FLOOD FREQUENCY ANALYSIS OF KADUNA RIVER
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CHAPTER ONE
INTRODUCTION
- Background to study
Flood is a frequent natural disaster and can cause adversity to the country and people. Urban flood is a complicated phenomenon owing to the inhomogeneity of the urban surface (Ozdemiret al., 2013). Over the past few decades, urban flood intensity has risen worldwide in response to two primary factors urbanization and climate change (Berndtssonet al., 2019). The increase in population and economic growth with the changing development trend has resulted in an unprecedented expansion of urbanized land.
In the present world, land and water are the most important things. Many cities developed independently along a number of river valleys throughout the world because rivers supplied a continuous if not always dependable flow of water for domestic, agricultural, industrial, navigational and waste disposal purposes. In Nigeria, flooding effects are similar to the rest of the world. According to Etuonovbe, (2011) flooding displaces more people than any other disaster in Nigeria because about 20 percent of the population are at risk of flooding. Recently, in 2018, 34 states out of 36 states of the country experienced one or more cases of flooding which has affected about 1.9 million people, destroyed 82,000 houses; displaced 210,000 people and devastated crops and livestock. Consequently, a state of emergencies in nine states of the Nation was declared by the Nigerian government (HKRC, 2018). Flood has been the highest occurring natural hazard in Nigeria, causing severe damages to lives and properties (Aderogba, 2012). It has become an annual event in many regions of the country occurring in the form of coastal floods, river floods, flash floods and urban floods (Komolafe et al., 2015).
Kaduna the capital city of Kaduna State Nigeria along Kaduna River is one of them (Alayende, 2010). Areas along Kaduna River are subjected to flooding at the peak of the raining seasons during August – September. On several occasions the river overflow it banks and spills flood waters into the adjoining properties on the floodplain. The flooding was attributed to the occurrence of high rainfalls over a period of days in the catchment (Jimoh and Ayodeji, 2003).
This flooding is virtually displacing desertification in Northern Nigeria as some states in the North who hitherto were desert threatened, now suffer serious flood calamities (Nigeria Tribune, 1999). It was reported also that the occurrence of flood in both Kano and Adamawa States led to the submergence of the whole town and washing away of both crops and livestock (Terra Daily, 2004).
Flood frequency analysis is widely used for the estimation of flood return periods and their probability based on the long term discharge data. Frequency analysis of extreme flood events is significant for flood hazard and risk assessment and subsequently mitigating their devastating impacts (Benameur et al., 2017; Millington et al. 2011; Renardet al., 2013). Evaluating flood frequency and determining the design flood are the final goals for hydrological analysis and the beginning of integrated flood control (Stedinger, 2012). A design flood is used in comprehensive flood management to assess the flood defense capacities of facilities and to protect human lives and properties within a watershed (Roggeret al., 2012).
Flood frequency approaches vary from statistical methods, i.e., directly applied on the observed annual maximum flood series, to adopting rainfall–runoff simulation models that transform design rainfalls to flood discharges (Saghafian et al., 2014). Statistical approach using measured annual peak discharge data is considered the standard method for estimating a flood quantile.
However, a large error may occur in estimating low-frequency floods when the sample size is not long enough (Katzet al., 2002).
Furthermore, due to changes in land use, achieving stationarity of data for flood frequency analysis has become more difficult (Beighley et al., 2003). Because changes in land use that occur with urbanization affect the frequency of floods (Archer, et al., 2010), studies are being conducted to adjust the past flood data to the present time condition. According to the research results of (Beven, K 2007), the change in land use clearly affects the hydrological responses of watersheds; however, quantifying these effects are very difficult. Therefore, even if sufficient continuously measured flood data are available for the site of interest, adjusting the flood data measured in the past to the present land use is very difficult and limits the computation of accurate flood quantiles.
Meanwhile, the design storm method is used to estimate flood quantiles by applying rainfall quantiles determined from rainfall frequency analysis to the rainfall-runoff model. This method requires three basic assumptions; the selection of the design rainfall hyetograph (rainfall duration and time distribution), the selection of the antecedent soil moisture conditions before the storm event, and the equality of the return periods between the rainfall quantiles and computed flood quantiles (Alfieriet al., 2008).
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