Managing hydrological infrastructure assets for improved flood control in coastal mega-cities of developing nations
R.I. Ogie⁎, P. Perez, K.T. Win, K. Michael
University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
Every year, coastal mega-cities situated in developing nations suffer severe losses associated with flood hazards. In response to this problem, these cities often rely on engineering interventions or structural measures, which typically necessitate an informed management of hydrological infrastructure assets such as waterways or drainage channels, detention reservoirs, high-protection levees, seawalls, dikes, dams, pumping stations and floodgates. Unfortunately, flood management outcomes, based on the use of these hydrological infrastructure assets, are undermined by lack of data and resources to support decision makers. The aim of this study is to provide strategic action plans to address this problem. First, the study reviews literature on flood-related issues and interventions in several coastal mega-cities situated in developing nations. Then, outputs of the review are synthesized into threats, opportunities, weaknesses and strengths common to these cities in relation to infrastructure-based approach to flood management. Using this information, situational analysis is carried out and appropriate strategies are recommended to help support informed management of hydrological infrastructure assets as means of improving flood control in coastal mega-cities situated in developing nations.
Flooding is a major problem in coastal urban areas (Takagi et al., 2016). This problem is expected to worsen due to climate change through increased frequency and intensity of extreme weather events and sea-level rise (Lau et al., 2010; Waters et al., 2003). Coastal mega-cities in developing nations (hereafter abbreviated as“CMDN”) are highly vulnerable to severe flood hazards due to their physical geography, population explosion, rapid urbanisation, and inadequate or poorly managed flood control infrastructure (Dewan, 2013; Gasper et al., 2011; Li, 2003). When compared to developed nations, it is observed that the average number of victims from natural hazards, including flooding, is 150 times greater and economic losses are around 20 times higher in developing countries (Wenzel et al., 2007). This situation calls for improved response to flood hazards in developing nations.In responding to the problem of flooding, coastal cities in developing nations often adopt structural and non-structural measures to flood mitigation (Caljouw et al., 2005). Structural measures or engineering interventions rely on efficient management of hydrological infrastructure assets, including waterways or drainage channels, detention reservoirs, high-protection levees, seawalls,dikes, dams, and other hydraulic components such as pumping stations and floodgates (Arthurton, 1998; Chan et al., 2012; Okoyeand Ojeh, 2015; Wilby and Keenan, 2012). The challenge of efficiently managing these often aging and deteriorating infrastructure assets in order to improve flood control outcomes is enormous, particularly under the prevailing conditions in developing nations, which typically include inadequate funding, skills shortage and the lack of actionable data to support decision making (Brecht et al., 2012; Sarzynski, 2015). Inefficient decisions related to the maintenance and real-time operations of flood control infrastructure often result in sub-optimal outcomes that further exacerbate flood hazards, potentially causing significant loss of life and property damage worth billions of dollars (Brinkman and Hartman, 2008; Few, 2003; Ward et al., 2011). Managing authorities in CMDN are therefore faced with the need to carefully assess the challenges and opportunities unique to their environment and consequently design suitable strategies to improve the management of their hydrological infrastructure assets.The management of hydrological infrastructure assets is a concept that applies infrastructure asset management to hydrological systems.
“Asset management”, in the context of infrastructure systems, is a concept that is difficult to define and one that means different things to different people (Lemer, 2000). Adopting a widely accepted definition proposed by the Federal Highway Ad-ministration (FHWA), infrastructure asset management can be defined as a systematic process of maintaining, upgrading, and op-erating infrastructure cost-effectively (U.S. Department of Transportation, 1999). Infrastructure asset management requires optimised decision making that is justified by evidence (Pantelias, 2005). This approach to infrastructure management decision making often requires a significant amount of actionable data, often lacking in developing countries (Halfawy, 2008). Hence, an important aspect of infrastructure asset management that is quite relevant to developing countries entails the development of capabilities, methodologies and computerised tools to help generate the quantitative data required to support managers at different levels in the decision making process (Pantelias, 2005).
At an infrastructure network level, decision-making focuses on network-wide optimisation of maintenance, construction and operation of infrastructure assets, which are often widely distributed within a large geographical area as determined by the physical extent of the entire network under consideration (Pantelias, 2005). Network level decision-making involves system-wide planning and an evidence-based structured approach to identifying key assets, vulnerable components and areas of priority within a specific infrastructure network, so that limited resources can be judiciously channelled to the maintenance, rehabilitation, extension, and operational efficiency of the given network (Haas et al., 1994). The absence of actionable data to drive this city-scale network-level thinking and decision making can further increase the complexity and sophistication of infrastructure management processes,creating the risk of imprecise decisions that often lead to infrastructure failure and economic loss (Halfawy, 2008).In the context of CMDN, the lack of accurate and reliable data about the geographical location, physical attributes and current conditions of assets in the hydrological network can lead to infrastructure failure associated with imprecise flood control decisions (Nasir and Muhammad, 2011). A recent study focusing on flooding in–Jakarta (Indonesia) reports that flooding is often associated with preventable failures of the flood control infrastructure and a lack of actionable data that limits the efforts of decision makers and managing authorities (Sedlar, 2016). Henceforth, to minimise flood hazards in CMDN, there is a need to design new strategies of sourcing actionable data or reliable information to support network level decision making and management of hydrological infra-structure assets. This study aims to address this problem by gathering, categorising and analysing relevant situational information about the threats, opportunities, weaknesses and strengths (TOWS) that are common to several case studies, in relation to improving infra-structure-based approach to flood management in CMDN. Relevant situational information used for the study are gathered through a literature review process. Following the review, a comprehensive description of coastal mega-cities of developing nations (CMDN) is presented. A situational analysis is then carried out using the TOWS strategy tool and information retrieved from literature. Based on the outcome of the situational analysis, the study further contributes by recommending appropriate strategies to help support an informed management of hydrological infrastructure assets as a means of improving flood control in CMDN. The methodology adopted is further described in the following section.
Several CMDN suffer from flooding and the experiences have been well documented in the literature. A structured synthesising of these studies can generate a rich and reliable information resource that can potentially aid in understanding commonalities in terms of causal factors, aggravating conditions, mitigation strategies, constraints, and institutional response to the problem of flooding in CMDN. This review first focused on retrieving relevant information about flood-related issues and interventions from each CMDN. Then, this information was organised into threats, opportunities, weaknesses and strengths, as required for TOWS analysis (Ravanavar and Charantimath, 2012). Using this information, TOWS analysis was subsequently carried out and a set of strategic action plans recommended.2.2. Literature review process TOWS analysis has never been carried out in order to establish strategies of improving infrastructure-based approach to flood management in CMDN. To perform such analysis, it was necessary to first carry out a systematic review of literature in order to synthesise the required information. A literature search was performed and articles identified by using a combination of relevant keywords as shown in Fig. 2. Academic databases such as Web of Science and Scopus were initially used to search for articles, but very scanty records were returned. This is mainly because academic databases such as Web of Science and Scopus do not search beyond titles and abstracts and there are only a few studies reporting on CMDN within their titles and abstracts. However, the Google Scholar search engine was found to be particularly useful for finding articles that have discussed CMDN within the body of the text.Consequently, a total of 14,700 articles were initially retrieved after removal of duplicates. Depending on the focus of the study, these articles were assigned into one of four groups, namely Categories A (n= 1), B (n= 321), C (n= 235), and D (n= 14, 143) as presented in Fig. 2. Note that n represents number of articles. The description of each category is shown in Fig. 2. All articles in Category D (i.e., n= 14, 143) were considered irrelevant to the study and as such excluded from the analysis. The eligibility of articles in Categories A, B, and C were further assessed and additional articles excluded based on several criteria defined in Fig. 2.Eventually, only 64 articles (i.e., Categories A (n= 1), B (n= 16) and C (n= 47)) were found to be relevant for the analysis. For each article that was identified as relevant to the study, we documented information common to CMDN, which could either be considered as a threat, opportunity, weakness or strength, as required for TOWS analysis.
2.3. A brief description of TOWS analysis
TOWS analysis is an advancement of SWOT analysis developed by Weihrich (1982). In SWOT analysis, the aim is to assess in a systematic manner, the external opportunities and threats as well as the internal strengths and weaknesses that may impact a given project or venture. Essentially, SWOT analysis involves the basic process of listing strengths, weaknesses, opportunities and threats.TOWS analysis, on the other hand, is a much more structured and strategy-oriented process (Weihrich, 1982). Trainer (2004) refers to it as“Turning Opportunities and Weaknesses into Strengths”. In this study, TOWS analysis was chosen over SWOT because it is considered a much more useful tool in the sense that it goes a step further by taking the list generated from SWOT analysis and translating that into actual strategies (Long and Zhu, 2007). Unlike SWOT, strategies can be easily developed from TOWS analysis by effectively combining: a) internal strengths with external opportunities and threats, and b) internal weaknesses with external opportunities and threats (Ravanavar and Charantimath, 2012). This strategy generation technique is an advantage of TOWS over SWOT analysis and further information about how this was achieved in the current study is presented in Section 2.5.
2.4. Justification for TOWS analysis
TOWS analysis as a strategic planning tool originated from the field of business management, but has since gained wide application in other disciplines (Weihrich, 1982). TOWS analysis has been previously applied as a city-scale strategy tool for improving planning and decision making (Long and Zhu, 2007; Zi et al., 2009). The success of previous applications demonstrates that TOWS analysis is a viable tool for understanding complex urban issues and for developing strategic action plans.Flooding in CMDN has been described elsewhere as a critical problem requiring urgent strategic measures (Chan et al., 2012).Having studied this problem in detail,Li (2003)concluded that“coastal mega-cities represent a market place where one can find all kinds of things:various opportunities and also all kinds of problems appearing in an unbelievable concentration”. This view of coastal mega-cities lends itself to the concept of TOWS analysis, where the opportunities, strengths, weaknesses and threats pertaining to the central issue are identified and assessed systematically to generate strategic action plans.
2.5. Applying TOWS analysis
In this study, TOWS analysis is applied as a strategy tool to improve engineering or structural approach to the complex problem of flooding in CMDN. More precisely, the study applies TOWS analysis to develop strategies for sourcing actionable data or reliable information needed to support the management of hydrological infrastructure assets in CMDN. As recommended by Ravanavar and Charantimath (2012), the strategy generation process involves matching external opportunities and threats with internal strengths and weaknesses (see Table 1). Basically, by using a TOWS matrix, four different questions are put forward (see Table 1) to better understand the situation in CMDN. The results of this situational analysis eventually provide the basis for recommending a set of strategic action plans. The results are presented and discussed in the following section.
3. Results and discussions
3.1. Defining coastal mega-cities
The answer to the question, “what is a coastal mega-city?” is one that has been defined inconsistently in the literature (Sekovskiet al., 2012). The inconsistency in defining a coastal mega-city arises from two aspects. First, there are several definitions of what makes for a coastal zone (Klein et al., 2003a, 2003b). For example, Nicholls (1995) defined a coastal zone based on a simple criterion, i.e., the likelihood that a rise in sea level up to 0.5 m would have significant impact on areas within the city boundaries. A standard way to estimate the likelihood of coastal inundation is to determine the elevation and exact distance of the city from the coast (Sekovski et al., 2012). Using this criterion, coastal cities have been defined as those in a“near-coastal zone”, i.e., an area within 100 m elevation and 100 km distance from the coastline (Nicholls and Small, 2002; Sekovski et al., 2012). In contrast, Pelling and Blackburn (2014)defined a coastal zone as an area within 50 m elevation and 100 km distance from the coast. Based on distance from the coast, Vallega (2001)described coastal areas as the belts extending 60 km from the coastline. Using the“near-coastal zone”criterion, Sao Paulo, which is less than 50 km from the coast, is disqualified from the list of coastal cities because it is positioned 800 m above the sea level (Nicholls, 1995; Sekovski et al., 2012). However, classification of coastal areas based on the near-coastal zone criterion does not consider the deltaic setting and large-scale features of coasts (e.g. coastal plain, continental shelf, large bays,estuaries, lagoons, coastal dune fields, river estuaries, etc.), which have been used previously as defining factors for coastal zones (Inman and Nordstrom, 1971; Sekovski et al., 2012). For example, even though the cities of Kolkata, Cairo and Dhaka do not meet the near-coastal zone criterion, they are still classified as coastal cities because of their deltaic setting (Sekovski et al., 2012).The second reason for the inconsistency in the definition of a coastal mega-city is the difference in the representation of the minimum population that qualifies a city as a“mega-city” (Sekovski et al., 2012). Cross (2001)and Sekovski et al. (2012) draw attention to the fact that mega-cities have been defined inconsistently in the literature, with minimum population thresholds of 1 million (e.g.,Barrett and Johnson, 2002; Mitchell, 1999), 8 million (e.g., Nicholls, 1995; Li, 2003; Wenzel et al., 2007) and 10 million(e.g.,Mage et al., 1996; Marshall, 2008; Sale et al., 2014). In this study, the minimum population threshold of 10 million is adopted for mega-cities in line with the United Nations (UN, 2016). Accordingly, there are currently 31 mega-cities worldwide, 14 of whichare sitting on coastal zones within developing nations as shown in Fig. 1(UN, 2016). These 14 CMDN have rapidly growing po-pulations that sum up to approximately 201 million (UN, 2016). It should be noted that in Fig. 1, the number in front of each city represents its population in million. The focus of this study will be on these 14 CMDN.
3.2. TOWS analysis results
As earlier stated, the information used for this study was derived from the analysis of 64 scientific articles related to flood control in CMDN. Results have been classified according to a TOWS approach: threats, opportunities, weaknesses and strengths that are common to flood control in CMDN.
S1: Large and growing population (Klein et al., 2003b; Li, 2003; Li et al., 2015). Large urban population means increased market size, cheap labour, greater economies of scale, improved innovation, attractiveness to local and international investors, all of which stimulate the economy and help to attain the level of sustained urban growth needed to enable CMDN respond adequately to extreme weather events such as flooding (Birdsall and Sinding, 2001; Kelley, 2001).
S2: Accumulated local knowledge (Parker and Mitchell, 1995; Wilby and Keenan, 2012; Wisner, 2003).
S3: Proliferation of social networks and online sharing culture in developing nations (Adelekan, 2009; Seto, 2011). The increasing access to internet and ownership of mobile devices are particularly fueling the use of social media in developing countries (Ali, 2011; Wike and Oates, 2014).
S4: Self-organisation and citizen-driven adaptive practice (Affeltranger, 2001; Djalante, 2012; Klein, 2002; Wilhelm, 2011).
W1: High poverty, unemployment and illiteracy rate (Cohen, 2004; Kraas, 2007; Li, 2003; Mitchell, 1999; Newton et al., 2012 Varis, 2013).
W2: Inadequate housing and high settlement density with large proportion, often 30–40%, of the urban population living in squatter and slum settlements (Adikari et al., 2010; Klein et al., 2003b; Li, 2003; McBean and Rodgers, 2010; Newton et al., 2012; Yeung, 2001).
W3: High level of environmental pollution, including greenhouse gas emissions (Karl and Trenberth, 2003; Li, 2003; Sekovskiet al., 2012; Newton et al., 2012).
W4: High volume of urban waste without adequate waste collection and disposal facilities, resulting in coastal littering andmillions of tonnes of untreated sewage and municipal wastes in water bodies (Jiang et al., 2001; Li, 2003; Sekovski et al., 2012; Tibbetts, 2002; Newton et al., 2012; Yeung, 2001).
W5: Unplanned construction on low-lying floodplains (Douglas et al., 2008; Li, 2003; Miguez et al., 1970; Newton et al., 2012; Timmerman and White, 1997).
W6: Lack of adequate maintenance and management of aging and deteriorating hydrological infrastructure assets (Chan et al.,2012; Douglas et al., 2008; Li, 2003; Miguez et al., 1970; Munji et al., 2013).
W7: Rapid urbanisation, including decrease in coastal vegetation, and excessive groundwater withdrawal from mining and construction activities (Douglas et al., 2008; Fuchs, 2010; Pelling and Blackburn, 2014; Sekovski et al., 2012; Newton et al., 2012;Patterson and Hardy, 2008).
W8: Shortage offiscal resources and government support (Chuenpagdee and Pauly, 2004; McBean and Rodgers, 2010; Sanyal andLu, 2004; Nyong, 2009; Yeung, 2001).
W9: Low quality drinking water and associated poor health outcomes (Li, 2003; Sekovski et al., 2012; Varis, 2013).
W10: Lack of reliable data such as terrain elevation, hydrograph, socio-economic, demographic, land-use and infrastructure-related data (Brecht et al., 2012; Fuchs et al., 2011). When data is available, issues of accessibility, temporal and locational accuracy,and incompatibility of data across government agencies limit use for planning and research purposes (Fuchs et al., 2011).
W11: Lack of well-developed insurance markets to aid quick recovery and reconstruction from flood losses (Faisal et al., 1999; Fuchs et al., 2011; Surminski and Oramas-Dorta, 2014).
O1: Remote sensing and related technology that facilitates improved mapping, visualisation and response to natural hazards(Cervone et al., 2017; Fuchs et al., 2011; Klein et al., 2003a, 2003b; Levy, 2005; Sanyal and Lu, 2004; Schnebele et al., 2015).
O2: Citizen-driven data collection through social media and mobile crowdsensing applications (Cervone et al., 2017; Muller et al.,2015; Schnebele et al., 2015).
O3: Internet of Things (IoT) enabled low-cost sensors for environmental monitoring (Basha and Rus, 2007; Keoduangsine and Goodwin, 2012; Muller et al., 2015; Rachman et al., 2008).
O4: Graph-based network thinking of river channels and potential applications of graph-theoretic algorithms in improving decision making related to large-scale urban drainage systems and spatially distributed flood control infrastructure (Cui et al., 2009;McDonald et al., 2011; Ogie et al., 2017a).
O5: Hydrologic and hydraulic models for facilitatingflood risk management (Budiyono et al., 2016; Dasgupta et al., 2013;Mujumdar and Kumar, 2012) More broadly, hydrologic and hydraulic models that have potentials to be used in flood risk management include LISFLOOD-FP, TR-20, MIKE FLOOD, HEC-1, HEC-2, HEC-FFA, HEC-FDA, HEC-5, HEC-RAS, ISIS, KINEROS, DIVAST,Delft-FLS, and SOBEK 1D2D (Bates and De Roo, 2000; Dasgupta et al., 2013; Gilles and Moore, 2010; Hagen et al., 2010; Levy, 2005; Ogden et al., 2001).
T1: Subsiding land (Li, 2003; Klein et al., 2003a, 2003b; Kovats and Akhtar, 2008).
T2: Natural environmental problems such as flat topography, high sea temperatures, sea-level rise and frequent rainfall occurring over a long duration (Arthurton, 1998; Baklanov et al., 2016; Douglas et al., 2008; Li, 2003; Munji et al., 2013; Nicholls, 1995).
T3: Spatial variability in rainfall (Douglas et al., 2008; Fay et al., 2010). The inability to predict rainfall pattern limits the ability to plan and prepare for urban floods(Douglas et al., 2008).
T4: Influx of the urban population to the coastline (Li, 2003; Sagala et al., 2013; Surjan et al., 2016). Some people may move to the coastline because they desire to live adjacent to the sea while others may find themselves in flood-prone dwellings simply because they cannot afford the cost of living in dry land residential zones (Li, 2003; Surjan et al., 2016).Based on the strengths, weaknesses, opportunities and threats identified above, four questions are put forwards as per the TOWS matrix shown in Table 1. An attempt to answer these questions produced results presented in four tables, i.e., Tables 2, 3, 4 and 5.
Based on information in Tables 2, 3, 4 and 5, a set of strategic actions are recommended.
i. Accumulated local knowledge is an important strength of CMDN. This knowledge needs to be better explored when seeking to improve understanding and response to issues such as land subsidence, changing patterns in rainfall, increasing urban congestion,and other natural environmental problems (e.g. high sea temperatures, sea-level rise, etc.). The proliferation of social networks and online sharing culture should also be harnessed as an instrument for improving monitoring and mobilisation of community action against the aforementioned issues. This approach builds upon self-organisation and citizen-driven adaptive practice, which offer cost-effective solutions to CMDN (Wilhelm, 2011; Perez et al., 2015).
ii. A large and growing population means increased market size, cheap labour, greater economies of scale and attractiveness to local and international investors, all of which help to stimulate the economy of CMDN (Birdsall and Sinding, 2001; Kelley, 2001). This is a strength for CMDN. However, when the urban population are allowed to move to the coastline in an uncontrolled fashion, it can pose a significant threat to the management of CMDN and can also undermine the success of flood control strategies in these CMDN. To address this issue, effort should be geared towards ensuring that the urban poor, who cannot afford the cost of living in dry low-flood risk land be provided with alternative housing arrangements outside slums. In locating or arranging for such alternative housing scheme, it is important to have a balanced consideration of the fact that there are many uneducated and unemployed youths who would prefer to live closer to the coast where it is easier for them to monitor and take advantage of the informal economy, including unskilled work such as fishing and cargo container labour (Seto, 2011). Moreover, to ease the housing pressure in CMDN, economically stimulating activities such as the establishment of factories, higher educational institutions, etc. should be sited in less dense inland neighbouring suburbs. It is also vital to have proper land zoning schemes, with appropriate policies to limit the construction of residential buildings on low-lying flood plains (Hartmann, 2009). This will help limit movement of people to coastal areas and also reduce exposure to flood hazards. Furthermore, research can contribute in addressing the problem of excessive influx of people into CMDN if reliable and accurate data is made available and accessible.
iii. Land subsidence is also a significant threat to many deltaic CMDN because it leads to increased exposure to flooding. The results of the analysis (Table 3) show that several actions can be taken to minimise land subsidence. These include prohibiting unplanned construction and building on low-lying flood plains. Citizens as well as mining and construction companies should be sensitised about how their activities may lead to excessive groundwater withdrawal, which aggravates the problem of land subsidence. Again, research can contribute in addressing the problem of land subsidence in CMDN if reliable and accurate data is made available and accessible.
iv. Spatial variability in rainfall can pose a significant threat to successful planning and management of floods in CMDN if precipitation patterns are not well understand. Rainfall monitoring stations provide a useful way to improve understanding of spatial patterns in precipitation, but adequate measures must be put in place to ensure sensing devices are protected against theft. These monitoring devices must be properly maintained and kept functional, particularly during the monsoon seasons when the frequency of rainfall is high. Importantly, the shortage of fiscal resources means that the government can only afford a limited number of monitoring station. The locations of these monitoring stations should be optimised algorithmically to improve understanding of spatial variability in rainfall. No doubt, insights from research can further contribute to improve understanding of spatial variability in rainfall, including causal factors, if reliable and accurate data is made available and accessible. For example,the relationship between greenhouse gas emission and climate change in CMDN can be better understood through research.
v. Natural environmental characteristics such as unfavourable topography, sea-level rise and frequent rainfall can hinder flood mitigation strategies in CMDN. Based on Table 3, several actions can be taken to minimise the impact of these threats. First, urbanisation processes that result in decreased coastal vegetation should be minimised because of the consequential reduction in the shielding effect and protection that trees and other vegetation provide to coastlines during excessive rainfall and flood. Secondly, unwholesome activities such as solid waste disposal in water bodies, high levels of greenhouse gas emission, and unplanned construction and building in low-lying floodplains should be significantly minimised because they aggravate the natural environmental problems of excessive rainfall and associated flooding. To minimise flood impacts during days of excessive rainfall, managing authorities should strive to make optimal decisions related to real-time operations of flood control infra-structure and drainage channels. More so, limited resources should be judiciously spent on measures that are most effective in reducing losses associated with excessive rainfall and flooding. This will require in-depth planning and research as data avail-ability permits. Importantly, such research effort should address ways in which local insurance market can be developed to cover losses associated with natural environmental problems such as floods.
Specific strategies focusing on opportunities:
i. The opportunity exists to apply remote sensing technology for improving flood control and decisions related to the maintenance and real-time operations of hydrological infrastructure assets within CMDN. However, the results (Table 2) show that several factors limit widespread application of remoting sensing technology in CMDN. These factors include cost, shortage of expertise and lack of relevant hydrological data to complement the use of remote sensing data as input variables in flood modelling and management.
ii. Similarly, opportunity exists for CMDN to apply hydraulic and hydrologic models for flood risk management, but shortage of expertise and lack of relevant hydrological data needed as input variables also weakens the realisation of this opportunity. The situation is worsened by the presence of excessive waste in water bodies, potentially increasing fluid viscosity to conditions that limit the usefulness of outputs from hydraulic and hydrologic models.
iii. A great opportunity available to CMDN is graph-based network modelling and analysis of hydraulic/hydrological infrastructure system as a tool for improving flood management (Ogie et al., 2017a). The geospatial data required to construct graph-based network models of city-wide hydrological infrastructure system can be crowdsourced by capitalising on the growing population of citizens equipped with mobile sensing devices such as smart phones. Citizens can provide local knowledge to improve the ac-curacy of the outputs from the network modelling and analysis. Hydrological infrastructure network modelling and analysis is considered appropriate for data-starved CMDN because the underlying graph theory provides a rigorous mathematical basis for computational analysis (e.g. network vulnerability analysis), using very little data obtainable at the time and allowing for further improvement from the initial results as additional data becomes available in the future (Bunn et al., 2000; Ogie et al., 2017b).However, in taking full advantage of the opportunity to apply graph-based network approach to flood management in CMDN, several factors need to be taken into consideration. First, previously mapped network topology should be regularly updated as changes occur due to uncontrolled construction on flood plains. Proper records of real-time operational conditions of drainage or hydrological infrastructure assets should be maintained as this may provide useful input for network analysis. To improve the scope of application and usefulness of graph-based network analysis results, hydrological data as well as other contextual in-formation, including demographic characteristics of the targeted community should be made available and accessible. Where there is shortage of expertise, training may be required (Yeung, 2001). In addition, research can contribute by demonstrating through case studies, the various ways in which graph-based network analysis can be applied as a veritable tool to improve flood management in CMDN.
iv. Another important opportunity available to CMDN is IoT enabled low-cost sensors for environmental monitoring. For example,cheap and reliable water-level sensors can be developed locally at very low cost (Ogie et al., 2017c). However, the shortage of fiscal resources means that the government can only afford a limited number of monitoring devices. The locations of these sensors should therefore be optimised algorithmically to improve availability of water-level information in different parts of the city.Local knowledge at community level should also be considered as potential source of information to guide the design and optimal placement of these monitoring devices. As earlier mentioned, it is important to put in place adequate measures to protect the sensors from theft. Where self-organisation and citizen-driven adaptive practice are strong, citizens are more likely to take ownership of safeguarding installed sensors within their local neighbourhood. The sensors should be kept functional at all times by ensuring proper maintenance, including battery/power replacement. Accumulated solid waste should be removed from waterways to prevent interference with proper functioning of sensors.
v. Citizen-driven data collection via social media and mobile crowdsensing provides a unique opportunity for CMDN to enrich their flood management decisions with near real-time information (Perez et al., 2015). The huge and growing population combined with the proliferation of social networks and online sharing culture in these mega-cities strengthens this opportunity. In this sense, each one of the millions of citizens equipped with a smart mobile device is a potential human sensor in the data collection initiative. To fully harness this opportunity, significant effort is required to develop mobile crowdsensing techniques and principles that enable flood management authorities make more accurate and informed decisions using insights from the huge volumes of urban environmental data generated by citizens via social media, particularly during flooding and emergency situations.Several authors (e.g., Eilander et al., 2016; Holderness and Turpin, 2015; Perez et al., 2015) have begun demonstrating how crowdsourced social media data can be harnessed to support flood management decisions in developing mega-cities. The guiding principle for relying on such data is the concept of the“wisdom of the crowd”, wherein any single observation is assumed to be inherently unreliable and the focus is on the reliable patterns that only emerge from harnessing large amounts of independent consistent observations (Eilander et al., 2016). It is important to remember that at the core of any successful mobile crowdsensing application or social media-based collection of urban data are people acting as human sensors (Ogie, 2016). Hence, programmes that help to reduce poverty, illiteracy and unemployment should be encouraged so that more people are equipped with the required resources (e.g. smart phones, paid internet access, etc.) and intellectual capability to participate usefully, both in terms of the quality and quantity of data contributed. Importantly, the urban poor living in slums should be given hope and treated as relevant stakeholders in the co-management of coastal communities, rather than being treated as illegal elements of society,waiting to be demolished (Burra, 2005). This approach will reduce apathy and unwillingness of so-called illegal slum dwellers to contribute georeferenced data due to fear of Government tracking them.
3.3.1. Overview of TOWS outcome
The ability to manage hydrological infrastructure assets in an informed manner will no doubt help a great deal in improving flood control outcomes in CMDN. This study has applied TOWS analysis to generate strategies for making actionable data and relevant information available to managing authorities in CMDN. Three solutions are identified as viable means of facilitating informed management of hydrological infrastructure assets in CMDN, namely citizen-driven data collection via social media and mobile crowd-sensing, IoT-enabled low-cost sensors for environmental monitoring, and graph-based network modelling and analysis of hydraulic/hydrological infrastructure system. For the sake of simplicity, citizen-driven data collection via social media and mobile crowd-sensing can be represented as“social network”; IoT-enabled low-cost sensors for environmental monitoring as“sensor or cybernetwork”; and graph-based network modelling and analysis of hydraulic/hydrological infrastructure system as“physical network”.The adoption of these three solutions together, i.e., a“cyber-physical-social network”approach will culminate in what we refer to int his study as data-driven management of hydrological infrastructure assets. Basically, these solutions will enable CMDN to transition the management of their hydrological infrastructure assets from a data-starved approach that is characterised by elements of assumptions and suboptimal flood control outcomes to a data-driven strategy that facilitates speedy, accurate, and efficient operational decisions (Saputelli et al., 2006). According to the U.S. Army War College, strategy comprises three components, namely the“ends”, “means and “ways” (Yarger, 2008). The “ends” are the goals to be achieved; the “means” are the resources available to achieve the goals and the “ways” describe how to apply the resources (means) in order to achieve the goals (ends) (Yarger, 2008). In that sense, this study has addressed the “ends” and “means” dimensions, where the “end” is to adopt data-driven management of hydrological infrastructure assets as an avenue for improving flood control outcomes in CMDN and the “means” are the three viable solutions already identified as the cyber-physical-social network approach. To address the“ways”component of strategy, which was not covered in this paper, future studies will seek to demonstrate how the three identified solutions can be implemented in CMDN.
To improve flood control outcomes in coastal mega-cities of developing nations (CMDN), there is a need for better informed approaches to the management of hydrological infrastructure assets such as waterways or drainage channels, detention reservoirs,high-protection levees, seawalls, dikes, dams, pumping stations and floodgates. Achieving this approach requires proper under-standing of the prevailing conditions in these cities, both favourable and the unfavourable. Hence, CMDN must carefully assess the challenges and opportunities unique to their environment and consequently design suitable strategies of generating relevant in-formation to improve the management of their hydrological infrastructure assets.To address this issue, this study reviewed literature and retrieved relevant information about the strengths, weaknesses, opportunities and threats that are common to CMDN. Using this information, the study implemented TOWS analysis, resulting in the recommendation of appropriate strategies to help support an informed management of hydrological infrastructure assets as a means of improving flood control in CMDN. In a nutshell, it was noted that remote sensing technology and hydraulic/hydrologic models exist as avenues to support informed management of hydrological infrastructure assets, but several prevailing factors in CMDN limit their viability. Other viable solutions were recommended, including citizen-driven data collection via social media and mobile crowd-sensing (social network), IoT-enabled low-cost sensors for environmental monitoring (sensor or cyber network), and graph-based network modelling and analysis of hydraulic/hydrological infrastructure system (physical network). Future studies will seek to demonstrate how the three identified solutions can be implemented in CMDN. It is important to note that the focus of this study is on improving structural measures to flood mitigation in CMDN and does not in any way undermine the crucial role of non-structural measures such as land-use zoning, risk management and emergency planning, building codes,flood insurance, institutional arrangement, etc. Finally, one limitation of this study is the fact that it does not consider the distinct characteristics that may be unique to each individual coastal mega-city situated in developing nations. Where such distinct characteristics exist, it is advisable to consider them together with interpreting and applying the results of this study to that specific city.
This work was supported by the Australian National Data Service (ANDS) through the National Collaborative Research Infrastructure Strategy Program [ANDS MODC 15, 2014], the Department of Foreign Affairs and Trade, Australia (DFAT) [agreement number 71984] and the University of Wollongong Global Challenges Program (PetaJakarta.org)
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Citation: R.I. Ogie, P. Perez, K.T. Win, K. Michael, 2018, “Managing hydrological infrastructure assets for improved ﬂood control in coastal mega-cities of developing nations”, Urban Climate, 24, pp. 763-777.