Research Articles (Earth Sciences)

Permanent URI for this collection

https://hdl.handle.net/10566/273

Browse

Now showing 1 - 20 of 313

Land use and land cover changes in sub-catchments of Zimbabwe and their implications on wetland and catchment soil water conditions
(Elsevier Ltd, 2025) Dube Timothy; Mupepi Oshneck; Marambanyika Thomas
This study evaluated land use and land cover changes in the Shashe and Tugwi and Zibagwe sub-catchments from 2017 to 2023, with a focus on their impacts on dry season wetland extent and condition. Utilizing the Google Earth Engine Cloud Computing platform, Sentinel-2 Level 1C data were processed using Support Vector Machine (SVM) classification algorithm to analyse these changes. The Soil Moisture Active Passive level 4 (SMAP L4) soil moisture and the Normalised Difference Vegetation Index (NDVI) were computed to determine the influence of catchment level land cover change on soil moisture conditions. This study considered the influence of land cover on wetland conditions and catchment level soil moisture levels which got minimum attention in previous wetland studies. The study highlights that bare land in Tugwi and Zibagwe increased more rapidly (601.1 %) than in the drier Shashe sub-catchment. However, the wetland area decreased more in Shashe, indicating greater wetland degradation despite the slight difference (0.4 %). The analysis revealed that wetlands experienced an overall 11.8 % loss in Shashe and 11.4 % loss in Tugwi-Zibagwe. Results indicate that 5.2 %, 3.4 % and 2.3 % of the wetland area was replaced by grassland, shrubland and bare land respectively in Tugwi and Zibagwe combined whilst 4.8 %, 3.6 % and 2.32 % of the wetland area were replaced by bare land, grassland and shrubland respectively in Shashe. Statistically significant weak positive correlations were confirmed between soil moisture and NDVI in Tugwi and Zibagwe combined (r = 0.28; p = 0.04) and Shashe (r = 0.43; p = 0.02). Rainfall had stronger correlation with soil moisture in Tugwi and Zibagwe (r = 0.43; p = 0.19) and Shashe (r = 0.62; p = 0.38) which were not statistically significant indicating more influence of land cover on soil moisture than rainfall. The findings accentuate the critical need for sustainable land use practices to mitigate the adverse effects on natural land cover and wetland ecosystems. The rapid expansion of bare land and reduction in wetlands underscore the pressing challenges posed by land cover changes, particularly in regions experiencing increasing aridity. •Land use and cover changes in the three sub-catchments are assessed.•The influence of land cover change on wetland extent and soil moisture conditions are analysed.•The relationship between root zone soil moisture and rainfall and land cover change is analysed.
A multi-source data approach to carbon stock prediction using bayesian hierarchical geostatistical models in plantation forest ecosystems
(Taylor and Francis Ltd., 2024) Dube, Timothy; Chinembiri, Tsikai Solomon; Mutanga, Onisimo
Modeling of environmental phenomena is usually confounded by the influence of multiple factors existing at different time and spatial scales. Bayesian modeling is presumed to be the best approach for modeling such complex systems. Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. We therefore investigated how two related multi-data sources of new generation remote sensing derived ancillary information influence C stock prediction required for building sustainable capacity in C monitoring and reporting. Two mainstream models constructed from Landsat-8 and Sentinel-2 derived vegetation indices coupled with climatic and topographic covariates were used to predict C stocks using forest inventory data collected using spatial coverage sampling. A multi-source data driven approach to C stock prediction yielded slightly lower predictions for both the Landsat-8 ((Formula presented.) and the Sentinel-2 ((Formula presented.) -based C stock models than C stock predictions published in related studies. Distance to settlements ((Formula presented.)) and (Formula presented.) are significant predictors of C stock with the Sentinel-2-based C stock model outperforming its Landsat-8 model variant in terms of prediction accuracy. The Sentinel-2-based C stock model resulted in a 1.17 MgCha−1 Root Mean Square Error (RMSE) with a ((Formula presented.) 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha−1 RMSE with a ((Formula presented.) associated 95% credible interval. Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. Both climatic and topographic derived ancillary data are not significant predictors of C stock under the present modeling conditions. Accurate and precise accounting of C stock for climate change mitigation and action can best be done at landscape scales rather than local scale as the scale of variation for climate-change-related variables vary at larger spatial scales than the ones utilized in the present study.
Available satellite data for monitoring small and seasonally flooded wetlands in semi-arid environments of Southern Africa
(John Wiley and Sons Ltd, 2024) Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic
Time-series monitoring of wetland eco-hydrological dynamics using remote sensing continues to be an attractive and practical tool, mainly due to its ability to overcome challenges related to in situ data availability. However, acquiring seamless and cloud-free data for accurate and routine wetlands monitoring remains a persistent challenge. In this study, we aimed to evaluate the availability of satellite scenes in the google earth engine (GEE) catalogue that could facilitate the monitoring of eco-hydrological dynamics in small and seasonally flooded wetlands within the semi-arid environments of southern Africa. The study covered a 20-year period from 2000 to 2020, with a specific focus on the Nylsvley floodplain as a case study. The study conducted a comprehensive assessment of available products on the GEE platform, including Landsat thematic mapper (TM), enhanced thematic mapper plus (ETM+), operational land imager (OLI), sentinel-1 and sentinel-2. The identified images underwent rigorous filtering and screening based on varying cloud-cover percentages (0%, 1%–10%, 11%–25% and 26%–50%). The results revealed a considerable number of satellite products (1376) available for the study period. Specifically, there were 492 landsat images, 394 sentinel-1 images and 490 sentinel-2 images. Amongst these, sentinel-2 and landsat-7 had the highest number of images (69% and 76%, respectively) with cloud-cover percentages ranging from 0% to 20%. However, images with cloud cover exceeding 26% were excluded from the analysis. Further analysis indicated that using satellite images with 0% cloud cover resulted in an overall accuracy (OA) ranging between 69% and 72%, while 1%–10% cloud cover had an OA ranging between 68% and 70%, and 11%–25% cloud cover had an OA ranging between 69% and 80.55% for both the dry and wet seasons. Overall, the classification results demonstrated satisfactory OAs (68%–82%) for all scenes, with some inaccuracies observed for certain classes, notably bare surface and long grass. These inaccuracies were particularly evident when using landsat-7 scenes, attributable to the spatial resolution of the data. The findings emphasised the availability of a substantial amount of archival satellite data, capable of monitoring small and seasonally flooded wetlands, providing valuable insights into the eco-hydrological dynamics of these ecosystems. Moreover, the study highlighted the benefits of cloud-computing platforms like GEE in addressing challenges associated with big data filtering, processing and analytics, thereby enhancing environmental monitoring and assessments, which may have been limited by the unavailability of advanced processing tools and seamless cloud-free data.
Assessment of the spatiotemporal dynamics of the hydrological state of non-perennial river systems and identification of flow-contributing areas
(South African Water Research Commission, 2024) Maswanganye, Sagwati E; Dube, Timothy; Jovanovic, Nebo; Kapangaziwiri, Evison; Mazvimavi, Dominic
Non-perennial rivers (NPRs) have three hydrological states; each state has its importance, function and implication for water resource management. The dynamics of these states have been inadequately assessed and understood. Hence, this study sought to determine the spatiotemporal variations in the hydrological conditions of NPRs, focusing on the Touws river–karoo drylands and Molototsi river within the semi-arid region of the Limpopo province of South Africa. Additionally, the study aimed to delineate and characterize the primary areas contributing to runoff in these two river systems. Sentinel-1 and sentinel-2 satellite data sources were employed in this study. Specifically, the modified normalized difference water index (MNDWI) derived from sentinel-2 was utilized to delineate water surface areas along the two rivers. Subsequently, these derived datasets were utilized to assess the hydrological states over a 32-month period (2019–2022). Based on the presence of water, the river’s state was classified as flowing, pooled, or dry. The results showed that remote sensing can be used to determine the hydrological state of the two river systems with ~90% overall accuracy. However, there is about a 30% chance that a flow event can be missed using Sentinel-2 due to clouds and temporal resolution. Some of these gaps can be filled using synthetic aperture radar (SAR) data (Sentinel-1), as demonstrated with the Molototsi river. In the Molototsi catchment, the upper catchment contributes the majority of flows. For the Touws river, the southwestern part of the catchment was determined as the major contributing area for the observed flows. This suggests that the chosen observation site might not be representative of upper catchment dynamics; therefore, a monitoring site in the upper catchment is required. This study provided hydrological information and an approach that can be used to monitor the hydrological states for better understanding and management of NPRs and catchments
Assessment of Huixian Karst wetland for local water augmentation in Guilin, China
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Qi, Jihong; Xu, Yongxin; Kanyerere, Thokozani
Due to the rapid exploitation of water resources in the Huixian karst wetland in the southwest of China in the past decades, the wetland has suffered from shrinkage in size and serious degradation of ecological functioning. To assess how much water within the wetland could still be taken out for local supply purposes while the wetland can still be kept in its normal ecological functioning. Through the use of multi-methods, including field surveys by a multi-disciplinary team, water balance, Remote sensing, GIS and numerical simulation, this paper characterizes the wetland regime of the study area and finally determines scenarios of water resource utilization for local water supply within acceptable parameters of wetland ecological health. Through the analysis of the methods, it was found that the hydrological characteristics of the study area were conditioned by not only the karst water but also the regional precipitation fluctuations. A zone of mobile watersheds for lake Mudong was established as opposed to a conventional single watershed. If the wetland ecosystem is kept at the current status of class III, a scenario of withdrawal of up to 20% of lake inflows could be accommodated. The results and their approaches would provide much-needed information for the protection of the wetland and its sustainable water utilization per se. It would offer a basic reference for similar problems in karst areas of southwest China and other areas alike.
Remote sensing crop water productivity and water use for sustainable agriculture during extreme weather events in South Africa
(Elsevier B.V., 2024) Mpakairi, Kudzai Shaun; Dube, Timothy; Sibanda, Mbulisi
The impact of climate variability and extreme weather events on agricultural productivity in arid environments has become a focal point in contemporary research. Monitoring crop water productivity (CWP) is critical and urgently required especially in the arid regions where agriculture consumes an above-average portion of the available fresh water resources. In this context, this study aimed to demonstrate the utility of remotely sensed data in assessing CWP and water use dynamics across diverse crop types in South Africa during the El Niño (2018/19) and non-El Niño (2021/22) events. In addressing the objective, the study also assessed the intra- and inter-annual variations in crop water productivity for diverse crop types including, grains, grapes, citrus fruits, teas, planted pastures, and oil seeds. The study used potential evapotranspiration and biomass derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to estimate CWP from 2017 to 2021. This period included El Niño (2018/19) and non-El Niño (2021/22) years. The results showed that potential evapotranspiration (PET) derived from MODIS was related to the PET estimated from weather stations (R2 > 0.6; RMSE < 21.90; p-value < 0.001). In terms of water use, planted pastures had the highest water use 114 mm/month), while teas and citrus fruits had the lowest water use (6 mm/month). Citrus fruits, grapes and teas consistently had the lowest annual mean crop water productivity (<0.02 kg/m3/annually), while oil seeds had the highest annual mean crop water productivity (>0.1 kg/m3/annually). Lastly, there were no significant differences (p-value > 0.05) between the CWP for all the crops observed between El Niño (2018/19) and non-El Niño (2021/22) periods, suggesting the effectiveness of adaptation measures and interventions during this period. These results provide a simple, spatially explicit framework, relevant to understanding crop-water use, laying the groundwork for informed decision-making and sustainable agricultural practices. Integrating these findings into policy frameworks and agricultural strategies is paramount for ensuring food security and resilience in a changing climate. © 2024
The influence of physicochemical variables on plant species richness and distribution in the coastal salt marshes of the Berg River Estuary, South Africa
(Elsevier, 2024) Mngomezulu, Nomcebo T; Rajkaran, Anusha; Veldkornet, Dimitri A
The continuous distribution of coastal salt marsh habitats along an elevation gradient can be disrupted by tidal creeks running through them. Tidal creeks wind through salt marshes and create different environmental con ditions for adjacent habitats. While studies have emphasized the importance of tidal creeks as links facilitating interactions in salt marshes, few have studied plant communities and physiochemical conditions associated with tidal creeks. This study determined the influence of creek physicochemical variables on the diversity and dis tribution of coastal salt marsh plants. Six transects in the lower reaches of the Berg River Estuary, South Africa were sampled over two seasons at sites with either the presence or absence of creeks. Species composition and abundance were analysed by replicate quadrats and paired with physicochemical variables (groundwater and sediment). The k-means of 20 species in 334 quadrats revealed four distinct clusters of salt marsh habitats, creeks, intertidal salt marsh, supratidal salt marsh and reeds. Species richness was higher along transects with creeks (16) compared to those with no creeks (5). The physiochemical variables, groundwater temperature, pH, dissolved oxygen, conductivity, and sediment variables (redox potential, organic content, percentage silt and percentage sand), significantly influenced the abundance of creek species. This study highlights the importance of tidal creeks in forming unique vegetation communities in salt marshes, where they act as refugia for intertidal species. It is suggested that tidal creek communities should be included in salt marsh vegetation descriptions and monitored in association with physicochemical variables in response to climate change.
Seasonal variations in water use of japanese plum orchards under micro-sprinkler and drip irrigation methods using fruitlook data
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Mashabatu, Munashe; Motsei, Nonofo; Jovanovic, Nebojsa
South Africa is considered one of the driest countries, and its water insecurity challenges are exacerbated by climate change and variability, depletion, and degradation, among other factors. The challenges of water insecurity are exacerbated by some of the introduced crops, like the Japanese plums (Prunus salicina Lindl.) grown in South Africa, as they consume a lot of water. The Japanese plums are grown under irrigation to supplement low and erratic rainfall in the country. There is little information on the water requirements of Japanese plums (particularly in water-scarce regions), a gap addressed by this study. Therefore, the study aims to quantify and compare the seasonal water use of high-performing, full-bearing Japanese plum orchards under drip and micro-sprinkler irrigation in the Western Cape Province, using readily available satellite data from the FruitLook platform. The seasonal water use volumes of selected plum orchards were compared at provincial and farm scales. At a provincial scale, micro-sprinkler-irrigated orchards consumed significantly more water (up to 19%) than drip-irrigated orchards, whilst drip-irrigated orchards experienced an average 38% greater water deficit. Results were more variable at the farm scale, which was attributed to the influence of site-specific soil, climate, and crop conditions on the performance of the irrigation methods. Therefore, a blanket approach cannot be used when selecting an irrigation method and design. Instead, a case-by-case approach is recommended, which takes into account the root distribution, soil texture, and planting density, among other factors. The generated knowledge facilitates allocating and licensing water resources, developing accurate irrigation scheduling, and promoting improved water use efficiency.
Seasonal and spatial dynamics of surface water resources in the tropical semi-arid area of the Letaba catchment: Insights from google earth engine, landscape metrics, and sentinel-2 imagery
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Dube, Timothy; Mashala, Makgabo Johanna; Ayisi, Kingsley Kwabena
Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments.
Local perspectives, regional consequences: the socio-environmental impacts of sand harvesting in southern Africa
(Elsevier Ltd, 2025) Jovanovic,Nebojsa; Smigaj,Magdalena; Walker,David
After water, sand is the most exploited resource on Earth, with extraction rates often exceeding the sustainable supply, impacting ecosystems and local communities. Still, there is very little information on the situation in southern Africa, despite the rapid economic growth in the region and associated increase in sand demand. This study aimed to address this gap by identifying the implications of sand extraction on local communities and the ecosystem, drawing upon the perspectives of local stakeholders. Qualitative data collected in Botswana, South Africa and Mozambique through stakeholder interviews, revealed a suite of environmental and social issues surrounding both licensed and unlicensed operations. The experienced negative impacts and benefits were occasionally contradictory in nature, strongly depending on characteristics relating to geography, and the type of sand harvesting activity. We subsequently explored links between experienced benefits, impacts and current regulatory frameworks through development of a Driver-Pressure-State-Impact-Response (DPSIR) framework, which highlighted that careful mining site selection and adherence to regulations could minimise socio-environmental impacts whilst achieving benefits. The findings of the study provided insights on the main obstacles for alleviating sand harvesting-related impacts and existing knowledge gaps that need to be first addressed to inform the development of more sustainable sand harvesting practices.
Bush encroachment with climate change in protected and communal areas: a species distribution modelling approach
(Elsevier B.V., 2025) Maphanga, Thabang; Dube, Timothy; Sibanda, Mbulisi
Savanna rangelands have experienced widespread degradation due to bush encroachment, raising significant concerns among conservationists and rural communities. In the context of climate change, these ecosystem shifts are likely to intensify, especially in South Africa's semi-arid regions. Understanding the impacts of climate variability and change on species distribution within these rangelands is crucial for mitigating further ecosystem disruption. Environmental factors, along with climatic variables, can accelerate the process of bush encroachment, threatening both biodiversity and land use. Early identification of areas vulnerable to invasion is key to developing effective and cost-efficient management strategies. This study aims to model the distribution of invasive species across protected and communal landscapes under long-term climate change projections. A Random Forest (RF) model produced the highest accuracy metrics for Area under the curve (AUC) = 0.99 and True Skill Statistic (TSS)=0.97, while a MaxEnt model recorded the second highest AUC (0.98) and TSS (0.97). The results show a clear difference between the current and future scenarios of the spatial distribution in all the models. Applying a species distribution model (SDM) using both MaxEnt and RF produced a higher degree of prediction accuracy because RF is susceptible to overfitting training data while MaxEnt can produce predictable and complex results. Moreover, the overall predictions using the ensemble model demonstrated an increase in areas suitable for encroachment under RCP 8.5 but a decrease in the bush encroachment rate under RCP 2.6. These findings underscore the critical need for proactive management strategies to mitigate bush encroachment, particularly under high-emission scenarios, ensuring the sustainability of semi-arid savanna rangelands in the face of climate change.
Stonefly systematics: past, present, and future
(Oxford University Press, 2025) Machingura, James; Eichert, Anna; De Almeida, Lucas Henrique; Du, Yu-Zhou; Duarte, Tácio; Fochetti, Romolo; Hotaling, Scott; Huo, Qing-Bo; Jouault, Corentin; Kirkaldy, Abigail Puleng; Letsch, Harald; Li, Weihai; López-Rodríguez, Manuel Jesús; Mcculloch, Graham; Mo, Raorao; Mtow, Shodo; Pessacq, Pablo; Rippel, Mellis Layra Soares; Rivera-Pomar, Rolando; Sproul, John S; Sarmento, Felipe Ribeiro Pereira; Sroka, Pavel; Tierno De Figueroa, José Manuel; Ware, Jessica
Stoneflies (Insecta: Plecoptera) are a widespread group of freshwater insects known for their ecological significance and sensitivity to environmental change. This diverse order encompasses over 4,000 species across 17 families, with the number of described species predicted to increase substantially over the coming years. This review surveys the past and present landscape of stonefly systematics, emphasizing recent advancements in our understanding of the phylogenetic relationships within this group to the ordinal, subordinal, and family level. We highlight the need for expanded biodiversity surveys, particularly in underexplored regions such as high-elevation ecosystems, the Southern Hemisphere, and the Arctic, and identify the key challenges impeding the advancement of systematic research, in particular the decline in taxonomic expertise. Looking forward, we outline a vision for the future of stonefly systematic research, advocating for increased inclusivity, collaborative research efforts, and the integration of advanced molecular methodologies.
Thermo-mechanical intrusion-wall rock interaction and granite emplacement mechanisms of the peninsula granite at the Sea Point contact, Cape Town, South Africa
(Elsevier Ltd, 2025) Mhlanga, Musa; Bailie, Russell; Reinhardt, Jürgen
The Sea Point contact, Cape Town, South Africa exposes the intrusive contact between the ∼540 Ma S-type Peninsula Granite and the ∼560–555 Ma metasedimentary rocks of the Malmesbury Group of the Pan-African Saldania Belt. The western Saldania Belt was subjected to low-grade greenschist facies metamorphism and deformation during the ∼560–540 Ma Saldanian orogeny. The Peninsula Granite intruded as a series of numerous granite sheets which made use of the pre-existing country rock anisotropy in order to propagate. These are the steeply dipping S0 bedding due to folding during the Saldanian orogeny, and a steeply dipping axial planar S2 foliation to the F2 folds developed during the dominant D2 deformation. Magma overpressure relative to tensile stresses in the country rock and regional NE-SW-orientated compressional stresses allowed intrusion of variably crystal-laden magma along the anisotropies. The granitic sheets are commonly concentrated in the hinge zones of F2 folds, where structural traps facilitated magma “trapping.” Filter pressing at the tail of the magma-filled hydrofracture caused closing during magma through-flow resulting in the entrapping of magmatic crystals, most notably K-feldspar megacrysts, in the wall rock as well as xenoliths dislodged during magma infiltration and stoping, and possibly magma flow. Magma stresses have brought about the alignment of K-feldspar megacrysts as well as the long axes of xenoliths parallel to the orientation of granite sheets and wall rock septa in the complex lit-par-lit zone and adjacent to the contact.
Using multisource remotely sensed data and cloudcomputing approaches to map non-native species in thesemi-arid savannah rangelands of Mpumalanga, South Africa
(Taylor & Francis, 2025) Maphanga, Thabang; Dube, Timothy; Sibanda, Mbulisi; Gxokwe, Siyamthanda
Semi-arid savannah rangelands are diverse environments (in terms of species) that play an important role in sustaining biodiversity and providing ecosystem services. However, the emergence of non-native species, as well as bush encroachment, are currently threatening these (semi-arid rangeland and grassland) ecosystems. The purpose of this study was therefore to map and quantify the spatial extents of non-native woody vegetation in the Kruger National Park and surrounding communal areas in Mpumalanga, South Africa. To achieve the study’s objectives, Sentinel-1 and Sentinel-2 remotely sensed data were combined and analysed using the random forest (RF) machine-learning algorithm in the Google Earth Engine (GEE) cloud computing platform. Specifically, spectral bands and selected spectral derivatives, e.g. enhanced vegetation index (EVI2), normalized difference moisture index (NDMI) and normalized difference phenology index (NDPI) were computed and used to map non-native woody vegetation. After optimizing the model combination, the classification outputs had an overall accuracy of 70%, with class accuracies such as producer’s accuracy (PA) and user’s accuracy (UA) ranging from 67% to 95%.It was shown in this study that using Sentinel-2 and Sentinel-1 data together led to better overall accuracy than using single sensor models when mapping semi-arid savannah rangelands. It was also found in this study that the overall classification accuracy of non-native (invasive) species using optical sensors was higher than in previous studies. On a free platform like GEE, it was possible to utilize advanced classification processes to fully exploit the informative content of Sentinel-1 and Sentinel-2 data.
Zambezi river basin aquifer systems: opportunities and challenges in using freely available data sources and groundwater flow modelling for spatial exploratory analysis
(Elsevier B.V., 2025) Mengistu, Haile; Banda, Kawawa; Crestaz, Ezio
A groundwater flow model was implemented over the Zambezi River Basin using the state-of-the-art DHI-WASY finite element code Feflow. The analysis was based upon different freely available datasets that include a hydrologically consistent digital elevation model from HYDROSHEDS, the BGS (British Geological Survey) quantitative hydrogeological maps, and the regional hydrogeological SADC-GMI database. The model implementation was aimed at: (i) to identify and analyse challenges and limitations (data scarcity, accuracy of regional datasets, impact of geological, tectonic and hydrogeological complexity on model reliability) in applying groundwater flow modelling at basin scale; (ii) to perform an exploratory spatial analysis with reference to the magnitude and spatial distribution of effective recharge, aquifers’ properties and interlinks between surface water and aquifer systems (surface water – groundwater interactions). High uncertainty is generally associated with the estimation of hydrological and hydrogeological parameters, whose high spatial variability is not necessarily captured by the regional data products. This study evaluates how integrating freely available datasets (such as the DEM, BGS maps) influences model accuracy and uncertainty, particularly in terms of parameter estimation. The findings illustrates that, despite the limitations, freely available datasets can still effectively be used to develop a groundwater model that captures regional piezometric trends and provides insights into spatial variability. This demonstrates the potential for using such models in similar data-scarce regions. The modeling approach is expected to provide valuable quantitative insights into groundwater trends and variability, helping to identify key areas of uncertainty and guiding future data collection and model refinement efforts.
Assessing the impacts of water diversion project on water resource system sustainability
(John Wiley and Sons Inc, 2025) Liu, Dedi; Chen, Wen; Zhang, Ruikang
Interbasin water diversion project has been considered as an effective way to assure water resource system sustainability. In order to assess the impacts of water diversion on sustainability, we propose a framework in terms of reliability, resilience, and vulnerability. The estimated water availability from hydrological models and the projected water demand are input to a water resource allocation model. The water resource allocation model allocates the two available water sources (i.e., the local and the diverted water) in the water-receiving areas. The differences of the allocated water resources between these two water sources are figured out to quantify the impacts of water diversion on water resource system sustainability. The water-receiving area of Bailong River Water Diversion Project, located in Gansu, China, was selected as a case study. The results show that compared to the reference planning years, the runoff in future planning years will be reduced, while their water demands will almost increase under all scenarios. Although the current designed water diversion scheme is effective in increasing resilience, there is still potential for increasing resilience through optimizing the designed scheme. Further, the more unfavorable the water supply and demand conditions are, the larger the space for optimizing the system sustainability. This study can help understand the impacts of water diversion on water resource system sustainability in a changing environment.
Machine learning and spatio temporal analysis for assessing ecological impacts of the billion tree afforestation project
(John Wiley and Sons Ltd, 2025) Dube, Timothy; Mehmood, Kaleem; Anees, Shoaib Ahmad
This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel-2 imagery, we observed an increase in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high-confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R2 of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data-driven afforestation efforts and inform sustainable environmental management practices.
A validation of fruitlook data using eddy covariance in a fully mature and high-density japanese plum orchard in the western cape, south africa
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Mashabatu, Munashe; Motsei, Nonofo; Jovanovic, Nebojsa
The cultivation of Japanese plums (Prunus salicina Lindl.) in South Africa has increased over the years, yet their water use is unknown. Their cultivation in the Western Cape Province of South Africa is highly dependent on supplementary irrigation, indicating their high water use demand. This study used remote sensing techniques to estimate the actual evapotranspiration (ETc act) of the Japanese plums to assess their water use on a large scale. The accuracy of the procedure had to be validated before getting to tangible conclusions. The eddy covariance was used to measure ETc act in an African Delight plum orchard to validate the FruitLook remote sensing data for the 2023–2024 hydrological year and irrigation season. The seasonal and annual plum crop water requirements measured using the eddy covariance system were 751 and 996 mm, while those estimated by FruitLook were 744 and 948 mm, respectively. Although FruitLook slightly underestimated plum ETc act by a Pbias of −6.15%, it performed well with a Nash–Sutcliffe efficiency (NSE) of 0.91. FruitLook underestimated evapotranspiration mainly during the peak summer season with full vegetation cover when the model may inaccurately represent irrigation impacts, soil moisture availability, and localized advection effects, better captured by the eddy covariance system. Based on the results, FruitLook proved to be sufficiently accurate for large-scale applications to estimate evapotranspiration in Japanese plum orchards in the Western Cape.
Ccu2pdsnse4 and cu2pdsn(s,se)4 palladium-substituted kesterite nanomaterials for thin-film solar cells
(American Chemical Society, 2025) Nwambaekwe, Kelechi; Yussuf, Sodiq; Tshobeni, Ziyanda; Ikpo, Chinwe; January, Jaymi; Cox, Meleskow; Ekwere, Precious; Iwuoha, Emmanuel
Kesterites are being studied intensively as sustainable absorber materials for solar cell development. However, elements such as Zn and Cu exhibit antisite defects that function as charge traps and recombination centers that affect the light absorption and carrier transport efficiencies of kesterite solar cells. The substitution of Zn or Cu with other metals is one of the strategies used to improve the photovoltaic performance of kesterites. This study focuses on the preparation and photovoltaics of Cu2PdSnSe4 (CPTSe) and Cu2PdSn(S,Se)4 (CPTSSe) kesterite nanoparticles (containing Pd instead of Zn) by a modified solvothermal (polyol) microwave synthesis method. The nanomaterials exhibited a tetragonal kesterite crystal structure with polydispersed morphology and average crystallite sizes of 22 and 17 nm for CPTSe and CPTSSe, respectively. DAMMIF ab initio analysis of the small-angle X-ray scattering data determined the shape of CPTSe and CPTSSe nanomaterials to be ellipsoidal. Ultraviolet-visible (UV-vis) spectroscopy revealed red-shift absorption properties, with bandgap energy values of 1.13 eV (CPTSe) and 1.20 eV (CPTSSe), thereby making them suitable light absorber materials for photovoltaic applications. Photoluminescence spectroscopy characterization confirmed the attenuation of defect concentrations in CPTSe and CPTSSe compared to the Zn analogue, which positively impacts the charge-carrier transport and recombination properties. A preliminary test of the materials in superstrate photovoltaic cell devices yielded power conversion efficiency values of 1.32% (CPTSe) and 3.5% (CPTSSe). The CPTSe- and CPTSSe-based photovoltaic devices maintained ∼70% mean open-circuit voltage (Voc), which is a significant improvement over the ∼20% Voc retained by Zn-based kesterites after 24 days.
Spatiotemporal analysis of surface urban heat Island intensity and the role of vegetation in six major Pakistani cities
(Elsevier B.V., 2025) Dube, Timothy; Anees, Shoaib Ahmad; Mehmood, Kaleem
The Urban Heat Island (UHI) phenomenon exacerbates thermal discomfort in urban areas and significantly contributes to urban overheating when combined with climate change. This study investigates the spatiotemporal patterns of Surface Urban Heat Island Intensity (SUHII) in six major cities of Pakistan, focusing on the interplay between urban expansion, vegetation cover, and SUHII. To quantify SUHII dynamics, the impact of urban sprawl and vegetation changes was analyzed. The study offers critical insights into the implications for urban planning and policymaking in Pakistan. Using remote sensing data from Landsat satellites, analyzed with Geographic Information Systems (GIS) techniques, estimates of SUHII, urban expansion, and vegetation cover were derived. Specifically, imagery from Landsat-5 (2010−2013) and Landsat-8 (2014–2022), obtained from the US Geological Survey (USGS), was employed. Statistical analyses, including Pearson's correlation and linear regression, were conducted to assess relationships between these variables from 2010 to 2022. SUHII was found to increase annually by 0.18 °C in Islamabad and 0.19 °C in Peshawar, with corresponding urban expansion rates of 8.07 km2 (8967.75 pixels) and 1.67 km2 (1860.42 pixels) per year, respectively. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) were inversely correlated with SUHII, explaining up to 50 % of the variance in Peshawar. However, weaker correlations in Lahore suggest the presence of additional factors influencing SUHII. A distinct spatial relationship between increased vegetation and cooler areas was observed. For instance, Islamabad has greater vegetation cover and cool zones over 41.5 km2. In contrast, Lahore's hot spots spanned 127.1 km2, compared to Abbottabad's 10.4 km2, underscoring the thermal impact of reduced vegetation. The findings emphasize the effectiveness of urban greening, particularly in Islamabad's neutral thermal regions, in mitigating SUHII. This study offers important insights for urban planners in developing sustainable, climate-resilient cities within similar urban contexts. While the results are specific to Pakistani cities, the role of vegetation in mitigating SUHII may hold broader relevance for urban planning strategies in comparable settings.

Browse

Recent Submissions