Conceptualization of urban hydrogeology within the context of water sensitive urban design: case study of Cape Flats Aquifer

Magister Scientiae - MSc (Environ & Water Science)Urban hydrogeology can be used to facilitate a decision-making process regarding the implementation of water sensitive urban design (WSUD) to manage water systems of periurban cities. This thesis was aimed at providing explanation of how that approach can be applied in Cape Town using Cape Flats Aquifer as a case study. To achieve this main objective, three specific objectives were set, namely, objective 1 which focused on estimating aquifer parameters using Theis analytical flow solution, in order to identify areas for implementation of managed aquifer recharge (MAR) suggested by WSUD principles; Objective 2 focused on conceptualizing groundwater flow system of Cape Flats Aquifer using the Finite Difference Method (FDM), in order to predict aquifer behaviour under stresses caused by the implementation of WSUD; Objective 3 focused on assessing gw-sw interaction using Principal Aquifer Setting, environmental isotope, and hydrochemical analysis, in-order to identify where and when groundwater surface water interaction is occurring, and thus informing the prevention strategies of the negative effluence of such exchanges on WSUD. The analysis of data collected through pumping test approach which were conducted in March, October 2015 and June 2016, showed that average transmissivity ranged from 15.08m2/d to 2525.59m2/d, with Phillipi Borehole (BG00153) having the highest and Westridge borehole 1 (G32961) having the lowest transmissivity values based on Theis solution by Aqua test analysis. Theis solution by excel spreadsheet analysis showed that average transmissivity ranged from 11.30m2/d to 387.10m2/d with Phill (BG00153) having the highest transmissivity and Bellville 2 (BG46052) having the lowest transmissivity. Storativity values ranged from 10-3 to 10-1 with Phillipi borehole (BG00153) having the highest storativity and Lenteguer borehole 1(BG00139) having the lowest values from both analysis. Average transmissivity visual maps showed that highest transmissivity values within the Cape Flats Aquifer can be obtained around the Phillipi area towards the southern part of the aquifer. Storativity maps also showed that the greatest storativity values can be obtained around Phillipi and Lenteguer area. These findings reveal that MAR would be feasible to implement around the Phillipi and Lenteguer area, where aquifer storage and discharge rates are higher

Multispectral remote sensing of wetlands in semi-arid and arid areas: A review on applications, challenges and possible future research directions

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions

Scenarios analysis using water-sensitive urban design principles: A case study of the Cape Flats Aquifer in South Africa

A feasibility assessment was undertaken on the application ofwater-sensitive urban design (WSUD) for the Cape Flats Aquifer in Cape Town, South Africa, at the local scale. The study contributes towards the planning of water-sensitive cities in the future. A three-dimensional steady-state groundwater flow model was applied to the Cape Flats Aquifer to predict WSUD scenarios by incorporating managed aquifer recharge (MAR). Analysis of the scenarios of varying recharge estimates and groundwater abstraction rates, predicted using the model, indicated that the water-table distribution and outflows from identified groundwater balance components show direct proportionality to the varying recharge scenarios. A notable increase in these outflows was observed when the recharge rate was increased by 50%. Varying groundwater abstraction scenarios indicated that with increasing abstraction rates, water levels and outflows fromgroundwater balance components also decreased accordingly. A notable decline in water levels and outflows was established at an abstraction rate of 2.5 and 5 L/s, respectively. Similar to the previous regional studies in the area, the results from the predicted scenarios show that there is a potential for applying WSUD, particularly MAR, at site-specific scale within the Cape Flats Aquifer. However, shallow groundwater levels during wet seasons limit the opportunities for application of WSUD in the area. This finding would provide an important reference to the ongoing debate on the Cape Town water crisis and similar environmental conditions where WSUD is considered

Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa.

Wetlands in drylands have high inter- and intra-annual ecohydrological variations that are driven to a great extent by climate variability and anthropogenic influences. The Ramsar Convention on Wetlands encourages the development of frameworks for national action and international cooperation for ensuring conservation and wise use of wetlands and their resources at local, national and regional scales. However, the implementation of these frameworks remains a challenge. This is mainly due to limited availability of high-resolution data and suitable big data processing techniques for assessing and monitoring wetland ecohydrological dynamics at large spatial scales, particularly in the sub-Saharan African region. The availability of cloud computing platforms such as Google Earth Engine (GEE) offers unique big data handling and processing opportunities to address some of these challenges. In this study, we applied the GEE cloud computing platform to monitor the long-term ecohydrological dynamics of a seasonally flooded part of the Nylsvley floodplain wetland complex in north-eastern South Africa over a 20-year period (2000–2020)

Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales

Leveraging Google Earth engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

Although significant scientific research strides have been made in mapping the spatial extents and ecohydrological dynamics of wetlands in semi-arid environments, the focus on small wetlands remains a challenge. This is due to the sensing characteristics of remote sensing platforms and lack of robust data processing techniques. Advancements in data analytic tools, such as the introduction of Google Earth Engine (GEE) platform provides unique opportunities for improved assessment of small and scattered wetlands. This study thus assessed the capabilities of GEE cloud-computing platform in characterising small seasonal flooded wetlands, using the new generation Sentinel 2 data from 2016 to 2020. Specifically, the study assessed the spectral separability of different land cover classes for two different wetlands detected, using Sentinel-2 multi-year composite water and vegetation indices and to identify the most suitable GEE machine learning algorithm for accurately detecting and mapping semi-arid seasonal wetlands. This was achieved using the object based Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and Naïve Bayes (NB) advanced algorithms in GEE. The results demonstrated the capabilities of using the GEE platform to characterize wetlands with acceptable accuracy. All algorithms showed superiority, in mapping the two wetlands except for the NB method, which had lowest overall classification accuracy. These findings underscore the relevance of the GEE platform, Sentinel-2 data and advanced algorithms in characterizing small and seasonal semi-arid wetlands