Kawawa Banda

Land use and land cover (LULC) change is a major driver of hydrological variability in sub-Saharan Africa’s tropical river basins, yet its long-term impacts on water availability remain poorly understood. This study evaluated the influence of LULC change on water availability in Zambia’s Upper Kafue Basin from 1994 to 2024. Multi-temporal Landsat imagery was classified using Random Forest, Support Vector Machine, Decision Tree, K-Nearest Neighbours, and ensemble machine learning models. Hydro-climatic variables were derived from CHIRPS precipitation, MODIS evapotranspiration (ET), and FAO WaPOR datasets. Spatial LULC–ET relationships were analyzed via Geographically Weighted Regression (GWR), while hydro-climatic impacts were assessed using water balance indicators and the Budyko framework.Forest cover declined from 43.12% to 31.76%, while cropland expanded from 35.67% to 47.38%. Annual ET rose from 903 to 952.4 mm, whereas water availability dropped from 226 to ~55 mm (74.5%). Annual precipitation decreased slightly from 1,129 to 1,010 mm, and the evaporative index increased from 0.80 to 0.94, showing higher atmospheric water return. Budyko analysis yielded a mean aridity index of 1.63 and a catchment parameter ($\omega$) of 3.49, reflecting moderately water-limited conditions. Attribution analysis attributed 70.7% of the decline in water availability to reduced precipitation and 29.3% to increased ET. The GWR model showed low explanatory power (mean local $R^2 = 0.06$), suggesting influence from additional climatic and subsurface processes. Overall, results revealed progressive decoupling between precipitation and water availability, emphasizing the joint impact of climatic aridity and land surface dynamics on hydrological resilience.

This study investigated the levels of trace elements in two fish species, Kariba bream (Oreochromis mortimeri) and Kapenta (Limnothrissa miodon) from Lake Kariba in two consecutive years, 2021 and 2022. Both Kariba Bream fresh/sun-dried muscle and sun-dried Kapenta tissues were digested using a microwave digestion system and analyzed using Inductively Coupled Plasma Mass Spectrometry. Mean concentration of trace elements between the first and second sampling differed significantly ( P > 0.05). In the first sampling, concentrations of trace elements in sun-dried bream occurred in the order Fe > Al > Cr > Zn > Mn > As while Se, Cu, Cd, Pb and Hg were not detected. Sun-dried Kapenta displayed Al > Fe > Zn > Mn > Cr > Cu > As > Se with Cd, Pb and Hg not detected. Fresh bream showed Fe > Al > Zn > Cr > Mn > Cu > Se, with Pb, As, Cd and Hg not detected. In the second sampling, the order was, Fe > Al > Zn > Cr > Mn > Se > Cu > As in sun-dried bream; Fe > Zn > Mn > Al > Cr > Cu > Se > As in sun-dried Kapenta with Cd, Pb and Hg not detected in both. Fresh bream showed; Fe > Al > Zn > Mn > Cr > Se > Cu > As > Pb with Cd and Hg not detected. Notably, the concentrations of Fe, Mn and Cr in some samples exceeded the recommended limits by FAO/WHO. The THQ and HI for some elements were above the threshold value of 1, indicating potential risk of non-carcinogenic to human health for long-term consumption. These findings highlight the need for further investigation into post-fish handling, including land-use activities in and around the lake.

This study evaluates the economic value of Provisioning Ecosystem Services (PrES) across five districts in Zambia’s Lower Kafue Catchment. Using the Direct Market Valuation (DMV) method, household surveys, and field data, the research addresses a critical gap in Zambia’s natural-capital accounting and regional poverty-reduction policies. The paper identified eleven primary resource categories, with fish and crops dominating local utilization at over 31%. Across the ten marketed provisioning service categories valued, the total gross monetary value of all assessed services was estimated at USD 3,271,938.60, yielding USD 2,102,082.95 in direct cash income for households. After deducting harvesting, processing, and management costs, the net monetary value was USD 1,169,855.68. Fisheries emerged as the primary economic driver, contributing a net benefit of USD 728,968.75 (62.3% of the total net value), reflecting the productivity of the Kafue Flats flood system and the cost-efficiency of small-scale fishing. Crop production contributed USD 112,278 in net terms (9.6% of total net value), though high input costs held its net-to-gross margin to just 23.6%. Livestock, timber, firewood, charcoal, and wetland and wild products together contributed the remaining net value, providing essential supplementary income and safety nets for households across the catchment. Critically, 78% of surveyed households reported declining resource access over the past decade, driven by climate pressures, population growth, and overfishing. Coping mechanisms introduce heavy, hidden economic burdens. These findings present a compelling case for integrating ecosystem service values into national development planning to protect rural livelihoods.

Soil erosion and sediment transport are major drivers of land degradation, reservoir siltation, and declining water quality, especially in data-scarce river basins where field observations are limited. While the Revised Universal Soil Loss Equation (RUSLE) is widely used to estimate soil erosion, it does not quantify the proportion of eroded material that reaches river channels. This limitation, this study integrated RUSLE with a slope-connectivity-based sediment delivery ratio model in Google Earth Engine to assess soil erosion, sediment delivery, and sediment yield across the Upper Zambezi River basin. Freely available geospatial datasets on rainfall, vegetation, land cover, elevation, and soils were used to derive the RUSLE factors. Mean soil erosion rates ranged from 13.52 to 26.42 t ha⁻¹ yr⁻¹ across the six sub-basins, exceeding globally accepted soil-loss tolerance thresholds. Mean sediment delivery ratios were low, varying from 0.037 to 0.052, which indicated substantial sediment retention. Gross sediment yield showed strong spatial differences, with Cuando Chobe exporting the highest annual sediment load of 10.26 million t yr⁻¹, while Lungue Bungo recorded the highest localised sediment production. Model validation using field-measured total suspended solids showed a strong positive relationship with sediment-yield classes (Spearman’s ρ = 0.778, p < 0.0001), and the Kruskal-Wallis test confirmed classes' significant differences (H = 18.998, p = 0.0008). Generally, the integrated RUSLE-SC-SDR framework provides a scalable, reproducible, and computationally efficient approach for basin-scale soil erosion and sediment-yield assessment in data-scarce environments, supporting the identification of critical sediment source areas and evidence-based watershed management and conservation planning.

This study evaluated cost-effective approaches and reliable moisture variation indicators for improving soil moisture monitoring and forecasting across Zambia’s three agro-ecological regions (AER I, II, and III). The research aimed to identify the most reliable soil moisture indicators under varying soil profiles and meteorological conditions. Low-cost soil moisture profile probes were installed at five representative locations, where soil moisture data were collected from September 2024 to May 2025 at depths of 15 cm, 30 cm, 45 cm, and 60 cm. Soil samples were analyzed for bulk density and particle size distribution, with textures ranging from sandy loam to sandy clay loam and bulk densities between 2.37 and 2.71 g/cm³. Sentinel-2 Surface Reflectance data were used to derive NDVI, SAVI, and EVI at 10 m spatial resolution and five-day temporal frequency. Results showed strong regional differences in correlation strength between satellite-derived and in situ soil moisture observations. Strong correlations were observed in AER II (R² = 0.898; p = 3.27 × 10⁻⁶) and AER III (R² = 0.893; p = 5.43 × 10⁻¹⁹), indicating reliable agreement between remote sensing products and TEROS 54 sensor measurements. In contrast, weaker correlations in AER I, Livingstone (R² = 0.572), were linked to sandy soils with rapid drainage and lower moisture retention. NDVI, SAVI, and EVI showed significant correlations in Monze and Mpongwe, while Mpika exhibited weaker relationships due to coarse-textured soils and high bulk density. The findings highlight the importance of soil texture and depth in determining remote sensing reliability.

Background: Investigating the influencing factors of groundwater drought offers critical insights for the sustainable management of groundwater-dependent ecosystems (GDEs). The Upper Zambezi Catchment hosts a large-scale alluvial aquifer system, which is vulnerable to the effects of climate change to sustain GDEs. The study aims to: (a) characterize the spatial-temporal distribution of groundwater drought in the catchment, (b) identify hydrological and terrestrial drivers affecting groundwater drought, (c) rank the drivers according to their impact on the groundwater distribution/system, and (d) explore groundwater management actions under drought conditions i.e. disaster risk management. Methods: Influencing factors, which include meterological drought indicators (such as Standardized Precipitation Evapotranspiration Index, SPEI), teleconnection factors (ENSO, PDO and AMO), and anthropogenic factors (land use and land cover (LULC)), were investigated and quantitatively compared based on Spearman correlation analysis and a decision tree machine learning model (extreme gradient boosting, XGBoost). Structural Equation Modelling (SEM) was then used to explain latent (important) factors in the nexus of climate variability—LULC dynamics to groundwater response. Results: The study reveals that LULC types, particularly water bodies, cropland and bare land, exert the greatest influence on groundwater drought responses under teleconnection patterns attributed to ENSO, rather than through changes in the net water balance. This highlights the critical role of surface cover dynamics in shaping subsurface hydrological responses, with significant implications for the sustainability of groundwater-dependent ecosystems. Conclusions: This study is novel in its application of XGBoost and SEM to unravel the complex nexus between climate variability, LULC, and groundwater dynamics within an ecosystem context, under data-scarcity conditions. This understanding is not only critical for sustaining groundwater availability but also for preserving the integrity and functioning of groundwater-dependent ecosystems.