Adjustment of peak streamflows of a tropical river for urbanization.

Author:Amini, Ata
 
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INTRODUCTION

Streams are affected by runoff from rainfall and snowmelt moving as overland flow and subsurface flow. A major landuse change is the urbanization and characterized mainly by low infiltration rate. The sensitivity of the river floods to landuse change showed to be effectively dependent on the climatic behavior and the geomorphologic characteristics of the river basin (2). The impact of urbanization is more significant on catchment located in tropical region compared with that located either in arid or semiarid region. Therefore, urbanized catchment in tropical region experiences regular and intense flooding. For instance, referring to a tropical catchment, a 30% urbanization in a basin persuades a 24% increase of the annual mean discharge (3). Langat River as a tropical catchment is experienced rapid urbanization. The growth of urbanization results in rapid creation of large impervious areas is producing significant problems such as regular flooding due to inadequate drainage facilities. In many studies and despite of the specific nature of the modeling approach, it is most usual to fit a model to the data describing landuse change, verify that model on data and use the models to predict the future (1). Reliable estimation of discharge is needed for good design of urban drainage systems. Urbanization makes the historical record of Langat River non-homogenous and this makes the existing mathematical models for the historical data unsuitable due to poor estimated output. The literature does not identify a single method considered best for adjusting a flood record or homogenizing the historical data. Each method depends on the data used to calibrate the model. In addition, the databases used to calibrate the methods are very sparse (6). The objectives of this study are to analyze the change in flow behavior due to the impact of urbanization in Langat River catchment and to propose a methodology for both adjusting streamflow record and calibrate the model parameters from the non-homogenous data. The presented methodology optimizes the adjusted annual maximum streamflow to meet both homogeneity and best model fitness at Langat River and other catchments that are facing similar issues.

Study site, data acquisition and data processing: The area of interest for this study is Langat River catchment at Dengkil, Selangor, Malaysia. This area is located south of Kuala Lumpur, the capital of Malaysia. Hydrometeorologically, the basin experiences two types of monsoons, i.e., the Northeast (November to March) and the Southwest (May to September). The average annual rainfall in the study area is about 2400 mm and the wettest months (April and November) show rainfall amount above 250 mm, while the lowest rainfall occurs in June, about the average of 100 mm (5). Topographically, Langat basin can be divided into three regions, i.e., the mountainous area in the north, the undulating land in the centre of the basin and the flat flood plain at the downstream of Langat River.

During the last decades, Langat River catchment is subjected to intensive urbanization. Figure 1 shows the degree of urbanization that occurred in the area. The available streamflow record is from 1960 up to 2003 and the record is used for analyzing the impact of urbanization on streamflow increment in Langat River. The historical record exhibited a clear change in streamflow between the period before urbanization and that after urbanization. From 1960-1982, the period is considered as before urbanization while the period from 1983 up to 2003 is selected as the period that witnessed intensive urbanization at Langat River catchment.

[FIGURE 1 OMITTED]

The changes in maximum streamflow as a result of changes in the landuse for Langat River catchment were analyzed. Average annual runoff increased due to the decrease in forest area and development in agricultural and urban areas from 1983-2003. Based on confidence interval with t-test, the range of mean for peak streamflows before urbanization (1960-1983) at 5% significance level is calculated.

The calculated mean ranges from 99.78-149.4. The mean of peak streamflows after urbanization (1983-2003) was found to be significantly different from the above-mentioned range. This difference represents non-homogeneous peak streamflow record. Recorded data after urbanization shows great increase in the values of mean and standard deviation as shown in Table 1.

Table 1: Comparison between mean and standard deviation for streamflow of Langat River before (1960-1982) and after (1983-2003) urbanization Parameters Period from Period from Increment 1960-1982 1983-2003 percentage Mean ([m.sup.3] [sec.sup.-1]) 124.2 248.6 100.3 Standard deviation 60.3 123.0 104.0 Estimation of extreme flood is a main application of hydrology. The estimated flood is mainly used in design of water resource projects and flood-plain management. Therefore, to illustrate the changes, an analytical attempt to estimate flood frequency using Lognormal distribution that has best fitness, was performed for periods before and after urbanization and the results are shown in Table 2.

Table 2: Flood peaks for different return periods before and after urbanization for Langat River Peak flow ([m.sup.3] [sec.sup.-1]) Return Before After urbanization Increment percentage period urbanization (1983-2003) (year) (1960-1982) 2 113 234 107 5 159 324 104 10 191 385 102 50 261 519 99 100 292 577 98 MATERIALS AND METHODS

The historical daily record of streamflow for Langat River for a period of 44 years (1960-2003) is used in this study. The annual maximum streamflows are selected from the historical record. For the historical streamflow record, any month with incomplete daily record is considered as a gap. The gaps are filled using linear stochastic model called Thomas-Fiering model. This model is based on the first-order Markov model and represents a set of 12 regression equations. The well-known Thomas-Fiering model equation is described as (11):

[[x.sub.i,j] - [[bar.Q].sub.j]/[s.sub.j]] = [[x.sub.i,j-1] - [[bar.Q.sub.j-1]/[S.sub.j-1]] [a.sub.ij] [square root of (1 - [r.sub.j.sup.2])] (1)

Where:

[X.sub.[i,j]] = Predicted discharge for the jth month from the (j-1)th month at time i

[bar.Q.sub.j] = The mean monthly discharges during month j

[S.sub.j] = The standard deviation monthly discharges during month j

[a.sub.ij] = Independent standard normal variable at time i in the jth month

[r.sub.j] = The serial correlation coefficient for discharge in the jth month from the (j-1)th month

Negative values obtained from applying Eq. 1 are ignored.

Time series model: Time series modeling is the analysis of a temporally distributed series of data or the synthesis of a model for prediction in which time is an independent variable. An aim of time series and stochastic hydrology models are to produce synthetic streamflow series that are statistically related to observed streamflow series. Statistical similarity involved sequences that have statistics and...

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