. In total, we identified 31 individual bat species. We show that even when acoustic surveys are conducted in remote areas and over multiple years, it is possible to correct for biases and obtain representative richness estimates. Changes in habitat heterogeneity will have detrimental effects on the high richness reported here and human land-use change, specifically agriculture, must be mediated in a system such as the Angolan Miombo Woodland. Full article

Satellite salinity data from the Soil Moisture and Ocean Salinity (SMOS) mission was recently enhanced, increasing the spatial extent near the coast that eluded earlier versions. In a pilot attempt we assimilate this data into a coastal ocean model (ROMS) using variational assimilation and, for the first time, investigate the impact on the simulation of a major river plume (the Congo River). Four experiments were undertaken consisting of a control (without data assimilation) and the assimilation of either sea surface height (SSH), SMOS and the combination of both, SMOS SSH. Several metrics specific to the plume were utilised, including the area of the plume, distance to the centre of mass, orientation and average salinity. The assimilation of SMOS and combined SMOS SSH consistently produced the best results in the plume analysis. Argo float salinity profiles provided independent verification of the forecast. The SMOS or SMOS SSH forecast produced the closest agreement for Argo profiles over the whole domain (outside and inside the plume) for three of four months analysed, improving over the control and a persistence baseline. The number of samples of Argo floats determined to be inside the plume were limited. Nevertheless, for the limited plume-detected floats the largest improvements were found for the SMOS or SMOS SSH forecast for two of the four months. Full article

Good quality data on precipitation are a prerequisite for applications like short-term weather forecasts, medium-term humanitarian assistance, and long-term climate modelling. In Sub-Saharan Africa, however, the meteorological station networks are frequently insufficient, as in the Cuvelai-Basin in Namibia and Angola. This paper analyses six rainfall products (ARC2.0, CHIRPS2.0, CRU-TS3.23, GPCCv7, PERSIANN-CDR, and TAMSAT) with respect to their performance in a crop model (APSIM) to obtain nutritional scores of a household’s requirements for dietary energy and further macronutrients. All products were calibrated to an observed time series using Quantile Mapping. The crop model output was compared against official yield data. The results show that the products (i) reproduce well the Basin’s spatial patterns, and (ii) temporally agree to station records (r = 0.84). However, differences exist in absolute annual rainfall (range: 154 mm), rainfall intensities, dry spell duration, rainy day counts, and the rainy season onset. Though calibration aligns key characteristics, the remaining differences lead to varying crop model results. While the model well reproduces official yield data using the observed rainfall time series (r = 0.52), the products’ results are heterogeneous (e.g., CHIRPS: r = 0.18). Overall, 97% of a household’s dietary energy demand is met. The study emphasizes the importance of considering the differences among multiple rainfall products when ground measurements are scarce. Full article

Drought is one of the major threats to societies in Sub-Saharan Africa, as the majority of the population highly depends on rain-fed subsistence agriculture and traditional water supply systems. Hot-spot areas of potential drought impact need to be identified to reduce risk and adapt a growing population to a changing environment. This paper presents the Blended Drought Index (BDI), an integrated tool for estimating the impact of drought as a climate-induced hazard in the semi-arid Cuvelai-Basin of Angola and Namibia. It incorporates meteorological and agricultural drought characteristics that impair the population’s ability to ensure food and water security. The BDI uses a copula function to combine common standardized drought indicators that describe precipitation, evapotranspiration, soil moisture and vegetation conditions. Satellite remote sensing products were processed to analyze drought frequency, severity and duration. As the primary result, an integrated drought hazard map was built to spatially depict drought hot-spots. Temporally, the BDI correlates well with millet/sorghum yield (r = 0.51) and local water consumption (r = −0.45) and outperforms conventional indicators. In the light of a drought’s multifaceted impact on society, the BDI is a simple and transferable tool to identify areas highly threatened by drought in an integrated manner. Full article

Climate change is altering hydrological processes with varying degrees in various regions of the world and remains a threat to water resources projects in southern Africa. The likely negative impacts of changes in Africa may be worse than in most other regions of the world. This study is an evaluation of the possible impacts of climate change on water resources and hydropower production potential in Kwanza River Basin, Angola. The regional climate data, the basis for future climate scenarios, is used in the hydrological model HBV to assess climate change impacts on water resources in the Kwanza River Basin. Evaluation of changes in hydropower production potential is carried out using an energy model. The simulations show that annual rainfall in 2080 would increase by approximately 16% with increasing inter-annual variability of rainfall and dry season river flow and later onset of the rainy season. The simulation results show that for the Kwanza River Basin the effects as a result of changes in the future climate, in general, will be positive. Consequently, the increase in water resources will lead to increased hydropower production potential in the basin by up to 10%. Full article

In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP) metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution) and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1) remote sensing variables; (2) climatic variables; and (3) all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic natural resource management applications. Full article