Development of Master Recession Curve for ...

Development of Master Recession Curve for Attanagalu Oya Basin in Sri Lanka -A Comparative Evaluation

Research Monograph November 2016

Non-Funded Research of Professor N.T.S Wijesekera Department of Civil Engineering University of Moratuwa

Development of Master Recession Curve for Attanagalu Oya Basin in Sri Lanka

1

Contents

Introduction ............................................................................................................................................. 5 1.1

Importance of Baseflow Separation .......................................................................................................................... 5

1.2

Baseflow Separation Methods .................................................................................................................................. 5

1.3

Master Recession Curve ............................................................................................................................................ 6

2

Objective.................................................................................................................................................... 6

3

Design (Analysis) ................................................................................................................................... 7 3.1

Selected Events .......................................................................................................................................................... 7

3.2

Linearity Assumption ................................................................................................................................................. 7

3.3

Linear Regressions of Individual Events..................................................................................................................... 8

3.4

Reorganising Recession of Individual Events ............................................................................................................. 8

3.5

Best fit equation for K as a function of Log Q............................................................................................................ 9

3.6

Identification of the MRC –USGS ............................................................................................................................. 10

3.7

Linear Recessions .................................................................................................................................................... 11

3.8

Comparison of Two Methods .................................................................................................................................. 16

4

Results and Discussion ....................................................................................................................... 20

5

Conclusions ............................................................................................................................................ 24

6

References .............................................................................................................................................. 25

7

Selected Recession Periods .............................................................................................................. 27

8

Log Linear Relationships of Selected Recessions ..................................................................... 39

9

Rearranged Recessions ...................................................................................................................... 51

10 Manual Master Recession Curve ..................................................................................................... 54 11 Recession Index and the Log Streamflow relationship .......................................................... 55

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Figure 1:

Figure 2:

Graph of Recession Index (K) as a Function of Log Q ......................................................................................................9

Figure 3:

Index values................................................................................................................................................................................... 10

Figure 4: Figure 4: Figure 6:

Graph of Recession Index (K) as a Function of Log Q after the removal of three high Recession The USGS fitted Polynomial for the Karasnagala Streamflow data ........................................................................ 12

The Two Linear Regressions for the Karasnagala Streamflow data ..................................................................... 13

The Two Linear Regressions and the USGS Polynomial for the Karasnagala Streamflow data................ 14

Figure 7:

Event of 1973 Selected for the Comparison of Two Methods ........................................................................... 15

Figure 2:

Thiessen Average Rainfall and Measured Streamflow 1971 ..................................................................................... 18

Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9:

Figure 10: Figure 11: Figure 12: Figure 13: Figure 14: Figure 15: Figure 16: Figure 17: Figure 18: Figure 19: Figure 20: Figure 21: Figure 22: Figure 23: Figure 24: Figure 25: Figure 26: Figure 27: Figure 28: Figure 29: Figure 30: Figure 31: Figure 32: Figure 33:

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Baseflow Separation from the two methods..................................................................................................................... 17

Thiessen Average Rainfall and Measured Streamflow 1972 ..................................................................................... 18 Thiessen Average Rainfall and Measured Streamflow 1973 ..................................................................................... 19 Thiessen Average Rainfall and Measured Streamflow 1974 ..................................................................................... 19 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 27 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 27 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 28 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 28 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 29 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 29 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 30 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 30 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 31 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 31 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 32 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 32 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 33 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 33 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 34 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 34 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 35 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 35 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 36 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 36 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 37 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 37 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 38 Selected Hydrograph Recession Periods 1-47 ................................................................................................................. 38

Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 39 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 39 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 40 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 40

Figure 34:

Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 41

Figure 36:

Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 42

Figure 35: Figure 37: Figure 38: Figure 39: Figure 40: Figure 41: Figure 42: Figure 43: Figure 44: Figure 45: Figure 46: Figure 47: Figure 48: Figure 49: Figure 50: Figure 51: Figure 52: Figure 53: Figure 54: Figure 55:

Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 41 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 42 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 43

Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 43 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 44 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 44 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 45 Log-Linear Assumptions for the Selected Recessions 1-47 ....................................................................................... 45



Rearranged Linear Recessions Considering the Time after the Observed Last Peak – Semi Logarithmic Plot ....................................................................................................................................................................................................... 51

Rearranged Linear Recessions Considering the Time after the Observed Last Peak - Normal Axis 52

Figure 56:

Manually Fitted Master Recession Curve

Figure 57:

Manually Fitted Master Recession Curve without observed events and linear regressions ........ 54

Figure 58: Table 1:

Table 2:

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with the individual observed events and linear

regressions ..................................................................................................................................................................................... 53 Recession Index and the Log Streamflow relationship with Data Labels ................................................. 55 Baseflow Recession Constant (K) & Avg Streamflow for Selected Events........................................................... 22 Runoff Coefficient Comparison with Baseflow Separation methods .................................................................... 24

Development of Master Recession Curve for Attanagalu Oya Basin in Sri Lanka 1

Introduction

1.1

Importance of Baseflow Separation

Increase in the demand for quality fresh water due to population increase on one hand and the

variability of rainfall amidst climate change along with the pollution caused by human activities on the other hand has given rise to the need for accurate estimation of direct runoff and ground water

contributions. Hence suitably capturing watershed runoff components with baseflow separation and

recession analysis is important. Baseflow separation is important for the identification of direct runoff and groundwater contributions from catchments. Calibration of watershed models to the shape of the base flow recession curve is a way to capture the important relationship between groundwater discharge and subsurface water storage in a catchment (Jepsen, Harmon, and Shi. 2016). Baseflow

Separation is important to identify water quality concerns during baseflow and stormflow Stewart (2015).

Baseflow Separation which a major activity in the hydrograph

separation is important for runoff

coefficient determination (Merz, R., Blöschl, G., & Parajka, J. (2006)), forecasting low flows, low-flow frequency analysis, describing aquifer Characteristics, development of unit hydrographs (Vogel, R. M.,

& Kroll, C. N. (1996)) and the determination of the degree of pollution in surface runoff and groundwater (Baker, R. J., & Hunchak-Kariouk, K. (2006)). Stewart, M. K. (2014) recommending a new

baseflow separation and recession analysis approach has indicated that in order to avoid misleading results, it is necessary to apply baseflow separation before recession analysis.

1.2

Baseflow Separation Methods

Methods used to evaluate the discharge components from a hydrograph are of three types as baseflow

separation, frequency analysis and recession analysis. Mostly used baseflow separation and recession

analysis techniques are graphical procedures which assumes that the hydrograph recession segments

correspond to discharge from a combination of linear reservoirs. Identification of the baseflow commencement and its end in a hydrograph is necessary to separate baseflow.

There are many

techniques to determine the portioning of streamflow hydrographs including empirical and trial and

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error methods. The high variability of streamflow recessions in hydrographs resulting from different streamflow events causes a difficulty.

Though there are many techniques including empirical, and trial and error methods available to determine the hydrograph partitions, the high variability reported in the recession behaviour of individual hydrograph recession segments has made the separation less straight forward (Brodie, R. S., and S. Hostetler (2005)) (Merz, Blöschl, & Parajka(2006)) Tallaksen (1995). Baseflow commencement and ceasing of a particular hydrograph can be captured by using the periods of time when the streamflow hydrograph is coincident with the master recession curve (Chow, MAIDMENT, & Mays 1988)). 1.3

Master Recession Curve

Baseflow separation of multi peak streamflow hydrographs, requires the extension of the falling limb and one method to carry out this task is by developing a master recession curve for the watershed. An extensive illustration of the use of graphical methods for baseflow separation from multi-peak hydrographs is in Pettyjohn, Wayne and Henning (1979). The method of Base flow separation using the master recession curve dates back to 1933 when Horton described a perceptual model of infiltration processes (Beven 2004). The USGS method for master recession curve development describes the technique to identify the parameters for a polynomial expression of time as a function of the logarithm of streamflow (Rutledge 1998). Chen, Zhang, Xue, Zhang, Wei (2012) in their work on estimation of baseflow recession constants and effective hydraulic parameters in the karst basins of southwest China showed that the MRC can be fitted well by two segments of Q vs t lines. In the absence of published research on the development of the MRC for Sri Lankan watersheds and considering the importance of identifying the direct runoff and groundwater contributions from a watershed and observing the reported issues regarding the graphical methods, a master recession curve for the Attanagalu Oya river at Karasnagala, was developed to carry out baseflow separation.

2

Objective

The objective of the present work was to develop a master recession curve (MRC) for the Karasnagala

watershed in the western province of Sri Lanka while evaluating the applicability of the USGS method

through an investigation of the applicability of MRC development using a single polynomial expression.

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3

Design (Analysis)

3.1

Selected Events

Thiessen averaged watershed rainfall and the streamflow for the Karasnagala watershed were

available for the period from 1971 – 1974 (Figure 8, Figure 9, Figure 10and Figure 11). This dataset was used to identify rainless durations to separate streamflow recessions. Forty-seven (47) rainless

periods were identified for the preparation of the Master Recession Curve (MRC). These selections are shown in (Figure 12-Figure 35) 3.2

Linearity Assumption

Subsequent to the identification of recession periods, for each segment, the best linear equation for

time as a function of the Logarithm of streamflow was computed by assuming a linear relationship between the LogQ and time the recession constant was computed for each recession. Though this

assumption that groundwater recession behaves in the form of a linear function, there can be many

factors that causes non-linearity.

The USGS Water-Resources Investigations Report (USGS Report 98-4148) by Rutledge describing the assumption and the underlying non-linearity clearly and in details. The following is the extract from

the said report; “Although the linear model of streamflow recession (on the graph that shows flow on a

logarithmic scale as a function of time on a linear scale) may be applicable (Barnes, 1939; Ineson and Downing, 1964; Rorabaugh, 1964; Bevans, 1986), many factors can cause nonlinearity, most of which cause continuous variation in recession slope as streamflow recedes. Physical and mathematical models have shown that the presence of the moving free surface (water table) can cause nonlinearity of streamflow recession for a stream fed by an unconfined aquifer (Ibrahim and Brutsaert, 1965; Hornberger and others, 1970), and this nonlinearity can be enhanced by variable head at the ground-water-outflow boundary (Werner and Sundquist, 1951; Singh, 1969; Singh and Stall, 1971). Nonlinearity can result from the gain or loss of ground water because of leakage or evapotranspiration (Singh, 1969; Daniel, 1976) or from geologic heterogeneities in the basin (Horton, 1933; Riggs, 1964; Ineson and Downing, 1964; Trainer and Watkins, 1974, 1975; Petras, 1986). Anderson and Burt (1980) suggest that curvature can result from "combined changes in the slope and size of the saturated wedge." Wood and others (1972) developed nonlinear MRC’s for several streams in eastern Pennsylvania, and Nutbrown and Downing (1976) indicate that nonlinearity can be common for even the simplest of ground-water systems in England. The methods described here allow for the possibility that the MRC can be nonlinear. For the purpose of constructing the MRC, it is assumed that the nonlinearity of

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the MRC is slight compared with the nonlinearity of streamflow recession during times when surface (direct) runoff is significant or when the profile of the ground-water head distribution has not yet become nearly stable. Therefore, it is possible to extract segments of continuous recession from the record and select "nearlinear" parts of each segment that are indicative of the MRC.

3.3

Linear Regressions of Individual Events

As briefed in the previous section considering the mathematical convenience and based on the associated

order of magnitudes, it was assumed that the recession index, which is the time per log cycle of streamflow recession, varies linearly with the logarithm of flow (Log Q).to develop the MRC as practiced. However, it is recommended that future research make attempts to test the validity of this assumption. The linear regressions for each of the 47 events are shown in Figure 36 to Figure 59. These relationships enabled the extraction of the recession index (K) for each segment. The recession segment indices for each

event are in Error! Reference source not found.. Together with each recession index, the mean of observed streamflow for associated segment is shown. This pair is then used as a reference for further

computations associated with the development of the Master Recession Curve. Literature mentions

about stepping effects that would be apparent in the selected hydrograph recession and recommends

to take care when selecting recessions that are not too short to reflect the linearity while not too long to cause disturbances due to stepping effects as a result of rounding the data at the recording stage. Though several cases of stepping was observed, due to data limitations, no corrective actions were taken. Creation of simple trend lines was done to obtain the recession constant. 3.4

Reorganising Recession of Individual Events

The master recession curve was developed by arranging the events according to the shape and the magnitude of the streamflow values during the event. This was a trial and error exercise and requires

care to identify non-overlapping units to ensure similar behaviour of the watershed. During this

development effort it was felt that the rearrangements should be in agreement with the conceptualisation of watershed responses to similar streamflow ranges and also compatible the

expected similarity of behaviour during varying events and varying catchment moisture levels.

Accordingly using trial and error based on the judgement of watershed hydrologic behaviour, the time after peak was adjusted for reordering. The master recession curve(MRC) with the fitted linear recessions is shown in the Figure 60 and in Figure 61. The MRC when isolated showed three distinct

recession behaviour as the flow moves away from the observed last peak. This probably highlights the

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catchment flow regimes which are commonly labelled into two groups known as intermediate flow and baseflow (Figure 63). This was compared with the stepping effect that had to be taken care of during the individual recession constant determination. It was noted that this three segment behaviour is not related to the stepping of an individual recession. 3.5

Best fit equation for K as a function of Log Q

The best fit equation for K as a function of Log Q shown in Figure 1 did not demonstrate a linear trend

as expected. The graph with data labels are in Figure 64.

Log ( Average Streamflow of Recession Segment in mm)

2.50

y = 7.5881x - 0.027 R² = 0.3803

2.00 1.50 1.00 0.50 0.00 -0.50

0

0.05

0.1

0.15

0.2

0.25

0.3

-1.00 -1.50 Recession Index (K)

Figure 1:

Graph of Recession Index (K) as a Function of Log Q

The regression curve showed the tendency of a linear behaviour but the linear regression showed poor

results with a R2 value of 0.38. Attempts were made to remove outliers by looking at the data set. The

higher values of recession constants appeared unrealistic for the baseflow recession and hence the

three values were removed to evaluate the difference. The removal of the higher K values improved the regression constant to a value of 0.56 (Figure 2)

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y = 11.727x - 0.2781 R² = 0.558


2.50 2.00 1.50

1.16

1.00

0.730.71 0.49 0.43

0.50 0.00 -0.50 -1.00

0

-0.11 -0.13 -0.18 0.05 -0.25 -0.27 -0.38 -0.39 -0.50 -0.57 -0.58 -0.68 -0.89

1.97 1.85 1.81 1.66 1.61 1.56 1.58 1.39 1.36 1.21 1.281.29 1.09 1.00 1.06 0.98 0.94 0.96 0.95 0.86 0.79 0.75 0.65 0.54 0.06 0.14 0.1

0.15

0.2

-1.50 Recession Index (K)

Figure 2:

Graph of Recession Index (K) as a Function of Log Q after the removal of three

high Recession Index values.

3.6

Identification of the MRC –USGS

The values rectified were then used to identify the regression to derive the polynomial expression of time as a function of Log (Streamflow). The polynomial of the USGS method takes the form as follows.

T = A(LogQ2)+B(LogQ)+C; where T is time, and A,B and C are coefficients. Microsoft Excel Solver

program was used to identify the best fit parameters for the Attanagalu Oya data.

A, B and C valued for Attanagalu Oya dataset were 15.24, -54.16 and 61.60 respectively. The USGS

modelled polynomial with the base values from observed streamflow is shown in Figure 3. The

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modelled MRC shows that there is an averaging effect which had produced a single curve matching the two varying decay patterns. 3.7

Linear Recessions

In this work an attempt was taken to compare the USGS master recession curve with two linear

approximations visible in the base dataset. Two regression curves were developed and they are shown

in the Figure 4. The regression equations for the recession with a higher time constant is y = -0.0401x

+ 2.0623 while the one with a lower time constant is y = -0.0084x + 0.2929. In these equations y represented the streamflow while the x axis denotes time. The two regressions were named as intermediate flow recession and the baseflow recession.

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2.5

Log(Streamflow in mm)

2 1.5 1

x = 15.24 (Log y2) + -54.16 (Log y)+ 61.60

0.5 0

0

20

40

60

80

100

120

-0.5 -1 -1.5

Base Data of Karasnagala Figure 3:

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Modelled

The USGS fitted Polynomial for the Karasnagala Streamflow data

140

160

2.5 2

y = -0.0401x + 2.0623 R² = 0.9475

1.5


1 0.5 0

0

20

40

60

80

100

120

-0.5 y = -0.0084x + 0.2929 R² = 0.9213

-1 -1.5

Figure 4:

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The Two Linear Regressions for the Karasnagala Streamflow data

140

160

2.5 2 1.5


1 0.5 0

0

20

40

60

80

100

120

140

-0.5 -1

-1.5 Base Data of Karasnagala Linear (Intermediate Flow Recession)

Figure 5:

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USGS Best fit Polynomial Linear (Base Flow Recession)

The Two Linear Regressions and the USGS Polynomial for the Karasnagala Streamflow data

160

10

0

10 1

15 20 25

Figure 6:

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27-Sep

20-Sep

13-Sep

6-Sep

30-Aug

23-Aug

30 16-Aug

0

Event of 1973 Selected for the Comparison of Two Methods

Rainfall (mm/day)

Stream Flow (mm/day)

5

3.8

Comparison of Two Methods

The curves plotted in the same graph are in the Figure 4

This clearly reflects the averaging effect of the USGS method and the separation of two characteristics

by two regressions. The USGS method provides an easiness in the computations with a single function while losing the matching with actual observations. In the two linear regressions representing the intermediate flows and low flows, it is clear that the representation is better but the computations would require extra efforts.

An event was selected from the Attanagalu Oya Dataset to separate the baseflow to carry out a

comparative evaluation of the use of USGS polynomial and the two linear regressions. The selected

hydrograph is shown in the Figure 6. In this event there had been some intermittent rainfall but the response on the streamflow does not reflect as significant. Therefore, this event was chosen for the comparative evaluation. Baseflow separation using the two methods is shown in

Figure 7. The baseflow separation identified the starting point of the event by observing the

streamflow behaviour and then the recession prior to the event was continued up to the peak flow of the event. After determining the end of the events with the use of shapes corresponding to the

respective master recession curves the baseflow at the peak was connected to this point (the construction lines for each baseflow separation method is in Figure 7.

Baseflow separation is often carried out to capture the direct runoff during a rainfall event. Hence the

event direct runoff from the two methods were computed (Table 2: Runoff Coefficient Comparison with Baseflow Separation methods). Runoff Coefficient values from the two methods are also in the same Table.

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2.5

2

1.5

1

0.5

0

0

5

10

15

20

25

30

35

40

45

-0.5

Log(Measured Streamflow) Res BF low St Line Rising BF high St Line

Figure 7:

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BF low Recession Limb USGS USGS Separation

Baseflow Separation from the two methods

Res BF high St Line Rising Limb USGS BF Log Linear

50

Figure 8:

Thiessen Average Rainfall and Measured Streamflow 1971

Figure 9:


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Figure 10:


Figure 11:


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4

Results and Discussion

Variation of the recession constant (K) for each event and the corresponding average streamflow

within the selected period is in Table 1. The recession constant values showed a significant variability within the range from 7x10-17 to 0.16090.

Manually fitted master recession curve for the Karasnagala streamflow data showed the expected decay behaviour with a stepped appearance at two locations (Figure 63). The polynomial obtained

using the USGS method showed an averaging behaviour while the consideration of a two linear recession curves appeared having a better match with the individual points from observations (Figure 5).

Use of the MRC developed by both methods demonstrated the capability to assist the separation of

baseflow from an event from the set of observations. Results indicated that the commencement point

of the baseflow recession for each of the methods was significantly different. This was caused by the subjective nature of the fitting the MRC to the observed recession (

Figure 7). During the event separation keeping to the same lines of the other graphical methods, the

minimum point of baseflow was assumed as the point directly under the peak of the hydrograph. However, the commencement of the baseflow was taken as the starting point of the recession reflected by the MRC fitting.

The use of both MRC techniques showed that the point of cessation of direct runoff was different to the that given by the empirical relationship by Linsley, Kohler and Paulhus (1982). This needs to be investigated with many case study applications. The Karasnagala watershed of Attanagalu Oya

indicated that the recession period commenced after 5 days and 7 days respectively for the USGS

method and the two regression method. The empirical value (Linsley, Kohler and Paulhus 1982) for Karasnagala watershed is two days. Selected event for baseflow separation which lasted seven days had experienced 65 mm of rain per unit area and it is felt that the wetness was on the high side from average. Hence it is recommended that relatively dry events are also evaluated.

The direct runoff from the event with the two methods showed values of 18.5 mm and 19.90 mm from

the USGS method and the two regression method respectively. These values resulted in two different

event runoff coefficients. MRC curve from USGS method indicated that the event runoff value is 0.28

while the two regression curve method produced a value of 0.30. According to the runoff coefficient

values obtained for the selected event it is noted that the results from the two methods are very close to each other.

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Table 1:

Baseflow Recession Constant (K) & Avg Streamflow for Selected Events

Baseflow Recession

Month and

Identity

Year

1

Jan-71

2.09

0.0399

3

Mar-71

5.33

0.1274

5

Mar-71

2 4 6

Feb-71

Mar-71 Apr-71

7

May-71

9

Jul-71

8

Jun-71

10

Aug-71

12

Oct-71

11

Sep-71

13

Nov-71

15

Dec-71

14 16 17 18 19 20

Nov-71 Dec-71 Jan-72 Jan-72 Jan-72

Feb-72

21

Mar-72

23

Mar-72

22 24 25

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Average SF(mm)

Event

Mar-72 May-72 Jun-72

within Selected Period

1.55 2.16 1.82

Constant K

0.0216 0.0525 0.027

3.07

0.1609

5.15

0.1546

5.11 2.53 2.03 2.88 4.49 3.95 2.86 1.17 3.70 1.79 1.12 0.78 0.68 0.56 0.56

0.0767 0.0784 0.1415 0.1013 0.0931 0.0983 0.1346 0.0882 0.1324 0.1056 0.0695 0.0051 0.0036 0.0026 7E-17

0.61

0.0488

2.74

0.0848

6.22

0.114

Event

Month and

Average SF(mm)

Baseflow Recession

Identity

Year

within Selected

Constant K

Period 26

Jun-72

2.74

0.2019

28

Aug-72

0.88

0.0175

30

Aug-72

0.90

0.0277

27 29

Aug-72

31

Nov-72

33

Dec-72

32 34 35 36 37

Nov-72 Dec-72 Dec-72 Jan-73

Feb-73

38

Mar-73

40

Jun-73

39

Mar-73

41

Aug-73

43

Nov-73

45

Dec-73

42 44 46 47

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Jul-72

Sep-73

Nov-73 Jan-74

Feb-74

1.51 2.30 7.26 5.33 3.21 3.03 2.89 0.84 0.70

0.173

0.2376 0.1119 0.0805 0.0457 0.1519 0.2412 0.0245 0.043

0.51

0.0518

4.84

0.1199

2.71

0.0834

0.41 3.62 5.11 2.23 2.52 2.64 0.77

3E-16

0.1201 0.1382 0.1517 0.1478 0.1065 0.016

Table 2:

Runoff Coefficient Comparison with Baseflow Separation methods

#

5

Description

Value

1

Total Event Rainfall

65.43mm

2

Total Event Runoff

54.62mm

3

Total Direct Runoff - USGS Method

18.50mm

4

Total Direct Runoff - 2 Regression Method

19.90mm

5

Runoff Coeft- USGS

6

Runoff Coeft- 2 Linear Regressions

0.28 0.30

Conclusions

1. Available streamflow data of Karasnagala watershed showed the possibility of developing a

representative master recession curve. Present case study demonstrated the methodology to develop MRC curves for the watersheds to carry out baseflow separation.

2. The master recession curve developed using the USGS method resulted in an easy to use single

polynomial while the attempt to obtain a closer approximation to the observations produced two linear regressions

3. Gradients of the two linear regression curves had a decay constant of -0.0401 for higher streamflow values while the same for lower recession component was -0.0084

4. Direct Runoff from both methods were of the same range while the runoff coefficient from both methods did not differ significantly

5. Since both methods produced similar results it is easier to use a single polynomial as the MRC for baseflow separation

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6

1.

References

Baker, R. J., & Hunchak-Kariouk, K. (2006). Relations of water quality to streamflow, season, and land use for four tributaries to the Toms River, Ocean County, New Jersey, 1994-99. US Department of the Interior, US Geological Survey.

2.

Brodie, R. S., & Hostetler, S. (2005, November). A review of techniques for analysing baseflow from stream hydrographs. In Proceedings of the NZHS-IAH-NZSSS 2005 conference (Vol. 28).

3.

Chen, X., Zhang, Y. F., Xue, X., Zhang, Z., & Wei, L. (2012). Estimation of baseflow recession constants and effective hydraulic parameters in the karst basins of southwest China. Hydrology Research, 43(12), 102-112.

4.

Merz, R., Blöschl, G., & Parajka, J. (2006). Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 331(3), 591-604.

5.

Pettyjohn, Wayne A., and Roger J. Henning. "Preliminary estimate of regional effective ground-water recharge rates in Ohio." (1979).

6.

Raaii, Ahmad Ali, "Modeling recession and low flow characteristics of Iowa streams " (1995). Retrospective Theses and Dissertations. Paper 10972.

7.

Rutledge, A. T. Computer programs for describing the recession of ground-water discharge and for estimating mean ground-water recharge and discharge from streamflow records: Update. US Department of the Interior, US Geological Survey, 1998.

8.

Stewart, M. K. (2014). New baseflow separation and recession analysis approaches for streamflow. Hydrol. Earth Syst. Sci. Discuss, 11(6), 7089-7131.

9.

Tallaksen, L. M. (1995). A review of baseflow recession analysis. Journal of hydrology, 165(1), 349-370

10. Ven, T. C., MAIDMENT, D., & Mays, L. W. (1988). Applied hydrology, McGraw-Hill 11. Vogel, R. M., & Kroll, C. N. (1996). Estimation of baseflow recession constants. Water resources management, 10(4), 303-320. 12. Jepsen, S. M., T. C. Harmon, and Y. Shi. "Watershed model calibration to the base flow recession curve with and without evapotranspiration effects." Water Resources Research 52.4 (2016): 2919-2933. 13. Stewart, M. K. "Promising new baseflow separation and recession analysis methods applied to streamflow at Glendhu Catchment, New Zealand." Hydrology and Earth System Sciences 19.6 (2015): 2587-2603. 14. Beven, Keith. "Robert E. Horton's perceptual model of infiltration processes." Hydrological processes 18.17 (2004): 3447-3460. 15. Pettyjohn, Wayne A., and Roger J. Henning. "Preliminary estimate of regional effective ground-water recharge rates in Ohio." (1979). 16. CE322 Basic Hydrology Jorge A. Ramirez http://www.engr.colostate.edu/

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17. Linsley, R.K., Jr., Kohler, M.A., and Paulhus, J.L.H., 1982, Hydrology for engineers (3d ed.), New York, McGraw-Hill, 508 p.

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7

Selected Recession Periods

10

0 Rainfall (mm/day)

Selected Hydrograph Recession Periods 1-47

SF (mm)

Figure 12:

5

5

Figure 13:

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14-Mar

12-Mar

10-Mar

8-Mar

6-Mar

10 4-Mar

0


Figure 14:


Figure 15:


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Figure 16:


Figure 17:


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Figure 18:


Figure 19:


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Figure 20:


Figure 21:


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Figure 22:


Figure 23:


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Figure 24:


Figure 25:


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Figure 26:


Figure 27:


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Figure 28:


Figure 29:


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Figure 30:


Figure 31:


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Figure 32:


Figure 33:


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Figure 34:


Figure 35:


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8

Log Linear Relationships of Selected Recessions

Figure 36:

Log-Linear Assumptions for the Selected Recessions 1-47

Figure 37:


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Figure 38:


Figure 39:


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Figure 40:


Figure 41:


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Figure 42:


Figure 43:


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Figure 44:


Figure 45:


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Figure 46:


Figure 47:


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Figure 48:


Figure 49:


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Figure 50:


Figure 51:


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Figure 52:


Figure 53:


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Figure 54:


Figure 55:


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Figure 56:


Figure 57:


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Figure 58:


Figure 59:


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9

Rearranged Recessions

Streamflow (mm)

100

10

1

0.1 0

20

40

60

80

100

120

140

Time after last peak of Streamflow (Re-arranged Days) Figure 60:

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Rearranged Linear Recessions Considering the Time after the Observed Last Peak – Semi Logarithmic Plot

160

18 16

Streamflow (mm)

14 12 10 8 6 4 2 0 0

20

40

60

80

100

120

140


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Rearranged Linear Recessions Considering the Time after the Observed Last Peak - Normal Axis

160

3.5 3


2.5 2 1.5 1 0.5 0

0

20

40

60

80

100

120

140

-0.5 -1 -1.5


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Manually Fitted Master Recession Curve with the individual observed events and linear regressions

160

10 Manual Master Recession Curve

2.5 2


1.5 1 0.5 0

0

20

40

60

80

100

120

-0.5 -1 -1.5

Time after last peak of Streamflow (Re-arranged Days)

Figure 63:

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Manually Fitted Master Recession Curve without observed events and linear regressions

140

160

11 Recession Index and the Log Streamflow relationship

y = 7.5881x - 0.027 R² = 0.3803


2.50

Figure 64:

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2.00 1.66 1.61

1.50 1.16

1.00 0.50 0.00 -0.50 -1.00

0.49 0.43

0

0.73 0.71

-0.11 -0.13 -0.18 0.05 -0.25-0.27 -0.38 -0.39 -0.50 -0.57 -0.58 -0.68 -0.89

1.97 1.85 1.81 1.56 1.58 1.36 1.29 1.28

1.39 1.21 0.98 0.94 0.96 0.95 0.79 0.54

0.06

1.061.09 0.86 0.75 0.65

1.00

0.95

1.00 0.77

0.37

0.14

0.1

0.15

-1.50 Recession Index (K)

Recession Index and the Log Streamflow relationship with Data Labels

0.2

0.25

0.3

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