Browse
Recent Submissions
Item Open Access Impacts of climate change on streamflow and hydrological extremes in the South Phuthiatsana Catchment, Lesotho(National University of Lesotho, 2025) Maphutseng, Makoanye; Khaba, LiphapangGlobal climate change is predicted to significantly modify hydrological processes, which will have a big impact on ecosystem sustainability, flood risk, and water availability. Knowing how future climate changes may impact river systems is especially important in southern Africa, where population increase and fluctuating rainfall are already placing a strain on water resources. One such critical system is the South Phuthiatsana catchment, which serves as a vital supply of water for Maseru and the other metropolitan areas. This study explores how projected climatic shifts may influence streamflow behavior and the occurrence of hydrological extremes within the South Phuthiatsana watershed, an essential source of water for Maseru and surrounding urban communities. Bias-adjusted data from the MPI-ESM1- 2-LR global climate model, along with two sample emissions trajectories for the mid-21st century (2041–2080), were used to project climate inputs. Suboptimal performance indicators showed that the process-based model (SWAT+), which was used to simulate streamflow, was not very reliable in capturing historical daily flow patterns. SWAT+ was therefore thought to be insufficient for predicting future hydrological reactions in this context. A machine learning approach using the XGBoost algorithm was adopted to address this challenge. This data-driven model was trained on bias-corrected climate variables and observed streamflow, providing a more reliable tool for future streamflow prediction. The results from XGBoost revealed substantial and complex hydrological shifts. A consistent warming trend combined with highly variable seasonal precipitation patterns was evident across both emission scenarios. Extreme high flows, represented by the 98th percentile (Q98), are projected to decline by more than 52% compared to historical values, suggesting a reduced risk of flooding. In contrast, low flows are expected to increase dramatically; the 1st percentile (Q1) flow is projected to rise from near-zero values historically to approximately 9.0 m³/s, indicating a significant shift toward more perennial flow conditions. Mid-range flows (Q25, Q50, and Q75) are also expected to increase substantially, depending on the flow percentile and scenario. While the absolute magnitude of low flows improves, the number v of days with historically low flow conditions may still increase during certain months, highlighting a shift in the intra-annual flow variability. These findings point to a future with altered hydrological regimes in the South Phuthiatsana catchment characterised by diminished flood peaks, elevated baseflows, and more frequent low- flow conditions during critical periods. Despite initial limitations with the process-based model, the machine learning approach provided robust insights that form a valuable foundation for developing adaptive, forward-looking water resource management strategies. These results underscore the need for resilient planning to ensure long-term water security under evolving climate conditionsItem Open Access Effects of sedimentation on water quality in the Metolong Reservoir, Maseru, Lesotho(National University of Lesotho, 2025) Makhakhe, Matseliso Celestina; Raliengoane, TebesiReservoir sedimentation is a significant environmental challenge affecting the sustainability of water resources, particularly in Lesotho, where soil erosion and land degradation are prevalent due to intensive land–use activities and fragile ecosystems. Metolong Reservoir is a critical water supply for approximately two–thirds of Lesotho's population, challenged with rapid sedimentation which lowers the water quality and threatens the long–term viability of the reservoir, yet no known studies have been done to correlate sedimentation and water quality in the area. This study investigated how sedimentation affects water quality in the Metolong Reservoir by quantifying sediment accumulation in the Metolong Reservoir from 2020 to 2022 and analyzing the impact of sedimentation on key water quality parameters. The study employed historical bathymetric and water quality data (2020–2022), complemented by GIS–based spatial analysis and R statistical modeling to assess the spatial and temporal relationships between sediment deposition and changes in water quality parameters, following the causal–comparative research design. Water quality parameters analyzed were aluminum, Electrical Conductivity (EC), iron, manganese, nitrates, nitrites, phosphates, sulphates, TDS and turbidity. Bathymetric analysis revealed a total sediment accumulation of approximately 1,705,583 m3 (2.68% of reservoir capacity) between 2020 and 2022, concentrated primarily near the reservoir’s middle and towards the dam, resulting in an annual storage loss of 1.34%.Linear regression analysis revealedturbidity as the most significant at (p<0.05).The study also identified turbidity and nitrates as key water quality parameters significantly influenced by sedimentation, with turbidity showing the strongest correlation (r = 0.60) and nitrates showing a moderate correlation (r = 0.2) with sediment volume suggesting possible links with upstream land use and nutrient runoff. There was an increase in nutrients and heavy metals concentration from 2020–2022, indicating a need for intervention, though most water quality parameters were still within WHO and South African water quality standards. The findings confirm that sedimentation negatively affects water quality, underscoring the need for integrated catchment management strategies, including sediment control, land–use planning, and systematic water quality monitoring to safeguard reservoir operations and public health.Item Open Access Water demand forecasting using machine learning approach(National University of Lesotho, 2025) Mahamo, Qenehelo; Khaba, LiphapangThe increasing challenges related to water security, exacerbated by rapid urbanization, population growth, and climate variability, necessitate accurate and reliable forecasting methodologies to support sustainable water resources planning. This study explores water demand forecasting in Ha-Foso, Lesotho, by evaluating three machine learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). Utilizing time series data sets covering meteorological inputs (2012-2022), population, and water consumption records (2017-2024), the study assesses the influence of climatic and demographic variables, specifically precipitation, maximum and minimum temperatures, and population, on domestic water consumption. The research first used MLR to assess the influence of population, maximum temperature, minimum temperature, precipitation, and other factors on water demand. Subsequently, the study evaluated the predictive performance of MLR, SVR, and ANN models. Performance was evaluated using performance metrics, including the coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The regression analysis consistently identified population as the only statistically significant predictor of water demand (p<0.001), while climatic variables showed no significant influence during the study period. In the comparative evaluation, the SVR demonstrated thehighest accuracy and generalization capacity, outperforming ANN and MLR, with the least error metrics in both the training phase and the testing phase. The 2-year forecast highlighted the distinct behaviours of each model, with the SVR and ANN models providing more moderate growth projections compared to the steep, linear increase predicted by the MLR model. This study presents the potential of machine learning, particularly SVR and ANN, in addressing the intricate, non-linear relationships inherent in water demand forecasts, delivering precise and actionable water demand forecasts for peri-urban settings in Lesotho. The findings suggest the adoption of advanced architectures and the incorporation of socio- demographic variables to strengthen predictive capacity. The outputs are expected to support utility companies such as Water and Sewerage Company (WASCO) in strategic planning, conservation, and infrastructure investment decisions.Item Open Access Governance institutions in Range Resource Management in Lesotho(National University of Lesotho, 2025) Pheku, Motlotla Agnes Lineo Rabethane; Marake, Makoala VThis study explores the roles and interactions of dual governance institutions, chieftainship and local community councils (LCCs)-in managing range resources in Qacha’s Nek district, Lesotho. Specifically, it focuses on the Range Management Area (RMA) programme and the traditional Maboeella system, examining their effectiveness before and after the 1997 decentralization reforms. To achieve this, a mixed-methods approach was employed, combining quantitative data from structured surveys (n=90) with qualitative insights from key informant interviews and focus group discussions involving chiefs, councillors, and community members. Quantitative data were analyzed using descriptive statistics and Pearson correlation, whereas qualitative data were examined thematically using Taguette software. Overall, the findings reveal that while both governance institutions contribute to range resource management, traditional chieftainship continues to enjoy greater community trust due to its cultural legitimacy and grassroots presence. In contrast, although councillors are more aligned with formal policy structures, they often lack local influence and enforcement capacity. Consequently, the coexistence of these systems has led to overlapping roles, institutional friction, and limited coordination. Nevertheless, examples of effective collaboration demonstrate that clearly defined responsibilities and inclusive decision-making can enhance governance outcomes. Therefore, the study recommends the development of a hybrid governance model that integrates the strengths of bothsystems. This should be supported by improved communication, formalized collaboration frameworks, and legislative reforms to clarify roles and strengthen accountability. Ultimately, these insights offer practical guidance for harmonizing governance structures to promote sustainable natural resource management in Lesotho and similar contexts.Item Open Access Landslide sucespibility mapping(National University of Lesotho, 2025) Mocheka, Matseliso; Mapeshoane, BotleLandslide has a serious impact on development and countries’ economy. This study presents a geospatial analysis of landslide susceptibility within the Malibamatšo Sub-Catchment, Lesotho, using remote sensing imagery and GIS-based Frequency Ratio (FR) model. The frequency ratio (FR) was used to assess susceptibility to landslides using ten conditioning factors including topography, climate and distance to streams and roads. The conditioning factors datasets were derived from various sources such as DEM, NASA and cadastral maps. The FR weighted thematic layers were then overlaid through numerical addition using the Raster Calculator to generate the Landslide Susceptibility Index (LSI) map which was then classified into 5 categories. Contributing factors of landslide in Malibamatšo Sub-Catchment based on the total frequency ratio (FR) are distance to roads (FR = 15.09), slope aspect (8.79), LULC (5.99) and rainfall (5.09). Areas located within 200 meters of roads exhibited a high landslide susceptibility, with a Frequency Ratio (FR) of up to 4.82. Northeast-facing slopes, areas with bare or agricultural land cover, and regions experiencing high rainfall distribution are generally more susceptible to landslides. Topographic factors such as slope gradient, curvature and elevation are relatively less important, however, Moderate slopes (11–22ᵒ) are more prone to landslides than steeper slopes. Soil texture is the least contributing factor (FR = 1.15). The resulting Landslide Susceptibility Map (LSM) classifies the landscape into five susceptibility zones ranging from very low to very high risk. Almost 20% of the catchment is susceptible to the occurrence of landslides. Areas most susceptible to landslides are villagesnear the national roads and associated with agricultural activities such as Ha Lejone, Ha Taunyane, Ha Sephapo, Taung, Ha Nkisi, and Liphofung. The model's accuracy was validated using the success rate curve (SRC), yielding an Area Under the Curve (AUC) value of 0.89, indicating a high predictive performance. The study’s findings support informed decision- making for land use planning, infrastructure development, and the implementation of mitigation strategies in landslide-prone areasItem Open Access Evaluation of water distribution system and its impact in Ha Foso, Berea, Lesotho(National University of Lesotho, 2025) Mofolo, Tsepang C; Jelili, Musibau OThe interplay between urbanization, water demand and supply, and resource management is a critical issue, particularly as population concentration intensifies, with the implications for peri-urban areas. Rapid urbanization in Lesotho, for instance, has exerted pressures on the water infrastructure network, particularly in newly developing peri-urban communities like Ha Foso, where water supply challenges have become an issue. This study, therefore, evaluates the performance, adequacy, and reliability of Ha Foso’s water distribution system. EPANET software was used to conduct hydraulic analysis assessing parameters such as pressure levels, flow rates, and network efficiency, among others. Complementing the technical analysis, quantitative and qualitative data were obtained from a sample of 270 households, while participant interviews were conducted with water consumers and utility personnel. Logistic regression analysis was used to explore the relationship between infrastructure performance and users’ satisfaction, providing a robust analysis for understanding the system’s impact on the community. The study identifies systemic inefficiencies in the distribution network, such as low water pressure as well as low frequency, duration, shortage of, and less reliable water supply, and dissatisfied consumers. By integrating technical information (through assessments) with community perspectives, this study provides innovative solutions, such as adding booster pumps in households that receive low pressure for water infrastructure management. It also suggests measures, including installing pressure-reducing valves in high- pressure zones to protect infrastructure for improved water access and resilience of the water distribution network, towards achieving equitable and sustainable water resource management.Item Open Access Assessment of heavy metals and their impacts on aquatic ecosystems and river health in the Mohokare river using SASS-5(National University of Lesotho, 2025) Sepono, Sephooko; George, Mosotho JTo improve freshwater monitoring in Southern Africa, this study recommends integrating chemical analysis with biological biomonitoring frameworks like SASS-5 to detect ecological degradation early. This investigation assessed heavy metal contamination and its ecological impacts on the Mohokare River, a transboundary river flowing between Lesotho and South Africa. Sampling was conducted at six strategically selected sites: Matsoaing (control site in Butha-Buthe), Matlakeng, Mohloka-qala, Ha Fusi, Ha Setho, and Bolikela (in Mafeteng). These sites represent gradients of pollution from upstream pristine conditions to downstream urban and agricultural zones. Water and sediment samples were analyzed for Fe, Zn, Cu, Mn, and Pb using Atomic Absorption Spectroscopy (AAS). Sediment extraction involved Van Veen grab sampling and aqua regia digestion (HNO₃ and HCl) under controlled heating. Biological assessments were conducted using the South African Scoring System version 5 (SASS-5) and Average Score Per Taxon (ASPT) metrics, with macroinvertebrates sampled via standardized kick sampling. Results showed that Pb concentrations in water exceeded the South African aquatic ecosystem standard (0.01 mg/L) at all sites, reaching up to 0.065 mg/L at Bolikela. Fe concentrations also surpassed acceptable levels (0.3 mg/L) at four sites, peaking at 2.45 mg/L. In contrast, sediment-bound metal concentrations remained within Canadian Sediment Quality Guidelines. The cleanest site, Matsoaing, recorded the highest SASS-5 (104) and ASPT (7.43) scores, indicating excellent ecological condition, while Ha Setho and Bolikela, located downstream of Maseru, had the lowest scores (SASS-5: 38 and 46; ASPT: 4.22 and 4.60 respectively). Multivariate analyses including Spearman’s correlation and Principal Component Analysis (PCA) revealed strong negative correlations between Pb, Fe, and biological indices. These statistical insights helped pinpoint pollution sources and demonstrated a spatial pattern aligning with land-use impacts, especially urban effluents near Maseru and agricultural runoff downstream. This study demonstrates that elevated heavy metal concentrations, particularly Pb, are linked to macroinvertebrate diversity loss and deteriorating river health. It emphasizes the value of combined chemical-biological assessments in identifying pollution hotspots and guiding catchment management in data-scarce, Transboundary Rivers.Item Open Access The perceptions of the community in water, energy, food and ecosystem Nexus (WEFE NEXUS)(National University of Lesotho, 2025) Mahloane, Kamohelo Jeanett; Nkhabutlane, PulaneWater, energy, food, and ecosystems, as the essential resources underpinning human existence, are critical to the sustainable development of humanity. The local community exerts a significant influence in fostering the sustainable development of these resources. There exists a limited information regarding the effectiveness of nexus resource management in facilitating livelihoods and ensuring resource security. These challenges are associated with the insufficient knowledge within local communities concerning the utilization and exploitation of water, energy, and food resources; this lack of awareness lead to trade-offs, particularly in local and marginalized regions. Through the analysis of data collected from a local community through a questionnaire approach, this research endeavors to investigate the perceptions of the local community regarding WEFE nexus. The findings suggest that community perceptions of nexus resources are comprehensible through the lenses of social, natural, economic, human, physical, and environmental indicators. The results indicate that people's perceptions of nexus resources are based more on the advantages of individual resources than on how they are related to one another. This might be the outcome of how the community views a certain nexus resource; that is food and water, from the four nexus sectors. The community's primary nexus resources in the study region are food and water. This indicates, that there is a missing link between cross-sectorial resource utilization and management, and full-scale adoption of the WEFE nexus to enhance living conditions. Our results indicate that the livelihood advantage of individual nexus resources is the main focus in the community under study, and that there is a lack of knowledge regarding the usage and management of WEFE nexus resources. Based on these findings, we recommend the government and other interested stakeholders to take further steps to enhance the local community's awareness of nexus resources so that they may better comprehend the connections between WEFE sectorsItem Open Access Impact of altitudinal variation on selected soil properties and carbon dynamics on the alpine wetlands of Lesotho(National University of Lesotho, 2025) Mochala, Mosiuoa; Nthebere, KnightThe study entitled, “Impact of Altitudinal Variation on Selected Soil Properties and Carbon Dynamics in the Alpine Wetlands of Lesotho” was conducted in the on-going project entitled, “Carbon Modelling and Omics Approaches for Screening of Soil Microbes for Climate Change Adaptation in the Alpine Wetlands of Lesotho,” initiated in November, 2024. The study was designed in blocks (sub-catchments) with six altitudinal variations (from 2500 to 3155 m a.s.l), equivalent to alpine wetlands from three sub-catchments (Senqunyane, Khubelu and Sani) as follows: Khorong (2500-2550 m a.s.l) and Tenesolo (2552-2600 m a.s.l) in Senqunyane; Khamoqana (2839-2880 m a.s.l) and Khalong-la- Lichelete (2891-29950 m a.s.l) in Sani; and Lets’eng-la-Likhama (3040-3080 m a.s.l) and Koting-Sa-ha Ramosetsana (3087-3155 m a.s.l) in Khubelu. Each treatment was replicated four times. The soil texture was loam to sandy loam across the alpine wetlands. The alpine wetland soils were slightly acidic and non-saline. The findings of the study revealed that bulk density (BD) decreased with increasing altitude 0-15 cm soil depth and was significantly lower (1.08 Mg m-3) in Koting-Sa- ha Ramosetsana (KSHM) compared to other wetlands. The KSHM also showed significantly higher infiltration rate (IR) of 2.17 cm hr-1, maximum water holding capacity (MWHC) of 57.51% and saturated hydraulic conductivity (Ksat) of 2.70 cm hr-1 Ksat at 0-15 cm soil depth whereas, Tenesolo recorded the higher BD and the least IR, MWHC and Ksat. Soil organic carbon (SOC) and Calcium (Ca) were significantly higher in KSHM and increased with increasing altitude, except Khorong (KRN) which did not follow this increasing trend, i.e., KRN (2500-2550 m a.s.l) was exhibited with higher contents of SOC and Ca. The electrical conductivity, cation exchange capacity and macronutrients (nitrogen, phosphorus and potassium) availability of the soil were non-significant. Soil enzyme activities declined significantly with increase in altitude due to lowertemperatures at higher elevations, limiting microbial activity. The dehydrogenase, fluorescein di-acetate and β-galactosidase activities were 3.92 and 45.33%, 1.82 and 32.20% and 9.29 and 15.11% lower in KSHM (3087-3155 m a.s.l) compared to Tenesolo (2552-2600 m a.s.l) and Khorong (2500-2550 m a.s.l), respectively. Higher carbon pools viz., very labile (CVL), labile (CL), less labile (CLL) and non-labile (CNL) and total organic carbon (TOC) were recorded under KSHM compared to all other wetlands at varied altitudes. Passive pool of carbon (CPSV) was dominant over active carbon pool (CACT) with 75–79% contribution towards TOC. Both CPSV and CACT were higher in KSHM (higher elevation site). The Soil Quality Index (SQI) was enhanced (42.54% and 42.51%) at both upper (Koting-sa-ha Ramosetsana) and lower (Khorong) elevation wetlands, indicating that altitude alone does not fully determine soil quality. Instead, wetland condition, vegetation cover, and site-specific environmental factors are critical in shaping soil functionality and regulating carbon processes in alpine wetland ecosystem. Therefore, further research needs to consider synergistic factors including the slope, topography and soil degradation level in addition to the altitude.Item Open Access IMPACT OF ALTITUDINAL VARIATION ON SELECTED SOIL PROPERTIES AND CARBON DYNAMICS IN THE ALPINE WETLANDS OF LESOTHO(2025-07-01) Mochala, MThe study entitled, “Impact of Altitudinal Variation on Selected Soil Properties and Carbon Dynamics in the Alpine Wetlands of Lesotho” was conducted in the on-going project entitled, “Carbon Modelling and Omics Approaches for Screening of Soil Microbes for Climate Change Adaptation in the Alpine Wetlands of Lesotho,” initiated in November, 2024. The study was designed in blocks (sub-catchments) with six altitudinal variations (from 2500 to 3155 m a.s.l), equivalent to alpine wetlands from three sub-catchments (Senqunyane, Khubelu and Sani) as follows: Khorong (2500-2550 m a.s.l) and Tenesolo (2552-2600 m a.s.l) in Senqunyane; Khamoqana (2839-2880 m a.s.l) and Khalong-la- Lichelete (2891-29950 m a.s.l) in Sani; and Lets’eng-la-Likhama (3040-3080 m a.s.l) and Koting-Sa-ha Ramosetsana (3087-3155 m a.s.l) in Khubelu. Each treatment was replicated four times. The soil texture was loam to sandy loam across the alpine wetlands. The alpine wetland soils were slightly acidic and non-saline. The findings of the study revealed that bulk density (BD) decreased with increasing altitude 0-15 cm soil depth and was significantly lower (1.08 Mg m-3) in Koting-Sa- ha Ramosetsana (KSHM) compared to other wetlands. The KSHM also showed significantly higher infiltration rate (IR) of 2.17 cm hr-1, maximum water holding capacity (MWHC) of 57.51% and saturated hydraulic conductivity (Ksat) of 2.70 cm hr-1 Ksat at 0-15 cm soil depth whereas, Tenesolo recorded the higher BD and the least IR, MWHC and Ksat. Soil organic carbon (SOC) and Calcium (Ca) were significantly higher in KSHM and increased with increasing altitude, except Khorong (KRN) which did not follow this increasing trend, i.e., KRN (2500-2550 m a.s.l) was exhibited with higher contents of SOC and Ca. The electrical conductivity, cation exchange capacity and macronutrients (nitrogen, phosphorus and potassium) availability of the soil were non-significant. Soil enzyme activities declined significantly with increase in altitude due to lower temperatures at higher elevations, limiting microbial activity. The dehydrogenase, fluorescein di-acetate and β-galactosidase activities were 3.92 and 45.33%, 1.82 and 32.20% and 9.29 and 15.11% lower in KSHM (3087-3155 m a.s.l) compared to Tenesolo (2552-2600 m a.s.l) and Khorong (2500-2550 m a.s.l), respectively. Higher carbon pools viz., very labile (CVL), labile (CL), less labile (CLL) and non-labile (CNL) and total organic carbon (TOC) were recorded under KSHM compared to all other wetlands at varied altitudes. Passive pool of carbon (CPSV) was dominant over active carbon pool (CACT) with 75–79% contribution towards TOC. Both CPSV and CACT were higher in KSHM (higher elevation site). The Soil Quality Index (SQI) was enhanced (42.54% and 42.51%) at both upper (Koting-sa-ha Ramosetsana) and lower (Khorong) elevation wetlands, indicating that altitude alone does not fully determine soil quality. Instead, wetland condition, vegetation cover, and site-specific environmental factors are critical in shaping soil functionality and regulating carbon processes in alpine wetland ecosystem. Therefore, further research needs to consider synergistic factors including the slope, topography and soil degradation level in addition to the altitude.Item Open Access IMPACT OF SOIL CONDITION, TOPOGRAPHY AND LAND USE ON THE EROSION CHARACTERISTICS OF PHULENG-E-NYANE, HA MANTSEBO: A USLE ANALYSIS THE NATIONAL UNIVERSITY OF LESOTHO(2025-10-01)Soil erosion is a natural yet complex process resulting in the detachment and movement of soil by agents including water and wind, often accelerated by anthropogenic activities such as agriculture and land use changes. It threatens soil fertility, agricultural productivity and ecosystem health making its assessment crucial for sustainable land management. In this study, the impact of soil erosion causing factors on the erosion characteristics of Phuleng-e-Nyane Ha-Mantšebo were evaluated using the Universal Soil Loss Equation (USLE) model. A randomized complete block design in a split plot arrangement was employed to assess soil erosion across the area. The main plot consisted of two farming systems, cropland and long-term fallow land. Within each farming system, the subplot factor was the topo-sequence position comprising four levels: summit, shoulder, back-slope and toe-slope. The soil erosion factors used to determine the total soil loss in USLE include, rainfall erosivity index, soil erodibility factor, topographic factor, crop management factor and conservation factor. Disturbed and undisturbed soil samples were collected, whereby, the disturbed soil samples were collected using soil auger at 30 cm depth and undisturbed soil samples using the core samples. The secondary rainfall data for Moshoeshoe I International Airport was collected from the Lesotho Meteorological services while the slope length was measured using the 100 m fiberglass open reel measuring tape. Google earth was used to look at land use and land cover overtime. Correlation analysis was used to examine the relationship between soil loss and the contributing factors. The localized soil loss prediction model was developed. The total soil loss from study area was calculated at 12.25 Mg ha⁻¹ yr⁻¹ with cropped land contributing about 95.2% of the loss (about 53.7% from the north transect and 41.5% from the southwest transect) while 4.8% was from the long-term fallow land approximately shared equally among the two transects. The stepwise regression analysis revealed that land use is the most influencing factor on soil loss from the area, followed by topography. The study highlights the importance of integrating effective management practices to sustain soil health and reduce erosion hazards.Item Open Access IMPACTS OF CLIMATE CHANGE ON STREAMFLOW AND HYDROLOGICAL EXTREMES IN THE SOUTH PHUTHIATSANA CATCHMENT, LESOTHO(2025-10-01) Maphutseng, MGlobal climate change is predicted to significantly modify hydrological processes, which will have a big impact on ecosystem sustainability, flood risk, and water availability. Knowing how future climate changes may impact river systems is especially important in southern Africa, where population increase and fluctuating rainfall are already placing a strain on water resources. One such critical system is the South Phuthiatsana catchment, which serves as a vital supply of water for Maseru and the other metropolitan areas. This study explores how projected climatic shifts may influence streamflow behavior and the occurrence of hydrological extremes within the South Phuthiatsana watershed, an essential source of water for Maseru and surrounding urban communities. Bias-adjusted data from the MPI-ESM1-2-LR global climate model, along with two sample emissions trajectories for the mid-21st century (2041–2080), were used to project climate inputs. Suboptimal performance indicators showed that the process-based model (SWAT+), which was used to simulate streamflow, was not very reliable in capturing historical daily flow patterns. SWAT+ was therefore thought to be insufficient for predicting future hydrological reactions in this context. A machine learning approach using the XGBoost algorithm was adopted to address this challenge. This data-driven model was trained on bias-corrected climate variables and observed streamflow, providing a more reliable tool for future streamflow prediction. The results from XGBoost revealed substantial and complex hydrological shifts. A consistent warming trend combined with highly variable seasonal precipitation patterns was evident across both emission scenarios. Extreme high flows, represented by the 98th percentile (Q98), are projected to decline by more than 52% compared to historical values, suggesting a reduced risk of flooding. In contrast, low flows are expected to increase dramatically; the 1st percentile (Q1) flow is projected to rise from near-zero values historically to approximately 9.0 m³/s, indicating a significant shift toward more perennial flow conditions. Mid-range flows (Q25, Q50, and Q75) are also expected to increase substantially, depending on the flow percentile and scenario. While the absolute magnitude of low flows improves, the number v of days with historically low flow conditions may still increase during certain months, highlighting a shift in the intra-annual flow variability. These findings point to a future with altered hydrological regimes in the South Phuthiatsana catchment characterised by diminished flood peaks, elevated baseflows, and more frequent low-flow conditions during critical periods. Despite initial limitations with the process-based model, the machine learning approach provided robust insights that form a valuable foundation for developing adaptive, forward-looking water resource management strategies. These results underscore the need for resilient planning to ensure long-term water security under evolving climate conditions.Item Open Access SOCIOECONOMIC AND ENVIRONMENTAL DIMENSIONS OF ABANDONED MAQALIKA DAM TO THE SURROUNDING COMMUNITIES IN MASERU, LESOTHO(2025-10-01) Moloisane. R.MMaqalika Dam was constructed in 1983 to supply potable water for households and other uses in Maseru. However, since its abandonment for some time it has suffered progressive degradation due to pollution, sedimentation, and unregulated urban encroachment. Against that backdrop, this study examined the socioeconomic and environmental dimensions of the abandonment of Maqalika Dam in Maseru Lesotho. A mixed-methods approach comprising household surveys (n=310), key informant interviews with institutional stakeholders, and field observations was used with tabulations and content analysis to assess the multifaceted consequences of the dam’s discontinued use. Findings indicated that over 55% of nearby residents experienced livelihood disruptions, especially in irrigation, livestock watering, and small-scale fishing. Environmentally, over 94% of respondents reported pollution, waste dumping, and eutrophication as prominent challenges which posed risks to public health and aquatic life. Institutional neglect, rapid urbanization, and intentional pollution were identified as key drivers of abandonment. Although Metolong Dam now supplies Maseru, the Maqalika site remains a source of socio-environmental concern. Stakeholders proposed strategies including pollution source mapping, ecological rehabilitation, and participatory reuse planning. The study concludes that there is an urgent need for integrated water resource governance, emphasizing rehabilitation or sustainable repurposing to transform abandoned urban dams from liabilities into community assets.Item Open Access EFFECTS OF SEDIMENTATION ON WATER QUALITY IN THE METOLONG RESERVOIR, MASERU, LESOTHO(2025-07-01) Makhakhe, MReservoir sedimentation is a significant environmental challenge affecting the sustainability of water resources, particularly in Lesotho, where soil erosion and land degradation are prevalent due to intensive land–use activities and fragile ecosystems. Metolong Reservoir is a critical water supply for approximately two–thirds of Lesotho's population, challenged with rapid sedimentation which lowers the water quality and threatens the long–term viability of the reservoir, yet no known studies have been done to correlate sedimentation and water quality in the area. This study investigated how sedimentation affects water quality in the Metolong Reservoir by quantifying sediment accumulation in the Metolong Reservoir from 2020 to 2022 and analyzing the impact of sedimentation on key water quality parameters. The study employed historical bathymetric and water quality data (2020–2022), complemented by GIS–based spatial analysis and R statistical modeling to assess the spatial and temporal relationships between sediment deposition and changes in water quality parameters, following the causal–comparative research design. Water quality parameters analyzed were aluminum, Electrical Conductivity (EC), iron, manganese, nitrates, nitrites, phosphates, sulphates, TDS and turbidity. Bathymetric analysis revealed a total sediment accumulation of approximately 1,705,583 m3 (2.68% of reservoir capacity) between 2020 and 2022, concentrated primarily near the reservoir’s middle and towards the dam, resulting in an annual storage loss of 1.34%.Linear regression analysis revealed turbidity as the most significant at (p<0.05).The study also identified turbidity and nitrates as key water quality parameters significantly influenced by sedimentation, with turbidity showing the strongest correlation (r = 0.60) and nitrates showing a moderate correlation (r = 0.2) with sediment volume suggesting possible links with upstream land use and nutrient runoff. There was an increase in nutrients and heavy metals concentration from 2020–2022, indicating a need for intervention, though most water quality parameters were still within WHO and South African water quality standards. The findings confirm that sedimentation negatively affects water quality, underscoring the need for integrated catchment management strategies, including sediment control, land–use planning, and systematic water quality monitoring to safeguard reservoir operations and public health.Item Open Access THE PERCEPTIONS OF THE COMMUNITY IN WATER, ENERGY, FOOD, AND ECOSYSTEM NEXUS (WEFE NEXUS) A CASE STUDY AT HA SEEISO METOLONG, MASERU(2025-07-01) Mahloane. KWater, energy, food, and ecosystems, as the essential resources underpinning human existence, are critical to the sustainable development of humanity. The local community exerts a significant influence in fostering the sustainable development of these resources. There exists a limited information regarding the effectiveness of nexus resource management in facilitating livelihoods and ensuring resource security. These challenges are associated with the insufficient knowledge within local communities concerning the utilization and exploitation of water, energy, and food resources; this lack of awareness lead to trade-offs, particularly in local and marginalized regions. Through the analysis of data collected from a local community through a questionnaire approach, this research endeavors to investigate the perceptions of the local community regarding WEFE nexus. The findings suggest that community perceptions of nexus resources are comprehensible through the lenses of social, natural, economic, human, physical, and environmental indicators. The results indicate that people's perceptions of nexus resources are based more on the advantages of individual resources than on how they are related to one another. This might be the outcome of how the community views a certain nexus resource; that is food and water, from the four nexus sectors. The community's primary nexus resources in the study region are food and water. This indicates, that there is a missing link between cross-sectorial resource utilization and management, and full-scale adoption of the WEFE nexus to enhance living conditions. Our results indicate that the livelihood advantage of individual nexus resources is the main focus in the community under study, and that there is a lack of knowledge regarding the usage and management of WEFE nexus resources. Based on these findings, we recommend the government and other interested stakeholders to take further steps to enhance the local community's awareness of nexus resources so that they may better comprehend the connections between WEFE sectors.Item Open Access WATER DEMAND FORECASTING USING MACHINE LEARNING APPROACH(2025-10-01) Mahamo, QThe increasing challenges related to water security, exacerbated by rapid urbanization, population growth, and climate variability, necessitate accurate and reliable forecasting methodologies to support sustainable water resources planning. This study explores water demand forecasting in Ha-Foso, Lesotho, by evaluating three machine learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). Utilizing time series data sets covering meteorological inputs (2012-2022), population, and water consumption records (2017-2024), the study assesses the influence of climatic and demographic variables, specifically precipitation, maximum and minimum temperatures, and population, on domestic water consumption. The research first used MLR to assess the influence of population, maximum temperature, minimum temperature, precipitation, and other factors on water demand. Subsequently, the study evaluated the predictive performance of MLR, SVR, and ANN models. Performance was evaluated using performance metrics, including the coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The regression analysis consistently identified population as the only statistically significant predictor of water demand (p<0.001), while climatic variables showed no significant influence during the study period. In the comparative evaluation, the SVR demonstrated the highest accuracy and generalization capacity, outperforming ANN and MLR, with the least error metrics in both the training phase and the testing phase. The 2-year forecast highlighted the distinct behaviours of each model, with the SVR and ANN models providing more moderate growth projections compared to the steep, linear increase predicted by the MLR model. This study presents the potential of machine learning, particularly SVR and ANN, in addressing the intricate, non-linear relationships inherent in water demand forecasts, delivering precise and actionable water demand forecasts for peri-urban settings in Lesotho. The findings suggest the adoption of advanced architectures and the incorporation of socio-demographic variables to strengthen predictive capacity. The outputs are expected to support utility companies such as Water and Sewerage Company (WASCO) in strategic planning, conservation, and infrastructure investment decisions.