The Thailand floods in 2011 caused unprecedented economic damage in the Chao
Phraya River basin. To diagnose the flood hazard characteristics, this study
analyses the hydrologic sensitivity of flood runoff and inundation to
rainfall. The motivation is to address why the seemingly insignificant
monsoon rainfall, or 1.2 times more rainfall than for past large floods,
including the ones in 1995 and 2006, resulted in such devastating flooding.
To quantify the hydrologic sensitivity, this study simulated long-term
rainfall–runoff and inundation for the entire river basin
(160 000
The 2011 large-scale floods over the Chao Phraya River basin resulted in the worst ever economic flood damage to Thailand (The World Bank, 2012). The flooding appeared to be induced mainly by rainfall from five typhoons and tropical depressions between May and October. The total rainfall in the 6 months during the monsoon season was approximately 1400 mm, while previous large-scale floods were caused by a total rainfall of approximately 1200 mm. The average monsoon-season rainfall in this region is about 1000 mm. Therefore estimating how the additional 200 mm of rainfall magnifies the runoff and flood inundation is essential to understand the flood characteristics in this basin.
Oldenborgh et al. (2012) analysed a long-term rainfall pattern in the region with the Global Precipitation Climatology Centre (GPCC) V5 product. Based on the analysis, they concluded that the 2011 monsoon rainfall was not very unusual from a viewpoint of large-scale meteorology. Instead they stressed that the main causes of the unprecedented flood damage lay in non-meteorological factors, including reservoir management and conversion of agricultural land into industrial complexes. On the other hand, Komori et al. (2012) highlighted the fact that the seemingly insignificant rainfall may contribute significantly to the increase in runoff volume in the Chao Phraya River basin. They conceptually explained that the 1.4 times more rainfall than normal years might result in 2.5 times more runoff than normal years under a constant evapotranspiration assumption. Kotsuki and Tanaka (2013) performed a hydrologic simulation with a land surface model and concluded also that runoff is highly sensitive to rainfall (2.25 times more than average) in a naturalized condition excluding dam effects.
These studies are in line with hydrologic sensitivity analyses. Schaake (1990)
introduced an elasticity index to quantify the runoff change to precipitation
change as Eq. (1).
A possible reason for the lack of study on inundation elasticity may be associated with the difficulty in the quantification of flood inundation volume, especially for the long term in large river basins. Most of the existing inundation models are only applied to floodplains and constrained by boundary conditions of upstream river discharges or inflow to the floodplains. Those boundary conditions are generally difficult to define if multiple locations are inundated in a large river basin. Taking multiple inundations at various locations and their interactions in large river basins into account, this study employs a rainfall–runoff–inundation (RRI) model (Sayama et al., 2012). The model simultaneously simulates rainfall–runoff and flood inundation processes on a 2-D basis at a river basin scale. Since these two processes interact with each other, the concept of the RRI model with rainfall forcing is thought to be suitable to estimate the elasticity of runoff and flood inundation.
The objective of this study is to quantify the sensitivity of flood runoff and inundation volumes to diagnose the characteristics of the 2011 Thailand floods. We first calibrate the RRI model based on river discharge and test the inundation simulation result using remote sensing as well as the peak inundation water depths of 2011. Then we run the model continuously for 52 years (1960–2011) without the effect of dams and for 32 years (1980–2011) with the effect of two major dams. Based on the simulation results, we analysed the relationship among rainfall, runoff and inundation volumes for different years, including 2011 for the entire Chao Phraya River basin. Finally, we applied a regression model to the simulated historic runoff and inundation to estimate their elasticity indices.
The Chao Phraya River drains from an area of 160 000
The climate of the area is characterized by tropical monsoon. Average annual rainfall is between 1000 and 1400 mm and 85 % of the total rainfall occurs in April to October. In addition to the frontal rain from the monsoon, the basin receives rainfall from typhoons or tropical low depressions from the northern Pacific Ocean (JICA, 2013).
In 2011, five typhoons and tropical depressions hit the northern part of the
basin. As a result, approximately 1400 mm of rainfall fell in the wet
season. The Bhumibol and Sirikit dam reservoirs were 45 % filled and
51 % filled by 15 April 2011, respectively, and then both were 95 % filled by
5 October and 14 September 2011 (Komori et al., 2013; Mateo et
al., 2014). The peak discharge at Nakhon Sawan was 4686
This section explains the overview of the RRI model and its application to the Chao Phraya River basin, followed by the elasticity estimation method adopted in this study.
Map of the Chao Phraya River basin, Thailand.
The RRI model is a 2-D model capable of simultaneously simulating rainfall–runoff and flood inundation (Fig. 2) (Sayama et al., 2012). The model deals with land and river channels separately. In a grid cell where a river channel is located, the model assumes that both land and river are positioned within the same grid cell. The channel is discretized as a single line along its centerline of the overlying slope grid cell. The flow on the land grid cells is calculated with the 2-D diffusive wave model, while the channel flow is calculated with the 1-D diffusive wave model.
All the land grid cells can receive rainfall and contribute to rainfall–runoff flowing through other land grid cells and river channels. Meanwhile, they are subject to inundation due to multiple causes: overtopping from river channels, expansion of inundation water from surrounding land grid cells, accumulation of local rainwater or any combination of the three. Hence, the RRI model does not structurally distinguish between rainfall–runoff and flood inundation processes; instead, it solves water flow hydrodynamically. In terms of its application to an entire river basin with rainfall input, the model is similar to grid cell-based distributed rainfall–runoff models. While typical rainfall–runoff models fix flow directions at each grid cell based on surface topography, the RRI model changes flow directions dynamically. In this regard, the RRI model resembles 2-D flood inundation models (e.g. Iwasa and Inoue, 1982). Nevertheless, unlike many other flood inundation models, the application of the RRI model is not limited to floodplains. It is applicable to an entire river basin. It simulates flow interactions between land and river channels with considerations of levees, so that the RRI model does not require specifying an overflowing point and its overtopping discharge, which are typically required as boundary conditions when using inundation models. Another feature of the RRI model is the acceptance of rainfall and potential evapotranspiration as model input. It estimates actual evapotranspiration based on the soil moisture conditions and simulates surface and subsurface flow processes including flood inundation. The application of an integrated equation for surface and subsurface flows, numerically solved by an adaptive time step Runge–Kutta algorithm (Cash and Karp, 1990; Press et al., 1992), enables the RRI model to run fast and stable calculations, even for a large river basin with mountainous and plain areas.
Schematic diagram of the rainfall–runoff–inundation (RRI) model (Sayama et al., 2012).
The RRI model is applied to the entire Chao Phraya River basin. As the model
was being set up, the DEM (digital elevation model), flow direction and
flow accumulation were delineated from HydroSHEDS 30
The model input is rainfall and potential evapotranspiration. Daily rainfall records were observed at about 400 stations fairly equally distributed in the whole basin and used after Thiessen polygon interpolation. Potential evapotranspiration was estimated with the Penman–Monthieth equation based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (Uppala et al., 2005). The Ecoclimap data set (Tchuente et al., 2010), provided by Meteo France, was also used to identify seasonal and spatial variations of leaf area index.
To set model parameters, the area was first classified into two areas: mountains and plains. The forest area is mainly distributed in upstream mountainous regions, where downslope subsurface flow and surface flow are simulated by taking both surface and subsurface flows into account (see detail in Sayama et al., 2012). On the other hand, the cultivated area including some urban areas is distributed mainly in downstream plain regions, where vertical infiltration and surface flow are simulated with the Green–Ampt (G–A) model.
With respect to the effect of two major dams (Bhumibol and Sirikit), this study conducted two sets of simulation. The first one was a naturalized case, which did not take the dam effects into account, and used as the baseline simulation for water balance analysis. The simulation period for this case was 1960–2011 (52 years). The second case was to simulate water-regulated conditions, which used reservoir outflow records as boundary conditions for the two dam reservoirs. This regulated case was compared with the naturalized one to understand how the dams contributed to reduce flood runoff and inundation. The simulation period for the regulated case was limited to 1980–2011 (32 years) due to the availability of dam release records.
Based on the 52-year continuous rainfall–runoff–inundation simulation, we analyse basin-wide water balance for all the monsoon seasons. We calculate spatially averaged rainfall, simulated actual evapotranspiration, runoff, catchment storage and flood inundation. The runoff in this study is defined as all the water volume flowing out from the river basin; i.e. discharge at the mouth of the Chao Phraya River basin as well as some flooded water flowing out directly from the basin into the sea. The catchment water storage is the total volumes of cumulative infiltrated volume within the G–A model, water height equivalent in soil and surface as well as water volume stored in the rivers. If surface water depths exceed 0.5 m due to accumulation of surface water, the volume of the water on land surface is considered as flood inundation volume and excluded from the catchment storage. Note that total volumes of simulated catchment storage and flood inundation are divided by area of the basin, so that all the water balance components have the same unit as average water depths in mm.
For the water balance analysis, the selection of period is very important. In
this study, since our goal is to assess the relationship between rainfall and
flood inundation volume, we first look at a period whose rainfall amount has
the highest correlation with the maximum flood inundation volume. More
specifically, by setting the maximum flood inundation date as the ending
point of the water balance calculation period and changing the duration from
1 to 7 months, we calculated correlation coefficients between rainfall
amount during the selected period and the maximum flood inundation volume.
For each simulation year, we calculate rainfall
The focus of this study is to understand the relationship between each term
of the water balance equation including the flood inundation volume. We
primarily focused on the d
To understand the general characteristics of the elasticity index, we
exemplify a simple linear model of runoff
Figure 3 shows simulated and observed monthly discharge. We split the period
between 1980 and 2011 into a calibration period (1980–1999) and a validation
period (2000–2011). Model parameters were then manually calibrated by
focusing on the naturalized monthly discharge, marked at C2, and evaluated at
other upstream locations. The naturalized discharge is estimated to avoid the
effect of dams by adding inflow and subtracting outflow from the two major
dam reservoirs to the observed monthly discharge. Table 1 shows the
calibrated parameters for mountain and plain areas. Note that the following
sensitivity analysis covers the period of 1960–2011. However, due to the
reliability of observed discharges, we only focused here on 1980–2011 for
the model evaluation. We used three metrics including Nash–Sutcliffe
efficiency (NSE), coefficient of determination (
The hydrograph in Fig. 3 shows that the model can reproduce C2 monthly river
discharge well for both calibration and validation periods. The evaluation
statistics were NSE
Model parameter setting. The entire river basin is categorized into
two regions: mountain and plain areas. A type S-S (surface
Model performance evaluated by Nash–Sutcliffe efficiency (NSE),
coefficient of determination (
Simulated and observed monthly averaged river discharges at
We also test the RRI model performance in terms of peak flood inundation extent. Figure 4 shows the simulated annual maximum flood inundation depths (upper panel) and remote sensing composites (lower panel). For 2011, we referenced information released by UNITAR's Operational Satellite Applications Programme (UNOSAT), which used multiple-satellite information for estimating the maximum flood extent of the 2011 flood event in Thailand. For previous years for which no UNOSAT information is available, we used composite images produced by the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand.
For 2011, Fig. 4 shows that the model tends to underestimate inundation
extent identified by remote sensing, especially upstream of the C2 location.
According to the evaluation statistics defined in Appendix A, 57 % of the
remote sensing inundation extent was identified by the RRI model (i.e. the
hit ratio was 0.57) and 72 % of the simulated inundation extent agreed
with the remote sensing (i.e. the true ratio was 0.72) in 2011 (see Table 3).
In addition, the negative normalized error (NE
Model performance of flood inundation extent and area compared with remote sensing. TR is the true ratio, HR is the hit ratio and NE is the normalized error (see Appendix A for their definitions).
According to the lower panels in Fig. 4, the basin also suffered severe flood
inundation in the years 2006 and 2010. For these 2 years also, the figure
shows model underestimation similar to the case in 2011. The underestimation,
which is confirmed by low HR values (0.36 in 2006 and 0.29 in 2010) and
negative NE values (
For the other years, including 2005, 2007, 2008 and 2009, which have
relatively small inundation extents, simulating perfect flood inundation
extent is even more difficult. Both the TR and HR are as low as
0.14
Simulated (upper panel) and remotely sensed (lower panel) annual maximum flood inundation extents from 2005 to 2011. The source of the remote sensed extents is GISTDA for 2005–2010 and UNOSAT for 2011.
In summary, our comparison between the modelled and remotely sensed inundation extents indicates that the model, compared to remote sensing, tends to underestimate the inundation area, especially in severe flood years, whereas in the other normal years, although the model performs poorly in representing exact locations of flood inundation, it performs fairly well in estimating the total area of inundation.
To assess the model performance of peak inundation levels, we also conducted
a post-flood field survey in 2011 (Sayama et al., 2015). We used a
high-specification GPS and handheld laser telemeter to
measure flood marks at 18 points along rivers and 23 points on the
floodplains. The average mean error and root mean square error were
Although we acknowledge that the RRI model when applied to the entire Chao Phraya River basin contains the above mentioned uncertainty, the inundation volume estimations are also constrained by the water balance; i.e. river discharge at C2 points is reasonably approximated with local rainfall information and evaporation estimates. Based on model performance checking, we further applied the model for the sensitivity analysis of flood runoff and inundation volumes described in the following section.
After the model was set up, we ran the model for 52 years from 1960 to 2011. The
simulation results were then analysed to estimate all the water balance
components described in Sect. 3.3. Figure 5 shows the daily values of
cumulative rainfall, cumulative actual evapotranspiration, catchment storage,
cumulative runoff and flood inundation, respectively. The solid red line
shows the 2011 result, while the other grey thin lines show the results
from the remaining 51 years. The average values are shown by the solid
black lines. In general, the cumulative rainfall, cumulative runoff and flood
inundation in 2011 are higher than in other years. The effect of early monsoon
rainfall in March 2011 is remarkable (93 mm) compared with the average year
(27 mm). As a result, the catchment water storage also shows rapid increase
in March 2011, though the storage in January and February was close to the
average. The estimated minimum and maximum catchment water storage in 2011
was about 500 and 1000 mm, respectively, while they were about 400 and
800 mm in the average year. The high catchment storage volume, together with
the additional rainfall during the rainy season, caused inundation starting
from as early as June and July, when other years still do not show
significant flood inundation. Regarding the seasonal variations in ET, Tanaka
et al. (2008) reported that tropical evergreen forest in the mountainous area
of the Chao Phraya river basin has a deep soil layer (
To analyse the relationship between rainfall amounts for different duration
and peak inundation volumes, Fig. 6 plots cumulative rainfall counted
backwards from the peak inundation of each year (
Simulated water balance components for 2011 (red lines), the other
51 years (grey lines) and average values (black lines) in 1960–2011:
Relationships between annual maximum flood inundation volumes
(
Figure 7 shows the relationship between 6-month rainfall
The 6-month rainfall was about 1400 mm in 2011 and about 1200 mm in past
large-scale floods in 1995 and 2002, while the average 6-month rainfall is
about 1000 mm (Fig. 7). In the case of 2011, the estimated
Relationship between cumulative rainfall (
Regarding the runoff component, we need careful interpretation of the figure.
As we mentioned above, the ending point of the period for the water balance
calculation was set to be at the peak of flood inundation for each year.
However, for a better understanding of runoff volume, it is necessary to extend
the period to cover flood runoff even after its flood inundation peak. For
this purpose, we extended the water balance calculation period for 2 months
after its inundation peak, so that the inundated water receded and turned
into runoff and other water balance components. As a result, the runoff ratio
(
In the above discussions, we primarily focused on d
Parameters of the regression analysis with 6-month cumulative rainfall in Figs. 7 and 8.
The above discussions assume naturalized conditions without considering two
major dam reservoirs. Figure 8a and b show the effects of the two dams on
flood inundation and runoff volumes, respectively. Since the analysis with
dams can only be conducted from 1980 due to the dam release records
availability, in this figure we plot the results only for the periods
without the dams. Figure 8a suggests that the two main dam reservoirs
contributed to the reduction of
Summary sensitivity analysis of flood runoff and inundation volumes with the effects of dams.
Figure 9 summarizes the results of elasticity estimations with the effects of dam reservoirs. The figure compares three different magnitudes of monsoon rainfall (i.e. 1000, 1200 and 1400 mm). In 2011 the runoff is estimated to be 329 mm (during 8 months), while its runoff in average years is 132 mm. Therefore 1.4 times more rainfall resulted in 2.5 times more runoff compared to average years. The ratio agrees with what has been reported by Komori et al. (2012) as 2.5 times and Kotsuki and Tanaka (2013) as 2.25 times.
The runoff elasticity (
The main focus of this study was to quantify flood inundation volume and its
elasticity. The estimated elasticity for flood inundation (
The elasticity estimation presented in this study is the combination of model simulation-based and regression-based approach. In this approach, we generated synthetic records of flood runoff and inundation from the model simulation and regressed linear lines to estimate the relationship between rainfall and other water balance components. The advantage of this approach is that it avoids assuming artificial spatial and temporal rainfall patterns typically necessary for the synthetic model-based approach. Instead of using historic records of flood inundation, whose direct observation does not exist, we used the RRI model to estimate historic flood runoff and inundation volumes.
In this approach, errors in the simulation can be the main source of the uncertainty in the estimations. The second uncertainty is induced by the deviations from the regression lines shown in Figs. 7 and 8. To reduce the second uncertainty, it is necessary to match the temporal scale of target rainfall suitable for management objectives. This study choose 6 month rainfall prior to the peak of flood inundation as the basis for the analysis (and 2 more months for the total runoff analysis). Another reason for the deviation is due to the linear regression between rainfall and other variables. Although this study employs the linear regression because of its simplicity and robustness within the range of historic rainfall, linear regression may be inadequate for analysing unprecedented extreme events in the future.
The deviations from the regression line in Figs. 7 and 8 indicate that flooding cannot be simply quantified only with 6-month rainfall. For example, a spatially and temporally concentrated rainfall pattern with wet antecedent conditions signifies runoff and inundation compared to other cases with a similar magnitude of rainfall. In that case, the plot may be above the linear regression line. To consider the natural variability, the presented approach used a hydrologic–hydrodynamic model, then analysed the results for sensitivity estimations.
Furthermore, as pointed out by Sankarasubramanian (2001), it is important to employ multiple hydrologic models and parameters to evaluate the sensitivity since each model and its parameters can respond differently to different input. Unfortunately this study could not conduct such multiple simulations with different models because running long-term and large-scale inundation simulations are still computationally expensive. In future research, it will be important to evaluate the elasticity with different models and their parameters.
Regardless of all the possible uncertainty described above, our main target of this study was to provide the first-order estimations of water balance components and their elasticity, which helps to quantify how much rainfall turns into flood runoff and inundation volumes in the region for better flood risk management.
This study estimated the elasticity of flood runoff and inundation in the Chao Phraya River basin. Due to the flat topography with comparatively small bankfull river drainage, the delta suffers from frequent flood inundations. In this kind of environment, estimations of flood runoff and inundation and their sensitivities are essential for better flood risk management. The objective of this study was to quantify the sensitivity of flood runoff and inundation to address why the 2011 Thailand flood became so catastrophic with 1.2 times more or an additional 200 mm rainfall than for past large floods including the ones in 1995 and 2006.
Our analysis suggested that inundation volumes in this basin have the
highest correlation with rainfall amount in the previous 6 months. In the
case of 2011, the basin received about 1400 mm of rainfall in the rainy
season, and 9 % of the total rainfall flooded at the peak of inundation
with the dam operations. The elasticity of flood inundation volume to
rainfall
The analysis shows two important implications for flood management. The first
one is for the diagnostic analysis of flood events. In the case of the 2011
flood, dam operations and other diversion channel management were claimed to
be primary causes of the devastating disaster. Seemingly small rainfall
variability (i.e. 200 mm in 6 months) compared to past experienced flood
events in the region tends to draw less attention to the magnitude of the
event itself. However, our analysis suggested that the flood inundation
volume was about 1.6 times (
The second implication is for climate change impact assessment. The analysis
indicated the high sensitivity of flood inundation volume to rainfall
variability in this basin. The presented d
To evaluate the model performance with respect to simulated discharge
against observed discharge, we used the following three metrics.
Nash–Sutcliffe model efficiency (NSE): Coefficient of determination (squared correlation coefficient) ( Relative volume error (VE): where
To evaluate the model performance for flood inundation extents, we used the
following three indices including True Ratio (TR), hit ratio (HR) and
normalized error (NE) of flood inundation area and defined as follows:
The authors would like to express deep gratitude to the Royal Irrigation Department (RID) and Thai Meteorological Department (TMD) for providing us with observation records. The discussions with the JICA project team on a Comprehensive Flood Management Plan for the Chao Phraya River Basin, Anurak Sriariyawat at Chulalongkorn University in Thailand, Daisuke Komori at Tohoku University and Taichi Tebakari at Toyama Prefectural University helped us understand the 2011 flood hazards in Thailand. The authors would like to thank Susumu Fujioka, Tomoki Ushiyama, Atsuhiro Yorozuya and Kuniyoshi Takeuchi at ICHARM for their support during the post-flood data correction and analysis. This research was funded by MEXT KAKENHI (Grant-in-Aid for Scientific Research (C), 24560633) and grants supported by the Public Works Research Institute of Japan. Edited by: B. Merz Reviewed by: two anonymous referees