Flood prediction in the karstic suoimuoi

In this study, a distributed flood modelling approach, WetSpa, was performed by modifying model representations of some of the predominant features and processes of the karstic Suoimuoi

Eds O Batelaan, M Dusar, J Masschelein, Vu Thanh Tam, Tran Tan Van, Nguyen Xuan Khien

FLOOD PREDICTION IN THE KARSTIC SUOIMUOI CATCHMENT,

VIETNAM

Y.B LIU1, O BATELAAN1, N.T HUONG2, V.T TAM2 and F DE SMEDT1

1 Dept Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Belgium

yongbliu@vub.ac.be

2 Research Institute of Geology and Mineral Resources, Thanh Xuan-Hanoi-Vietnam

Abstract The major obstacles to modelling flood processes in karstic areas are a lack of

understanding and model representations of the distinctive features and processes associated with runoff generation in those regions and a lack of field data In this study, a distributed flood modelling approach, WetSpa, was performed by modifying model representations of some of the predominant features and processes of the karstic Suoimuoi catchment with complex-terrain and mixed land use in the northwest Vietnam The model was calibrated based on 15 months of hourly hydrometeorological data, topography, land use and soil types in GIS format, and used to continuously simulate both baseflow and fast-responding overland, conduit and channel flows during stormflow periods Considerable variability in simulation accuracy was found among storm events and within the catchment The simulation results showed that the model represents reasonably well stormflows generated by rainfall events in the study catchment, and the potential of using distributed flood simulation for estimating future flood conditions under changing land use conditions It is argued therefore that the WetSpa approach is suitable for application in karstic areas under human and natural pressure

Keywords: flood prediction, WetSpa, GIS, karstic Suoimuoi catchment

1 Introduction

The Suoimuoi River catchment is situated in

the mountainous Da River basin in the

Northwest Vietnam It covers an area of 273

catchment outlet The catchment is confined by

two regional deep fault systems trending in

NW-SE direction, the Son La Fault on the east

and the Da River Fault on the west A range of

non-limestone and limestone rocks of different

formations are exposed within the catchment

as shown in Fig 1 There is almost no surface

water drainage in the karst area Instead, closed

depressions exist here and there with cave

systems developed in the bottom or in the rock

walls (Tam, 2003) The karst aquifers receive

water, mainly by the regional groundwater

flow, with additional important in-situ

recharge by rainfall, surface water and exotic

water from higher-lying non-karstic areas The

movement of karst groundwater is closely

controlled by these tectonic conditions The

groundwater is mainly stored in fractures,

crushed zones and caves Along the river

course, there exist a number of karst

springs/resurgences and sinkholes in which the

interaction between karst groundwater and

surface runoff occurs (Tam et al., 2001)

The Suoimuoi catchment is characterized

by a humid subtropical climate and influenced

by the monsoon regime prevailing in Northern Vietnam Two distinct seasons can be observed

in the area: the dry winter lasting from November to April and the extensive rainfall summer from May to October The yearly mean temperature is 21.1°C, the mean annual precipitation is 1450 mm of which about 85%

Suoi Muoi sink hole

#

N

#

Limestone formation Hamrong Banpap and Chiengpa c Holocene

Camthuy and Nam tham Upper Donggiao

Streams Boundary

Fig 1: Distribution of karst limestone in the

Suoimuoi catchment

falls during the rainy season in summer

According to Tam (2003), the total discharge

of the Suoimuoi River tributaries located in the

non-limestone area west of the Sonla fault

contributes only 7-10% to the total river

discharge measured at the Suoimuoi sinkhole

However, their discharge contribution can rise

up to 25% of the total river discharge during

storms due to surface runoff generated from

the steep non-limestone rocks

During the period 2000-2003, an extensive

hydrological and geophysical survey was

conducted to study the mechanisms of

hydrogeological processes in the Suoimuoi

catchment Many sophisticated methods, such

as computer modelling, hydrogeological

mapping, tracer and pumping test, etc., are

performed to analyze complex groundwater

systems However, computer modelling is

difficult to realize accounting for

turbulent-flow conduits in the karst areas Even dye

tracing, which is usually considered the most

convincing tool for delineating groundwater

basins in karst, is not physically or

economically feasible in some cases, and

rarely gives more than an outline of the major

conduit flow Geophysical surveys may help to

delineate the local geologic framework and

major conduits, but the surveys cannot

determine detailed flow patterns and divides in

karst areas However, all these methods

provide significant information and analysis in

delineating drainage systems and determining

hydrological characteristics of the karst

aquifer In this paper, a flood simulation

approach for the Suoimuoi catchment using the

modified WetSpa hydrological model is

presented, for which modelling processes and

parameters are adjusted separately for the

limestone and non-limestone areas based on 15

months of hourly hydrometeorological data

2 Methodology

The watershed model approach used in this

study is a modification of the WetSpa model,

which was originally developed by Wang et al

(1997) to study the Water and Energy Transfer

between Soil, Plant and Atmosphere, and

adapted to flood prediction on hourly basis by

De Smedt et al (2000) and Liu et al (2002,

2003) The hydrological processes are

simulated in a grid-based schematisation of a

river basin including precipitation,

interception, depression, surface runoff,

infiltration, evapotranspiration, soil moisture storage, interflow, percolation, groundwater storage and discharge The model uses the spatial information of catchment topography, soil type and land use, and recorded meteorological data to predict river flow hydrographs and spatially distributed hydrological characteristics, such as soil moisture, infiltration rates, groundwater recharge, surface water retention or runoff, etc For the non-limestone areas in the Suoimuoi catchment, the WetSpa model is applied, for which the runoff of each grid cell

is calculated by a modified rational method

s CP

where Vs is the amount of surface runoff [L], P the net precipitation [L] (rainfall minus interception), θ the soil moisture content [L³/L³], θs the saturated soil moisture content [L³/L³], and C a potential runoff coefficient [-], which is assumed to depend upon slope, soil type and soil cover and interpolated from values collected from literature Next, the generated runoff is routed to the catchment outlet along its flow path using the method of

linear diffusive wave approximation (Liu et

al., 2003)

⎥

⎥

⎦

⎤

⎢

⎢

⎣

−

=

0 2

2 0 3

0

2

1

t t

t t t

t t

U

σ π

where U(t) [T-1] is the flow path unit response function at time t, t0 [T] and σ [T] are mean flow time and its standard deviation The parameters t0 and σ are spatially distributed, so that each flow path has different parameters depending on the length of the flow path and the physical characteristics of the flow path elements The total direct flow at the catchment outlet is calculated by the convolution integral of all flow path responses subjected to the spatially distributed runoff computed for each grid cell The infiltrated water into the soil is used for consequent percolation, interflow and evapotranspiration, which are controlled by the moisture content, hydraulic gradient and soil textures The groundwater flow is simulated using a linear reservoir method on a small subcatchment scale forming the baseflow of river discharge The schemes of WetSpa model are not valid in simulating processes of the kastic area

in the catchment due to the change of hydrological regime Water may flow overland

Trans-KARST 2004 Liu et al

from ridge tops, and then enter the ground in

upland regions through recharge features and

resurgent at springs in low areas Diffuse

infiltration can also take place through the soil

or through epikarst On steep slopes that do not

readily develop sinkholes, diffuse infiltration

can occur through the soil or into bedrock

fissures Moreover, it is difficult to identify

groundwater flow paths and divides in karst

aquifers, which arises from the extreme

heterogeneity and anisotropy of the karst

aquifer, and from changes in groundwater

patterns with different stages of flow For

example, groundwater flow paths, divides, and

basin boundaries can shift in response to rising

groundwater levels during and after major

precipitation events Taking account of the

above specific characteristics, the WetSpa

model is modified as follows in order to better

represent the predominant hydrological

features of the karst area in the catchment

Surface runoff coefficient is set to zero to

reflect the condition that almost no surface

flow is apparent in the karst areas

Water that contributes to conduit runoff in

the unsaturated zone is estimated taking

account of the effects of slope, soil type, land

use, moisture content, and is assumed to be a

linear function of the expected surface runoff

in the WetSpa model, i.e

contributes to conduit flow [L], and α [-] is a

global parameter within the range 0 – 1 and

realized through model optimization

Routing of conduit flow is accomplished by

the method of diffusive wave approximation as

described in Eq 2, but its concentration time

and variance are adjusted based on the analysis

of observed hydrographs

The parameter of hydraulic conductivity

and other soil features (porosity, pore size

distribution index, etc.) are readjusted through

model optimization

Groundwater flow is simulated using a

linear reservoir method, for which the flow

recession coefficient is obtained from the

analysis of observed hydrographs

Through above modification, the WetSpa

model is used to simulate the flow responses to

storm events in the karstic Suoimuoi

catchment Specifically, the pattern of

individual groundwater flow paths tends to

have a strong vertical component in the unsaturated zone and a strong tendency to follow the strike in the saturated zone Conduits carry high-velocity turbulent flow, and they include caves that are large enough to explore The statements about preferred flow routes in this study are supported by the

mapping of accessible conduits (Hung et al.,

2002) The total hydrographs at the catchment outlet are obtained by summation of the direct flow, interflow and groundwater flow from the non-limestone areas and the conduit flow and groundwater flow from limestone areas

3 Application

The measured hydrometeorological data during October 2000 to March 2001 are used

to calibrate model parameters in this study The hourly stream flow into the Suoimuoi sinkhole was captured by an automated water level logger The recorded hourly series of water level was converted to the flow hydrograph by a well calibrated rating curve that was constructed on the basis of many discharge-water level pairs measured at different stages of the stream flow (Tam, 2003) The resulting hydrographs are used in the baseflow separation and the model validation Hourly precipitation was monitored

by an automated logger located 4 km upstream

of the Suoimuoi sinkhole, and was assumed uniformly distributed over the catchment In addition, the data of potential evapotranspiration and air temperature were collected from a nearby gauging station, which are used as input to the WetSpa model

Topographical maps at scale 1:50,000 were available and cover the entire Suoimuoi catchment Based on these maps consisting of

a 20m contour level, A DEM with 50m spatial resolution and the surface drainage network with drainage density of 0.66 km/km2 were created as shown in Fig 2 The topography of the catchment is characterized by highlands in the upper part and lowlands in the lower part

of the catchment Elevation ranges from 539 to 1815m with an average catchment slope of 33.2% The major soil types of the catchment are Cambisol (43.7%) distributed in the highland areas and bed rock (22%) distributed

in the lowland areas in which mature karst landscapes are characterized Other soil types are Fluvisol, Luvisol, Leptosol, Travertin, Acrisol and Nitrosol, which are distributed

over the catchment All these soil types were

converted to the USGS soil texture classes for

use in the WetSpa model (Huong, 2002) The

distinguished land use types are: close canopy

forest (1.7%), open canopy forest (4.2%),

shrub (40.4%), grass land (5.6%), upland fields

(38.3%), paddy fields (5.2%), residential area

(4.5%) and open water (0.01%) and are

distributed as shown in Fig 3 The above land

use categories were further converted to the

WetSpa land use types based on vegetation and

land use assessment (Huong, 2002)

Model parameters are identified firstly

using GIS tools and lookup tables, which relate

default model parameters to the base maps, or

the combination of base maps Starting from

the 50 by 50 m pixel resolution digital

elevation map, hydrologic features including

surface slope, flow direction, flow

accumulation, flow length, stream network,

drainage area and sub-catchments are

delineated The threshold for determining the

stream network is set to 50, i.e the cell is

considered to be drained by streams or

conduits when the total drained area becomes

greater than 0.125 km² The threshold for

delineating subcatchments and main streams is

set to 1000 Maps of porosity, field capacity,

wilting point, residual moisture, saturated

hydraulic conductivity and pore size

distribution index are obtained from the soil

type map Maps of root depth, Manning’s

roughness coefficient and interception storage

capacity are derived from the land use map

Maps of default runoff coefficient and

depression storage capacity are calculated from

the slope, soil type and land use class

combinations The residential areas are mainly distributed besides the Suoimuoi river channel

as villages or small towns Due to the grid size, the residential cell is assumed 10% covered by impervious materials (roof, road, etc.), and the rest covered by farmland The average flow depth is estimated using the power law relationship (Molnar and Ramirez, 1998) with

an exceeding probability of a 2-year return period resulting in a minimum overland flow depth of 0.005 m and the channel flow depth

of 1.0 m at the catchment outlet By combining the maps of the average flow depth, the Manning’s roughness coefficient and surface slope, average flow velocity in each cell is calculated using Manning’s equation, which results in a minimum value of 0.005 m/s for overland flow, and up to 2.5 m/s for some parts

of the main river Next, the celerity and dispersion coefficient at each cell are produced, and the values of concentration time and its standard deviation for each contributing

cell are generated as described by Liu et al

(2003) With the above information, the unit flow path response functions are calculated from each cell to the sub-basin outlet and from the sub-basin outlet to the basin outlet

In dealing with the specific problems of karst areas in the Suoimuoi catchment, the WetSpa model is modified, the surface runoff coefficient is set to zero, and the conduit flow and groundwater flow are estimated separately

by a conceptual method and a linear reservoir method The volume of water contributed to the conduit flow in the unsaturated zone is assumed to be a linear function of the surface runoff in the non-limestone areas under the

Elevation ( m)

53 9 - 616

61 6 - 726

72 6 - 836

83 6 - 946

94 6 - 1055

10 55 - 1165

11 65 - 1275

12 75 - 1385

13 85 - 1495

14 95 - 1815

Streams Boundary

N

Fig 2: Topographic map of the Suoimuoi

catchment

N

Land use Paddy field Grass land Open canopy forest Close canopy forest Upland field Shrub Residential area

Streams Boundary

Fig 3: Land use map of the Suoimuoi catchment

Trans-KARST 2004 Liu et al

same condition of slope, soil type and land use

Likewise, the concentration time of conduit

flow is estimated by the calculated surface

flow time multiplied by a correction factor

Using GIS tools and the hydrological

modelling extension, the average calculated

flow time of the karst areas is computed by

integration of the flow time of each grid cell

weighed by its percentage of area and flow

coefficient A correction coefficient is then

obtained by comparing the value with

observed hydrographs at the catchment outlet,

and applied to each karst cell in the catchment

Additionally, model parameters in the karst

areas, such as the hydraulic conductivity, pore

size distribution index, etc., are adjusted during

model calibration by multiplying a correction

factor for each of them The groundwater

recession constant at the catchment outlet is

found from the baseflow separated from the

observed hydrographs This value is around

0.018 day-1 according to Tam (2003), and is

adjusted for each subcatchment based on its

slope, drainage area and geological features

Model calibration is implemented by

comparing the simulated hydrograph with the

observed hydrograph Each of the correction

factors and functions involved the use of

coefficients is determined using an

independent model optimization process

(Doherty and Johnston, 2003) The objective

function is the sums of squares of the

difference between observed and predicted

flows at the Suoimuoi sinkhole The correction

factor for estimating the volume of conduit flow is found around 0.15 The concentration time of conduit flow is about 1.5 times the surface runoff, while the hydraulic conductivity is about 2.5 times the default value and the soil pore size distribution index

is around 1.0, which leads to a very high percolation to the saturated zone in the karst areas A graphical comparison between observed and predicted hydrographs during the simulation period is presented in Fig 4

It can be seen from the figure that the hydrograph at the Suoimuoi sinkhole is well reproduced by the model Four statistical evaluation criteria were applied to the 15 months simulation results to assess the model performance It is found that the WetSpa model reproduces the observed water volume with -3.4% under estimation The model Nash efficiency for reproducing the river discharges

is 69% (Nash and Sutcliffe, 1970) The adapted Nash efficiency for reproducing low flows is 85%, and for high flows 70%, which indicate that the model is suitable for water balance simulation and flood prediction in the karstic Suoimuoi catchment, but the accuracy

of peak discharge prediction needs to be improved The model is also able to simulate the spatial variation of other hydrological characteristics at each time step, including surface runoff, infiltration, actual evapotranspiration, groundwater recharge, etc This gives the advantage of computer automation and analyzing the effects of

Fig 4: Observed and calculated flow hydrographs at Logger station during 2000-2001

topography, land use and soil type on the

hydrologic behaviour in a river basin

4 Conclusions

A test of a GIS-based modelling approach for

flood prediction in the karstic Suoimuoi

catchment was described The model uses a

modified rational method to calculate surface

runoff in non-limestone areas and conduit flow

in limestone areas based on the spatial

characteristics of topography, soil type, land

use and moisture condition Flow into the

outlet sinkhole was routed with the linear

diffusive wave approximation method, while

the concentration time of conduit flow is

multiplied by a correction coefficient Total

discharge at the basin outlet was calculated by

summing predicted flow from both

non-limestone and non-limestone areas in the

catchment The model was calibrated on a

15-month flow data series collected at the

Suoimuoi sinkhole The results of the

calibration show that in general flow

hydrographs are well predicted, especially the

baseflow at the catchment outlet However, the

predictions of peak discharge for some of the

storms are not satisfied indicating the need for

improved methods of runoff volume

calculation flow routing in karstic catchments

As described in the paper, the karstic

aquifers in the Suoimuoi catchment possess

large underground reservoirs of water, but

these reservoirs are difficult to exploit because

little is known about their hydraulic behaviour

A simple hydrological model, like the WetSpa

model used in this study, can provide useful

information about the behaviour of such

complex flow system The model explicitly

acknowledges the lack of detailed knowledge

about the location and size of conduits and

other flow paths with fewer data requirements

and calibration parameters In addition, the

effects of topography, soil type and land use on

potential runoff, recharge and outflow can also

be evaluated Work is continuing on

incorporating a more physical-based approach

in estimation of runoff volume and flow

transport into the model to study the complex

hydrological behaviour of the river catchment

5 References

De Smedt, F., Liu, Y.B and Gebremeskel, S., 2000

Hydrological modelling on a catchment scale

using GIS and remote sensed land use information, ed., Brebbia, C.A., 295-304, Risk Analyses II, WIT press, Southampton, Boston Doherty, J and Johnston, J.M., 2003 Methodologies for calibration and predictive analysis of a watershed model, Journal of the American Water Resources Association, 39(2), 251-265

Hung, L.Q., Dinh, N.Q., Batelaan, O., Tam, V.T and Lagrou, D., 2002 Remote sensing and GIS-based analysis of cave development in the Suoimuoi Catchment (Son La - NW Vietnam), Journal of Cave and Karst Studies, 64(1), 23-33 Huong, N.T., Application of the WetSpa model to the Suoimuoi catchment, Vietnam, MSc Thesis, Inter-University Programme in Water Resources Engineering, Katholieke Universiteit Leuven and Vrije Universiteit Brussel, Belgium, 2002 Liu, Y.B., Gebremeskel, S, De Smedt, F, Hoffmann, L and Pfister, L., 2003 A diffusive transport approach for flow routing in GIS-based flood modelling, Journal of Hydrology,

283, 91-106

Liu, Y.B., Gebremeskel, S., De Smedt, F and Pfister, L., 2002 Flood prediction with the WetSpa model on catchment scale, eds., Wu et al., 499-507, Flood Defence ‘2002, Science Press, New York Ltd

Molnar, P and Ramirez, J.A., 1998 Energy dissipation theories and optimal channel characteristics of river networks, Water Resources Research, 34(7), 1809-1818

Nash, J.E and Sutcliffe, J.V., 1970 River flow forecasting through conceptual models, Part 1:

A discussion of principles, Journal of Hydrology, 10, 282-290

Tam, V.T., 2003 Characterization of a Kastic system by an integrative and multi-approach study, a case study of Suoi Muoi and Nam La catchments in the Northwest Vietnam, Doctoral Thesis, Vrije Universiteit Brussel, Belgium Tam, V.T., Vu, T.M.N and Batelaan, O., 2001 Hydrological characteristics of a karst mountainous catchment in the Northwest of Vietnam, Acta Geologica Sinica, Journal of the Geological Society of China, 75(3), 260-268 Wang, Z.M., Batelaan, O and De Smedt, F., 1997

A distributed model for water and energy transfer between soil, plants and atmosphere (WetSpa), Physics and Chemistry of the Earth, 21(3), 189-193