Regional Flood Estimation Methods Samples †MyAssignmenthelp.com

Question: Discuss about the Regional Flood Estimation Methods. Answer: Introduction Floods are natural disasters that lead to loss of life and destruction of property (Syngellakis, 2016). They might be caused by heavy downpours, poor drainage, or the type of slopes in the area. Therefore floods pose a lot of danger not only to the community but also to human life. Due to this danger, different regional flood estimation methods have been developed with an aim to better predict the occurrence of floods. Without proper planning and management of floods, it can be a disaster, but if managed well, it can only be a hazard. The public and planning departments in government institutions require reliable and accurate estimates of large floods to promote flood risk management structures and policies (Wohl, 2000, p. 334). In this paper different regional planning methods will be analyzed. This will be done via literature analysis of the understanding of different regional flood estimation methods. Most of the papers analyzed are downloaded from Google scholar and other credible sites and will form the basis for this discussion. All the different regional flood estimation methods will be analyzed for their efficiency, accuracy, and reliability. This study will provide a frame work for understanding flood estimation methods, which can be very useful for developing policies related to floods forecasting and management. Derivation of Methods Used in Regional Flood Estimation The methods for regional flood estimation are divided into three parts: the selection of the area to be studied, developing a technique for indexing of flood values for the catchment chosen (based on the physical characteristics of the catchment), and the development of a regional flood frequency curve (to enable the estimation of the a flood of a set period from index values obtained earlier) (Roy and Mistri, 2013). This systematic approach provides a very good baseline for estimating floods. Region of Influence Approach This method focuses on the collection of data from stations in a well-defined region. This methods is important in enhancing the estimation of at-site quantiles. In this method, a region of influence is identified for all the gauging stations which consist of a given set of gauged stations near the selected station. In order to measure the proximity of each station, a p-dimensional Euclidian distance space in which the attributes are variables related to the identification of the stations which are similar in high flow rates. The model equation for the distance is shown below (Burn, 1990): Djk-- Euclidian distance from site j to k P Attributes used in measuring the distance --standardized values used in the measurement of attribute i, for the site j (Burn, 1990). The value of the distance from the equation above gives a measure of how each station is close to each other (Burn, 1990). Next, is to identify the region of influence, by choosing a threshold value that acts as a cut-off point for the distance measures (Burn, 1990). All the stations whose distance is more than the threshold value are eliminated from the region of influence. In this method, conventional regionalization techniques are used to select for the choice of the cut-off value. Another method of identifying the threshold value is to correlate the candidate station with the sites near the cutoff value (Burn, 1990). This makes sure that the stations selected are representative stations for the region of influence. A weighting function is used to show the relative significance of each of the gauging stations in the region of influence in relation to the at-site extreme flows. This function is depicted by the equation below (Burn, 1990): Where: WFjk --weighting for station k in the region of influence for site j THL-- parameter n is a constant When the region of influence has been determined for each site, it is now possible to predict the extreme flow rates at each site in relation to all the information from the other stations that are in the same region of influence. This gives a better representation of the flow rates, and enhance flood estimation in that given region (BURN, 1990). This method has been touted to be very efficient in regional flood estimation. It is not only efficient but also provide very accurate flood forecast. The method is also very flexible since it allows the inclusion of information from surrounding stations in the same region of influence. The method is also very versatile in that it can be combined with other different extreme flow rates estimators to provide better results. This is because it is easy to vary the threshold distance for the region of influence and the attributes to be used in the measurement of similarity for the stations to be added in the region of influence, and the weighting function used for reflecting the importance of all the stations in the region (Tasker et al., 1996). Canonical Correlation Analysis Another method for regional flood estimation is the use of canonical correlation analysis. This method has not been widely used but is slowly gaining popularity in the field of hydrology (Ouarda et al., 2001). When two sets of variables are represented by flood peaks and watershed characteristics, their correlation structures can be investigated using canonical correlation analysis. This method is very important in multivariate statistics since it provides a framework for factorial discriminant analysis correspondent analysis and multivariate analysis. It provides a method to establish the interaction between two groups of variables, through the identification of linear combinations between the first group and the second group. The first attempt to use CCA in hydrology was made by Wong (1963) and Snyder (9162). Other contributors were, Wallis (1967), Matalas and Reiher (9167). Torranin attempted to apply the method of CCA in 1972 in coastal monthly precipitation forecasts. This shows that this method has a long history in the application of regional flood forecasting. In 1990 Cavadias initiated the use of CCA in the estimation of maximum annual flood distribution in Canada. This was a pioneering work that ushered the use of CCN in regional flood estimation. In a hydrological system, flood statistics and catchment attributes are related by a multiple regression models. This multiple regression model has residuals that are interpolated spatially using a kriging method, which is used to minimize biases. In 2004, Ouarda and Chokmani came up with a kriging procedure method in a physiographical space, which was a multidimensional space defined by the catchment characteristics. Then they constructed a physiographical space that represented the distance between catchments based on their similaritythis was based on their catchment attributes (Schumann, 2011, p. 110). This enabled them to map hydrological catchment areas based on their characteristics for regional flood estimation methods. According to Kumar and Chatterjee, (2006), CCA can be very useful in finding homogenous zones or sub regions in the hydrological systems for reliable, and accurate regional flood estimationit is efficient, accurate, and saves a lot of time. Even as this method is advantageous it assumes similarity of hydrological basins, which naturally is not the case, most hydrological basins are not similar (Beran et al., 1990, p. 171). This might introduce an error in the method, which might lead to inaccurate results. Regional Flood Frequency Analysis Regional flood frequency analysis was developed by Smith in 1989. This method was based on a model that related to large quintiles, which is modeled by a Pareto distribution; that is generalized. In 1991, Arnell and Gabrielle developed this method further by incorporating two components: generalized extreme value and extreme value distributions. They were able to show that when a large region is divided into sub-regions more precise estimates can be achieved. Subsequently, Farquharson et al. in 1992 used the regional frequency curves through a GEV distribution, to map 162 stations in Africa. This shows the power of the method for regional flood estimation (Hamed and Rao, 1999, p. 60). More so, it is used to estimate the expected flood quantile of magnitude Qt at a given project location. The return period T is used to estimate the rarity of the flooding event. This method also allows for the forecast of flood quantile estimates in a given site; in relation to the flood data recorded in other gauging sites found in the same hydrological region (Cunnane, 1988). That is, if one of the sites does not have flood data, it can be estimated using other stations in the neighborhood. Some Regional flood frequency analysis assume that a given region is homogenous: that all the gauging stations' characteristics are homogeneous. This allows for estimation of flood volumes using other stations. One of this methods is the index flood method. This homogeneity allows for highly accurate estimates that are even more accurate compared to at-site estimation. Other methods of regional flood frequency analysis do not require homogeneity of the stations. Some of this methods are the joint multivariate estimation method and Bayesian method. However, even though homogeneity is not required in this methods, it increases the accuracy of the estimates. This method mostly relies on regional regression models to estimate quantiles using physiographic basin characteristics. However, according to Wohl, 2000 (p. 334) the reliance of hydrographs for this regression models poses serious challenges since the distribution of the critical inflows and critical duration is not clear. This ambiguity puts into question the accuracy and reliability of the method. Use of GIS and Remote Sensing Technology in Regional Flood Estimation Most of the conventional means used for flood monitoring and estimation, fail to record or estimate extreme flooding events (Sanyal and Lu, 2004). However, remote sensing techniques in collaboration with geographical information systems (GIS) have the capability to monitor this extreme events. This makes them be a better method for regional flood estimation than all the other methods discussed above. According to Dzurik and Theriaque, (1996, p. 257) most wetlands cover large areas that are not accessible via conventional means, which makes GIS and remote sensing to be a very good tool for flood forecasting in this areas. This remote sensing techniques cover large areas, even the inaccessible areas, and in collaboration with GIS makes a very good tool for data flood analysis in the watersheds. GIS tool used in this case provides a digital representation of the watershed characteristics, which can be used in hydrological modeling. Some of the characteristics of the watershed represented by GIS are natural ground cover, imperviousness, stream networks, and the delineation of the watershed (Dzurik and Theriaque, 1996, p. 257). These components when incorporated into the GIS tool can be used in flood forecasting and floodplain management. Soil moisture data collected by GIS and remote sensing techniques can also be useful in flood estimation model(Lijiao Lou et al., 2014, p. 82). Even in as much as this method is advantageous, high-level training is required for the use of GIS and remote sensing (Dijk and Bos, 2013, p. 36). This makes it not to be accessible to most people. Also, the process is time-consuming and requires a lot of resources; this makes it an expensive endeavor compared to other methods discussed above. Since the methods rely on satellite imageries, it is also susceptible to atmospheric weather conditions such as cloudiness, and the methods for removing such are also time-consuming (Wallis J.R, 1988, p. 171). Overall, given the availability of resources, it can be a very reliable method that would produce accurate results for even inaccessible areas. Conclusion All the methods mentioned are reliable; however, it depends on how they are employed and used. Overall, this literature review was able to establish that GIS and remote sensing was more reliable when large areas are to be considered, while canonical correlation analysis was least used of the methods. References Beran, M., Water, I.I.C, 1990. Regionalization in hydrology. International Association of Hydrological Sciences. BURN, D.H., 1990. An appraisal of the region of influence approach to flood frequency analysis. Hydrological Sciences Journal, 35(2), pp.149165. Cunnane, C., 1988. Methods and merits of regional flood frequency analysis. Journal of Hydrology, 100(1), pp.269290. Dijk, A. van and Bos, M.G., 2013. GIS and Remote Sensing Techniques in Land- and Water-management. Springer Science Business Media. Dzurik, A.A. and Theriaque, D.A., 1996. Water Resources Planning. Rowman Littlefield. Hamed, K. and Rao, A.R., 1999. Flood Frequency Analysis. CRC Press. Kumar, R. and Chatterjee, C., 2006. Closure to Regional Flood Frequency Analysis Using L-Moments for North Brahmaputra Region of India by Rakesh Kumar and Chandranath Chatterjee. Journal of Hydrologic Engineering, 11(4), pp.380382. Lijiao Lou, Baojiang Liu, and Mengjie Jin, 2014. 2014 International Conference on Information GIS and Resource Management. DEStech Publications, Inc. Available from: https://books.google.co.ke/books? Ouarda, T.B.M.J., Girard, C., Cavadias, G.S., and Bobe, B., 2001. Regional flood frequency estimation with canonical correlation analysis. Journal of Hydrology, 254(1), pp.157173. Roy, S. and Mistri, B., 2013. Estimation of Peak Flood Discharge for an Ungauged River: A Case Study of the Kunur River, West Bengal [Online]. Available from: https://www.hindawi.com/archive/2013/214140/ [Accessed 25 August 2017]. Sanyal, J. and Lu, X.X., 2004. Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review. Natural Hazards, 33(2), pp.283301. Schumann, A.H., 2011. Flood Risk Assessment and Management: How to Specify Hydrological Loads, Their Consequences and Uncertainties. Springer Science Business Media. Syngellakis, S., 2016. Management of Natural Disasters. WIT Press. Tasker, G.D., Hodge, S.A., and Barks, C.S., 1996. Region of influence regression for estimating the 50?year flood at ungaged sites. JAWRA Journal of the American Water Resources Association, 32(1), pp.163170. Wallis J.R, 1988. Environmental Software. Computational Mechanics Publications. Available from:https://books.google.co.ke/books? Wohl, E.E., 2000. Inland Flood Hazards: Human, Riparian, and Aquatic Communities. Cambridge University Press.