Topographic Map Of Stations At The Hulu

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02 Nov 2017

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INTRODUCTION

The principal aim of this report is to describe in detail, the seasonal low flow frequency behaviour of the Sungai Langat river for use to identify and rectify future encounters with the application of water. The data can also be useful in the assessment of the sufficiency of stream flow for waste access, in evaluating the quantity of stream flow available for future developments and in hydrograph separation studies and also in the implementation of state water laws.

There are different approaches to use when analysing low flow frequency, therefore the methods favoured is will be deliberated upon. This methods are used because they show a clearer view of hydrological characteristics and percentage value of the occurrence of flow rates.

In order to investigate special patterns which exist in low flow frequency distribution, regional methods could be a useful way by using the annual minimum low flow series (1 day, 7 days and 30 days mean flow) from catchments. Serial correlations of these data were checked in order to achieve satisfaction for the assumption of independence between years.

Problem statement

objectives

Scope of study

Literature review

Data collection

Data analysis

Results and Discussion

Suggestions and Conclusion

Figure 3.0: Research methodology flow diagram

3.1 STUDY AREA

The study area for this project is the Sungai Langat river basin at Dengkil of Selangor in Malaysia. The Langat river is in the north side of Malaysia with the co-ordinates, 20 55’ 0" N to 1010 21’ 0" E direction. The Sungai Langat (Sg Langat) basin is drained by three major tributaries; Sungai Langat, Sungai Semenyih, and Sungai Labu. The main tributary, Sungai Langat, flows about 182 km from the main range (Banjaran Titiwangsa) at the North east of Hulu Langat District in the south-southwest direction, and draining into the straits of Malacca. Sungai Langat originates from the hilly and forested areas in the western slope of Banjaran Ttiwangsa, northeast of Hulu Langat.

Figure 3.1: Flow patterns view around the Sungai Langat River basin

Figure 3.2: Topographic Map of stations at the Hulu Langat district

3.2 HYDRO –CLIMATOLOGICAL CHARACTERISTICS

Based on various stations located in Sungai Langat river basin, a long period of historical daily stream flow and rainfall data are available.

3.2.1 Rainfall data

Sg. Langat river Basin has numerous rainfall stations, a lot of which are still opened and some of which are closed. Some records were taken as far back as the early 1900’s which illustrates how old some stations are. DID has complied and published the rainfall records belonging to all stations up to 1990. The published records have been brought up by DID hydrology branch located at Ampang in Kuala Lumpur.

3.2.2Stream Flow Data

To perform the analysis of low flow which is also to estimate the risk of low flow hazards occurring, stream flow information had to be utilized. The department of irrigation and drainage (DID) collects and maintains such records. These stream flow records are collected at established gauging stations and indirect measurements of average or mean discharge for various time periods are made. There are stream flow gauging stations at three locations in the Sg. Langat River basin, one of which is has been closed since 1967. The numbers and names of all the stations are shown in table 3.1

Station Number

Station Name

2816441

Sg. Langat at Dengkil

2917401

Sg. Langat di Kajang

2917442

Sg. Langat di Kajang

Table 3.1: Stream flow stations at the Sg. (Sungai) Langat River basin

3.3 DATA COLLECTION AND PROCESSING

In this case study without the stream flow data the low flow frequency analysis cannot be performed, therefore the stream flow data is extremely essential for the analysis of low flow.

The daily stream flow information of Sg. Langat at Dengkil (St No: 2816441) from 1962 to 2012 was collected from the Department of Irrigation and Drainage (DID) situated at Ampang Kuala Lumpur, Malaysia. Table 3.2 illustrates an example of a typical daily stream flow data recorded and collected. The minimum flow, average flow and the maximum flow for each month has been calculated and recorded.

Table 3.2: Typical Streamflow Data for 1996 at the Dengkil station (DID, 2012)

3.4 THEORETICAL FRAMEWORKS

3.4.1 Return period (T)

The return period (T) of low flow can be defined as a statistical measure of how often low flow of a given magnitude occurs to be exceeded or equalled. Since the risk of failure is considered, which is connected to the design, the return period (T) of a hydrology even and the design life (L) of a proposed project are very important inputs to any design exercise. The risk of failure can be known as a function of the design life and the return period.

The return period (T) is known as:

The probability of the low flow which will be in the next year is referred to as:

Only where ‘P’ is presenting the probability of low flow which is being exceeded or equalled in ‘T’ years.

The low flow will not occur in the next year based on the probability as follows:

3.4.2 Sample moments

There are four very important parameters when dealing with low flow frequency, these parameters are:

Mean

Standard deviation

Coefficient of skewness

Coefficient of variation

Estimation of the sample mean stream flow is known below as:

Where:

= Mean observed (historical) flow

= Total number of flow

= number of observed flow

The sample estimation of variance is a measure of the variability of data given by:

The standard deviation is:

The sample coefficient of skewness is a measure of the lack of symmetry given by:

A coefficient of variation is calculated as a ratio of the standard deviation of the distribution to the mean of the same distribution

3.5 LOW FLOW FREQUENCY ANALYSIS METHOD

A low flow frequency analysis evaluates the probability of flows occurring and remaining below a specified (low) design threshold for a given length of time. It is important to assess the probability that the stream will almost always have sufficient flow to meet the required demand for water. Therefore such probabilities are estimated using a low flow frequency analysis at the site. Customarily the analysis is carried out with regard to the minimum discharge aggregated over a period of d days in each year – the ‘d-day annual minimum’ or  daily flow series.

Frequency analysis is intended to relate the magnitude of events to their frequency of occurrence via probability distribution. Distinguishing between the population and the sample is considered as a vital issue in frequency analysis. Frequency modelling has been classified as a univariate method, and it is also a practical method which deals with random variables. Estimating either magnitudes of random variables or probabilities is the intention of a univariate prediction. The determination of population is the first step to achieve this goal. Getting use of sample information to identify the relevant population density function is the purpose of a univariate data analysis which could be proceeds through the PDF (probability density function) being the univariate model which could be helpful to make probability statements. The input requirements for frequency modelling contain a probability distribution and a data series that are based on assumption, the occurrence of the random variable has to be illustrated by it. The lowest peak discharge to occur each year of records is included in the data series. In order to estimate the population, sample information has to be used which explains the process of analysis. The population contents of a mathematical is considered to be the function of one or even more parameters. To fit the hydrological data series, various probability density function have been used which is as following:

3.5.1 Gumbel distribution (Type I extreme value distribution)

The Gumbel distribution is also known as the Type I extreme value distribution. Gumbel in (1975) presented the extreme value family of distribution for the low flow frequency analysis which is considered as the most important factor usually being used in engineering and hydrology in Malaysia and worldwide.

This distribution entails performing low flow analysis, using frequency factors related to graphical methods. Graphical procedures form a very useful visual method of verifying whether a theoretical distribution fits an empirical distribution. This procedure is also known as the regression method. The Gumbel distribution has been used successfully to estimate return periods.

When applying the distribution a very important factor to consider is the probability plotting position. There are many probability plotting positions in use, examples are; Grintorgen, Blom, Landwehr, Weibull and so on. These plotting positions are used to estimate the Cumulative Distribution Function of Gumbel distribution.

Ying and Pandey (2005) investigated the use of eleven probability plotting positions to fit the Gumbel distribution using flow discharge data obtained from the Surma basin. They found that the Weibull method fits best the Gumbel distribution. A paper written by Yahaya et al. (2012) Compared 17 of these probability plotting position and found Weibull method is the best in terms of prediction of accuracy of values for large sample size when performing Gumbel distribution. Choosing the best probability position is important for estimating parameters of distributions and hence in determining the best estimate for return periods, therefore for this paper the Weibull method will be used for Gumbel distribution.

The procedure by which the Gumbel distribution was used in the estimation of low flow frequency has been illustrated below:

The steps below has to be used when analysing low flow frequency by using probability plot (Weibull method) :

The annual minimum series of low flow data (Qmin) which belongs to 50 years that has been obtained purposely for Sungai Langat has to be ranked.

Based on descending order (lowest - highest), the rank number (m) for the (Qmin) has to be given.

The return period as equation below has to be calculated (Weibull formula).

Find the probability (P) which is

Plot Probability (P) vs (Q) on Gumbel’s probability paper

After plotting a regression equation will be found, which is the mathematical model for predicting magnitudes, which will come in the form of

equ (4.1)

By using plot, (P) is the probability that a given flow (Q) will not be equalled or exceeded. For low flow non-exceedence is of interest.

3.5.2 Flow Duration Curve Method

A flow duration curve (FDC) could be demonstrated as a tool which describes the relationship between the magnitude and frequency of daily, weekly and monthly stream flow, considering a particular river basin. It will provide an estimation representing the percentage of time a given stream flow was equalled or exceeded over a historical period. Graphical view of the overall historical variability which comes together with stream flow in a river basin will be provided by a FDC which could be considered as simple, yet comprehensive. The complement of the cumulative distribution function (CDF) of daily stream flow is known as a FDC. FDC is basically a plot of Qp and each value of discharge Q has a corresponding exceedance probability ‘P’. The steps below will be used when to carry out the flow duration method.

Sort (rank) average daily discharges for period of record from the largest value to the smallest value, involving a total of n values.

Assign each daily or monthly flow value a rank (M), starting with 1 for the largest daily flow value.

Calculate Weibull exceedence probability (P) as follows:

Where

P = Is the probability that a given flow will be equalled or exceeded (percentage of time)

M = Is the ranked position on the listing (dimensionless).

n = is the number of events for the period of record (dimensionless).

Plot the flow discharge versus the Weibull exceedence probability

The observed stream flows are functioned by the quantile ‘Qp’. It is usually termed the empirical quantile function since this function depends on empirical observation. Statistician term the complement of the CDF the (survival) distribution function. The fact that most applications involves survival data arises in so many different fields consisting of medicine, demography and manufacturing (Anderson and Veath, 1988).



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