Recent flood dynamics of the Mekong Delta have raised concerns about an increased flood risk downstream in the Vietnamese Mekong Delta. Accelerated high dike building on the floodplains of the upper delta to allow triple cropping of rice has been linked to higher river water levels in the downstream city of Can Tho. This paper assesses the hydraulic impacts of upstream dike construction on the flood hazard downstream in the Vietnamese Mekong Delta. We combined the existing one-dimensional (1-D) Mekong Delta hydrodynamic model with a quasi-two-dimensional (2-D) approach. First we calibrated and validated the model using flood data from 2011 and 2013. We then applied the model to explore the downstream water dynamics under various scenarios of high dike construction in An Giang Province and the Long Xuyen Quadrangle. Calculations of water balances allowed us to trace the propagation and distribution of flood volumes over the delta under the different scenarios. Model results indicate that extensive construction of high dikes on the upstream floodplains has had limited effect on peak river water levels downstream in Can Tho. Instead, the model shows that the impacts of dike construction, in terms of peak river water levels, are concentrated and amplified in the upstream reaches of the delta. According to our water balance analysis, river water levels in Can Tho have remained relatively stable, as greater volumes of floodwater have been diverted away from the Long Xuyen Quadrangle than the retention volume lost due to dike construction. Our findings expand on previous work on the impacts of water control infrastructure on flood risk and floodwater regimes across the delta.
The Vietnamese Mekong Delta (VMD) is popularly known as the rice bowl of
Vietnam, as it provides about half of the nation's food volume
(Käkönen, 2008). The delta owes much of its agricultural productivity
to seasonal flooding, though severe flood years have dire consequences for
local populations. Severe flooding is relatively frequent too, having
occurred, for example, in 2000, 2001, 2002, and 2011. In general, while extreme flooding poses a threat to
people and properties, the benefits of small to medium floods outweigh the
disadvantages. In particular, the fertile sediment and fish conveyed by the
floodwaters help create an optimal environment for agricultural livelihoods
(Käkönen, 2008; Hung, 2012). Tri et al. (2013) and Marchand
et al. (2014) calculated that the seasonal floods transport some 160 million
tons of fluvial sediment annually. Lu et al. (2014) estimated 67 million tons
per year. Some 1.86 tons of fish, worth USD 2.6 billion, were supplied by
the floods in 2000. Flooding also improves soil quality by flushing fields,
which reduces acidity and agrochemical residues, while contributing to
wetland protection and biodiversity conservation (Howie, 2011; Danh and
Mushtaq, 2011; Hung, 2012). Historically the Vietnamese have adapted their
farming systems to exploit the benefits of flooding (Wesselink et al., 2015;
Ngan et al., 2018). One example is cultivation of floating rice (
Vietnam's
Today, great expanses of the VMD floodplains are covered by intensively
cultivated rice fields enclosed by low dikes or high dikes. This intensified
land use, however, has coincided with an increased flood risk downstream in
the delta, around the city of Can Tho. Comparing water levels in 2011 with
those in 2000, lower water levels were observed upstream in the more recent
year, with higher levels measured downstream. At the upstream station of Tan
Chau, for example, water levels in 2011 were 0.63
Several studies have concluded that the flood risk in the VMD has increased over time. Numerous reasons have been proposed, such as climate change, sea level rise, hydropower projects, land subsidence, and local rainfall. Wassmann et al. (2004) concluded based on a hydraulic model that the higher water levels in the delta were caused by sea level rise in association with climate change. Fujihara et al. (2015) investigated the impacts of upstream runoff, sea level rise, and land subsidence on flood levels. They found that flood depths would be significantly increased in 19 tide-dominated areas, and that land subsidence and sea level rise would worsen inundation. Lauri et al. (2012) and Hoang et al. (2016) explored potential impacts of climate change and reservoir management scenarios on the future hydrology of the Mekong River. Numerous authors have considered the effects of climate change and sea level rise on flood propagation, inundated area, and sediment transport (Apel et al., 2012; Hung, 2012; Quang et al., 2012; Manh et al., 2014).
Some studies have honed in on the effects of infrastructure development on
VMD flood levels. Hoa et al. (2008) used the HydroGIS hydrodynamic model to
evaluate the effects of the infrastructural changes from 1996 to 2004 on
floodwater levels and flood protection efficacy. They concluded that
infrastructure works, such as dredging canals, raising embankments, and
upgrading roads, likely mitigated the overall extent of flooding but
increased flood depth by 20 to 30
Dikes and other water control infrastructures prevent floodwaters from
entering agricultural fields. They may therefore increase floodwater flows
downstream. Indeed, although floodwater volumes were less in 2011 than in
2000, the water levels observed downstream were higher in 2011 than in 2000.
Duong et al. (2014) and Marchand et al. (2014) proposed that the higher
downstream river water levels observed during the 2011 floods could be due to
the construction of higher dikes. Fujihara et al. (2015) pointed out the need
for more research to understand the impacts of high dike construction.
Despite the rapid expansion of high dike systems for triple rice cultivation
in the upper Mekong Delta, few modeling
studies have as yet assessed the implications of such dikes for floodwater
regimes. Defined as “the prevailing characteristics and
distribution of flood pulses and variability within and across years, is
controlled by geography, geology, climate, and human modifications and drives
physical and ecological processes within floodplain ecosystems, affecting the
diversity, abundance, and communities of species” (Whipple et al., 2017).
The study presented in this paper aimed to fill these knowledge gaps by using 1-D and quasi2-D modeling to test the hypothesis that large-scale high dike construction reduces the flood retention capacity of the floodplains and increases water levels and the corresponding flood risk downstream. We first examined the impacts of dike construction on flood dynamics, focusing particularly on changes in river water levels and the spatial distribution of floods on the VMD floodplains. We then developed and calibrated a hydrodynamic model for the entire VMD to simulate flooding under different dike construction scenarios. Using the simulation results we calculated water balances to identify and quantify changes in flood dynamics. The modeling results enabled us to analyze changes in flood patterns and river water levels across the VMD due to dike construction. Finally, we analyze and discuss some of the accompanying uncertainties, closing with a number of conclusions.
The Mekong Delta covers some 5 million ha, extending down from Kratie in Cambodia through the VMD to the Gulf of Thailand and South China Sea. At Chaktomuk, its main river, the Mekong, meets the Tonlé Sap River, which in the wet and dry season, respectively, adds and abstracts water to and from the more northern Tonlé Sap Lake. Under Phnom Penh, the Mekong again divides, entering Vietnam in two branches: the Mekong River (called the Tien River in Vietnam) and the Bassac River (called the Hau River in Vietnam) (Manh et al., 2014; Kummu et al., 2014).
Located in the North Pacific monsoon climate (Tamura et al., 2010; Manh
et al., 2014), the Mekong Delta is strongly impacted by both flooding
upstream and the tidal flows of the Gulf of Thailand and South China Sea.
Flooding occurs in the wet season, from July/August to November/December,
beginning when the annual average discharge at Kratie exceeds
13 600
Location of the Mekong Delta and Long Xuyen Quadrangle (LXQ).
The LXQ and Plain of Reeds floodplains, due to their huge water retention
capacity, play a key role in moderating peak floods. Floodwaters originate
from the two main rivers and overland from Cambodia. As the aim of this study
is to examine the effects of water control infrastructure on floodwater
levels and distribution, we focused on the LXQ, as it has undergone the most
extensive development of high dikes during the past decades. Most
agricultural areas on the LXQ floodplains are protected by low dikes or high
dikes. Low dikes allow floodwaters to overflow into the fields after the
harvest of the second crop in mid-August. High dikes prevent floods
year-round, enabling cultivation of a third rice crop (Howie, 2011). This has
made the LXQ one of the VMD's highest productivity rice areas (Quang et al.,
2012). The LXQ encompasses parts of three provinces, including a large part
of An Giang and Kien Giang provinces and a small part of Can Tho Province
(see also Fig. 1). The LXQ has 0.49 million ha of floodplains, located on the
northern delta, west of the Hau River. Between the river and the dense
network of canals that has long been a feature of this region, numerous dikes
have been built, some topped by roads. Statistics from the Department of
Agricultural and Rural Development show an enormous increase in the area
protected by high dikes in An Giang Province, from 2591
We developed a one-dimensional (1-D) hydrodynamic model using the Mike 11 software developed by the Danish Hydraulic Institute (DHI). This is an implicit finite difference model for 1-D unsteady flow computation. In addition, it can be applied to a quasi-two-dimensional (quasi2-D) flow simulation appropriate for detailed modeling of rivers, including special treatment of floodplains, road overtopping, culverts, gate openings, and weirs (Doulgeris et al., 2012). The modeling procedure allows use of kinematic, diffusive, or fully dynamic, vertically integrated equations for conservation of continuity and momentum (the Saint-Venant equations) to solve complex flow and mass transport problems (Patro et al., 2009; Dung et al., 2011; Manh et al., 2014). In the model, the Saint-Venant equations are formulated as follows (DHI, 2011).
Continuity equation:
We developed our model to represent the river network and floodplains of the Mekong Delta. Data on the Mekong Delta river network and physical properties were derived from the Southern Institute for Water Resources Research (SIWRR). The hydrodynamic module included in Mike 11 was applied to simulate flow dynamics and inundations. We incorporated four main components: (i) the river network, (ii) boundary conditions, (iii) a cross section and (iv) a set of other parameters. Although rainfall accounted for only a small percentage of surface water inflows, we nonetheless included it in the model using the Rainfall Runoff (RR) module.
The 2011 river network was imputed into the model based on available data.
The area of interest – from Kratie and the Tonlé Sap Lake in Cambodia to
the river mouths in Vietnam – encompassed 5 million
Boundary conditions for the model were set using discharges and water levels observed in 2011 and 2013. All daily data were provided by the National Centre for Hydro-Meteorological Forecasting (NCHMF) and SIWRR. Discharges from six stations were imputed for the upstream boundary conditions, while the downstream boundary conditions were provided by water levels measured by nine tide gauges near the coast. Upstream, the discharge at Kratie was the most important boundary input for drawing the main flood hydrograph to simulate discharges and water levels downstream for the VMD.
We embedded 13 000 cross sections in the model. These described the topography of the rivers and branches. Cross-section data were collected from various sources. Data concerning the major streams were very reliable, as these measurements were produced and regularly updated by national projects. For the branches, bathymetric data were used for most cross sections, though this process meant that accuracy was likely lower. These cross sections had, however, been tested in various SIWRR projects.
Our set of other parameters included river roughness, wind effects, and various components derived from DHI (2011). These described the physics of the Mekong Delta. Among them, the river roughness coefficient was the most important and sensitive parameter. River roughness was represented in the model as Manning coefficients, which we initially estimated based on published values corresponding to particular types of rivers and canals (Chow, 1959; Fabio et al., 2010; Dung et al., 2011). First, referring to Chow (1959), we set the Manning coefficients as 0.020 (irrigation channel, straight, on hard-packed smooth sand), 0.025 (earth channel excavated in alluvial silt soil, with deposits of sand on the bottom and grass growth), and 0.033 (natural channel, somewhat irregular side slopes, very little variation in cross section). These were used for all rivers and branches in the three initial model runs to identify changes in water levels and discharges of the main rivers. Second, we calibrated the model by modifying these numbers for the branches in the more coastal areas. After model fitness was satisfactory for the stations near the coast, we defined a range of Manning coefficients (0.024–0.017) for the Tien and Hau rivers. Rivers in the Cambodian part of the delta were given a range of 0.1–0.05, whereas a range of 0.03–0.025 was selected for the rivers and canals on the VMD floodplains. These parameters were optimized during the calibration process.
Daily rainfall data were derived from 37 meteorological stations (28 in Vietnam and 9 in Cambodia). Thiessen polygons were used to describe the contribution of surface water flows to river and canal discharge. In the model, we divided the Mekong Delta into 120 sub-regions, with data from rainfall gauges for each. The rainfall discharge had to be calibrated using the RR module provided with the Mike 11 NAM before it could be used for the hydraulic model simulations.
The flood model had to be calibrated and validated to ensure reliable
performance. For calibration, we used the severe flood year of 2011. To
validate the model, we used data from the 2013 flood season. These 2 years
were selected because the river and infrastructure network, land uses, and
dike locations were similar in both years. The Nash–Sutcliffe efficiency
(NSE) and correlation coefficients were used to check the model's
goodness-of-fit for the calibration and validation periods. The NSE is one of
the most commonly used efficiency criteria in hydrology. It measures how much
of the variability observed is explained by the simulation. A perfect
simulation has an NSE of 1 (Ritter and Muñoz-Carpena, 2013). The
correlation coefficient (
For the calibration and validation periods, we used hourly discharge and water level time series from 15 gauging stations, including 11 stations along the Tien and Hau rivers and 4 stations on the floodplains (Fig. 1). We selected these stations because (i) the objective of our study was to explore the water level dynamics in the main streams and LXQ and (ii) observational data were available from each.
In addition to calibration and validation for the 2011 and 2013 data, we assessed model performance for the 2000 flood hydrograph. Using flow data from 2000, including discharge at Kratie and water levels at nine tide gauges, we ran the model assuming the 2011 river network and land use system. Model outputs were compared to maximum river flows in the Hau River.
To simulate the hydraulic dynamics of the floodplains, the quasi2-D approach was combined with 1-D modeling. In the quasi2-D model, the floodplains were described as a network of fictitious river branches and spillovers with the main rivers. This approach had several advantages, i.e., (i) transferring some of the benefits of 2-D flow calculations and flow directions to the 1-D hydrological model; (ii) saving computation time because fewer input data were needed; and (iii) reliable model representation of physical processes (Lindenschmidt, 2008; Soumendra et al., 2010).
We used different approaches to model the floodplains in Cambodia and in Vietnam. The Cambodian floodplains without channels and dikes were simulated by wide cross sections using the 1-D method. For the LXQ, we applied the quasi2-D approach to formulate the hydrodynamic interactions between the floodplains and rivers under various dike construction scenarios. Although the Plain of Reeds itself was not a focus of this research, we included it in the model with the dikes as constructed in 2011, to better understand the hydraulic interactions between the Tien and Hau rivers via the Vam Nao River and tributaries. The LXQ floodplains are characterized by a dense network of dikes and channels, producing multitudes of compartmentalized fields for agriculture.
Our model has 554 dike compartments representative of the floodplains of the
VMD. Our modeling approach for simulating the interaction between rivers and
floodplains is to consider that each dike compartment is a flood cell. It
means each flood cell is a specifically defined and isolated geographical
area as a rectangle surrounded by real dikes and channels. This approach,
from Dung et al. (2011), is illustrated in Appendix A. In the figure, each
compartment was considered as a flood cell and modeled as a fictitious river
branch with a low and wide cross section, as extracted from a SRTM digital
elevation model (Shuttle Radar Topography Mission DEM,
Dike construction scenarios: (S1) no high dikes, (S2) dike infrastructure as in 2011, (S3) high dikes throughout An Giang Province, and (S4) high dikes throughout the Long Xuyen Quadrangle.
Various dike construction and land use scenarios were developed to explore
the impacts of dikes on flood dynamics (Fig. 2). The first scenario (S1)
provided a baseline to explore flood dynamics without the impact of high
dikes. High dikes are usually built at a height of
2.0–2.5
To understand why and where the water movements on the floodplains cause
changes in downstream flows, we calculated water balances for each scenario.
For the 1-D hydrological model representing the complex hydraulic situation
of the Mekong Delta, all components in the water balance equation were
estimated. The water balance equation is as follows:
From the output of the hydraulic model, we extracted discharge time-series data from canals along the closed boundaries of the LXQ to calculate flow volumes over the July to December period. Inflows include the water fluxes along the Vinh Te Canal and along the Hau River. Outflows were taken from the Cai San Canal and the canal along the Gulf of Thailand. The water balance was also computed for the Hau River. Here, the water fluxes at Chau Doc and the volume of the Tien River were input flows, while the output flows consisted of discharges along the Hau River to the LXQ, through the Cai San Canal, and at the point on the Hau River beyond the Cai San Canal. Rainfall volumes were calculated from the individual rainfall simulation files.
Correlation coefficient and Nash–Sutcliffe efficiency of water levels (WL) and discharges (
(–) Missing data due to unavailability of observed discharge data from station.
Time series of daily simulated and observed flows in 2011 at all stations used for model calibration.
Simulated and observed peak water levels for the 2000, 2011, and 2013 flood years at four stations along the Hau River.
Table 1 presents the calibration and validation results. Additionally, Fig. 3
presents the time-series plots for the streamflow results of 2011.
Model performance was also judged as good considering the small difference
between the peak water levels produced by the simulation and those observed
in 2011 and 2013 (Fig. 4). However, the peak values simulated were in most
cases lower than observed values. The discrepancy was greater for 2000 than
for 2011 and 2013. The simulation returned a slightly lower peak river water
level at Can Tho in 2000 (2.02
Tidal water levels in numbers of hours above various thresholds, observed at the My Thanh and Ben Trai stations in the 2000 and 2011 wet seasons (July to December).
Changes in river water level and origins at Can Tho (2000, 2011).
This raises the question of whether the changes in river water levels at Can
Tho are primarily attributable to changes in the floodplains and canal
networks between 2000 and 2011, or to the effect of the higher tidal
movements observed in the estuaries of the Tien and Hau rivers. Tidal flows
in these estuaries were markedly higher in 2011 than in the peak flood year
of 2000, suggesting potential backwater curve effects (Table 2). However, the
model results for 2000 (using the 2011 river and infrastructure network and
the 2000 river water level and tidal data) compared to those for 2011 (2011
river and infrastructure network and 2011 water levels) show just a modest
increase of 0.08
Figure 4 shows a good fit between the simulated and observed peak water levels for the floods in 2011 (calibration) and 2013 (validation). In the 2000 flood, the fitness is low due to the significant changes in physical topography such as river network and branches and river cross sections between the model setup of 2011 and the measured data in 2000.
Peak water levels under different dike construction scenarios in the boundary canals of the Long Xuyen Quadrangle.
Comparison of peak river water levels at stations along the Hau River resulting from different scenarios (note: LXQ is the Long Xuyen Quadrangle).
Simulation results indicate that if all high dikes were removed (S1), peak
river water levels would be much lower, especially in the upper part of the
Mekong Delta (Fig. 5). Compared to the 2011 situation (S2), peak river water
levels would be reduced by 66
Comparison of peak water levels at stations in the Long Xuyen Quadrangle (LXQ) resulting from different scenarios.
Comparison of peak water levels produced by the scenarios in different flood years.
The increases in river water levels from high dike expansion in An Giang
Province and the LXQ (S3 and S4) show a similar pattern to S2 (dike
infrastructure as in 2011) and S1 (no dikes). The model presents very slight
increases in river water levels (2–3
Paired sample
To assess the impact of different floods on peak river water levels, we ran
our four scenarios with the 2000 and 2013 flood hydrographs, compared to the
base runs for 2011. These simulations resulted in upstream concentrations of
water level increases for all of the three flood hydrographs (Fig. 7). The
largest increases in river water levels were found for the high dike
scenarios (S2, S3, and S4). These produced similar absolute increases in
relation to the no dike scenario (S1) under all three hydrographs. The
suggestion here is that peak levels in the Hau River are relatively
independent of the amount of floodwater and flow regime, as water volumes for
the simulations differed quite starkly, from
Observed and simulated peak water levels along the Hau River.
Across the four scenarios and the three flood hydrographs, our model results indicate pronounced increases in water levels in the upstream reaches, with levels remaining fairly constant downstream (Fig. 8). For scenarios S1 and S2 and the 2000, 2011, and 2013 flood hydrographs, we calculated coefficients of variation (CV) for the water levels. At Chau Doc, upstream, the CV was 0.47, diminishing to 0.07 downstream at Can Tho. Two explanations may account for the limited variability found in water levels downstream: (i) use of tidal water level data at the river estuary as a boundary condition for the model and (ii) the coast-to-upstream direction of our model calibration procedure.
The tidal water level data at the estuary of the Mekong were influenced by the (peak) river discharges in the years considered, with peak river flows particularly influencing river mouth levels at high and low tide. Thus, our model's boundary conditions were not only set by tidal movements, but also influenced by river discharges at the estuary mouth for the years considered. In calibrating our model, we first set the roughness coefficients for the coastal area to agree with recorded water levels before calibrating for river water levels and discharges in the upstream parts. This potentially reduced the variability in downstream water levels. Potential biases of water level would be propagated toward the upstream reaches and outer edges of the model.
On the other hand, the dissipation effect of a floodplain and river network as large as the Mekong Delta is expected to yield relatively smaller change amplitudes in downstream water levels, as changes are modulated across a large area. However, any further reduction in the floodplain area and its dissipation capacity would be expected to produce a markedly increased amplitude in downstream water levels.
Comparison of total inflow to system volume.
Water balance calculations for the Long Xuyen Quadrangle under the various scenarios. Red numbers indicate the difference with scenario S1 (no high dikes).
To further assess the model's simulation of the hydrodynamic characteristics
of the Mekong Delta, we conducted a water balance analysis of flood volumes
for the LXQ. Compared to the situation without high dikes (S1), the high dike
scenarios (S2, S3, and S4) produced a reduction of floodwaters flowing into
the LXQ (Fig. 9). The floodwater volume decreased from
The floodwater volumes reaching the LXQ diminish with large-scale high dike
construction, that is, in scenarios S2, S3, and S4, due to several factors.
First, overflow from the Cambodian floodplains into the Vinh Te Canal drops
from
The high dike scenarios (S2, S3, and S4) also resulted in changes in flow
directions of the modeled flood streams and in volumes. As a consequence,
there was only a slight increase in flood volume in the downstream (estuary)
reach of the Hau River. In the Vinh Te Canal, a flood stream amounting to
Recent flood dynamics of the Mekong Delta have raised concerns about an increased flood risk downstream in the VMD. Some authors suggest that a greater flood risk downstream might be linked to the prevalence of high dikes on the upper VMD floodplains (Hoa et al., 2007; Duong et al., 2014; Marchand et al., 2014; Fujihara et al., 2015). Using a 1-D hydrodynamic model combined with a quasi2-D approach (following Dung et al., 2011), we quantified the impacts of extensive high dike construction on floodwater levels and flood risk across the VMD. Most hydrodynamic studies of the Mekong Delta have retrofitted modeled changes (e.g., dikes and canal network) to past flood events (e.g., flood levels and flood area data). Whereas good fits are generally reported between model outputs and recorded water levels, these studies are unable to explain how flood volumes are distributed over the delta. We therefore elaborate one of the new studies to explore the 1-D with a quasi2-D model advantage, considering potential hydraulic impacts of existing and planning dike construction scenarios on the flood regimes in the VMD. We fill this knowledge gap by using water balance calculations to explain where floodwater delivers under the dike scenarios.
In our study, we calculated water balances for the flood scenarios and events
considered, to provide insight into the spatial redistribution of flood
volumes due to changes in dike prevalence. Our results show a clear impact of
dike construction on floodwater levels in the Hau River. The high dike
scenarios (S2, S3, and S4) produced a marked increase in peak river water
levels in the upstream reaches of the Hau River (
Regarding the flood hydrographs and floodwater volumes examined, representing
the flood conditions of 2000, 2011, and 2013, we found fairly limited effects
of extensive dike construction on the water levels of the Hau River and canal
network. Although total flood volumes differed markedly (
These results are consistent with those of other authors making use of 1-D
hydrodynamic models with quasi2-D approaches. Previous studies report water
level increases of
Our model performed well in the calibration (S2, 2011) and validation (S2, 2013) runs, in which the state of high dikes in 2011 was compared with the recorded water levels from gauging stations for the hydrographs of 2011 and 2013. Consistent with previous work, this suggests that our model setup and calibration were able to reproduce recorded water levels. Our simulation runs did not return a neat fit with the recorded water levels in the Hau River in the 2000 flood hydrograph (Fig. 8). Upstream, our scenarios returned lower than recorded values, and downstream at Can Tho our values were slightly higher. In part, this may be attributable to changes in the river and canal network between 2000 and 2011 (e.g., additional dredging and excavation). These may have altered the hydraulic properties of the Hau River in ways not captured in our scenarios.
The major known change in this period, that is, expansion of high dikes (from
At the outset of our study, we expected expansion of high dikes to produce greater discharges in the Hau River, resulting in a more pronounced backwater curve and higher water levels at Can Tho, such as those reported at the peak of the 2011 floods. However, this was not corroborated by our modeling results. Water levels at Can Tho were stable, the main changes in water levels being upstream. The relative stability of the water level at Can Tho can only be explained by a relative stability in discharge in the lower reaches of the Hau.
Our water balance analysis used the 2011 hydrograph for all of our scenarios
to show how water is redistributed over the delta in the various model
simulations. According to the scenario runs, the impacts of floodwater
retention losses in the LXQ due to high dike construction are concentrated in
the upstream and eastern reaches of the delta, with minimal impacts
downstream in the Hau River and at Can Tho. The simulation runs further show
increases in floodwater volumes and flood risk to be redirected toward the
Tien River and Plain of Reeds, as well as the Cambodian floodplains. To be
able to return fairly stable water levels downstream in the Hau River (at Can
Tho and the estuary mouth), reductions in the flood retention capacity of the
LXQ (S2, S3, and S4) are compensated for by reduced floodwater volumes
entering the system and the LXQ floodplains (
Some limitations need to be considered in our study. We could not fully validate the suggested reduced flood inflows to the Long Xuyen Quadrangle, and subsequent diversion of floodwaters to the Plain of Reeds and Cambodian floodplains, due to lack of monitoring data for these areas. Our model results regarding the spatial redistribution of floodwater volumes could have been influenced by the way we calibrated the model as well as the model uncertainty. The hydrodynamic model approach applied could also have influenced the accuracy of flood simulation and water balance equations. On a small scale, two-dimensional and three-dimensional hydrodynamic models (2-D and 3-D) are most suitable for simulating the flood dynamics of a complex floodplain. However, 2-D and 3-D models are at present difficult to apply to large areas, such as the Mekong Delta, due to the detailed data and computational capacity required (Soumendra et al., 2010; Dung et al., 2011). The aims of our study dictated a focus on a large part of the delta, as we were interested in the impacts of upstream water control measures on downstream river water levels. Given the constraints in data and available model configurations, we combined the 1-D model with a quasi2-D approach. Our modeling results are in line with previous studies applying similar methods. Our water balance analysis suggests that it would be recommendable to invest in better and more comprehensive data availability, as well as additional computational capacity, to enable more in-depth study of floodwater movements on the delta through 2-D and 3-D modeling.
Development of extensive high dikes to enable triple rice cultivation in the
upstream floodplains of the VMD has raised critical concerns about
environmental impacts, especially changing water flows and downstream flood
risk. We used a 1-D-quasi2-D modeling approach to assess the impacts of four
dike development scenarios on floodwater volumes and distributions on the
delta, focusing on changes in peak water levels and the delta-wide water
balance. Our study had three main findings.
All in all, our results indicate substantial impacts of large-scale dike
construction on peak flood levels, flood retention capacity, and the
delta-wide water balance in the Mekong Delta. Flood risk in the Mekong Delta
will likely increase as a direct consequence of high dike construction,
especially in view of the cumulative impacts of other factors, such as sea
level rise, land subsidence, and more extreme rainfall due to climate change.
Any plans for future expansion of high dikes should therefore be subject to
careful deliberation and detailed impact assessment. From a hydraulic
modeling perspective, dike impact assessment should be conducted on
a delta-wide scale and pay special attention to opportunities for model
calibration and validation for the Cambodian floodplains and Plain of Reeds.
Data used for the study are freely requested by sending emails to the correspondence author.
The left figure describes the 1-D-quasi2-D modeled river network of the VMD and the right figure shows a representative typical floodplain compartment. The approach is from Dung et al. (2011).
Graphs of correlation and Nash–Sutcliffe efficiency of daily simulated and observed flows in 2011 at all stations used for model calibration.
Time series of daily simulated and observed flows in 2013 at all stations used for model calibration.
Paired sample test for water level time series along the Hau River in 2011.
The authors declare that they have no conflict of interest.
This research for this paper was funded by the NUFFIC/NICHE VNM 104 project, which is co-funded by the Netherlands Government and Vietnam National University. We thank the experts at the Water Resources Management Group of Wageningen University, the Netherlands, and the Center of Water Resources Management at the Vietnam National University for their valuable comments and support. In addition, we are grateful to DHI for providing a Mike 11 model license during the study. Edited by: Dominic Mazvimavi Reviewed by: Jonathan Remo and two anonymous referees