Simulating Past Severe Flood Events to Evaluate the Effectiveness Of

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Journal of Hydrology: Regional Studies 10 (2017) 82–94

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Journal of Hydrology: Regional

Studies

jo urnal homepage: www.elsevier.com/locate/ejrh

Simulating past severe ﬂood events to evaluate the

effectiveness of nonstructural ﬂood countermeasures in the

upper Chao Phraya River Basin, Thailand

∗

Sarawut Jamrussri , Yuji Toda

Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

a r t i c l e i n f o a b s t r a c t

Article history: Floods devastate communities and result in large economic losses in the lower reaches of

Received 2 November 2016

the Chao Phraya River Basin, Thailand. The aim of this study was to determine whether

Received in revised form 30 January 2017

nonstructural flood countermeasures would help prevent flash floods in the upstream area

Accepted 7 February 2017

of the Upper Chao Phraya River Basin, and control the volume of ﬂoodwaters reaching the

downstream area.

Keywords:

Numerical models were used to quantify the effects of nonstructural measures, namely

Nonstructural ﬂood countermeasure

land use regulation, reforestation, and retention areas, and to examine how efﬁcient these

Scenarios analysis

proposed nonstructural approaches would have been during the severe ﬂood events that

Upper Chao Phraya River Basin

occurred in 1995, 2006 and 2011. Three scenarios, reforestation, retention areas, and the

Flood inundation analysis

combination of reforestation and retention areas, were developed for these nonstructural

ﬂood countermeasures, as outlined in the Thai Master Plan for Water Resources Manage-

ment.

Their effectiveness in the Upper Chao Phraya River Basin was quantitatively assessed by

comparing the model results for the actual conditions with the scenario results. Results

showed that the proposed nonstructural measures had considerable potential to reduce

peak discharges and ﬂood volumes in the Upper Chao Phraya River Basin. Integration of

these proposed nonstructural ﬂood countermeasures with the existing countermeasures

in the Chao Phraya River Basin may be the most practical way to cope with the challenges

of future ﬂood disasters.

BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Thailand, especially the Chao Phraya River Basin (CPRB), Thailand is accustomed to frequent ﬂood disasters. Indeed, CPRB

has dealt with numerous catastrophic ﬂoods in the past, those that occurred in 1995, 2006, and 2011. Thailand suffered from

extensive severe ﬂooding in the CPRB in 2011, which resulted in the vast inundation areas and caused signiﬁcant after-effects

on a scale that had not been experienced in Thailand’s recent history. The 2011 ﬂood, caused by excessive continuous rainfall

from ﬁve successive tropical storms, inundated an area of more than 30,000 km , and affected more than 13 million people

from July until December 2011 (Komori et al., 2012). The Ministry of Finance (2012) reported seven major industrial estates

∗

Corresponding author.

E-mail addresses: [email protected] (S. Jamrussri), [email protected] (Y. Toda).

http://dx.doi.org/10.1016/j.ejrh.2017.02.001

S. Jamrussri, Y. Toda / Journal of Hydrology: Regional Studies 10 (2017) 82–94 83

in the lower part of the CPRB were inundated to depths of up to 3 m by ﬂoodwaters and total damages and losses amounting

to US$ 46.5 billion from the 2011 ﬂood in Thailand.

The Master Plan for Water Resources Management, formulated by the Thai government in 2012 (SCWRM, 2012), states

that, to manage ﬂoods properly, structural and nonstructural measures should be applied synchronously. The structural

approach includes measures to store and divert water. Conversely, nonstructural measures aim to create room for the river,

meaning that increased areas of land should be available for ﬂoodwaters to spill into ﬂood retention areas. Unfortunately,

because of various issues, the application of this Master Plan was suspended. Therefore, to date, no actions have been taken

and no progress has been made towards implementing additional ﬂood countermeasures in the CPRB, except for minor

measures such as structural rehabilitation, dredging for drainage improvement, and dam deregulation.

Historically, the preferred ﬂood management countermeasures in the CPRB have been structural solutions, such as dams,

dykes, and embankments. Some scholars currently argue that, while structural approaches may provide a solution at the time

of the ﬂood, they have long-term impacts and, because the height of the physical defence needs to be continually increased

to deal with larger ﬂoods, they are unsustainable and impractical to maintain (Kundzewicz and Stakhiv, 2010; Lawler, 2009;

Wilby and Keenan, 2012). Other studies have reported that the structural approach is inadequate for large magnitude ﬂoods.

For example, structural measures were breached at 8 places along the Chao Phraya River during the 2011 ﬂood, such that

ﬂoodwaters spread unpredictably, causing enormous damages and losses in industrial estates in the lower reaches of the

CPRB. Conversely, there are many successful applications of nonstructural measures which are able to reduce ﬂood damage

(Bohm¨ et al., 2004; Simonovic and Li, 2003; Wheater and Evans, 2009). In the light of future climate change, new and holistic

approaches need to be adopted so that Thailand is sufﬁciently equipped to cope with and prevent ﬂooding on the same scale

as, or greater than, that experienced in the 2011 ﬂood. Basin-wide integrated ﬂood management, comprising structural and

nonstructural measures, is therefore considered the most suitable approach for managing future ﬂood events.

Numerous studies have analysed and reported aspects of the 2011 ﬂood (Komori et al., 2012; Sayama et al., 2015;

Supharatid, 2015; Trigg et al., 2013; Wongsa, 2014). To date, most of these studies have concentrated on the lower part of

the CPRB and on applications of structural ﬂood protections. None of these recent studies have investigated applications

of nonstructural ﬂood countermeasures in the upper CPRB. The focus of this study therefore was to explore the feasibility

of applying nonstructural approaches in the upper CPRB to prevent rapid ﬂooding in the upper reaches and to control the

volume of ﬂoodwaters arriving in the lower basin. The objective of this study was, through scenario analysis by hydrological

and hydrodynamic models, to examine the efﬁciency of nonstructural ﬂood countermeasures included in the Thai Master

Plan for Water Resources Management, namely land use regulation, reforestation, and retention areas, in the upper CPRB.

2. Materials and methods

2.1. Study area

The catchment area of the upper Chao Phraya River Basin (CPRB) covers 104,481 km , and accounts for about 65.71%

of the CPRB. The upper CPRB consists of four main rivers, the Ping, Wang, Yom and the Nan, which ﬂow downstream and

combine in Nakhon Sawan Province. The upper CPRB is mountainous (60%), and there are seven multipurpose dams with

a total reservoir effective capacity of 18 billion m in the Ping, Wang, and Nan River basins. The conﬂuence of the Ping and

Nan Rivers in Nakorn Sawan Province marks the origin of the Chao Phraya River, as illustrated in Fig. 1.

Floods in the CPRB are controlled by storing water in the seven reservoirs in the upper CPRB, from where excess discharge

then ﬂows downstream to the lower part of the watershed. Because of geographical location of upper CPRB, steep slope,

and dense forest (60%), the middle basin receives a large amount of runoff during monsoon seasons that causes ﬂooding in

middle reach of CPRB almost every year. In the middle reaches of CPRB, there are some low-lying areas that are generally

used for agriculture. Some of these areas are natural ﬂoodplains that cannot be cultivated in the rainy season because of the

ﬂooding. Analysis of historical hydrological data shows that communities in downstream start to be threatened when the

discharge of the Chao Phraya River at runoff station C.2 exceeds 2000 m /s. Furthermore, the ﬂood risk drastically increases

when the discharge at this station exceeded 3500 m /s which results in vast inundation areas and huge economic losses in

the lower reaches of the CPRB. Information about the discharge at runoff station C.2 is critical for making decisions about

ﬂood management for the lower CPRB. The discharge recorded at runoff station C.2 has exceeded 3500 m /s in all severe

historical ﬂood events in the CPRB, and the peak discharges in 1995, 2006, and 2011 at C.2 reached 4820, 5960, and 4686 m /s,

respectively.

2.2. Simulation models

In this study we used two numerical models, a hydrological model and a hydrodynamic model. The hydrological model

was used to simulate the discharge in the upper CPRB and the hydrodynamic model was used to investigate ﬂood routing

and analyse ﬂood inundation in the upper CPRB. Once the calibration and validation processes were completed, this study

applied the hydrological model to investigate the effects of reforestation and land use regulation on ﬂood discharge in the

upper CPRB. The ﬂood hydrographs derived from the hydrological model, such as ﬂow conditions and runoff between the

upstream and downstream boundary, were used as input data for hydrodynamic model. The hydrodynamic model was used

84 S. Jamrussri, Y. Toda / Journal of Hydrology: Regional Studies 10 (2017) 82–94

Fig. 1. The upper Chao Phraya River Basin (CPRB).

to investigate the ability of nonstructural ﬂood countermeasures to mitigate severe ﬂooding. This study used the severe

ﬂood events in 1995, 2006, and 2011 to compare with the results of each nonstructural ﬂood countermeasure simulation.

2.2.1. Hydrological model

The Soil and Water Assessment Tool (SWAT), operated on a daily time step, was used for this study. The SWAT model

is a semi-distributed physically-based hydrologic model (Arnold et al., 1998) that was developed to predict the impact of

land management on yields of water, sediment, and agricultural chemicals in large, complex watersheds. It has been used

extensively to predict water quantity and quality in response to various management and climate scenarios (Eckhardt et al.,

2003; Fohrer et al., 2001, 2005; Hernandez et al., 2000; Jha et al., 2004; Mango et al., 2011). The original model has been

updated to Arc SWAT 2012 (Winchell et al., 2009), which was developed speciﬁcally for the ArcGIS platform.

A digital elevation model (DEM), land use map, soil map, and weather data (as shown in Table 1) are the required inputs

for the SWAT model. The DEM, obtained from the Royal Thai Survey Department (RTSD), was used to derive the drainage

networks and eventually delineate the sub-basins. The land use type classiﬁcation and soil characteristics were obtained

from the Land Development Department (LDD). This study used land use data from 2009 for the SWAT model simulation

and land use data from 2002 was used to analyse trends when developing reforestation scenarios.

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Table 1

Data collection for hydrological model.

Sr. no. Data Type Duration Source

A. Temporal data (hydro-meteorological data)

1 Rainfall Daily 1993–2011 MET

2 Discharge Daily 1993–2011 RID and EGAT

3 Temperature Daily 1993–2011 MET

4 Relative humidity Daily 1993–2011 MET

5 Wind speed Daily 1993–2011 MET

6 Solar radiation Daily 1993–2011 MET

B. Spatial data

1 DEM Raster 30 m × 30 m RTSD

2 Land use Vector 2002, 2009 LDD

3 Soil type Vector 2015 LDD

Daily climate data, including precipitation, temperature, solar radiation, wind speed and humidity data, were obtained

from the Thai Meteorological Department (MET) for the period from 1993 to 2011. Daily precipitation data were obtained for

56 gauging stations, while daily temperature, solar radiation, wind speed, and humidity data were collected from 10 major

weather stations. Data for six hydrological runoff stations, namely P.7A, W.3A, Y.20, Y.6, N.7A and C.2, were obtained from

the Royal Irrigation Department (RID); inﬂow data from the Bhumibol and Sirikit Dams, obtained from Electricity Generating

Authority of Thailand (EGAT), were also used for model calibration and validation, as shown in Fig. 1.

2.2.2. Hydrodynamic model

Analysis of hydrodynamic and ﬂuvial data were carried out with iRIC (international River Interface Cooperative) Soft-

ware, an open source product developed under a bilateral cooperation between the U.S. Geological Survey (USGS) and the

Foundation of Hokkaido River Disaster Prevention Research Center, Japan. This study used the Nays2DFlood solver, which

can analyse unsteady two-dimensional river ﬂows and sedimentation processes at different scales (Nelson et al., 2016;

Wongsa, 2014). The high-order Godunov scheme, known as the Cubic Interpolation Psuedoparticle (CIP) method, was used

to solve the water ﬂow equations (iRIC Software, 2015). This method interpolates physical values between grid points at

the previous time step using cubic equations based on the assumption that the spatial gradients of those physical values

are also transported by similar convective equations. This method can calculate precise proﬁles of convectional variables

using information from a small number of adjacent cells. The computation procedure was used to calculate changes in the

flow fields and the floodplain configuration with time at infinitesimal intervals up to the Courant–Friedrichs–Lewy (CLF)

condition. The iRIC software and educational materials are freely available at www.i-ric.org.

3. Results and discussion

3.1. Model calibration and validation

3.1.1. Hydrological model

Because the SWAT model includes many parameters that can inﬂuence stream ﬂow or pollutant transport (van Griensven

et al., 2006), the most sensitive parameters for a given watershed are determined in the ﬁrst step of the calibration and

validation process (Arnold et al., 2012a,b). Sensitivity analysis that determines the rate of change in the model output

relative to changes in model inputs (parameters) can be used for this step. This study carried out sensitivity analysis of the

parameters to determine the most sensitive parameters for the model calibration. This study used the SUFI-2 algorithm

in SWAT-CUP to map all uncertainties (such as parameter, conceptual model, and input) of the 35 hydrological model

parameters, expressed as uniform distributions or ranges, and tried to capture most of the measured data that fell within

the 95% prediction uncertainty (95PPU) of the model in an iterative process. The 95PPU is calculated at the 2.5% and 97.5%

levels of the cumulative distribution of an output variable obtained through Latin hypercube sampling. To quantify the ﬁt

between simulation result, expressed as 95PPU, and observation expressed as a single (with some error associated with it)

we came up with two statistics: P-factor and R-factor (Abbaspour et al., 2004). The P-factor is the fraction of measured data

(plus its error) bracketed by the 95PPU band and varies from 0 to 1, where 1 indicates 100% bracketing of the measured data

within model prediction uncertainty (i.e., a perfect model simulation considering the uncertainty). The R-factor on the other

hand is the ratio of the average width of the 95PPU band and the standard deviation of the measured variable. In this study,

11 parameters were retained for model calibration by the sensitivity analysis, as shown in Table 2.

In this study, we simulated the 19-year period from 1993 to 2011 for eight sub-basins in four main river basins (Fig. 1). The

data for each station spanned different years, so this study used 2/3 of the data for calibration and the rest for validation. The

ﬁrst two years were used as a warm-up period to establish the initial conditions and were excluded from the analysis. This

study evaluated the model performance by comparing the model results with observed data using two statistical indicators,

coefficient of determination (R ) and Nash-Sutcliffe efficiency (NSE). The model parameters provided the best fit between

the calculated and observed daily hydrographs at eight runoff stations, as shown in Fig. 2. Model results were mostly well

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Table 2

Sensitivity ranking and category of the most sensitive parameters.

Rank Name Deﬁnition Category

1 CN2 Soil conversion service (SCS) runoff Very High

curve

number for moisture condition 2

2 SURLAG Surface runoff lag coefﬁcient Very High

3 SOL AWC Available water capacity of the soil High

layer (mm/mm soil)

4 ALPHA BF Baseﬂow alpha factor (days) High

5 REVAPMN Threshold depth of water in the High

shallow aquifer

for percolation to the deep aquifer

(mmH2O)

6 CH K2 Effective hydraulic conductivity in High

main channel alluvium (mm/h)

7 GW REVAP Groundwater “revap” coefﬁcient High

8 GWQMN Threshold depth of water in the High

shallow aquifer

required for return ﬂow to occur (mm)

9 ESCO Soil evaporation compensation factor High

10 SOL Z Maximum canopy index Soil depth High

11 RCHRG DP Deep aquifer percolation fraction High

Fig. 2. Comparison of simulated runoff using SWAT model and the observed data.

matched to the observed data in the basin having a good rainfall gauges distribution (in the PING-1, WANG-1, and NAN-1

sub-basins), and yielded acceptable values of the statistical indicators, as shown in Table 3. Some of the model results were

not well matched to the observed data, but this is quite normal in situations where there are limited rain gauges to represent

the catchment rainfall (in the YOM-1 sub-basin); further, some of stations were located in the ﬂood plain (in the PING-2,

NAN-2, and CPY-1 sub-basins). However, overall results generally indicated that the model performs well in simulating

ﬂow except around basins are limited rain gauges. The poor performance can be attributed to inadequate representation

of rainfall pattern due to small number of observation rain gauges and lower the rain gauge density. The investigation the

ability of the model to capture peak ﬂows is necessary for this study because this study attempted to explore the efﬁciency

of non-structural measure. However, the SWAT model showed good agreement between the simulated and observed yearly

peak ﬂows during the simulation periods (Fig. 3). R values for the yearly peak ﬂow of the 8 basins ranged from 0.53 to 0.96.

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Table 3

Model statistic values of daily runoff stations.

Statistical index PING-1 PING-2 WANG-1

Calibration Validation Calibration Validation Calibration Validation

R 0.617 0.589 0.411 0.429 0.673 0.639

NSE 0.598 0.555 0.376 0.410 0.595 0.586

Statistical index YOM-1 YOM-2 NAN-1

Calibration Validation Calibration Validation Calibration Validation

R 0.384 0.272 0.506 0.429 0.592 0.562

NSE 0.355 0.164 0.496 0.383 0.523 0.51

Statistical index NAN-2 CPY-1

Calibration Validation Calibration Validation

R 0.262 0.521 0.45 0.511

NSE 0.199 0.302 0.395 0.377

Fig. 3. Curves ﬁtted between simulated and observed yearly peak ﬂows.

The statistical values in the daily hydrographs and the yearly peak ﬂow therefore indicate that the SWAT model is capable

of capturing the hydrological characteristics of the upper CPRB and reproducing an acceptable daily hydrograph.

3.1.2. Hydrodynamic model

The iRIC model was applied to simulate the ﬂood conditions in the upper CPRB, from the Ping, Yom, and Nan Rivers to

Nakhon Sawan Province, the gate to the lower CPRB (Fig. 4). Flood inundations were simulated using the ﬂow conditions

from 1 September to 31 October in 1995, 2006, and 2011. For the upstream boundary conditions in hydrodynamic model, we

used the observed discharge data from runoff station P.17, Y.16 and N.5A for Ping, Yom and Nan Rivers respectively (shown

as simulation boundary in Fig. 4) and the simulated runoff between the upstream and downstream boundary was derived

from the results of SWAT model (shown as hydrological boundary in Fig. 4). The simulated (iRIC) and the measured (GISTDA

satellite map) ﬂood inundations are illustrated in Fig. 5. According to ﬂood inundation results from iRIC model in 2006 and

2011, the model tends to underestimate ﬂood extent identiﬁed by GISTDA satellite map, especially point A (in Fig. 5). It may

associate with the coarse ﬂoodplain elevation data (30 m × 30 m resolution) and iRIC model (parameters and the simulated

runoff derived from SWAT model). The other possible reason for the underestimation is that the satellite map may judge

88 S. Jamrussri, Y. Toda / Journal of Hydrology: Regional Studies 10 (2017) 82–94

Fig. 4. Boundary condition for the hydrodynamic model.

Table 4

Model statistic values of the ﬂood hydrograph at runoff station C.2.

Statistical index 1995 2006 2011

R 0.960 0.964 0.926

NSE 0.949 0.993 0.996

RMSE (m /s) 194.82 305.27 245.31

areas as ﬂooded even when they have shallow water depth such as in paddy ﬁelds. However, there was good agreement

between the ﬂood hydrographs for the simulated and observed data recorded at runoff station C.2 (Fig. 6). The overall model

performances are also shown in Table 4. The iRIC model produced good simulations of both the ﬂood inundation and ﬂood

hydrograph, which indicates that the model is capable of simulating severe ﬂood events in the upper CPRB, and that it was

suitable for this investigation of the effectiveness of nonstructural ﬂood countermeasures.

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Fig. 5. Comparison of ﬂood inundation between the simulated results (left) and GISTDA satellite data (right) (a) year 2006 and (b) year 2011.

Fig. 6. Comparison of ﬂood hydrograph at runoff station C.2 (a) year 2006 (b) year 2006 and (c) year 2011.

3.2. Proposed nonstructural ﬂood countermeasures in the upper CPRB

3.2.1. Reforestation scenarios

The predominant land use across a total area of 104,481 km in the upper CPRB in 2009 was classiﬁed into seven categories,

namely forest (62.1%), range-brush (16.4%), paddy ﬁeld (10.4%), agricultural area (7.2%), orchard (1.7%), water resources

(1.6%), and urban (0.6%). In line with the Thai Master Plan for Water Resources Management, this study then explored

the impact of reforestation on the daily discharge in the upper CPRB. Reforestation scenarios were developed to evaluate

their potential and to reconﬁrm that this approach should be adopted and implemented in the upper CPRB. To ensure the

reforestation scenarios were realistic, this study considered two conditions. In the ﬁrst, a maximum area of 6400 km was

reforested, which was proposed in the Master Plan for the increased forest area in the upper CPRB. In the second, reforestation

scenarios were formulated by considering the land use change from 2002 to 2009 (Table 5). Based on two conditions,

this study therefore developed three reforestation scenarios, namely 1) conversion of range brush to forest (6400 km ), 2)

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Table 5

Trends in land use change from 2002 to 2009.

Land use Land use in 2002 Land use in 2009 Land use change (2002–2009)

2 2

Area (km ) (%) Area (km ) (%)

Agricultural land 12,380 11.85 12,967 12.41 +587

Forest 57,622 55.15 48,015 45.96 −9607

Orchard 4190 4.01 5858 5.61 +1668

Paddy ﬁeld 18,348 17.56 15,616 14.95 −2732

Range brush 7032 6.73 15,761 15.09 +8729

Urban area 3017 2.89 3901 3.73 +884

Wet land 1892 1.81 2363 2.26 +471

Sum. 104,481 100 104,481 100 0

Table 6

Change in average annual runoff for the whole basin under different land use scenarios.

3 3

Land use change scenarios Annual runoff; Seasonal runoff; (m /s) (%change) Yearly peak ﬂow; (m /s) (%change) 3

(m /s) (%change)

Wet season Dry season

Current land use 1,331.85 1,900.74 783.16 4,705.75

Scenario 1. conversion of 1,324.70 1,887.13 783.50 4,540.86 (−3.50%)

range brush to forest (−0.54%) (−0.69%) (+0.04%)

100% of the master plan

proposal (6400 km )

Scenario 2. conversion of 1,325.74 1,890.16 784.17 4,680.35 (−0.54%)

orchard to forest (−0.46%) (−0.53%) (+0.13%)

30% of the master plan

proposal (1920 km )

Scenario 3. conversion of 1,331.77 1,900.48 785.87 4,687.05 (−0.40%)

agricultural land to forest (−0.01%) (+0.01%) (+0.35%)

10% of the master plan

proposal (640 km )

2 2

conversion of orchard to forest (1920 km ), and 3) conversion of agricultural land to forest (640 km ). Conversion refers to

change from one land cover to another.

The SWAT model was developed to predict the impact of land management practices on water in large complex water-

sheds with varying soils, land use and management conditions over long periods of time (Jha et al., 2004). SWAT therefore

provides a management tool to change the percentage of one land use to another such as conversion of range brush to

forest (Arnold et al., 2012a,b). Therefore, the effects of land use change were simulated by the SWAT model using 17 years of

runoff data (1995–2011) and the model results from the current land use (year 2009) were compared with various land use

scenarios within the basin. The changes in the annual and seasonal runoff and yearly peak discharge because of the various

land use scenarios are summarized in Table 6. The runoff in the wet season (Jul–Nov) and yearly peak ﬂow decreased in

response to the reforestation in every scenario. However, the runoff increased in the dry season (Dec–Jun).

3.2.2. Retention areas

In the upper CPRB, there are some low-lying areas that are generally used for agriculture, mostly paddy ﬁelds. Some of

these areas are natural ﬂoodplains that cannot be cultivated in the rainy season because of the ﬂooding. Floodplains connected

to the river channels provide water storage progressively which reduces peak ﬂood ﬂows and levels. The geomorphological

units in the central plain of Thailand correspond with ﬂood conditions. The depth of inundation in the back marshes located

in higher natural levees is around 4.5-5 m, whereas the depth of inundation in the lower part is less than 1 m and the sediment

found in natural levees is silty (Haruyama, 1993). From a basin-wide water management viewpoint, agricultural lands in

low-lying areas were implemented to one of options for ﬂood countermeasures. A number of studies have also witnessed

and conﬁrmed that retention areas can reduce the risk of ﬂooding in downstream urban areas (Förster et al., 2008; Huang

et al., 2007; Scholz and Sadowski, 2009; Scholz and Yang, 2010) and calls for a more integrated approach to regional and local

spatial planning (Camnasio and Becciu, 2011; Guo and Urbonas, 1996; Harrell and Ranjithan, 2003; Lammersen et al., 2002;

Montaldo et al., 2004; Yeh and Labadie, 1997). Moreover, retention areas play an important role in the control of pollution

caused by combined urban stormwater and sewer overﬂow (Tung, 1988) and can be adapted to act as water storage during

irrigation periods in order to reduce agricultural water shortages (Camnasio and Becciu 2011).

Therefore, the Royal Irrigation Department (RID) has proposed 5 lowland areas in the upper CPRB along the Yom and

Nan Rivers which should be used as ﬂoodwater retention areas, as shown in Fig. 4. These retention areas can accommodate

6 3 2

a ﬂood volume of around 1161 × 10 m , and cover an area of 835 km . Because of a longitudinal gradient (approximately

1/12,000) and low capacity of rivers (Sayama et al., 2015), water in the rivers usually spill over the river bank or embankment

to these 5 retention areas in almost every year. The ﬂoodwaters were stored in retention areas until the storage capacity was

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Table 7

The results of scenarios analysis at runoff station C.2.

Year Index Model Reforestation Retention area Combination

Value Value % diff Value % diff Value % diff

1995 Peak Flow (m /s) 4803 4780 0.48 4513 6.04 4438 7.60

Average Flow (m /s) 3212 3185 0.83 3007 6.38 2989 6.94

Flood volume over 3500 m /s 8612 8247 4.24 7857 8.76 7816 9.24

2006 Peak Flow (m /s) 5840 5816 0.40 5505 5.74 5375 7.96

Average Flow (m /s) 3361 3330 0.91 3141 6.55 3056 9.07

Flood volume over 3500 m /s 11,266 10,655 5.42 10,635 5.60 10,142 9.98

2011 Peak Flow (m /s) 4885 4874 0.22 4795 1.84 4790 1.94

Average Flow (m /s) 4182 4149 0.80 4076 2.53 4045 3.26

Flood volume over 3500 m /s 19,392 19,253 0.72 19,108 1.46 19,095 1.53

reached, and then the excess ﬂoodwaters moved over the crest of downstream roads. Moreover, Surface water connectivity

during flood inundation has been identified as important to many aspects of a floodplain’s functions. For example, floodplain

habitat heterogeneity is mainly a product of shifting water sources and different ﬂow paths (Tockner et al., 2000). Floodplain

channels provide important sediment distribution pathways onto a ﬂoodplains (Mertes et al., 1996; Day et al., 2008; Bertoldi

et al., 2009) as well as the deposition and erosion of sediment altering the structure and morphology of ﬂoodplains (Trigg

et al., 2012), thus the connectivity is both created by, and creates, the morphology of the ﬂoodplains (Tockner and Stanford,

2002). Furthermore, MRC (2008) stated that ﬂood inundation enriches soil with deposited sediments and nutrients beneﬁcial

to agriculture and rejuvenate breeding grounds for aquatic plants and ﬁsh. In their natural state, ﬂood plains are amongst

the most dynamic and heterogeneous ecosystems, showing complex patterns of variation over a wide range of temporal

and spatial scales. These patterns arise through the interactions between the dominant hydrogeomorphologic and ecologic

processes such as natural disturbance and succession (Junk et al., 1989; Tockner et al., 2010).

To date, these 5 retention areas have not been used because of the apathy of the stakeholders who include various

governmental institutions and civil society groups; the protest is particularly strong at the local and community levels.

Basically, 5 low-lying areas will naturally be submerged before the critical time arrival. In theory, if we are able to develop

these 5 retention areas to hold large amount of water during critical time, therefore, the ﬂood damage in community and

economic areas in lower watershed will be automatically decreased. From this reason, we considered these areas when

investigating the efﬁciency of retention areas as nonstructural ﬂood countermeasures for the upper CPRB. For the purposes

of this study, the initial water level at which discharge would be diverted from rivers to these retention areas for every single

ﬂood event was the same, meaning that the level at which water would be diverted was about 1 m higher than river bank.

The retention areas in this study were set as “in the initial of simulation period (hydrodynamic model), ﬂooded water shall

be drained from the river channels to retention areas”.

3.3. Nonstructural scenarios analysis

This study carried out scenario analysis to investigate the efﬁciency of the nonstructural ﬂood countermeasures outlined

in the Master Plan, namely reforestation and retention areas, as described in the previous section. These scenarios were then

further developed for three situations, namely 1) reforestation scenario that was carried out completely in line with the

master plan (an increase of 6400 km in the forest area); 2) retention areas scenario, and 3) a combination of reforestation and

retention areas scenarios. Three scenarios were simulated by iRIC model to investigate their efﬁciency. For the reforestation

scenario, an area of 6400 km in the whole upper CPRB was reforested by application of SWAT model and this scenario

could reduce the runoff in the wet season (Jul–Nov) and yearly peak ﬂow (Table 6). Therefore, the ﬂow conditions in the

upstream and the runoff between upstream and downstream of reforestation scenario simulation were adjusted based

on the change in discharge at each runoff station. The retention areas scenario was simulated based on the assumption

that retention areas are completely drained to accommodate ﬂood volume. The upstream boundary condition and runoff

between upstream and downstream of the retention areas scenario were not changed from the calibration simulation. Thus,

the combination of reforestation and retention areas scenarios used ﬂow conditions and ﬂood retarding storage from the

reforestation scenario and retention areas scenario respectively. The discharge of the Chao Phraya River at runoff station C.2

was used for 3 scenarios, and the analysis results are shown in Fig. 7 and Table 7. The scenario results are similar, and show

comparable decreases in the peak ﬂow and ﬂood volume at runoff station C.2. The effect in the river was clearly obvious

when the peak flow and runoff were reduced by reforestation and the flood overflow was stored in retention area.

The reforestation scenario does not have great potential for reducing the peak and average ﬂows but it did demonstrate

considerable potential for reducing ﬂood volumes that were designated as harmful. The results from the retention areas

scenario indicated that the ﬂoodwaters from rivers overﬂowed into the retention areas too early. It may come from the

initial of water diversion from river to retention areas, set was 1 m above river bank, and the low ﬂow capacities of the Yom

and Nan Rivers. These factors mean that the retention areas would not be able to accommodate the desired reduction in the

6 3

peak discharge and would not be able to reach their potential storage capacity (1161 × 10 m ) when discharge exceeded

92 S. Jamrussri, Y. Toda / Journal of Hydrology: Regional Studies 10 (2017) 82–94

Fig. 7. Comparison of the ﬂood hydrographs for each scenario at station C.2.(a) year 2006 (b) year 2006 and (c) year 2011.

3500 m /s. Despite this, the retention area scenario would have reduced the ﬂoodwater volumes in the 1995, 2006 and 2011

6 3 6 3 6 3

ﬂoods by 755 × 10 m , 631 × 10 m , and 284 × 10 m , respectively.

The results show that ﬂood control by nonstructural ﬂood countermeasures is not possible using only either reforesta-

tion or retention areas. This study, therefore, applied a scenario to test the combination of reforestation and retention

areas. For the 2006 ﬂood, the combination of the two nonstructural ﬂood countermeasures showed the highest potential

3 3

to reduce all aspects. During the 2006 event, the peak discharge, average ﬂow, and ﬂood volume were 465 m /s, 305 m /s

6 3

and 1124 × 10 m respectively. The combination of the nonstructural ﬂood countermeasures showed only a small potential

3 6 3

to reduce the peak discharge (95 m /s) and the ﬂood volume (297 × 10 m ) during the 2011 ﬂood, which may have been

attributable to the continuous heavy rainfall and complete saturation of the soil. Moreover, inundation in this event had

commenced in July 2011, and so the retention areas were not able to accommodate additional ﬂoodwaters because of the

high water level in the main channel.

4. Conclusions

Floods are natural phenomena; however, humans try to overcome this challenge with structural measures but the most

of the time they neglect the fact that water needs space. Nonstructural ﬂood countermeasures are necessary for the effective

flood management. Therefore, in this study we investigated the efficiency of nonstructural flood countermeasures to mitigate

the impact of severe ﬂooding. All scenario results showed that nonstructural ﬂood countermeasures had the potential to

reduce peak flow and flood volume in the upper CPRB and they also could indirectly alleviate flood damage in the lower

CPRB, which is a well-known economic and dense community area in Thailand. It can be stated that the nonstructural ﬂood

countermeasures have an ability to reduce peak discharge and to control ﬂood volume; however, they were not able to cope

with the severe ﬂoods in 1995, 2006, and 2011. Besides, it is crucial to preserve forest areas and 5 retention areas not to lose

their existing functions by land use regulations. Moreover, ﬂood inundation in ﬂoodplain (5 retention areas) is a complex

process which involves many factors such as ﬂow paths and its topographic control (Hudson and Colditz, 2003).

We proposed two main explanations for the low performance of these measures on severe ﬂooding. First, ﬂoodwaters

spilled from the rivers to retention areas too early because of the initial diversion level and the low capacities of the Yom

and Nan Rivers. The retention areas therefore could not accommodate ﬂoodwaters as much as their full storage capacity

when the discharge at runoff station C.2 exceeded 3500 m /s. Moreover, it seems that the proposed nonstructural ﬂood

countermeasures in the upper CPRB from the Thai Master Plan are insufﬁcient to overcome the severe ﬂooding. However,

the results of the hydrological and hydrodynamic models imply that the proposed nonstructural ﬂood countermeasures

in this study might prevent and alleviate small- to medium scale ﬂood damage (the discharge at runoff station C.2 ranges

S. Jamrussri, Y. Toda / Journal of Hydrology: Regional Studies 10 (2017) 82–94 93

from 2000–3500 m /s). To cope with the severe ﬂooding, the efﬁciency of the proposed nonstructural approaches should be

increased considerably. For example, the proposed reforestation areas (6400 km ) should be revised upwards because the

proposed value is just around 67% of the change in the forest area from 2002 to 2006 (9607 km ). Moreover, to increase the

efﬁciency of the nonstructural ﬂood countermeasures, we need to provide more possible retention areas to accommodate

the excess ﬂood volume. However, scenario results showed that nonstructural ﬂood countermeasures have the potential

to be used as a part of engineering solutions employed for reducing ﬂood peak and volume of the severe ﬂood in upper

CPRB. Thus, we hope that results of this study will contribute to further discussions in Thailand so that the most acceptable

alternatives are accepted.

Comprehensive ﬂood management, which integrates these proposed nonstructural ﬂood countermeasures with the exist-

ing structural measures in the CPRB, may be the most suitable approach for coping with and mitigating ﬂood damage in the

CPRB. Therefore, the existing physical structures should be improved and checked to ensure that they are ready to use. In

addition, a water management plan must be also developed for major reservoirs. The results of reforestation scenarios in the

hydrological model clearly showed that the amount of discharge in the upper dams was reduced. The reservoir operation

should be considered to increase the efficiency of nonstructural flood countermeasures. For hydrodynamic simulation, flood

inundation in ﬂoodplain is a complex process which involves many factors such as ﬂow paths and its topographic control

(Hudson and Colditz, 2003). Therefore, high-resolution topographic data should be utilized for ﬂoodplain analyses in order

to enhance understanding of ﬂoodplain processes (Hudson et al., 2013). This approach will facilitate the development of

more appropriate management strategies for CPRB. Moreover, ﬂood management needs developing with a long-term per-

spective. It is necessary to consider long-term impacts and develop adaptation measures accordingly. To avoid a recurrence

of the event in 2011 and to develop the sustainable ﬂood management in CPRB, further studies should incorporate climate

change. Significant benefits of nonstructural flood countermeasures in upper CPRB may accrue from taking climate change

into account.

Acknowledgements

The authors are grateful to the Royal Irrigation Department, Electricity Generating Authority of Thailand, Thai Meteorology

Department and Land Development Department for topographical, hydrological and historical data used in this study.

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