Factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana

Factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana Justice Agyei Ampofo, Ebenezer Owusu Sekyere, Raymond Adongo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4274764/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract This study assessed the factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana. The research used mixed research approach that was descriptive with data collection by both quantitative and qualitative methods. In addition, 400 residents from the Tamale Metropolis were sampled, and the data were analyzed through a not ordered least square (OLS) model and Kendall's coefficient concordance. The research has the overall performances of the model come out by using the R-squared and Adjusted R-squared values, which amount to the 0.745 and 0.720, respectively. Furthermore, flood incidence as an independent variable in the regression analysis has a positive coefficient of 1.678, which means that a rising flood incidence is linked to an intensification of flooding in Tamale Metropolis. Besides, the research found that the two adaptation measures being the use of flood barriers and sandbags were the most favored by the population. The fact that the usage of emergency supplies is based at the last position among the adaption strategies which are meant to avert the negative effects of flooding has been ranked as the least employed strategy of mitigating the effects of flooding. The results showed that during the flood season, water level rise, infrastructural damages, poor land use classification, economic status, water supply systems, sanitation facilities, roads network, the presence of low-lying areas, agricultural land, and government agencies have a major influence on the flooding situation in the Tamale Metropolis. This study recommends that the urban planning guidelines of the Tamale Metropolitan Assembly should be enforced and implemented to oversee the management and planning of the land use, management of unplanned development and reduction of the ecological degradation by infrastructure around flooding. Flooding Adaptation Strategies and Tamale Metropolis 1.0 Introduction Floods have been around since the beginning of time as an event that naturally occur, but their ability to wreak enormous damages on humanity is still active. The term flood implies infiltration of water onto out-of-water area by exceedingly heavy rainfall, thawing of snow or dam break (Hrushikesh, Gururaj & Pathak, 2023). This vice is one of the most destructive vices as it leads to deaths and destruction of infrastructure. It causes internal displacements, results in long-term economic and environment issues (Glago, 2021; Onwuka, et. al., 2015). Floods can be caused in different ways, such as river flooding, flash flood, coastal flooding, and urban flooding (Sowmya, John, & Shrivasthava, 2015). River floods are the most dominant type of flooding and remarkably, these events are preceded by long periods of intensive rainfall or snowmelt that overflows the rivers to a point where they exceed their bank capacity (Pomeroy, Stewart & Whitfield, 2016). Pronto floods, on the contrary, are usually sudden with very little sign of warning which usually affect the hilly regions or places with bad drainage systems (Kieu & Van Tran, 2021). Coastal flooding, offshore on the other hand, flooding in urban areas is accounted for by poor drainage systems. Floods negatively impact the international stage and are the reason behind the largest share of all natural hazards between 1998 and 2017 (Tembata et al., 2020). In the same time, floods became the global problems affecting around 2.3 billion people and causing 157, 000 deaths worldwide (Ganguly & Cahill, 2020). Statistics for the period show a devastating economic loss of over $ 662 billion during the flooding phenomenon in the year 2023, alone. (Kurt, 2023). On the one hand, we cannot deny the fact that the frequency and intensity of floods will be higher than thought in past considering climate change. The stormy effects of flood no longer threatening by climate change, leading to further global warming, more frequent and severe storms, which pickup infrastructure and natural systems for drainage. In Africa, the situation is not different, with an apparent fact that the continent, with its diverse topography and climatic conditions, is highly prone to hazards through floods (Alfieri et al., 2017). Nevertheless, heavy rains still remaining the main causes of the flood in Africa. Consequently, this intense rainfall which, in turn, is a significant floodwater threat, majorly, occurs in those areas that cannot be able to handle excess water. Similarly, in almost all cases, rapid urbanization, unless accompanied by sustained improvements in the infrastructure, leads to drainage systems that are unable to handle the water volume of heavy rain, thus resulting in floodgates opening in populated areas. Deforestation is not only an established factor for flooding incidents but also a threat for the dignity of African countries. The destruction of forests can lead to major imbalances in ecological systems, and may cause a weakened vegetation level to absorb extra water that can lead to amplified surface runoff rate, and thus to a higher risk of flooding (Gunnell et al., 2019). These floods over the years in Africa have caused deaths of human beings, displacement and homelessness, damages to social amenities, losses in agriculture, and spread of water borne diseases (Mugambiwa & Makhubele, 2021). The damage wreaked by flooding across Africa have a dual relationship with the flooding experienced in Ghana, its sovereign nation. Within the period of 1991–2018, more than one-third of all disasters recorded in Ghana were the result of floods; the Upper East, Upper West, Northern and Volta regions are the areas most endangered by floods in the whole country. (Ntim-Amo et al., 2022). Also, in the year 2015, there occurred a deluge occasioned by heavy rains in Accra, the capital city of Ghana. Only 300 people, nonresidents, were killed, and the number of the displaced was more than 2000. Yet, according to the latest report, floods have destroyed 1.7 million homes and incurred about $ 200 million in the form of infrastructure damage, loss of productivity, and emergence response costs. Town of Tamale is typically characterized climatically from a Sahelian region where the city experiences a greater low raining season, long duration of low raining season length, and the short duration high raining season duration that is always observed amongst the months of May and September (Chagomoka et al., 2018). This turns the city of Tamale to be by flooding during the rain months. Nevertheless, the situation is aggravated due to the human factors such as urbanization and population growth. The urban development of the city that was once designed as a metropolis has led to an expansion of the metropolitan area accompanied by an increased requirement in housing and infrastructure. Consequently, this causes the invasion of open spaces and natural watercourses (Mensah, Gough & Simon, 2018). The fast growth of the population and unrestrained urbanization as a consequence has heavily overtasked the drainage infrastructure in the city not only by intensifying the problems around flooding, but also by causing other issues. Urban development through building of roads, residential areas, and agricultural fields without proper storm water management systems in various regions of the city provokes the accumulation of pooled water on streets, residential housing and farms during heavy rainfall (Kaur & Gupta, 2022). Alongside with those, it facilitates troubles for the local citizens as well as destructs infrastructure and farming activities. In the long run, the floods impacts will be deemed unsurpassable. Floods led to the displacement of communities, loss of livelihoods, increased poverty levels, and social unrest. The destruction of infrastructure, such as roads, bridges, and buildings, hampers economic development and recovery (Islam et al., 2016). Flooding also carries pollutants and contaminants, posing risks to public health and the environment (Crawford et al., 2022). In response to the persistent issues of flooding, both local and national governments have implemented a range of flood adaptation strategies in dealing with the adverse effects of flooding on their livelihoods. This study therefore seeks to assess the factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana. 2.0 Methodology The study area being the Tamale Metropolis city was the one used. The Metropolitan of Tamale Municipality was enacted through government action and by Law, the Legislative Instrument (L.I. 2068) became the instrument of authority for the local government to function. Tamale is a double city- the capital of the Metropolitan and the Regional Northern Region. By using December 2018 as a deadline, these two regions became two separate institutions from the Northern region which they seceded from through the referendum held in December 2018. Interchangeably, there is Tamale Metropolis listed as part of 16 District and Municipal assemblies (MMDAs) under the Northern Region. Metropolis is that chiefly situated in the middle region of the Region whilst it borders Sagnarigu in the northwest, Mion District in the east and South to East Gonja. In the southernmost point is the Middle Gonja. The Metropolis is located in the geographical coordinates of the 9º16 and 9º34 North latitude and the 0º36 and 0º57 West longitude as shown in the extracted data from the Ghana Shared Space (GSS) (2022). 2.1 Research design and approach encompass the use of sampling methods, data collection technique, and the analysis methods The key objective of the study is to assess the factors contributing to the flooding menace in Tamale Metropolitan assembly and adaptation strategies employed by residents of the Northern Region in Ghana to minimize the effects of flooding. A descriptive study design will be used, with a mixed-methods data collection approach, because a thorough research is needed to be conducted. The population of the Accra metropolis based on the appropriate statistics from the Ghana Statistical Services (GSS) is 730,000 (2023). This population is for the study purpose, sample size then is determined, using Cochran’s (1977) sample size determination formula, sample size is calculated. Sample size (n) calculation by Cochran (1977) formula, mentioned below: ……………………………………………………………. ( 1 ) Where n = sample size N = target population of residents of the Tamale Metropolis e = marginal error (5%) N = 730,000 The sampled population size of 400 people from Tamale Metropolis is utilized by this study to determine the factors associated with floods and the adaptation mechanisms by residents to bring down the effects of flooding in the Northern Region. The procedure of choosing a limited quantity (sampling) of individuals or items from a bigger population is the content of (Sharma, 2017). The aim of sampling is to use the information gathered from the parameters identified within the sample to extrapolate information about the whole population (Martínez-Mesa et al., 2016). Sampling methods use randomized sampling as well as stratified sampling, cluster sampling, convenience sampling, and others. The method of sampling is based on how you want to conduct, the available resources, and the type of participants you want to represent. On the other hand, for data collection, these three types of sampling methods namely purposive sampling, simple random sampling, and convenience sampling were employed to pick a representative sample of the respondents. The zone wise of the Tamale metropolis into three groups hence Tamale South, Tamale Central and Tamale North was one of the institutional changes the analysis. A total of five neighborhoods were selected, and one community was randomly sample in each of them. The stakeholders to be sampled were from each of the 8 different communities and response from each 20 respondents were selected using the simple random sampling and convenience sampling technique. The sampling process led us to a total of 400 respondents as show in Table 1 . Table 1 Sample zones and communities for the Study No. Zonal name Community name Number of respondents 1 Tamele North Sogunayili 35 Gbalo 30 Jisonayili 20 Gumani 30 Fuo 15 2 Tamale Central Kunyevila 50 Nalung 60 Sawaba 30 Gumbihin South 20 Gumbihini North 30 3 Tamale South Kobilimahagu 20 Jakara-yili 40 Lamakara 60 Dungu 30 Nyohini, 50 Total 400 Source : Author’s Construct, (2023) The major information source, comprising of residents’ surveys, was used to have people respond to the questions regarding the flooding frequencies and consequences in lives and properties. This work based on the secondary data is focused on the institutions in Tamale which we considered to represent two districts of the Northern region. The next phase of information was that of the government institutions’ main responsibilities and linked to flood control and management as well as what they think as to the reasons and the required ways to deal with this in Tamale. Their main promoted bodies included town and country planning Tamale, The Ghana Meteorological Agency and National Disaster Management Organisation (NADMO). The data was analysed using the ordinary least squares (OLS) regression and Kendall's empirical definition of rank correlation (Kendall's rank-order coefficient) respectively. 2.2 Examination of factors regarding flood cases in Tamale metropolis The review process of the factors which affect flooding in the Tamale Metropolitan Assembly employed the use of ordinary least square (OLS) regression. OLS regression involves the examination of the imaginary numerical relationship between an explainable and an outcome variable. As we talk about the predictors of flooding in the Tamala Metropolitan area, this means becomes indispensable for the sustenance of the flood discussion, thus which shows that the factors contributing to the flooding phenomenon are greatly understood. In such incidence, gradually water is left to surface by multiple processes, most of which are interrelated. Consequently, OLS regression gives a researcher the opportunity to consider the multiple kinds of variables at once, thus, getting rid of the bewildering effect of the different occurrences of influence in a comprehensible manner. Among other applications, classic OLS model is able to conduct analysis not only to identify relationships between variables but also to predict future directions. This anticipation cancels out the need to respond to flooding in the study area. Thus, this facilitates to urban planning and disaster risk reduction through the predictive capability. Regression Model Flood Incidence = β0 + β1* Institutional factors + β2*Socioeconomic Factors + β3*Infrastructure + β4*Topography + β5*Land Use + β6*Community Awareness + ε ……………….( 2 ) Where β0, β1, β2, etc., are the coefficients and ε represents the error term. The variables that was used for the model are presented in Table 2 . Table 2 Description, Measurement and hypothesized sign of variables used in the regression model Variable Description Measurement Hypothesized sign Dependent Variable D 1 Flooding Yes = 1 and No = 0 + Independent Variables X 1 Age of respondent Number of years +/- X 2 Sex of respondent Male = 1 and Female = 0 +/- X 3 Income status Amount in GH ₡ + X 4 Educational status Educated = 1 and non-educated = 0 +/- X 5 Availability of economic development initiatives Yes = 1 and No = 0 + X 6 Access to a reliable and safe water supply system Yes = 1 and No = 0 +/- X 7 Availability of sanitation facilities Yes = 1 and No = 0 +/- X 8 Reliable transportation network Yes = 1 and No = 0 + X 9 Located in a low-lying area Yes = 1 and No = 0 + X 10 Land use pattern Residential = 1 Commercial = 0 +/- X 11 Policies or regulations availability Yes = 1 and No = 0 +/- X 12 Government agencies availability Yes = 1 and No = 0 + 2.3 Analysis of the effectiveness of existing adaptation strategies used by residents of the Tamale Metropolis in reducing flood-related vulnerabilities Flood victims apply different ways to outrun the puzzles of water rise. This type of strategies are called adaptation measures. Local adaptation strategies which were compiled from various sources and presented to respondents to rank them on the basis of impact. Unanimity among the strategies of adaptation rank recognition was tested by Kendall coefficient of concordance that we used to test the level of agreement among the respondents. In line with Legendre (2005), the Kendall’s coefficient of concordance is a measure of agreement among several (p) chosen judges who are responsible for grading an array of (n) supplied objects. Where W is an index that is used to measure the ratio of observed variance of the sum of ranks assigned to observations divided by the maximum possible variance of the ranks. The idea is to sum up, each of the adaptation methods will be ranked separately. If there recording of any change in statement corresponds to the expected manner, then the variability from the sum of these values was the most. The Kendall’s concordance coefficient (W) is therefore given by the equation: W = 12S/p 2 (n 3 - n) – pT……………………………………………………….…… ( 3 ) Where W denotes Kendall’s Concordance Coefficient, p denotes the number of adaptation strategies, n denotes the number of respondents (sample size), T denotes correlation factor for tied ranks and s denotes the sum of square statistics. The sum of a square statistic (S) is given as: S = ∑ (R i – R) 2 …………………………………………………………………… ( 4 ) Where: R i = rows sums of ranks R = the mean of R i The correlation factor for tied ranks (T) is also given as: T = ∑ (t k 3 - t k )……………………………………………………………………. ( 5 ) Where: t k = the number of ranks in each (k) of m groups of ties. X 2 = p (n − 1) W………………………………………………………………….. ( 6 ) p = number of adaptation strategies W = Kendall’s coefficients of concordance 3.0 Results and Discussion 3.1 Factors contributing to flooding in the Tamale Metropolis Flood Incidence The regression model result depicts that there is a positive association between flood incidence and an increase in flooding in Tamale Metropolis. The coefficient which is (1.678) indicates that as flood incidence increase, the flooding in the metropolis will also increase. The extremely low p-value of 0.00 was found to suggest that flood incidence is a highly significant predictor of flooding in our flood modelling (See Table 3 ). This can be interpreted as flooding linked to the increment in flood happenings also increases the probability of occurrence of flooding in the Tamale Metropolis. The revelation that there is a direct association between flood frequency and flood occurrence in Tamale city is useful and similar to previously published results. The example of a study on the flooding consequences on social services in Tamale Metropolis by Ghana Capitalizes on wiping away any doubt about the presence of floods in some areas in Ghana and the fact that flooding events result in socio-economic effects. The flooding events in the city now are a result of both natural forces like an increase in temperature and rainfall escalated by climate change and man-made problems, including inappropriate land use and uncontrolled center construction. Beside, finding out floods damage regression models in other areas and also using logistic regression for extraction of flood area shows a role of regression analysis in knowing as well as predicting flood-related phenomena (Youssef, Pradhan & Sefry, 2016). Such studies focus on the issue of pinpointing the actual ties between the various determinants and flooding. Its strategy does not deviate from the methodology applied in the regression study for the Youssef, Pradhan and Sefry, (2016). Infrastructure The generally positive coefficient (0.789) attached to infrastructure in the regression model reinforces the fact that it is highly correlated to increased flooding in the Tamale Metropolis. The observed p-value of 0.025, which is less than the higher critical value of 0.05, concludes that this variable is statistically significant. (Please refer to the last table in the question for results of tests). It is hinted that the condition of infrastructure is what leads to flooding in one way or the other in the Tamale Metropolis. The infrastructure-flooding relationship in an urban setting like the Tamale Metropolis is a pivot point for urban planners and disaster risk mitigation. This implies that successful companies are likely to have leadership and management teams that are mutually supportive and work cooperatively. The result of this indicate the general patterns of the study on infrastructure and the flood hazards in city centers. Ridha, Ross, and Mostafavi (2022) study on the impact of the climate change on infrastructures and flood risk perceptions in coastal urban are showed the public perception of flood risk in consideration to the plan and age of city infrastructure. As a result, research on the impacts of flooding on the built environment and infrastructure throughout the world have pointed out that the flood impacts ravaged housing, business, learning, health, and other necessary facilities (Dong et al., 2020; Espada, Apan & McDougall, 2017). The social services effects of flooding in Tamale Metropolis equally underline the causes and the socioeconomic impacts of floods, such as weather change, improper land management and unplanned development brains a major contributor to flooding. Land Use The positive sign of the coefficient (0.567) in the regression, specifying land use as the independent variable, reveals a positive relationship between certain kinds of land use and the degree of flooding in the Tamale Metropolis. The p-value below 0.05 and 0.018 for this model implies that the connection between land use and flooding has a statistically significant meaning (See Table 3 ). Resources regression analysis outputs concur with what has already been researched on the cause of flooding in Tamale Metropolitan area. Flooding in Tamale Metropolis illustrates the damages caused by substandard land use and poor settlement planning (Kayaga & Laskunerver, 2021). This only goes to show that the patterns of land use do play in important role in the flood risk in the urban place. As a result, an article on land use planning as a tool for environmental management in the metropolitan area of Tamale stress the strategic use of the planned land use and regulations in order to help in urban human settlement development (Fuseini, 2016). In the long run – poor planning and absence or weak regulations for land use would lead to devastation of the environment and maybe to creation of the flood risk conditions. Furthermore, the research on urban development in Ghana especially Tamale, and the type of urban governance response indicates the challenges and implications to undertake such a growth in urban infrastructure in rapidly growing metropolitan districts like the one in Tamale. It becomes that innovative this expansion carries can lead to increased flood risks in the urban area. Land uses’ effect on flooding is also proved by the studies of how land use control measures and practices cause flooding risk in the urban areas of Ghana. The studies draw the attention towards the fact that land use control procedures and practices shall create flood hazard with the demand to control the flood risk in urban areas through the adoption of effective and workable land using control. Income Status A positive correlation coefficient (0.678) found in the regression model indicates that there is a positive relationship between incomes and flooding in Tamale Metropolis, that is, high-income status is associated with more flooding. As can be seen from Table 3 , the percentage of income is such a crucial predictor of flooding that the p-value is 0.000 (highly significant). Privileged people in socio-economy and natural conditions are more involved in links between income status and flooding. A study by Kayaga et al. (2021), on the effect of flooding on social services in Tamale Metropolis, Ghana reiterates the social and economic effects of floods in the Metropolis, namely, loss of lives and property, and the entities that run the current transport system encounter inhibition, financial and health problems to inhabitants of flood-prone areas. However, this illness depends less on the place individuals and communities are being affected at whatever income level are supposed to exist in Tamale Metropolis. This study, which is on characterizing urban growth in Kumasi, Ghana, then concentrates on the rapid urbanization and population growth that is a hallmark of the dynamics in the Tamale metropolitan area and can exert pressure on infrastructural as well as service provision that may also influence the risk of flooding (Amoateng, 2016). These centres of urbanization will be with a new category of the income difference or income-specific vulnerability to flooding within the Metropolis. Water Supply System The negative (-0.234) coefficient associated with the existence of a source of ready and safe water in the regression model means that the availability of a reliable water system will lower the levels of flooding in the Tamale Metropolis. At the small level of p value of 0.005, the water resource management is perceptibly significant, suggesting water supply system may well be a reliable water allocation for flood mitigation (See Table 3 ). The linkage of unfailing and safe water supply networks and high-flooding to Tamale Metropolis contributes to the proper knowledge of flood risk and resilience. Hence the negative coefficient is when adequate water provisions encourage lower chances of flooding in places thus ascertained. What it means is that the Goal of water infrastructure to be part of the solution of disastrous floods is brought into question as well. Ghana Water Company Limited that the new water facility comprises of White Volta River intake and water treatment plant with the required infrastructure as an entity is aimed to provide treated water to the Tamale Metropolitan area. Another major argument is that the escalating flow of water from the rivers on the other hand may result to flooding. The accessibility of sustainable water infrastructure is imperative for addressing the ever-growing urban demands for water, and it might be helpful in reducing the risk of city areas flooding. Sanitation Facilities The estimation of regression model elucidates the fact that the positive coefficient (0.567) associated with the operation of sanitation facilities implies the evidence of increased flooding in the Tamale Metropolis. The very low p-value which was calculated as 0.001 itself highlights the significance of sanitation facilities to the general population (i.e. it shows they an effective predictor of flooding; see Table 3 ). It is crucial to consider the interaction of the used sanitation facilities and flooding in urban planning and environmental management as it is true in the case of Tamale Metropolis. The environmental sanitation crisis in the Tamale municipality, Ghana stresses the issues regarding environmental sanitation such as residents’ poor attitudes concerning environmental sanitation, weak institutional capacity and environmental sanitation negative impact on the city (Napari & Cobbinah, 2014). The scientists claim that unclean water and poor sanitation have great roles in increased floods and water pollution, hence, the health problems. More significantly, the positive coefficient that is also statistically significant in health of sanitation facilities in the model compel us to look at and handle sanitation challenges that are environmental in nature in order to curtail flood risk within Tamale Metropolis. Transportation Network The (-0.789) coefficient in the regression model with the disruption in the transport network is a negative number thus lit disruption in the transport is associated with the increase of flooding in the Tamale Metropolis. The highly insignificant p-value of 1 reduces its chance to influence the effectiveness of flood combat by 1% meaning that the network is a significant factor for a better flood mitigation. (See Table 3 ). The Transportation network and flooding in Greater Tamale are a fundamental thing to consider when urban planners and builders are trying to design and construct disaster resistant and sound cities. Road closures, accidents bridges, and imposable over paths hamper spreading of emergency work, evacuation actions, and decrease the status of essential commodities and services. The destruction of the transport system which is among the socioeconomic impacts of the floods in the Metro is one of the facets that has affected the lives of the people in the Tamale Metropolis. The research shows the things people deal with as a result of these disasters including the loss of transportation, adverse financial situation and the health problems. This proves clearly that a good transportation network supports critical services and smooth out help that comes during flood events in the Tamale metropolis. In the same way, the flooding experienced during heavy rainfall in Tamale Metropolis and Sagnarigu municipality paralyzed transportation, thus roadblocks for conveying people, and the movement of commodities and goods within the affected areas was affected. The negative coefficient and statistical significance of the transportation network in the regression model underscore the importance of resilient transportation infrastructure in flood risk management and disaster response. Low-Lying Area The positive coefficient (0.345) associated with being located in a low-lying area in the regression model suggests that being situated in a low-lying area is associated with increased flooding in the Tamale Metropolis. The low p-value of 0.004 indicates that the variable is statistically significant, highlighting the importance of the topographical factor in flood risk assessment (See Table 3 ). Low-lying areas are particularly susceptible to flooding due to their lower elevation, which result in the accumulation of water during heavy rainfall or flood events. The topographical characteristics of low-lying areas make them particularly vulnerable to flooding, especially in the absence of efficient drainage systems and flood mitigation measures (Ofori, 2023). Land Use Pattern Land use pattern is positively correlated with the regression model to the extent of 0.678 coefficient, which indicates that land use types of the Tamale Metropolis are associated with excessive flooding in the city. P-value, being less than 0.007, mean, that land use pattern is effectively significant among the factors to produce high floods (See Table 3 ). The grab of the nature's ecosystem causes the change the in the amount and severity of the flooding by means of decreasing the natural hydrological cycle, increasing the runoff, and reducing the infiltration. As poor land use practices such as unorganized and uncontrolled urbanization, deforestation and agricultural expansion increase, the contributed flooding risk rises due to the natural environment modification which, in turn, contributes to the increase in the vulnerability of the local communities to flooding. A study in Tamale Metropolitan Area on the Use of land Planning in providing Environmental management, highlighted the significance of the planning and regulation of human settlements, in efforts to evade environmental degradation and unruly human settlement development (Pogbekuu, 2010). The researcher urged implementation of an elaborate policy for environmental conservation, and land use planning for urbanization issues that affect Nairobi City. Secondly, another research by Fuseini and Kemp (2016), dealing with urban growth in Ghana's Tamale, discloses the infrastructural development challenges in this urban area due to inadequate resources and inefficiency in the government response to the issues of poor services including water supply, sanitation and waste management. The study suggests that this can be accomplished by putting in place sound urban governance systems and storm water management mechanisms to support sustainable urban growth and minimize the potential loss of lives through flood. Policies or Regulations The positive figure (0.456) related to the presence of policies or regulations in the regression model that results from the computation is interpreted as an increase in flooding in the Tamale metropolis attributable to the existence of policies or regulations. Notably, at p-value being 0.000, the variable is extremely significant, thus, suggesting a further investigation about the current policy setups in flood management (As per Table 3 ). While policies and legislations play an important part in mapping plans connected with land use planning, infrastructure development and disaster risk reduction in order to thwart flood risk and highlight urban resilience, they all have a collective role to play. This resonate that the policies and regulations can help in making the built environment resilient so that it can adapt when floods occur while taking into account the nature of different stakeholders' attributes deciding the severity of flood damages (Armah et al., 2010). Through their effectiveness in terms of policymaking and regulations, conditions and integrity in built infrastructure are determined and, as a result, communities are shaped as being either vulnerable or resilient to floods and other environmental risks. A positive and highly significant coefficient for the enforcement/regulatory variables in the regression model bring out policy as one of the important governance and regulatory frameworks in the management of flood risks and urban planning. These findings can be used to provide the basis to specifically develop intervention strategies and policy measures intended to enhance the effectiveness of the existing regulations and policies in regard to the reduction of flood risks and ensuring the resilience of the Tamale Metropolis in overcoming the challenges of environmental vulnerability. Government Agencies The positive sign (0.211) to the governmental bodies in the regression equation emphasizes them to be correlated with more floods in Tamale Metropolis. The p value being 0.045 (look at Table 3 ) which is less than the imputed significance level of 0.05 (see Table 3 ) stands for the fact that the variable is statistically significant. Government authorities being in charge of disaster risk management, urban design and infrastructures, either through their policies or the lack of them, end up leading to the higher exposure of communities to floods. Studies, such as one by Songsore (2020), highlight problems that urban growth in Ghana is confronting with urban authorities response – mainly by being unable to provide infrastructure and service provision on time, for example water supply, sanitation and waste management services. The study suggests that there is an imperative call for functional municipal government and appropriate infrastructure development to manage urban expansion. Moreover, this would reduce the vulnerability of the communities to environmental risks such as flooding. The positive slope and statically significant coefficient of government agencies in the regression model clearly reflect the role of a sound governance and regulation in reducing the extent of flood risks and the process of urban development. Hence, the resource allocation managers could capitalize on this knowledge and target interventions and policy measures that dominate the entire improvement of the effectiveness of government agencies in addressing flood vulnerability and increasing the resilience of the Tamale Metropolis to environmental hazards. The coefficient of determination (R-squared) and the R-adjusted value are the means to assess the model's overall fit, the results are respectively 0.745 and 0.720. The values arrived at indicate that the percentage variation of the independent variables can be accounted for 74.5% through the specific independent covariates. The F-statistic of 29.43 is significantly high (p-value is 0.000), and the conclusion might be useful to the model fitted for the data. R-squared is the value that measures exactly the portion of the dependent variable variance that is being explained by the constant variables in the model, namely the independent variables. In such situation, data values of R-squared of the variables 0.745 (within the range of 0 to 1) informs about 74.5% of variation in the dependent variable and is therefore considered as a quite strong relationship within the independent and dependent variables. R-squared Adjustment is the same as R-squared, except for the number of independent variables in the model or over fitting and over adjustment problems. An Adjusted R^2 value of 0.720 suggests that the model is instead a good fit for the data since it is a much higher value than the unadjusted R^2 value of 0.669. This indicates that the addition of the individual variables is indeed helping with fitting the model and is furthermore reducing the risk of over fitting. The enormously meaningful F-statistic of 29.43 (p-value = 0.000) also suggest that the model is applicable to the data, as the independent variables are highly correlated with the dependent variable (desirability), according to the latter (See Table 3 ). Table 3 OLS Regression Results on factors contributing to flooding in the Tamale Metropolis Variable Coefficient Standard Error t-value P-value Intercept 2.345 0.567 4.134 0.001 Flood Incidence 1.678*** 0.234 7.189 0.000 Infrastructure 0.789 0.345 2.287 0.025 Topography -0.234 0.178 -1.314 0.192 Land Use 0.567 0.234 2.423 0.018 Community Awareness -0.789* 0.456 -1.732 0.085 Age of respondent -0.147 0.105 -1.400 0.162 Sex of respondent -0.092 0.076 -1.211 0.227 Income status 0.678*** 0.123 5.512 0.000 Educational status 0.104 0.089 1.168 0.244 Economic development initiatives -0.008 0.102 0.078 0.938 Water supply system -0.234** 0.078 -2.987 0.005 Sanitation facilities 0.567*** 0.145 3.897 0.001 Transportation network -0.789*** 0.234 -3.367 0.001 Low-lying area 0.345** 0.112 3.071 0.004 Land use pattern 0.678** 0.234 -2.899 0.007 Policies or regulations 0.456*** 0.067 6.782 0.000 Government agencies 0.211 0.104 2.019 0.045 R-squared Adjusted R-squared F-statistic P-value 0.745 0.720 29.43 0.000 ***, ** and * denote that the variable is significant at less than 1%, 5% and 10% respectively Source: Field Survey Data (2023) 3.2 Adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding Kendall’s coefficient of concordance was used to measure the level of agreement among the respondents and was found to be statistically significant for the adaptation methods ranked during the mitigation of climatic anomalies in the metropolis. Table 4.00 below shows the Chi-square which gives us (df = 9) = 1060.809 and the asymptotic Sig stats which are (Sig. = 0.000) and it implies that respondents scored signals at 1%. The Kendall’s coefficient of concordance (W) = 0.795, that is to say, 79.5% of the elements assigned by respondents are in agreement. The research demonstrated holistically that the two tactics - of using flood barriers as well as sandbags - is the most adopted as a means of flood mitigation. Concern, which involves the use of concrete blockage means like sandbags and flood walls, aims to either hinder the development of flooding or to reduce its harmful effects. They are meant for keeping the properties and the structures above water during flooding party when the water level rises or during intense rainfall. The use of filling barriers and sandbags is indicated the front row adaptation strategy which is grind against the effects of flooding in the Tamale locality. Implementing this plan involves throwing physical obstacles such as sandbags or flood doors that aim to block inflow or reduce the flood damages effect. Flood risks of properties and infrastructures are contained with this technique during the times it rains heavily or water levels rise. The Tamale metropolis is experiencing heavy flooding in its flood-prone areas of the city causing different communities to be flooded year after year. The causes of flooding that we are experiencing in these parts are from natural sources and some of them are usually man-made. In the last twenty-seven years we have witnessed higher average temperatures of which the global climate change is a major contributor and also an alarming increase in the quantity of rainfall. Besides all these, such man-made causes as poor land use, unplanned construction of settlements, and haphazard discarding of other forms of refuse into the river and the banks of the river are highlighted as the major causes of flooding in urban city. In light of these challenges, the deployment of flood barriers and sandbags serves as a critical adaptation strategy to mitigate the impact of flooding in the Tamale metropolis. These physical barriers help protect life and property, prevent inundation, and reduce the socioeconomic impacts of floods in the flood-prone zones. Furthermore, the use of sandbags as flood barriers is a cost-effective and practical solution for protecting various areas from flooding. Sandbags are useful for blocking outer crevices of homes and containing rainfall in low-elevation terrain (Atufu & Holt, 2018), making them a valuable tool for flood protection. Furthermore, the use of early warning systems is ranked second adaptation strategies employed in mitigating the effects of flooding. Early warning systems play a crucial role in reducing flood-related vulnerabilities by providing timely information about impending floods. This allows for early evacuation and preparedness, ultimately minimizing the loss of life and property damage. In the context of the Tamale metropolis, early warning systems provide critical information to residents and authorities, allowing for timely evacuation and preparedness in the event of impending floods. This significantly reduce the impact of flooding on communities and infrastructure. Furthermore, the use of early warning systems is essential for enhancing disaster risk reduction and improving community resilience to flooding. These systems help empower communities with flood risk awareness and facilitate the adoption of fundamental strategies to mitigate the impact of flooding. The third adaptation strategies employed in mitigating the effects of flooding is developing community evacuation plans community evacuation plans are ranked third. These plans involve establishing procedures for safely evacuating residents from flood-prone areas to designated shelters or safe locations. Effective community evacuation plans are essential for ensuring the safety of residents during flood events. In the Tamale Metropolis, residence in flood prone areas usually move to higher grounds or even temporary relocate to stay with their love once till the raining season is over, before they return to their homes (Kuusaana & Eledi, 2015). In addition, building elevated structures is the fourth ranked adaptation strategies employed in mitigating the effects of flooding. Elevating structures such as homes, public buildings, and infrastructure above potential flood levels significantly reduce their vulnerability to flooding. This adaptation strategy helps minimize damage to properties and ensures that essential services remain operational during flood events. As a result, residence in flood prone areas raises their building foundation above sea level as strategy of preventing runoff water into their homes. Additionally, used of flood insurance coverage is ranked fifth ranked adaptation strategies employed in mitigating the effects of flooding. Flood insurance is a critical adaptation strategy as it provides financial protection to individuals and communities affected by flooding. It helps in covering the costs of repairing or rebuilding structures damaged by floods, thereby reducing the economic burden on affected households and businesses. Generally, it is businesses in flood-prone areas that tend to use flood insurance more than individuals. This is because businesses often have more assets and property to protect, making them more vulnerable to the financial impacts of flood damage. Implementing sustainable landscaping is ranked sixth adaptation strategies employed in mitigating the effects of flooding. Sustainable landscaping is a strategy that involves using techniques to promote natural water absorption and drainage, thereby reducing surface runoff and the risk of localized flooding (Collentine & Futter, 2018). This approach also enhance the resilience of flood prone areas within the in the Tamale metropolis to extreme weather events. Households with rain gardens are less likely to greatly affected flooding as compared to houses with concrete floor. Additionally, specific landscaping designs, such as laying mulch, choosing native plants with high water tolerance, and building rain gardens, help prevent flood damage (Sharath & Peter, 2019). Supporting local flood control measures is ranked seventh adaptation strategies employed in mitigating the effects of flooding. This includes advocating for and investing in infrastructure projects aimed at controlling flooding at the local level, such as expansion of drainage system within the flood prone areas. According to the Norizan, Hassan and Yusoff (2021), local flood control measures are essential for reducing the impact of floods and building more resilient communities. NADMO over the years has develop programs gear towards mitigating future losses from floods and other natural disasters. While the use of emergency supplies is ranked as the least adaptation strategy employed in mitigating the effects of flooding, it is still a crucial aspect of flood preparedness in the. In the event of a flood, emergency supplies such as food, water, and medical supplies are necessary to sustain individuals and communities during and after the flood event. Philpott and Casavant (2016), recommends having emergency supplies such as sandbags, shovels, sump pumps assist individuals in overcoming the challenges of flooding. Table 4 Adaptation strategies in reducing flood-related vulnerabilities Types of adaptation strategies used Mean Rank Ranking Obtaining flood insurance coverage 5.84 5th Using flood barriers and sandbags 2.09 1st Utilizing early warning systems 3.81 2nd Developing community evacuation plans 4.26 3rd Building elevated structures 5.31 4th Implementing sustainable landscaping 6.19 6th Managing stormwater effectively 6.83 8th Stocking emergency supplies 7.00 10th Participating in community flood preparedness programs 6.89 9th Supporting local flood control measures 6.80 7th N Kendall's Wa Chi-Square df Asymp. Sig. 400 0.795 1060.809 9 .000 Source : Field Data (2023) 4.0 Conclusion and Recommendation The study concluded that flood incidence, infrastructure, land use, income status, water supply system, sanitation facilities, transportation network, low-lying areas, land use patterns, policies or regulations, and government agencies all significantly contributes to flooding in the Tamale Metropolis. Also, the study further concluded that there is a substantial consensus among respondents regarding the effectiveness of adaptation strategies in mitigating flooding effects in the Tamale metropolis. Flood barriers and sandbags emerged as the foremost strategy, offering cost-effective protection for properties and infrastructure. Early warning systems ranked second, proving vital for timely evacuation and disaster preparedness. Community evacuation plans, elevated structures, flood insurance, sustainable landscaping, and local flood control measures also showcased their significance. Emergency supplies, though least prioritized, remain crucial for flood preparedness, emphasizing the multifaceted approach required for effective flood risk reduction. The study recommends that the Tamale Metropolitan Assembly should implement and enforce effective urban planning regulations to manage land use patterns, control unplanned development, and minimize the impact of infrastructure on flooding. In addition, the assembly should collaborate with relevant stakeholders to incorporate climate-resilient and sustainable urban development practices. Declarations Acknowledgments: The authors gratefully acknowledge the support provided by the participants whose valuable contributions enhanced this study. Special thanks are due to the editors of Discover Environment for their insightful suggestions that improved the quality of this manuscript. Author Contributions: Justice Agyei Ampofo, conceptualization, data collection, analysis, writing, of the draft manuscript and editing; Prof. Ebenezer Owusu Sekyere and Dr. Raymond Adongo gave guidance for the paper, reviewing, commenting and editing. All authors read and approved the final manuscript. Funding: The authors have not received any financial support for the publication of this article. Data availability: The datasets generated during the course of the study are included in this published article. Code availability: Not applicable Ethics approval and consent to participate All human and/or animal subjects involved in this study were treated in strict adherence to ethical principles and regulations established by the University for Development Studies, Tamale, Ghana. Data collection from participants was carried out with their informed consent, and subsequent analysis adhered to standardized methodologies developed by the Graduate School of the University for Development Studies, Tamale, Ghana. Participants were thoroughly briefed on the purpose and usage of their data, providing explicit consent for its publication. Moreover, the data collection process received approval from Dr. Dzigbodi Adzo Doke, the Head of the Department of Environment and Sustainability Sciences at the University for Development Studies, Tamale, Ghana. Dr. Doke's oversight ensured alignment with the university's ethics and regulations, guaranteeing that all procedures adhered to established guidelines and were conducted with compassion and respect. Competing interest The authors declared that they have no competing interests. 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The term flood implies infiltration of water onto out-of-water area by exceedingly heavy rainfall, thawing of snow or dam break (Hrushikesh, Gururaj \u0026amp; Pathak, 2023). This vice is one of the most destructive vices as it leads to deaths and destruction of infrastructure. It causes internal displacements, results in long-term economic and environment issues (Glago, 2021; Onwuka, et. al., 2015).\u003c/p\u003e \u003cp\u003eFloods can be caused in different ways, such as river flooding, flash flood, coastal flooding, and urban flooding (Sowmya, John, \u0026amp; Shrivasthava, 2015). River floods are the most dominant type of flooding and remarkably, these events are preceded by long periods of intensive rainfall or snowmelt that overflows the rivers to a point where they exceed their bank capacity (Pomeroy, Stewart \u0026amp; Whitfield, 2016). Pronto floods, on the contrary, are usually sudden with very little sign of warning which usually affect the hilly regions or places with bad drainage systems (Kieu \u0026amp; Van Tran, 2021). Coastal flooding, offshore on the other hand, flooding in urban areas is accounted for by poor drainage systems. Floods negatively impact the international stage and are the reason behind the largest share of all natural hazards between 1998 and 2017 (Tembata et al., 2020). In the same time, floods became the global problems affecting around 2.3\u0026nbsp;billion people and causing 157, 000 deaths worldwide (Ganguly \u0026amp; Cahill, 2020). Statistics for the period show a devastating economic loss of over \u003cspan\u003e$\u003c/span\u003e662\u0026nbsp;billion during the flooding phenomenon in the year 2023, alone. (Kurt, 2023). On the one hand, we cannot deny the fact that the frequency and intensity of floods will be higher than thought in past considering climate change. The stormy effects of flood no longer threatening by climate change, leading to further global warming, more frequent and severe storms, which pickup infrastructure and natural systems for drainage.\u003c/p\u003e \u003cp\u003eIn Africa, the situation is not different, with an apparent fact that the continent, with its diverse topography and climatic conditions, is highly prone to hazards through floods (Alfieri et al., 2017). Nevertheless, heavy rains still remaining the main causes of the flood in Africa. Consequently, this intense rainfall which, in turn, is a significant floodwater threat, majorly, occurs in those areas that cannot be able to handle excess water. Similarly, in almost all cases, rapid urbanization, unless accompanied by sustained improvements in the infrastructure, leads to drainage systems that are unable to handle the water volume of heavy rain, thus resulting in floodgates opening in populated areas. Deforestation is not only an established factor for flooding incidents but also a threat for the dignity of African countries. The destruction of forests can lead to major imbalances in ecological systems, and may cause a weakened vegetation level to absorb extra water that can lead to amplified surface runoff rate, and thus to a higher risk of flooding (Gunnell et al., 2019). These floods over the years in Africa have caused deaths of human beings, displacement and homelessness, damages to social amenities, losses in agriculture, and spread of water borne diseases (Mugambiwa \u0026amp; Makhubele, 2021).\u003c/p\u003e \u003cp\u003e The damage wreaked by flooding across Africa have a dual relationship with the flooding experienced in Ghana, its sovereign nation. Within the period of 1991\u0026ndash;2018, more than one-third of all disasters recorded in Ghana were the result of floods; the Upper East, Upper West, Northern and Volta regions are the areas most endangered by floods in the whole country. (Ntim-Amo et al., 2022). Also, in the year 2015, there occurred a deluge occasioned by heavy rains in Accra, the capital city of Ghana. Only 300 people, nonresidents, were killed, and the number of the displaced was more than 2000. Yet, according to the latest report, floods have destroyed 1.7\u0026nbsp;million homes and incurred about \u003cspan\u003e$\u003c/span\u003e200\u0026nbsp;million in the form of infrastructure damage, loss of productivity, and emergence response costs. Town of Tamale is typically characterized climatically from a Sahelian region where the city experiences a greater low raining season, long duration of low raining season length, and the short duration high raining season duration that is always observed amongst the months of May and September (Chagomoka et al., 2018). This turns the city of Tamale to be by flooding during the rain months. Nevertheless, the situation is aggravated due to the human factors such as urbanization and population growth. The urban development of the city that was once designed as a metropolis has led to an expansion of the metropolitan area accompanied by an increased requirement in housing and infrastructure. Consequently, this causes the invasion of open spaces and natural watercourses (Mensah, Gough \u0026amp; Simon, 2018). The fast growth of the population and unrestrained urbanization as a consequence has heavily overtasked the drainage infrastructure in the city not only by intensifying the problems around flooding, but also by causing other issues. Urban development through building of roads, residential areas, and agricultural fields without proper storm water management systems in various regions of the city provokes the accumulation of pooled water on streets, residential housing and farms during heavy rainfall (Kaur \u0026amp; Gupta, 2022). Alongside with those, it facilitates troubles for the local citizens as well as destructs infrastructure and farming activities. In the long run, the floods impacts will be deemed unsurpassable. Floods led to the displacement of communities, loss of livelihoods, increased poverty levels, and social unrest. The destruction of infrastructure, such as roads, bridges, and buildings, hampers economic development and recovery (Islam et al., 2016). Flooding also carries pollutants and contaminants, posing risks to public health and the environment (Crawford et al., 2022). In response to the persistent issues of flooding, both local and national governments have implemented a range of flood adaptation strategies in dealing with the adverse effects of flooding on their livelihoods. This study therefore seeks to assess the factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cp\u003eThe study area being the Tamale Metropolis city was the one used. The Metropolitan of Tamale Municipality was enacted through government action and by Law, the Legislative Instrument (L.I. 2068) became the instrument of authority for the local government to function. Tamale is a double city- the capital of the Metropolitan and the Regional Northern Region. By using December 2018 as a deadline, these two regions became two separate institutions from the Northern region which they seceded from through the referendum held in December 2018. Interchangeably, there is Tamale Metropolis listed as part of 16 District and Municipal assemblies (MMDAs) under the Northern Region. Metropolis is that chiefly situated in the middle region of the Region whilst it borders Sagnarigu in the northwest, Mion District in the east and South to East Gonja. In the southernmost point is the Middle Gonja. The Metropolis is located in the geographical coordinates of the 9\u0026ordm;16 and 9\u0026ordm;34 North latitude and the 0\u0026ordm;36 and 0\u0026ordm;57 West longitude as shown in the extracted data from the Ghana Shared Space (GSS) (2022).\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1 Research design and approach encompass the use of sampling methods, data collection technique, and the analysis methods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe key objective of the study is to assess the factors contributing to the flooding menace in Tamale Metropolitan assembly and adaptation strategies employed by residents of the Northern Region in Ghana to minimize the effects of flooding. A descriptive study design will be used, with a mixed-methods data collection approach, because a thorough research is needed to be conducted. The population of the Accra metropolis based on the appropriate statistics from the Ghana Statistical Services (GSS) is 730,000 (2023). This population is for the study purpose, sample size then is determined, using Cochran\u0026rsquo;s (1977) sample size determination formula, sample size is calculated. Sample size (n) calculation by Cochran (1977) formula, mentioned below:\u003c/p\u003e \u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere n\u0026thinsp;=\u0026thinsp;sample size\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;target population of residents of the Tamale Metropolis\u003c/p\u003e \u003cp\u003ee\u0026thinsp;=\u0026thinsp;marginal error (5%)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;730,000\u003c/p\u003e \u003cp\u003e\u003cimg 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A2gwj4KAKi87UwAJwwEbK54Hxa/N0ZI6Q3gMOOpaHbOBJiDvIEjEQNtGGo32tBa/ARNkHw0ICOB9AAeQ7YiAt3ulvSEZkvIfALg8DKZZoxXhO+q3HG/MgQIcAwcmiT1dJX4VarKXPigHHCEyZgXNwGyNgAUzw/6YPYEJbQPA1MOGgCQWCSZMSPsIA/J9MHGAQmQhaqMkwJKF1H3ENPFbLPlPQYp+MfEIa0fkvphWzAlaTk7MJP3kK53RwJgutAbjzUnSoPwC2luQ9kwX2cciDu6VhsRHhFeAFXAFz+I8W8DpWgFcfC3AUJ4D4JDfgE+FeoYD/QY4vPReRmo8ldXHjOilptLGPluzeWeIQLPhFfnVCUgQMMLMXMgX+KU2E5D8JxCMz0s/wQQTtFqdXL+ARjGUWTOERnvOR/0DGk5Gpg4hCSKstBrAhpgC6ihy4BDLl/bMgZiT/uvE6cy04LOpH2cqGnteU2Gs+EuLinFHn4BGe7k/QcwMlZy0KKDhGhy9nK00CjyUEMUZGgAa53pm/D94Wj8Wbcq25zjQp4BDJmt7popZSOVuxLPN4pyWfA2xzzafNdtjtRncGgvhp8jbmpEBC2EzSwIPAGM/mx4QMD9ykAIc+stLr/UJOJgBzJu8vSgEDcVsCziASA5igAOwEVwku3HUUUetcghirGZhZQMEtCMCMkDIr9bXgQMPaBxRt6Sv0BhkSNaFWc6P9rmGBFz4ioBHtHcPuXbDwSylOjIjY8xAB3/wtA5S0aZse44DfQo4mvVxeMH4DfJ04UasrQuJNkCBf8Rs2NGLStORGEfL4H8hDEwIAhKhY31Qx/kpaD05ARpOR7N3HCMoVHjquf/DucpBGeMFKABHv0iKMUdorv3QFAYMGNBKU8mvnf8POFyPphPXiOOiOzQIiVBxLHwsfhcm9kV7ppX2fBBxTHvAT/uJfdE+tnIymFt15yz+AQ5+n3gebfURfZVt1znQp4CDmu43UQlMZ4gQtucczfuov4QiBn70mQ+jTrIG2dRBzvVS02bCqWjWlp4fSwaY+WkG+apRcb4ZV8iS4zAWSSLEwCcqkWklQBC4hOCIwNBIpEAj5+oDuAS5D74GYeOOCIDRHhoJdvCSaRTaAq2C+ZTzIq6hhoMvgzkHSBG/h+Kr+P0ZYOSZAukg1aM0jnoInQmqP36fRhmZcX7Z9gwH+gRwUN+9uIROgY8XkCB19ALFy95WVKUtFhMgdrhoSqNrMDnM4kqhXcPLTi33a2qhdlPtzbjGTOsQMhUFEPlAAAWoybtAfALKq1V6am9hFsCTp1ELKzNF9EUwtaWpRFIVAKMt4JGoAxAjbEAthLe6WDt/aA+NTA+nEHi/ESP8aoycvcCQkxb53RdOYdoRAihMRRqQ9rQ37cOJKV+FdsEkw0fP1W/icvZGyLnqKKVq7QpRFfkpdaqDfv14+d48B4Z64CCITBSViLQN4T/quQVO2quIxCrC5SWsq70dsZEPwvVoFo2I0xV4eMlpGUwSQl0HGXa6BC1taAtCo0FmW9pCPltrT9CjPUGqk3uhiWhjKbp6hIZZwxfEx6KNceZ+k3p/9e9AkBbRiLcEFHhwoOrb7B8aln7qwKG9zFPgpj3gyEPpno9n6xk5rp06iwCifGySvPA7VrbKj5X/e54DQz1wdIclNAeO0HzW7k5/PXmuKAJ/guzKIYmADJAbEpyQoUkAGFoQja7Q4OFAvwaOwcPi5q9CK7B8HN9EZ02I5q/S9TPM6nwZkt7Cn9L13rp3JtBQbiBrtLO+re5dsZyNAwU4hsD3gEkjwlA3bYakoRJSJg8/xn8lsPw+IkaRhzMk8aevj6UAR19/wuX+Cgd6gQMFOHqBqaXLwoG+zoECHH39CZf7KxzoBQ4U4OgFppYuCwf6OgcKcPT1J1zur3CgFzhQgKMXmFq6LBzo6xwowNHXn3C5v8KBXuBAAY5eYGrpsnCgr3OgAEdff8Ll/goHeoED/wedHxBOJsPtrAAAAABJRU5ErkJggg==\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eThe sampled population size of 400 people from Tamale Metropolis is utilized by this study to determine the factors associated with floods and the adaptation mechanisms by residents to bring down the effects of flooding in the Northern Region. The procedure of choosing a limited quantity (sampling) of individuals or items from a bigger population is the content of (Sharma, 2017). The aim of sampling is to use the information gathered from the parameters identified within the sample to extrapolate information about the whole population (Mart\u0026iacute;nez-Mesa et al., 2016). Sampling methods use randomized sampling as well as stratified sampling, cluster sampling, convenience sampling, and others. The method of sampling is based on how you want to conduct, the available resources, and the type of participants you want to represent. On the other hand, for data collection, these three types of sampling methods namely purposive sampling, simple random sampling, and convenience sampling were employed to pick a representative sample of the respondents. The zone wise of the Tamale metropolis into three groups hence Tamale South, Tamale Central and Tamale North was one of the institutional changes the analysis. A total of five neighborhoods were selected, and one community was randomly sample in each of them. The stakeholders to be sampled were from each of the 8 different communities and response from each 20 respondents were selected using the simple random sampling and convenience sampling technique. The sampling process led us to a total of 400 respondents as show in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample zones and communities for the Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZonal name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunity name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of respondents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTamele North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSogunayili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGbalo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJisonayili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGumani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTamale Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKunyevila\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNalung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSawaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGumbihin South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGumbihini North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTamale South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKobilimahagu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJakara-yili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLamakara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDungu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNyohini,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e400\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author\u0026rsquo;s Construct, (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe major information source, comprising of residents\u0026rsquo; surveys, was used to have people respond to the questions regarding the flooding frequencies and consequences in lives and properties. This work based on the secondary data is focused on the institutions in Tamale which we considered to represent two districts of the Northern region. The next phase of information was that of the government institutions\u0026rsquo; main responsibilities and linked to flood control and management as well as what they think as to the reasons and the required ways to deal with this in Tamale. Their main promoted bodies included town and country planning Tamale, The Ghana Meteorological Agency and National Disaster Management Organisation (NADMO). The data was analysed using the ordinary least squares (OLS) regression and Kendall's empirical definition of rank correlation (Kendall's rank-order coefficient) respectively.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Examination of factors regarding flood cases in Tamale metropolis\u003c/h2\u003e \u003cp\u003eThe review process of the factors which affect flooding in the Tamale Metropolitan Assembly employed the use of ordinary least square (OLS) regression. OLS regression involves the examination of the imaginary numerical relationship between an explainable and an outcome variable. As we talk about the predictors of flooding in the Tamala Metropolitan area, this means becomes indispensable for the sustenance of the flood discussion, thus which shows that the factors contributing to the flooding phenomenon are greatly understood. In such incidence, gradually water is left to surface by multiple processes, most of which are interrelated. Consequently, OLS regression gives a researcher the opportunity to consider the multiple kinds of variables at once, thus, getting rid of the bewildering effect of the different occurrences of influence in a comprehensible manner. Among other applications, classic OLS model is able to conduct analysis not only to identify relationships between variables but also to predict future directions. This anticipation cancels out the need to respond to flooding in the study area. Thus, this facilitates to urban planning and disaster risk reduction through the predictive capability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRegression Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFlood Incidence\u0026thinsp;=\u0026thinsp;β0\u0026thinsp;+\u0026thinsp;β1* Institutional factors\u0026thinsp;+\u0026thinsp;β2*Socioeconomic Factors\u0026thinsp;+\u0026thinsp;β3*Infrastructure\u0026thinsp;+\u0026thinsp;β4*Topography\u0026thinsp;+\u0026thinsp;β5*Land Use\u0026thinsp;+\u0026thinsp;β6*Community Awareness\u0026thinsp;+\u0026thinsp;ε \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere β0, β1, β2, etc., are the coefficients and ε represents the error term.\u003c/p\u003e \u003cp\u003eThe variables that was used for the model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription, Measurement and hypothesized sign of variables used in the regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypothesized sign\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFlooding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1 and Female\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmount in GH ₡\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducated\u0026thinsp;=\u0026thinsp;1 and non-educated\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of economic development initiatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess to a reliable and safe water supply system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e7\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of sanitation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e8\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReliable transportation network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated in a low-lying area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eCommercial\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e11\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicies or regulations availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e12\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment agencies availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1 and No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Analysis of the effectiveness of existing adaptation strategies used by residents of the Tamale Metropolis in reducing flood-related vulnerabilities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFlood victims apply different ways to outrun the puzzles of water rise. This type of strategies are called adaptation measures. Local adaptation strategies which were compiled from various sources and presented to respondents to rank them on the basis of impact. Unanimity among the strategies of adaptation rank recognition was tested by Kendall coefficient of concordance that we used to test the level of agreement among the respondents. In line with Legendre (2005), the Kendall\u0026rsquo;s coefficient of concordance is a measure of agreement among several (p) chosen judges who are responsible for grading an array of (n) supplied objects.\u003c/p\u003e \u003cp\u003eWhere W is an index that is used to measure the ratio of observed variance of the sum of ranks assigned to observations divided by the maximum possible variance of the ranks. The idea is to sum up, each of the adaptation methods will be ranked separately. If there recording of any change in statement corresponds to the expected manner, then the variability from the sum of these values was the most. The Kendall\u0026rsquo;s concordance coefficient (W) is therefore given by the equation:\u003c/p\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;12S/p\u003csup\u003e2\u003c/sup\u003e (n\u003csup\u003e3\u003c/sup\u003e- n) \u0026ndash; pT\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere W denotes Kendall\u0026rsquo;s Concordance Coefficient, p denotes the number of adaptation strategies, n denotes the number of respondents (sample size), T denotes correlation factor for tied ranks and s denotes the sum of square statistics. The sum of a square statistic (S) is given as:\u003c/p\u003e \u003cp\u003eS = \u0026sum; (R\u003csub\u003ei\u003c/sub\u003e \u0026ndash; R) \u003csup\u003e2\u003c/sup\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere: R\u003csub\u003ei\u003c/sub\u003e = rows sums of ranks\u003c/p\u003e \u003cp\u003eR\u0026thinsp;=\u0026thinsp;the mean of R\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eThe correlation factor for tied ranks (T) is also given as:\u003c/p\u003e \u003cp\u003eT = \u0026sum; (t\u003csub\u003ek\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e- t\u003csup\u003ek\u003c/sup\u003e)\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere: t\u003csub\u003ek\u003c/sub\u003e = the number of ranks in each (k) of m groups of ties.\u003c/p\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;p (n \u0026minus;\u0026thinsp;1) W\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;number of adaptation strategies\u003c/p\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;Kendall\u0026rsquo;s coefficients of concordance\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results and Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Factors contributing to flooding in the Tamale Metropolis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFlood Incidence\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe regression model result depicts that there is a positive association between flood incidence and an increase in flooding in Tamale Metropolis. The coefficient which is (1.678) indicates that as flood incidence increase, the flooding in the metropolis will also increase. The extremely low p-value of 0.00 was found to suggest that flood incidence is a highly significant predictor of flooding in our flood modelling (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This can be interpreted as flooding linked to the increment in flood happenings also increases the probability of occurrence of flooding in the Tamale Metropolis. The revelation that there is a direct association between flood frequency and flood occurrence in Tamale city is useful and similar to previously published results. The example of a study on the flooding consequences on social services in Tamale Metropolis by Ghana Capitalizes on wiping away any doubt about the presence of floods in some areas in Ghana and the fact that flooding events result in socio-economic effects. The flooding events in the city now are a result of both natural forces like an increase in temperature and rainfall escalated by climate change and man-made problems, including inappropriate land use and uncontrolled center construction. Beside, finding out floods damage regression models in other areas and also using logistic regression for extraction of flood area shows a role of regression analysis in knowing as well as predicting flood-related phenomena (Youssef, Pradhan \u0026amp; Sefry, 2016). Such studies focus on the issue of pinpointing the actual ties between the various determinants and flooding. Its strategy does not deviate from the methodology applied in the regression study for the Youssef, Pradhan and Sefry, (2016).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInfrastructure\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe generally positive coefficient (0.789) attached to infrastructure in the regression model reinforces the fact that it is highly correlated to increased flooding in the Tamale Metropolis. The observed p-value of 0.025, which is less than the higher critical value of 0.05, concludes that this variable is statistically significant. (Please refer to the last table in the question for results of tests). It is hinted that the condition of infrastructure is what leads to flooding in one way or the other in the Tamale Metropolis. The infrastructure-flooding relationship in an urban setting like the Tamale Metropolis is a pivot point for urban planners and disaster risk mitigation. This implies that successful companies are likely to have leadership and management teams that are mutually supportive and work cooperatively. The result of this indicate the general patterns of the study on infrastructure and the flood hazards in city centers. Ridha, Ross, and Mostafavi (2022) study on the impact of the climate change on infrastructures and flood risk perceptions in coastal urban are showed the public perception of flood risk in consideration to the plan and age of city infrastructure. As a result, research on the impacts of flooding on the built environment and infrastructure throughout the world have pointed out that the flood impacts ravaged housing, business, learning, health, and other necessary facilities (Dong et al., 2020; Espada, Apan \u0026amp; McDougall, 2017). The social services effects of flooding in Tamale Metropolis equally underline the causes and the socioeconomic impacts of floods, such as weather change, improper land management and unplanned development brains a major contributor to flooding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLand Use\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe positive sign of the coefficient (0.567) in the regression, specifying land use as the independent variable, reveals a positive relationship between certain kinds of land use and the degree of flooding in the Tamale Metropolis. The p-value below 0.05 and 0.018 for this model implies that the connection between land use and flooding has a statistically significant meaning (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Resources regression analysis outputs concur with what has already been researched on the cause of flooding in Tamale Metropolitan area. Flooding in Tamale Metropolis illustrates the damages caused by substandard land use and poor settlement planning (Kayaga \u0026amp; Laskunerver, 2021). This only goes to show that the patterns of land use do play in important role in the flood risk in the urban place.\u003c/p\u003e \u003cp\u003eAs a result, an article on land use planning as a tool for environmental management in the metropolitan area of Tamale stress the strategic use of the planned land use and regulations in order to help in urban human settlement development (Fuseini, 2016). In the long run \u0026ndash; poor planning and absence or weak regulations for land use would lead to devastation of the environment and maybe to creation of the flood risk conditions. Furthermore, the research on urban development in Ghana especially Tamale, and the type of urban governance response indicates the challenges and implications to undertake such a growth in urban infrastructure in rapidly growing metropolitan districts like the one in Tamale. It becomes that innovative this expansion carries can lead to increased flood risks in the urban area. Land uses\u0026rsquo; effect on flooding is also proved by the studies of how land use control measures and practices cause flooding risk in the urban areas of Ghana. The studies draw the attention towards the fact that land use control procedures and practices shall create flood hazard with the demand to control the flood risk in urban areas through the adoption of effective and workable land using control.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIncome Status\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA positive correlation coefficient (0.678) found in the regression model indicates that there is a positive relationship between incomes and flooding in Tamale Metropolis, that is, high-income status is associated with more flooding. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the percentage of income is such a crucial predictor of flooding that the p-value is 0.000 (highly significant). Privileged people in socio-economy and natural conditions are more involved in links between income status and flooding. A study by Kayaga et al. (2021), on the effect of flooding on social services in Tamale Metropolis, Ghana reiterates the social and economic effects of floods in the Metropolis, namely, loss of lives and property, and the entities that run the current transport system encounter inhibition, financial and health problems to inhabitants of flood-prone areas. However, this illness depends less on the place individuals and communities are being affected at whatever income level are supposed to exist in Tamale Metropolis. This study, which is on characterizing urban growth in Kumasi, Ghana, then concentrates on the rapid urbanization and population growth that is a hallmark of the dynamics in the Tamale metropolitan area and can exert pressure on infrastructural as well as service provision that may also influence the risk of flooding (Amoateng, 2016). These centres of urbanization will be with a new category of the income difference or income-specific vulnerability to flooding within the Metropolis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWater Supply System\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe negative (-0.234) coefficient associated with the existence of a source of ready and safe water in the regression model means that the availability of a reliable water system will lower the levels of flooding in the Tamale Metropolis. At the small level of p value of 0.005, the water resource management is perceptibly significant, suggesting water supply system may well be a reliable water allocation for flood mitigation (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The linkage of unfailing and safe water supply networks and high-flooding to Tamale Metropolis contributes to the proper knowledge of flood risk and resilience. Hence the negative coefficient is when adequate water provisions encourage lower chances of flooding in places thus ascertained. What it means is that the Goal of water infrastructure to be part of the solution of disastrous floods is brought into question as well. Ghana Water Company Limited that the new water facility comprises of White Volta River intake and water treatment plant with the required infrastructure as an entity is aimed to provide treated water to the Tamale Metropolitan area. Another major argument is that the escalating flow of water from the rivers on the other hand may result to flooding. The accessibility of sustainable water infrastructure is imperative for addressing the ever-growing urban demands for water, and it might be helpful in reducing the risk of city areas flooding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSanitation Facilities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe estimation of regression model elucidates the fact that the positive coefficient (0.567) associated with the operation of sanitation facilities implies the evidence of increased flooding in the Tamale Metropolis. The very low p-value which was calculated as 0.001 itself highlights the significance of sanitation facilities to the general population (i.e. it shows they an effective predictor of flooding; see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is crucial to consider the interaction of the used sanitation facilities and flooding in urban planning and environmental management as it is true in the case of Tamale Metropolis. The environmental sanitation crisis in the Tamale municipality, Ghana stresses the issues regarding environmental sanitation such as residents\u0026rsquo; poor attitudes concerning environmental sanitation, weak institutional capacity and environmental sanitation negative impact on the city (Napari \u0026amp; Cobbinah, 2014). The scientists claim that unclean water and poor sanitation have great roles in increased floods and water pollution, hence, the health problems. More significantly, the positive coefficient that is also statistically significant in health of sanitation facilities in the model compel us to look at and handle sanitation challenges that are environmental in nature in order to curtail flood risk within Tamale Metropolis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransportation Network\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe (-0.789) coefficient in the regression model with the disruption in the transport network is a negative number thus lit disruption in the transport is associated with the increase of flooding in the Tamale Metropolis. The highly insignificant p-value of 1 reduces its chance to influence the effectiveness of flood combat by 1% meaning that the network is a significant factor for a better flood mitigation. (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Transportation network and flooding in Greater Tamale are a fundamental thing to consider when urban planners and builders are trying to design and construct disaster resistant and sound cities. Road closures, accidents bridges, and imposable over paths hamper spreading of emergency work, evacuation actions, and decrease the status of essential commodities and services. The destruction of the transport system which is among the socioeconomic impacts of the floods in the Metro is one of the facets that has affected the lives of the people in the Tamale Metropolis.\u003c/p\u003e \u003cp\u003eThe research shows the things people deal with as a result of these disasters including the loss of transportation, adverse financial situation and the health problems. This proves clearly that a good transportation network supports critical services and smooth out help that comes during flood events in the Tamale metropolis. In the same way, the flooding experienced during heavy rainfall in Tamale Metropolis and Sagnarigu municipality paralyzed transportation, thus roadblocks for conveying people, and the movement of commodities and goods within the affected areas was affected. The negative coefficient and statistical significance of the transportation network in the regression model underscore the importance of resilient transportation infrastructure in flood risk management and disaster response.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLow-Lying Area\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe positive coefficient (0.345) associated with being located in a low-lying area in the regression model suggests that being situated in a low-lying area is associated with increased flooding in the Tamale Metropolis. The low p-value of 0.004 indicates that the variable is statistically significant, highlighting the importance of the topographical factor in flood risk assessment (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Low-lying areas are particularly susceptible to flooding due to their lower elevation, which result in the accumulation of water during heavy rainfall or flood events. The topographical characteristics of low-lying areas make them particularly vulnerable to flooding, especially in the absence of efficient drainage systems and flood mitigation measures (Ofori, 2023).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLand Use Pattern\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLand use pattern is positively correlated with the regression model to the extent of 0.678 coefficient, which indicates that land use types of the Tamale Metropolis are associated with excessive flooding in the city. P-value, being less than 0.007, mean, that land use pattern is effectively significant among the factors to produce high floods (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The grab of the nature's ecosystem causes the change the in the amount and severity of the flooding by means of decreasing the natural hydrological cycle, increasing the runoff, and reducing the infiltration. As poor land use practices such as unorganized and uncontrolled urbanization, deforestation and agricultural expansion increase, the contributed flooding risk rises due to the natural environment modification which, in turn, contributes to the increase in the vulnerability of the local communities to flooding.\u003c/p\u003e \u003cp\u003eA study in Tamale Metropolitan Area on the Use of land Planning in providing Environmental management, highlighted the significance of the planning and regulation of human settlements, in efforts to evade environmental degradation and unruly human settlement development (Pogbekuu, 2010). The researcher urged implementation of an elaborate policy for environmental conservation, and land use planning for urbanization issues that affect Nairobi City. Secondly, another research by Fuseini and Kemp (2016), dealing with urban growth in Ghana's Tamale, discloses the infrastructural development challenges in this urban area due to inadequate resources and inefficiency in the government response to the issues of poor services including water supply, sanitation and waste management. The study suggests that this can be accomplished by putting in place sound urban governance systems and storm water management mechanisms to support sustainable urban growth and minimize the potential loss of lives through flood.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolicies or Regulations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe positive figure (0.456) related to the presence of policies or regulations in the regression model that results from the computation is interpreted as an increase in flooding in the Tamale metropolis attributable to the existence of policies or regulations. Notably, at p-value being 0.000, the variable is extremely significant, thus, suggesting a further investigation about the current policy setups in flood management (As per Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While policies and legislations play an important part in mapping plans connected with land use planning, infrastructure development and disaster risk reduction in order to thwart flood risk and highlight urban resilience, they all have a collective role to play.\u003c/p\u003e \u003cp\u003eThis resonate that the policies and regulations can help in making the built environment resilient so that it can adapt when floods occur while taking into account the nature of different stakeholders' attributes deciding the severity of flood damages (Armah et al., 2010). Through their effectiveness in terms of policymaking and regulations, conditions and integrity in built infrastructure are determined and, as a result, communities are shaped as being either vulnerable or resilient to floods and other environmental risks. A positive and highly significant coefficient for the enforcement/regulatory variables in the regression model bring out policy as one of the important governance and regulatory frameworks in the management of flood risks and urban planning. These findings can be used to provide the basis to specifically develop intervention strategies and policy measures intended to enhance the effectiveness of the existing regulations and policies in regard to the reduction of flood risks and ensuring the resilience of the Tamale Metropolis in overcoming the challenges of environmental vulnerability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGovernment Agencies\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe positive sign (0.211) to the governmental bodies in the regression equation emphasizes them to be correlated with more floods in Tamale Metropolis. The p value being 0.045 (look at Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) which is less than the imputed significance level of 0.05 (see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) stands for the fact that the variable is statistically significant. Government authorities being in charge of disaster risk management, urban design and infrastructures, either through their policies or the lack of them, end up leading to the higher exposure of communities to floods. Studies, such as one by Songsore (2020), highlight problems that urban growth in Ghana is confronting with urban authorities response \u0026ndash; mainly by being unable to provide infrastructure and service provision on time, for example water supply, sanitation and waste management services. The study suggests that there is an imperative call for functional municipal government and appropriate infrastructure development to manage urban expansion. Moreover, this would reduce the vulnerability of the communities to environmental risks such as flooding. The positive slope and statically significant coefficient of government agencies in the regression model clearly reflect the role of a sound governance and regulation in reducing the extent of flood risks and the process of urban development. Hence, the resource allocation managers could capitalize on this knowledge and target interventions and policy measures that dominate the entire improvement of the effectiveness of government agencies in addressing flood vulnerability and increasing the resilience of the Tamale Metropolis to environmental hazards.\u003c/p\u003e \u003cp\u003eThe coefficient of determination (R-squared) and the R-adjusted value are the means to assess the model's overall fit, the results are respectively 0.745 and 0.720. The values arrived at indicate that the percentage variation of the independent variables can be accounted for 74.5% through the specific independent covariates. The F-statistic of 29.43 is significantly high (p-value is 0.000), and the conclusion might be useful to the model fitted for the data. R-squared is the value that measures exactly the portion of the dependent variable variance that is being explained by the constant variables in the model, namely the independent variables. In such situation, data values of R-squared of the variables 0.745 (within the range of 0 to 1) informs about 74.5% of variation in the dependent variable and is therefore considered as a quite strong relationship within the independent and dependent variables. R-squared Adjustment is the same as R-squared, except for the number of independent variables in the model or over fitting and over adjustment problems. An Adjusted R^2 value of 0.720 suggests that the model is instead a good fit for the data since it is a much higher value than the unadjusted R^2 value of 0.669. This indicates that the addition of the individual variables is indeed helping with fitting the model and is furthermore reducing the risk of over fitting. The enormously meaningful F-statistic of 29.43 (p-value\u0026thinsp;=\u0026thinsp;0.000) also suggest that the model is applicable to the data, as the independent variables are highly correlated with the dependent variable (desirability), according to the latter (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOLS Regression Results on factors contributing to flooding in the Tamale Metropolis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlood Incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.678***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.789*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic development initiatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater supply system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.234**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanitation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.789***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-lying area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.345**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicies or regulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.456***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR-squared\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdjusted R-squared\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eF-statistic\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003cp\u003e0.720\u003c/p\u003e \u003cp\u003e29.43\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e***, ** and * denote that the variable is significant at less than 1%, 5% and 10% respectively\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Field Survey Data (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding\u003c/h2\u003e \u003cp\u003eKendall\u0026rsquo;s coefficient of concordance was used to measure the level of agreement among the respondents and was found to be statistically significant for the adaptation methods ranked during the mitigation of climatic anomalies in the metropolis. Table\u0026nbsp;4.00 below shows the Chi-square which gives us (df\u0026thinsp;=\u0026thinsp;9)\u0026thinsp;=\u0026thinsp;1060.809 and the asymptotic Sig stats which are (Sig. = 0.000) and it implies that respondents scored signals at 1%. The Kendall\u0026rsquo;s coefficient of concordance (W)\u0026thinsp;=\u0026thinsp;0.795, that is to say, 79.5% of the elements assigned by respondents are in agreement.\u003c/p\u003e \u003cp\u003eThe research demonstrated holistically that the two tactics - of using flood barriers as well as sandbags - is the most adopted as a means of flood mitigation. Concern, which involves the use of concrete blockage means like sandbags and flood walls, aims to either hinder the development of flooding or to reduce its harmful effects. They are meant for keeping the properties and the structures above water during flooding party when the water level rises or during intense rainfall.\u003c/p\u003e \u003cp\u003eThe use of filling barriers and sandbags is indicated the front row adaptation strategy which is grind against the effects of flooding in the Tamale locality. Implementing this plan involves throwing physical obstacles such as sandbags or flood doors that aim to block inflow or reduce the flood damages effect. Flood risks of properties and infrastructures are contained with this technique during the times it rains heavily or water levels rise.\u003c/p\u003e \u003cp\u003eThe Tamale metropolis is experiencing heavy flooding in its flood-prone areas of the city causing different communities to be flooded year after year. The causes of flooding that we are experiencing in these parts are from natural sources and some of them are usually man-made. In the last twenty-seven years we have witnessed higher average temperatures of which the global climate change is a major contributor and also an alarming increase in the quantity of rainfall. Besides all these, such man-made causes as poor land use, unplanned construction of settlements, and haphazard discarding of other forms of refuse into the river and the banks of the river are highlighted as the major causes of flooding in urban city. In light of these challenges, the deployment of flood barriers and sandbags serves as a critical adaptation strategy to mitigate the impact of flooding in the Tamale metropolis. These physical barriers help protect life and property, prevent inundation, and reduce the socioeconomic impacts of floods in the flood-prone zones. Furthermore, the use of sandbags as flood barriers is a cost-effective and practical solution for protecting various areas from flooding. Sandbags are useful for blocking outer crevices of homes and containing rainfall in low-elevation terrain (Atufu \u0026amp; Holt, 2018), making them a valuable tool for flood protection.\u003c/p\u003e \u003cp\u003eFurthermore, the use of early warning systems is ranked second adaptation strategies employed in mitigating the effects of flooding. Early warning systems play a crucial role in reducing flood-related vulnerabilities by providing timely information about impending floods. This allows for early evacuation and preparedness, ultimately minimizing the loss of life and property damage. In the context of the Tamale metropolis, early warning systems provide critical information to residents and authorities, allowing for timely evacuation and preparedness in the event of impending floods. This significantly reduce the impact of flooding on communities and infrastructure. Furthermore, the use of early warning systems is essential for enhancing disaster risk reduction and improving community resilience to flooding. These systems help empower communities with flood risk awareness and facilitate the adoption of fundamental strategies to mitigate the impact of flooding.\u003c/p\u003e \u003cp\u003eThe third adaptation strategies employed in mitigating the effects of flooding is developing community evacuation plans community evacuation plans are ranked third. These plans involve establishing procedures for safely evacuating residents from flood-prone areas to designated shelters or safe locations. Effective community evacuation plans are essential for ensuring the safety of residents during flood events. In the Tamale Metropolis, residence in flood prone areas usually move to higher grounds or even temporary relocate to stay with their love once till the raining season is over, before they return to their homes (Kuusaana \u0026amp; Eledi, 2015).\u003c/p\u003e \u003cp\u003eIn addition, building elevated structures is the fourth ranked adaptation strategies employed in mitigating the effects of flooding. Elevating structures such as homes, public buildings, and infrastructure above potential flood levels significantly reduce their vulnerability to flooding. This adaptation strategy helps minimize damage to properties and ensures that essential services remain operational during flood events. As a result, residence in flood prone areas raises their building foundation above sea level as strategy of preventing runoff water into their homes. Additionally, used of flood insurance coverage is ranked fifth ranked adaptation strategies employed in mitigating the effects of flooding. Flood insurance is a critical adaptation strategy as it provides financial protection to individuals and communities affected by flooding. It helps in covering the costs of repairing or rebuilding structures damaged by floods, thereby reducing the economic burden on affected households and businesses. Generally, it is businesses in flood-prone areas that tend to use flood insurance more than individuals. This is because businesses often have more assets and property to protect, making them more vulnerable to the financial impacts of flood damage.\u003c/p\u003e \u003cp\u003eImplementing sustainable landscaping is ranked sixth adaptation strategies employed in mitigating the effects of flooding. Sustainable landscaping is a strategy that involves using techniques to promote natural water absorption and drainage, thereby reducing surface runoff and the risk of localized flooding (Collentine \u0026amp; Futter, 2018). This approach also enhance the resilience of flood prone areas within the in the Tamale metropolis to extreme weather events. Households with rain gardens are less likely to greatly affected flooding as compared to houses with concrete floor. Additionally, specific landscaping designs, such as laying mulch, choosing native plants with high water tolerance, and building rain gardens, help prevent flood damage (Sharath \u0026amp; Peter, 2019).\u003c/p\u003e \u003cp\u003eSupporting local flood control measures is ranked seventh adaptation strategies employed in mitigating the effects of flooding. This includes advocating for and investing in infrastructure projects aimed at controlling flooding at the local level, such as expansion of drainage system within the flood prone areas. According to the Norizan, Hassan and Yusoff (2021), local flood control measures are essential for reducing the impact of floods and building more resilient communities. NADMO over the years has develop programs gear towards mitigating future losses from floods and other natural disasters.\u003c/p\u003e \u003cp\u003eWhile the use of emergency supplies is ranked as the least adaptation strategy employed in mitigating the effects of flooding, it is still a crucial aspect of flood preparedness in the. In the event of a flood, emergency supplies such as food, water, and medical supplies are necessary to sustain individuals and communities during and after the flood event. Philpott and Casavant (2016), recommends having emergency supplies such as sandbags, shovels, sump pumps assist individuals in overcoming the challenges of flooding.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdaptation strategies in reducing flood-related vulnerabilities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of adaptation strategies used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObtaining flood insurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsing flood barriers and sandbags\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilizing early warning systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeveloping community evacuation plans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding elevated structures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplementing sustainable landscaping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManaging stormwater effectively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStocking emergency supplies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipating in community flood preparedness programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupporting local flood control measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eKendall's Wa\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eChi-Square\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAsymp. Sig.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003cp\u003e0.795\u003c/p\u003e \u003cp\u003e1060.809\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource\u003c/b\u003e: Field Data (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Conclusion and Recommendation","content":"\u003cp\u003eThe study concluded that flood incidence, infrastructure, land use, income status, water supply system, sanitation facilities, transportation network, low-lying areas, land use patterns, policies or regulations, and government agencies all significantly contributes to flooding in the Tamale Metropolis. Also, the study further concluded that there is a substantial consensus among respondents regarding the effectiveness of adaptation strategies in mitigating flooding effects in the Tamale metropolis. Flood barriers and sandbags emerged as the foremost strategy, offering cost-effective protection for properties and infrastructure. Early warning systems ranked second, proving vital for timely evacuation and disaster preparedness. Community evacuation plans, elevated structures, flood insurance, sustainable landscaping, and local flood control measures also showcased their significance. Emergency supplies, though least prioritized, remain crucial for flood preparedness, emphasizing the multifaceted approach required for effective flood risk reduction. The study recommends that the Tamale Metropolitan Assembly should implement and enforce effective urban planning regulations to manage land use patterns, control unplanned development, and minimize the impact of infrastructure on flooding. In addition, the assembly should collaborate with relevant stakeholders to incorporate climate-resilient and sustainable urban development practices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the support provided by the participants whose valuable contributions enhanced this study. Special thanks are due to the editors of Discover Environment for their insightful suggestions that improved the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eJustice Agyei Ampofo, conceptualization, data collection, analysis, writing, of the draft manuscript and editing; Prof. Ebenezer Owusu Sekyere and Dr. Raymond Adongo gave guidance for the paper, reviewing, commenting and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors have not received any financial support for the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during the course of the study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll human and/or animal subjects involved in this study were treated in strict adherence to ethical principles and regulations established by the University for Development Studies, Tamale, Ghana. Data collection from participants was carried out with their informed consent, and subsequent analysis adhered to standardized methodologies developed by the Graduate School of the University for Development Studies, Tamale, Ghana. Participants were thoroughly briefed on the purpose and usage of their data, providing explicit consent for its publication. Moreover, the data collection process received approval from Dr. Dzigbodi Adzo Doke, the Head of the Department of Environment and Sustainability Sciences at the University for Development Studies, Tamale, Ghana. Dr. Doke\u0026apos;s oversight ensured alignment with the university\u0026apos;s ethics and regulations, guaranteeing that all procedures adhered to established guidelines and were conducted with compassion and respect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access:\u0026nbsp;\u003c/strong\u003eThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u0026rsquo;s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkaateba, M. A., Ahmed, A., \u0026amp; Inkoom, D. K. B. (2021). Chiefs, land professionals and hybrid planning in Tamale and Techiman, Ghana: Implications for sustainable urban development. \u003cem\u003eInternational Journal of Urban Sustainable Development\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 464-480.\u003c/li\u003e\n\u003cli\u003eAldrich, D. P., \u0026amp; Meyer, M. A. (2015). 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Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. \u003cem\u003eEnvironmental Earth Sciences\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(1), 12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Flooding, Adaptation Strategies and Tamale Metropolis","lastPublishedDoi":"10.21203/rs.3.rs-4274764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4274764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed the factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana. The research used mixed research approach that was descriptive with data collection by both quantitative and qualitative methods. In addition, 400 residents from the Tamale Metropolis were sampled, and the data were analyzed through a not ordered least square (OLS) model and Kendall's coefficient concordance. The research has the overall performances of the model come out by using the R-squared and Adjusted R-squared values, which amount to the 0.745 and 0.720, respectively. Furthermore, flood incidence as an independent variable in the regression analysis has a positive coefficient of 1.678, which means that a rising flood incidence is linked to an intensification of flooding in Tamale Metropolis. Besides, the research found that the two adaptation measures being the use of flood barriers and sandbags were the most favored by the population. The fact that the usage of emergency supplies is based at the last position among the adaption strategies which are meant to avert the negative effects of flooding has been ranked as the least employed strategy of mitigating the effects of flooding. The results showed that during the flood season, water level rise, infrastructural damages, poor land use classification, economic status, water supply systems, sanitation facilities, roads network, the presence of low-lying areas, agricultural land, and government agencies have a major influence on the flooding situation in the Tamale Metropolis. This study recommends that the urban planning guidelines of the Tamale Metropolitan Assembly should be enforced and implemented to oversee the management and planning of the land use, management of unplanned development and reduction of the ecological degradation by infrastructure around flooding.\u003c/p\u003e","manuscriptTitle":"Factors contributing to flooding and adaptation strategies employed by residents of the Tamale Metropolis to mitigate the effects of flooding in the Northern Region, Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 10:25:07","doi":"10.21203/rs.3.rs-4274764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-06-22T12:21:50+00:00","index":"hide","fulltext":""},{"type":"decision","content":"Revision requested","date":"2024-06-18T09:30:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-15T19:38:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-09T23:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268186929040332440498389479001001336620","date":"2024-06-09T15:05:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-08T19:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264953342232386445574920337606524025823","date":"2024-06-07T21:03:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94661319131593315911683839817599104359","date":"2024-06-04T15:51:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317681545268182041145710842582758031063","date":"2024-06-04T13:05:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-05T09:19:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277706582505024426928050337705837407950","date":"2024-04-30T15:29:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338380818291467511922655334687254031780","date":"2024-04-30T13:47:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60960107455588333691132419692931395390","date":"2024-04-30T12:29:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-30T12:03:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T06:37:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T06:36:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Environment","date":"2024-04-16T09:10:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9a628aa-c751-4155-926d-ecab2b68ffda","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-06T07:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 10:25:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4274764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4274764","identity":"rs-4274764","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}