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Available online at www.sciencedirect.com ScienceDirect Aquatic Procedia 4 ( 2015 ) 427 – 434 INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Flood Frequency Analysis of Tel Basin of Mahanadi River System, India using Annual Maximum and POT Flood Data Nibedita Guru a, Ramakar Jhab aResearch Scholar, Civil Engineering, NIT Rourkela, India, [email protected] bProfessor, Civil Engineering, NIT Rourkela, India, [email protected] Abstract Flood frequency analysis indicates the catchment characteristics, water availability and possible extreme hydrological conditions like floods and droughts at various locations of any river system. Such studies have been done in the past using long term annual maximum flood series for early warning, preparedness, mitigation and reduction of any kind of disasters. In the present study, Annual Maximum (AM) flood series and Peak over Threshold (POT) flood series were used to carry out flood frequency analysis for Tel basin of Mahanadi river system, India. The POT values were considered based on (a) commonly used standard practice and (b) flood values damaging the downstream areas and causing disaster in Mahanadi river system, India. To recognize the anomalies in tail behavior of the flood frequency distribution and for selecting appropriate flood frequency distributions, Quantile-Quantile plots (Q-Q plots) were used. The analysis was carried out for flood series data of two gauging stations Kesinga (upstream) and Kantamal (downstream) of Tel basin, Mahanadi river system, India for the years 1972-2009. Fourteen different flood frequency distributions were tried for AM and POT flood series data for 31 years for Kesinga and 38 years for Kantamal. The results obtained using Generalized Pareto (GP) distribution shows better results for AM flood data series with all goodness of fit tests. However, for POT flood data series LogNormal (3P) distribution showed best results followed by GP distributions with all goodness of fit test. The distributions most suitable for POT data sets are same for the distribution being used globally for flood forecasting. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (©http://creativecommons.org/licenses/by-nc-nd/4.0/ 2015 The Authors. Published by Elsevier B.V.). Peer-review-review under under responsibility responsibility of organizing of organizing committee committee of ICWRCOE of ICWRCOE 2015 2015. Keywords: Annual maximum series; Peak over threshold; flood frequency analysis; probability distribution; Q-Q plots ___________________________________________________________________________________________________________________ * Nibedita Guru. Tel.: +919438055906. E-mail address: [email protected] 2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi: 10.1016/j.aqpro.2015.02.057 428 Nibedita Guru and Ramakar Jha / Aquatic Procedia 4 ( 2015 ) 427 – 434 1. Introduction High flow exceeding danger levels and entering in flood plains is the result of heavy or continuous rainfall exceeding the absorptive capacity of soil, and the flow capacity of the streams. It causes widespread damage to property and life in different parts of the catchment. Despite the fascinating achievements of science and technology in the 21st century, floods and droughts continue to hit every generation of human beings, bringing suffering, death, and material losses. The knowledge of magnitude-frequency relationships can be used in the design of dams, spillway of dams, highway, bridges, culverts, water supply systems and flood control structures. In the past, flood frequency analysis techniques were developed to relate the magnitude of floods with their frequency of occurrences (Hosking and Wallis, 1997). Such studies have also been done to estimate flood based on catchment characteristics and statistical analysis. It is understood that a minimum of 30-40 years of records are needed for flood frequency analysis. If the length of records is too short, specifically on inadequate data situation, then regional flood frequency curves together with at-site mean provides consistent estimates of floods. Some of the studies carried out in the past are discussed below; In the year 1868, O’Connell performed one of the earliest studies on regional analyses of stream flows with simple empirical formulas that attempted to connect discharge to drainage area. The approach was very simple and the proposed formula was (1) Where = maximum discharge; A = drainage area; and C = coefficient related to the region. O’Connell selected a value of 0.5 for the exponent, considering the relationship between discharge and area as parabolic in the absence of sufficient data. The application of probability theory in flood estimation procedures was introduced by Fuller (1914) who calculated floods of different return periods for catchments in the U.S. With nearly 50 years of additional data, Fuller (1914) analyzed long records of dailyy fflowsl and peak flow particularly the data from the United States. He related the average of the maximum floods ( ) to the drainage area with an exponent of 0.8 (2) Hazen (1921) revised his own work and found some data sets plotted are as curved lines in log normal distribution. He suggested to use a three-parameter distribution including skewness and plot it on logarithmic probability paper. He made a note saying that “The coefficient of skewness is subjected to the objection that there is a tendency for its value to increase with the number of terms in the series”. Foster (1924) introduced the Pearson type III (P3) frequency distribution for describing the flood data. Gumbel (1941) brought the basis of analysis to a new level by applying extreme value theory. Using the findings of Fisher and Tippett (1928), Gumbel (1941) introduced the Extreme Value Type I distribution (EV1) to flood frequency analysis. Chow et.al (1988), related the magnitude of such extreme events with their frequency of occurrence through the use of probability distributions. By fitting the past observations to selected probability distributions, the probability of future high flow events can be predicted. Cunnane (1988) reviewed twelve different methods of regional flood frequency analysis, including well known methods such as the USWRC (U.S Water Resources Council) method , different variants of index flood methods, Bayesian methods and the two-component extreme value (TCEV) method and he rated the index flood using a regional algorithm based on PWMs as the best one. POT series are also denoted by some authors as Partial Duration Series (PDS) because the flood peaks can be considered as the maximum flow values during hydrograph Nibedita Guru and Ramakar Jha / Aquatic Procedia 4 ( 2015 ) 427 – 434 429 periods of variable length. An important advantage of the POT series is that when the selected base value is sufficiently high, small events that are not really floods are excluded. With the annual series, non-floods in dry years may have an undue influence on shape of the distribution. In river flood applications, for instance, the U.S. Water Resources Council (1976) considers consecutive peak floods as independent if the inter-event time exceeds a critical time and if an inter-event discharge drops below a critical flow. The dependence between the POT or PDS values is a function of the hydrological independence criterion used to divide the full series in its partial durations or of the parameters (e.g. threshold level) used to define the particular POT values (e.g. Lang et al., 1999). This paper discusses the method of choosing the threshold (the optimal number of upper extremes) in POT analysis of samples and distributions from (a) commonly used standard practice and (b) flood values damaging the downstream areas and causing disaster. An extreme value analysis methodology was used to recognize the anomalies in tail behavior of the flood frequency distribution by means of Quantile-Quantile plots (Q-Q plots). 2. Study area and Data Collection The Tel River originates in plain of Koraput district of Odisha, about 32 km to the west of Jorigam (Figure 1).It is the second largest river of Orissa and is an important tributary of the Mahanadi River. The river traverses a total length of 296 km to join the Mahanadi River on the right bank, 1.6 km below Sonepur. The total drainage area of the Tel River is about 22,818 km2, in which 11960 km2 lies up to Kesinga and 19600 km2 lies up to Kantamal gauging stations. The Tel sub-basin is bound between latitude 18° to 21° and between longitude 83° to 86° approximately. The normal annual rainfall of the entire Mahanadi basin is 1360 mm (16% coefficient of variation, CV) of which about 6%, i.e.1170 mm, occurs during the monsoon season (15 % CV) from June to September. Fig. 1.General Location Map of the Tel sub-basin Daily discharge data for the years 1972-2009 were collected from Central Water Commission, Bhubaneswar Figure 2 shows the daily mean discharge time series from 1972-2009 for Kantamal (downstream) and 1979 to 2009 for Kesinga (upstream) station of Tel basin. In addition, we fit a non-linear function to the time series, using locally weighted scatterplot smoothing (LOWESS). The LOWESS results illustrate that the series does not have major non-stationaries in frequency or variability. 430 Nibedita Guru and Ramakar Jha / Aquatic Procedia 4 ( 2015 ) 427 – 434 Fig. 2.Discharge time series including linear and non-linear trend of the Tel sub-basin, India 3. Materials and Method Annual Maximum (AM) and Peak over Threshold (POT) flood series was used in the present study for fitting different distributions. AM analysis is relatively straightforward; it employs only the largest event in each year, regardless of whether the second (or third) event is greater than the largest events in other years.

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