Urban and river flooding: Comparison of flood risk management approaches in the UK and China and an assessment of future knowledge needs

Increased urbanisation, economic growth, and long-term climate variability have made both the UK and China more susceptible to urban and river flooding, putting people and property at increased risk. This paper presents a review of the current flooding challenges that are affecting the UK and China and the actions that each country is undertaking to tackle these problems. Particular emphases in this paper are laid on (1) learning from previous flooding events in the UK and China, and (2) which management methodologies are commonly used to reduce flood risk. The paper concludes with a strategic research plan suggested by the authors, together with proposed ways to overcome identified knowledge gaps in flood management. Recommendations briefly comprise the engagement of all stakeholders to ensure a proactive approach to land use planning, early warning systems, and water-sensitive urban design or redesign through more effective policy, multi-level flood models, and data driven models of water quantity and quality

Multi-Risk Climate Mapping for the Adaptation of the Venice Metropolitan Area

Climate change risk reduction requires cities to undertake urgent decisions. One of the principal obstacles that hinders effective decision making is insufficient spatial knowledge frameworks. Cities climate adaptation planning must become strategic to rethink and transform urban fabrics holistically. Contemporary urban planning should merge future threats with older and unsolved criticalities, like social inequities, urban conflicts and \u201cdrosscapes\u201d. Retrofitting planning processes and redefining urban objectives requires the development of innovative spatial information frameworks. This paper proposes a combination of approaches to overcome knowledge production limits and to support climate adaptation planning. The research was undertaken in collaboration with the Metropolitan City of Venice and the Municipality of Venice, and required the production of a multi-risk climate atlas to support their future spatial planning efforts. The developed tool is a Spatial Decision Support System (SDSS), which aids adaptation actions and the coordination of strategies. The model recognises and assesses two climate impacts: Urban Heat Island and Flooding, representing the Metropolitan City of Venice (CMVE) as a case study in complexity. The model is composed from multiple assessment methodologies and maps both vulnerability and risk. The atlas links the morphological and functional conditions of urban fabrics and land use that triggers climate impacts. The atlas takes the exposure assessment of urban assets into account, using this parameter to describe local economies and social services, and map the uneven distribution of impacts. The resulting tool is therefore a replicable and scalable mapping assessment able to mediate between metropolitan and local level planning systems

Using sensor web technologies to help predict and monitor floods in urban areas

Includes abstract.Includes bibliographical references.Since flooding is worldwide one of the most common natural disasters, a number of flood prediction and monitoring approaches have been used. A lot of research has been conducted on the prediction and monitoring of floods by using hydrological models. The problem is that current hydrological models do not offer Disaster Management officials or township residents with timely data and information. In South Africa, possible flood warnings are usually communicated by Disaster Management officials using traditional approaches such as loudspeakers, radio and Television (TV). Making calls to warn residents about the possible occurrence of floods by using such means are, however, neither sufficient nor effective. As the result of improved communication, sensor, software and computing capabilities, the use of sensor networks and sensor web for predicting and monitoring environment have been considered in recent years. In order for sensor data such as sensor measurements, sensor descriptions and alerts to be integrated, the Open Geospatial Consortium (OGC) introduced the Sensor Web enablement (SWE) standards and suggested different specifications with respect to the geospatial sensor web. The first implementation of the sensor web framework is available. In this research, the results of using the sensor web technologies for predicting and monitoring floods in the urban areas are presented. The aim of this research project is to illustrate how the sensor web technology can help in the prediction and monitoring of floods in the urban areas, particularly in the Alexandra Township (Greater Johannesburg) which has experienced floods each and every year. The focus of this research is on the incorporation of the sensor data into the sensor web technology. The data used as input to sensor web and the hydrological model was historical rainfall data from the South African Weather Service (SAWS). Shuttle Radar Topography Mission (SRTM) free data from the internet was also used in this research

Effects of land use and land cover changes on water quality of the upper Umngeni River, KwaZulu-Natal Province, South Africa.

Doctor of Philosophy in Hydrology University of KwaZulu-Natal. Pietermaritzburg, 2017.Changes of land use and land cover are important drivers of the quality of water reaching a waterbody. These changes affect the catchment and modify the chemical composition of the atmosphere, and thus altering the cycle of nutrients and the flux of energy. With current developments in Geographic Information Systems (GIS) techniques, hydrological modelling and statistical analyses, one or a combination of many methods can be used to assess the relationships between land use and land cover (LULC) classes and water quality variables. However, all these approaches are reliant on the collection of field measurements, LULC data and water sampling. Typically funding for such long-term information is not generally available in Africa. A three-year study involving analysis of historical data, field work and desktop investigations was conducted in the upper reaches of the uMngeni Catchment (1653 km2), South Africa, to assess the spatial and temporal variation of land use and land cover and its influence on the flux of water, nutrients (nitrogen and phosphorus) and Escherichia coli (E. coli) in the catchment. This involved the analysis of historical land use and land cover information (1994, 2000, 2008 and 2011), analysis and processing of historical datasets of E. coli, electrical conductivity, ammonium, nitrate, soluble reactive phosphorus (SRP), total phosphorus (TP), total suspended solids (TSS), temperature and turbidity. A water quality index based on a long-term data base of water quality emanating from existing monitoring programmes was assessed. In addition, stations were established for river sampling (14) and collection of bulk atmospheric deposition (3) of ammonium, nitrates, SRP and TP, in the Midmar Dam catchment (927 km2). These were consolidated with the application and testing of the Hydrological Predictions for the Environment (HYPE) model in the catchment, in simulating streamflow, transport and dynamic of inorganic nitrogen and total phosphorus, resulting from LULC changes. Results showed that the natural vegetation declined by 17% between 1994 and 2011, coinciding with an increase in cultivated, urban/built-up and degraded lands by 6%, 4.5% and 3%, respectively. This resulted in high variability in the concentrations of water quality parameters, but Midmar and Albert Falls Dams retain over 20% of nutrients and sediment and approximately 85% of E. coli. It was concluded that these dramatic changes in LULC directly affect the chemical composition of water in the catchment. However, these linkages are complex, site-specific and vary from one sub-catchment to another and decision-making regarding water resources management in the catchment must recognise this. The level of E. coli in water is a major issue for human contact during recreational activities in the entire study area. Higher concentrations of E. coli, ammonium, nitrates, SRP and TP were attributed to the poor or lack of sanitation facilities in the informal settlements, dysfunctional sewage systems, effluent discharged from wastewater works, expansion of agricultural activities, as well as a runoff from livestock farming and urban areas. Moreover, water quality in the catchment ranged between “marginal” and “fair”, predominantly “marginal” in 90% of the sites and completely poorer in the Mthinzima Stream, an important tributary of Midmar Dam. A declining monitoring frequency and resultant poorly reporting of water quality in the catchment, led to a recommendation for the establishment of automatic or event-based samplers, which should provide the optimum information on nutrient loadings to the waterbodies. Bulk atmospheric deposition and river inflows into the Midmar Dam studies were conducted under severe drought conditions. Higher concentrations of NH4, NO3 and TP in precipitation samples than those of rivers were found because of the high retention of nutrients in the landscape. In terms of loading, the bulk atmospheric deposition provided significant quantities of NH4, while TP, SRP and nitrates were predominantly from river flows. Specific loads of DIN (nitrate + ammonium) and TP in the catchment were slightly higher that the previously reported values for the catchment and are comparable to the other human-disturbed catchments of the world. HYPE model has successfully simulated streamflow (1961-1999), DIN and TP (1989-1999). For simulations of streamflow NSE values = 0.7 in four out of the nine sites (at a monthly time-step) and NSE > 0 in eight out of nine sites (at a daily time-step). Major floods and drought events were represented very well in the model, with a general over-simulation of baseflow events. The water balance was captured well at calibration sites with over-simulation of streamflow on the Lions River (PBIAS=28%) and their under-simulation in outlet sub-catchments (PBIAS < 0). This is ascribed to the simplification of some processes in the model i.e. evapotranspiration, water release, water abstraction and inter-basin transfer. There has been good fit between the simulations and observations of TP and streamflow with a lagging of the observed values. However, mismatches were noted for DIN. Evaluation of seasonal distribution of DIN suggested that denitrification, crop uptake of DIN and dilution were intensive during the period of rainfall and high temperatures in the catchment, while TP was highly mobilised during rainfall events, due to its strong binding with the soil. The information from this study highlighted the current state of LULC changes, the sub-catchments with the potentiality to export high levels of DIN and TP, the complexity of the relationship between LULC-water quality, the gaps in existing data collection programmes, the catchment responses to LULC changes and the usefulness of hydrological models which may apply beyond the upper reaches of the uMngeni Catchment

Modelling future land-use change and assessing resultant streamflow responses: a case study of two diverse Southern African catchments.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Land-use and land cover (LULC) is a crucial constitute of the terrestrial ecosystem, impacting on numerous fundamental processes and characteristics such as land productivity, geomorphological process and the hydrological cycle. Assessing the hydrological impacts of land-use and land cover changes (LULCCs) has become one of many challenges in hydrological research. LULCCs modify hydrological processes such as evapotranspiration, infiltration and interception, consequently impacting on the hydrological regimes of a catchment. Understanding the implications of LULCCs on catchment hydrology is therefore fundamental for effective water resource planning and management, and land-use planning. Globally, numerous studies have documented the impacts of LULCCs on catchment hydrology, however in Southern Africa there exists a knowledge gap on the impacts of LULCCs on catchment hydrology, specifically future land-use and land cover change (LULCC). Therefore, the aim of this study was to simulate potential future land-use within two diverse South African catchments using an appropriate land-use change model and thereafter to assess streamflow responses to these future land-use scenarios using the ACRU hydrological model. Future land-use was simulated utilizing the Cellular Automata Markov (CA-Markov) model. The CA-Markov model is a hybrid land-use change model that integrates Markov chain, CA, Multi-Objective Land Allocation (MOLA) and Multi-Criteria Evaluation (MCE) concepts. CA-Markov simulated future land-use through the creation of conditional probability and transition probability matrices, suitability images and the utilization of a CA contiguity filter and socio-economic and biophysical drivers of LULCC. The results illustrated that within both catchments, increasing growth of anthropogenically driven LULC classes such as urban, agroforestry and agrarian areas inevitability contribute to the fragmentation, modification and deterioration of natural land-cover types. The model’s reliability and capability was assessed by running a validation, which was conducted by simulating changes between t1 (1990) and t2 (2013/14) to predict for t3 (2018). The predicted map produced for 2018 was then compared against the actual 2018 reclassified map, which served as a reference map. The obtained kappa values (Kstandard, Klocation and Kno) achieved during the validation were all above 80%, thus indicating the model’s reliability and capability in successfully predicting future LULC in the study sites. The assessment of future LULCC impacts on streamflow responses was achieved by utilizing the ACRU model. Historical and future scenarios of land-use were utilized as inputs into a preexisting ACRU model where all input parameters (e.g. climate, soils) remained constant with only changes made to the land cover parameters and area occupied by each land cover. The results illustrated that due to anthropogenic induced LULCC, the hydrological regime within the uMngeni catchment has been altered when compared to the baseline hydrological regime. Patterns of low (1:10 driest year) and high (1:10 wettest year) flows have changed significantly between the baseline and 1990. However, between 1990 and the future hydrological regime (2030 LU scenario) only a slight amplification of these impacts was evident. Mean annual streamflow increases and decreases were present in majority of Water Management Units (WMU’s), however, the Table Mountain, Pietermaritzburg, and Henley WMU’s illustrated greater increases in mean annual accumulated streamflows compared to other WMU’s while the New Hanover New Hanover and Karkloof WMU’s illustrated the greatest decreases in mean annual accumulated streamflows. Furthermore, results indicated that streamflow responses significantly increase in the presence of urban land-use. The impacts become evident as streamflows cascade through the catchment. The results also illustrated that streamflow responses were due to the nature of LULCC, viz urban land-use, commercial forestry, and agriculture combined with the location and extent of LULCCs. These results are beneficial for the implementation of proactive and sustainable water resource planning and land-use planning. Moreover, considering the simulated streamflow responses in relation to varying land-use scenarios, it is essential that water resource planning incorporate land-use location, nature and scale from not only the perspective of land-use effects, but also on hydrological responses in a catchment. Given the interdependence between streamflow responses and changes in land-use, water resource and land-use planning should not occur in silos. Overall, this study illustrated the importance of understanding and assessing land-use and water interactions in a water stressed region such as South Africa. Keywords: land-use and land cover changes, hybrid land-use change model, streamflow responses, land-use and water interactions, sustainable water resource planning