Parameterization Using ArcView GIS in Medium

8/11/2019 Parameterization Using ArcView GIS in Medium

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GIS and Remote Sensing in Hydrology, Water Resources and Environment (Proceedings ofICGRHWE held at the Three Gorges Dam, China, September 2003). IAHS Publ. 289, 2004 50

Parameterization using ArcView GIS in mediumand large watershed modelling

Y. B. LIU1, F. H. DE SMEDT

1, L. HOFFMANN

2 & L. PFISTER 2

1 Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2,1050 Brussels, [email protected]

2 Research Unit in Environment and Biotechnologies,Centre de Recherche Public-Gabriel Lippmann, 162a Avenue de la Faïencerie, L-1511 Luxembourg

Abstract This paper describes the development of a comprehensive subcatch-ment parameterization method for medium and large-scale watershedmodelling based upon the fully distributed WetSpa model. A simplifiedapproach is used in order to take advantage of the existing spatial analysisfunction within ArcView GIS and its Spatial Analyst and hydrologicalmodelling extensions. The model combines elevation, soil and land use datawithin a GIS framework, and predicts the flood hydrograph and water balanceover a river basin. The method of diffusive wave approximation is applied intracing water for both overland flow and channel flow, while the water andenergy budget are maintained for each very small watershed derived from thehigh resolution DEM. The method was applied to the Alzette River basin, an1176-km

2 medium-size watershed in the Grand-Duchy of Luxembourg. Good

agreement between the predicted and measured hydrograph has been achieved

according to the graphical comparison and statistical assessment.Key words Alzette River basin; ArcView; diffusive wave; watershed modelling

INTRODUCTION

Along with the rapid development of GIS technology and remote sensing techniques,

especially the concomitant availability of high resolution DEM and the advances in

integrating GIS with hydrological modelling, flood prediction with distributed models

tends to be more advantageous and competent. However, model simulation for long

time series on medium and large catchment scales (e.g. drainage areas are greater than

500 km2), with small grid sizes and short time intervals is tedious, costly, and time

consuming. This is because the computation time and the use of computer memory are

affected by the number of cells involved in the catchment. If the watershed is large or

the cell dimension too small, the number of cells increases so that the computation

time increases and the free memory of the computer is often insufficient, and therefore

the simulation is sometimes difficult or impossible to realize with a personal computer.

One approach to cope with this problem is to increase the cell size, which may

introduce errors by aggregation of spatial input data and misrepresentation of the true

catchment characteristics. Studies have shown that grid size has remarkable effects on

the simulation results of both runoff generation and flow routing. The coarser

resolution results in a lower peak discharge and earlier time-to-peak, especially for thestorm events of short duration and lower antecedent soil moisture condition (Molnar &

Julien, 2000). This is in agreement with the argument used by Beven (1995) that the



Parameterization using ArcView GIS in medium and large watershed modelling 51

aggregation approach towards macro-scale hydrological modelling, using averaged

parameter values, is inadequate for representing hydrological processes at a large scale.

In addition, terrain information is lost as a result of data aggregation at a larger grid-

cell size, and the high-resolution data is not fully utilized in the watershed modelling.

In this study, an operational method for the automated physiographic para-

meterization of a hydrological model is developed. The purpose is to provide an

enabling technology for GIS-based medium and large watershed modelling, utilizing

high-resolution information as much as possible. Using this technique, it is possible to

derive all necessary model parameters rapidly and accurately, and at different

discretizing levels by varying the number of GIS derived subcatchments. A case study

in the Alzette River basin, Grand-Duchy of Luxembourg, is presented.

METHODOLOGY

The modelling approach used in this study is based on the WetSpa (Water and Energy

Transfer between Soil, Plant and Atmosphere) model, which was originally developed

by Wang et al. (1996) and adapted for flood prediction on an hourly time scale by

De Smedt et al. (2000) and Liu et al. (2002). WetSpa is a raster-based simulation

model where computations are carried out on a cell-based pattern and at an hourly time

step. The model conceptualizes a basin hydrological system being composed of

atmosphere, canopy, root zone, transmission zone, and saturation zone layers. The

watershed is discretized into a number of grid cells, for which the water and energy

balance are maintained. The hydrological processes include: precipitation, interception,depression, surface runoff, infiltration, evapotranspiration, percolation, interflow,

overland and channel flow, and groundwater flow. The water budget computations for

interception storage, depression storage and soil moisture storage are carried out for

each cell, while the water balance for groundwater storage is computed on a small

subcatchment scale. A mixture of physical and empirical relationships is used to

describe the hydrological processes in the model. The model predicts flow hydro-

graphs, which can be defined for any numbers and locations in the channel network,

and can simulate the spatial distribution of catchment hydrological characteristics.

In order to deal with the computing time and memory problem when applying the

fully distributed WetSpa model to a medium or large river basin, the model is modified

to a semi-distributed pattern, where the water budget is computed for each very small

subcatchment, built up from high resolution DEM data, rather than large grid cells

with approximately the same area as the subcatchments. The GIS derived very small

subcatchment serves as a relatively homogeneous hydrological unit, with the same

precipitation and potential evapotranspiration. The advantage of this method is to

maintain the internal drainage structure of the subcatchment, for which surface runoff,

interflow and groundwater flow can be estimated at the subcatchment outlet, and the

respective water and energy are balanced for each subcatchment. Simulations using

large grid cells do not have this advantage, because the cell boundary may not

adequately reflect the true drainage boundary.

Using GIS ArcView, the meteorological and hydrometrical stations are geo-referenced to the base map and a spatial analysis of the precipitation and potential

evapotranspiration is undertaken for the entire catchment with ArcView Thiessen



Y. B. Liu et al.52

Polygons extension. The hourly mean precipitation and mean potential evapotranspira-

tion inputs to the model for each subcatchment are then calculated by weighting the

data of the neighbouring meteorological stations, for which the weight of a given

station is predetermined by its areas percentage of the whole catchment, using a high

resolution base map and the ArcView Spatial Analyst extension. In such a way, the

spatial distribution of meteorological variables is fairly taken into account in the

model. Model parameters such as interception storage capacity, depression storage

capacity, potential runoff coefficient, overland roughness coefficient, root depth, soil

property parameters, average travel time to the outlet, dispersion coefficient and so on,

are firstly calculated for each grid cell using ArcView lookup tables and the high

resolution DEM, soil type and land use maps, or a combination of the three base maps.

The mean parameters for each subcatchment are obtained by integrating the values

from all cells of that subcatchment. These results give a better estimation of the natural properties compared with the method using large grid cells, since a unique value is

assigned to each pixel for grid type data and therefore the grid re-sample enumeration

may cause errors in reflection of the cell characteristics. In general, coarser spatial

resolution tends to produce shorter flow paths, and hence decreases the flow time and

the hydrodynamic and geomorphological attenuation of the flood wave. In the

modified WetSpa model, the above drawbacks can be overcome, for which the

minority categories of land use and soil type are taken into account in each very small

subcatchment, and the flow time and dispersion coefficient are calculated based on the

high resolution DEM. The unit response functions for each simulation unit at their

outlets are then obtained by integration of the cell response functions within that

subcatchment weighted by the potential runoff coefficient, whereas the channel

response functions from each subcatchment outlet to the basin outlet are obtained by

integration of the cell response functions along the stream channel. If the discretization

of the watershed is highly intensive, the water may flow out of the subcatchment

within the first time step, and the process of flow routing within each subcatchment

can be omitted. However, this process is maintained in the model in case of coarse

discretization of the catchment or very small time intervals of the model simulation.

Using the above predetermined parameters for each subcatchment, the model is

operated within the ArcView interface and with the input meteorological data of

hourly precipitation and potential evapotranspiration of different gauging stations. The

division of the watershed should be performed according to the project purpose and thecomplexity of the terrain. Dividing the watershed into very small subcatchments is

necessary in order to take care of most of the spatial variability of the hydrological

processes. However, for a medium sized plain watershed, if the project purpose is

flood prediction only at the outlet of the watershed, dividing that watershed into a few

hundred or even tens of subcatchments might be sufficient. Then, the modified WetSpa

model could be considered as becoming more and more semidistributed as the

dimensions of the subcatchments increase. A few simulations are necessary to decide

how many subcatchments are necessary to meet various objectives.

APPLICATIONS

One case study of the modified WetSpa model was performed on the Alzette River

basin using available hydro-meteorological data from December 1996 to December




2000, for which topography and soil data were available in GIS form, while the land

use data was obtained from remote sensed images. The study area covers an area of

1176 km2 and is located mostly in the Grand-Duchy of Luxembourg, where high-

magnitude floods occurr frequently and have caused important damage since the early

1990s. Figure 1(a) shows the location of the study area as well as the river system and

monitoring network. The study area generally has a rolling topography, with elevation

ranging from 195 to 545 m and an average basin slope of 8.7%. The soil is mostly a

sandstone/limestone mixture with areas of clay and marl, and can be classified as

texture categories: loamy sand (16.7%), silt loam (8.1%), silt (21.4%), loam (22.7%),

sandy clay loam (10.9%), silt clay loam (15.4%) and clay loam (4.8%). The dominant

land use types in the catchment are deciduous shrub and forest (33.7%), grassland

(30.7%) and cropland (23.3). Urban area (11.2%) is also one of the major land use

types in the basin, as the study area is highly developed with several towns; Ettelbruck,Mersch, Luxembourg-city and Esch/Alzette. Other land use types are free water

surface (0.2%), and former mining areas (0.9%), which are located on the right bank

tributaries of the upstream catchment and disturb the local hydrological behaviour

during storm events. Figure 1(b) shows the land use of the watershed.

The climate in the region has a northern temperate humid oceanic regime without

extremes. Rainfall has a relatively uniform distribution over the year. High runoff

occurs in winter and low runoff occurs in summer due to the higher evapotranspiration.

The average annual precipitation in the region varies between 800 mm to 1000 mm,

Ettelbruck

Livange

Steinsel

Mersch

Hesperange

Pfaffenthal

#

#

##

Schoenfels

Mamer

Hagen

Reichlange

Bissen

National boundaryAlzette boundary

Stream networkDaily rain gaugeHourly rain gauge

Stream gauge

#

0 5 10km

N

EW

S

Fig. 1 (a) Study area and measurement networks. (b) Land use of the Alzette basin.

(a) (b)



Y. B. Liu et al.54

and the annual potential evapotranspiration is around 550 mm. A dense rainfall and

discharge monitoring network has been set up to study the hydrological behaviours of

the river basin since 1995 as shown in Fig. 1(a). Potential evapotranspiration is esti-

mated using the Penman-Monteith formula with measured meteorological data in

Luxembourg-city, and extended to each rainfall Thiessen polygon based on the

proportions of different land use type over the polygon (Drogue et al ., 2002). The daily

areal rainfall interpolated via Thiessen polygons is time disaggregated according to the

temporal structure of the four hourly reference raingauges.

Based on the point elevation measurements, a 50 × 50 m pixel resolution digital

elevation model was generated, and the digital land use and soil type map with the

same cell size were converted from the available land use and soil coverage. The cell-

based parameters were then identified using ArcView tools and lookup tables, which

relate default model parameters to each category of the individual base map, or thecombination of the three base maps. Starting from the high resolution DEM,

hydrological features including aspect, surface slope, flow direction, flow

accumulation, flow length, stream network, and drainage area were delineated. Maps

of porosity, field capacity, wilting point, residual moisture, saturated hydraulic

conductivity, and pore size distribution index were obtained from the soil type map.

Maps of root depth, Manning’s roughness coefficient and interception storage capacity

were derived from the land use map. Maps of default runoff coefficient and depression

storage capacity were calculated from the slope, soil type and land use class

combinations. The average hydraulic radius for each grid cell was calculated using a

power law relationship, which relates hydraulic radius with the controlling drained

area by a network constant and a geometry scaling exponent (Molnar & Ramirez,

1998) representing the average behaviour of the cell and the channel geometry. By

combining the maps of the average hydraulic radius, the Manning’s roughness

coefficient and the surface slope, average flow velocity in each cell was calculated

using Manning’s equation, which resulted in values of 0.005 m s-1 for overland flow on

upland areas in the watershed, and up to 2.7 m s-1 for some parts of the main river.

Next, the celerity and dispersion coefficients in each cell were produced according to

Henderson (1966). The contributing area was then determined from topographic data

for a particular downstream convergence point, i.e. normally the cells corresponding to

the stream flow gauging sites and the basin outlet. For each contributing grid cell, the

values of mean flow time and its standard deviation were calculated by integrationalong the flow paths. A spatial distribution of the mean flow time to the basin outlet is

shown in Fig. 2(a), in which the average flow time is <10 h for the main river and up

to 55 h for the most remote areas.

For the simulation of hydrographs both at the basin outlet and the flow monitoring

stations inside the catchment, and the interest in the spatial variability of hydrological

characteristics over the catchment, the basin was divided into 2276 subcatchments, as

shown in Fig. 2(b), corresponding to the threshold value of 100 when delineating the

stream network based on the flow accumulation theme. The areas of the GIS derived

subcatchments range from 0.025 to 3.8 km2 with an average subcatchment area of

0.52 km2 corresponding to a grid mesh with cell size of 720 m. The flow routing with-

in each subcatchment is not important if the GIS derived subcatchments are very small,as is the case in this study, since most excess water may flow out of the subcatchment




5 - 10 h

10 - 20 h20 - 30 h

30 - 40 h40 - 50 h

> 50 h

N

EW

S

0 5 10 km

N

EW

S

0 5 10 km

Fig. 2 (a) Computed flow time to the basin outlet. (b) River reaches and subwatersheds.

during the first time step with little attenuation. Routing of the flow is therefore mainly

governed by the channel geophysical conditions. However, flow routing within each

subcatchment becomes more and more important along with the increasing

subcatchment scale. In this study, stream net delineating thresholds of 200, 300, 500,

and 1000, resulting in the subcatchment numbers of 1207, 797, 455 and 233,

respectively, were also tested during model calibration, for which the predicted

hydrograph at the basin outlet did not change too much, but the simulation results for

the small watersheds inside the catchment were considerably affected.

Manual calibration of the model was realized at Ettelbruck for the period 1997–

1998 and validation for the period 1999–2000 using the available hourly precipitation,

potential evapotranspiration and stream discharge data at different stations. The parameters that need to be adjusted during model calibration are mainly interflow

scaling factor, groundwater flow recession coefficient, and plant coefficient, while

other distributed model parameters are predetermined using GIS ArcView before the

model operation and normally remain constant. The predetermined distributed

parameters of potential runoff coefficient, hydraulic radius, Manning’s coefficient etc.,

can also be adjusted during calibration if necessary. The predicted hydrographs at other

flow monitoring sites inside the catchment can also be seen as model verifications, but

only the simulation results at Ettelbruck, the basin outlet, are presented in this paper.

Figure 3 shows the observed vs the simulated stream flow and the estimated base flow

(interflow plus groundwater drainage) at Ettelbruck for a compound flood event thatoccurred in February and March 1997. Three large storms happened successively

during the period, and caused serious floods over the catchment. The total recorded

(a) (b)



Y. B. Liu et al.56

rainfall over the catchment during the period was 147.8 mm, with 56.5, 22.8 and 67.4

mm for the three storms, respectively, corresponding to observed peak discharges of

115, 105 and 190 m3 s-1 at Ettelbruck for each individual flood. As can be seen in Fig.

3, the predicted hydrograph fits the measured hydrograph very well. The volume of

surface runoff takes about 53% of the total flood volume, while the interflow and

groundwater flow is about 47% of the total flood volume. The first storm did not

produce a relevant big flood compared with its storm volume, as a large quantity of

rainfall was used for saturating the soil, while the second and the third storms

generated a higher proportion of runoff. This was demonstrated by the predicted

hydrograph, where the calculated surface runoff coefficient was 0.30, 0.35 and 0.39 for

the three floods, respectively. With the initial hydrological condition at the end of

calibration period, the validation results for the year 1999 and 2000 were in fairly good

agreement with the measured discharges. The model can reproduce high flows well, but low flows are a little overestimated. This may be because of the simplification of

the groundwater component in the model, or less accurate estimation of the evapo-

transpiration from groundwater storage during dry periods. Similar simulation results

can be obtained for other flow gauging sites inside the catchment, but the model

performance for the stations with small drained area is not as good as the stations with

large areas. However, by discretizing the large catchment into very small subcatch-

ments, the model is able to consider the precipitation, antecedent moisture and runoff-

generating processes in a spatially realistic manner based on topography, land use and

soil type, resulting in a fairly high accuracy for high flows, and the general hydrolog-

ical trends being well captured by the model.

Both numerical and graphical criteria were considered to provide an overall

indication of the model’s capabilities. Model biases are within the range of –0.08 to

0.05 for the whole simulation period. The model confidential coefficients are within

the range of 0.53 to 0.84, with a mean value of 0.73, and the Nash-Sutcliffe

coefficients (Nash & Sutcliffe, 1970) are within the range of 0.47 to 0.87 with mean

values of 0.74. It is found that better performance exists for the watershed with large

0

50

100

150

200

250

10/2/97 13/2/97 16/2/97 19/2/97 22/2/97 25/2/97 28/2/97 3/3/97 6/3/97 9/3/97 12/3/97 15/3/97

D i s c h a r g e ( m 3 / s )

0

3

6

9

12

15

P r e c i p i t a t i o n ( m m / h )Precipitation

Calculated outflow

Calculated baseflow

Observed outflow

Fig. 3 Calculated and observed hourly flows at Ettelbruck for the floods in February1997.




areas, while for small watersheds the performance is less satisfied. This indicates that

the modified WetSpa model can give a good representation of flow hydrographs for

larger catchments. For a small watershed, a fully distributed model based on a high

resolution DEM and with smaller time interval is preferable.

DISCUSSION AND CONCLUSIONS

A practical approach for dividing a large catchment into very small subcatchments,

based on high-resolution digital graphic data, is presented in this paper. The modified

WetSpa model uses elevation, soil and land use data in a simple way to estimate runoff

and soil moisture within root zone on the scale of GIS derived natural units, allowing

for the internal drainage structure of WetSpa for each small subcatchment. The dis-cretization of the catchment is implemented according to the model purpose, terrain

complexity, and the interest of spatial variable distributions. In the case of fine dis-

cretization, channel characteristics are more important in controlling the routing of

stream flow, which have to be properly determined. However, flow routing inside the

subcatchment has to be taken into account for coarse discretization, in order to predict

outflow hydrographs more accurately. The model has been applied successfully to the

1176-km2 Alzette River basin in Luxembourg with 4 years of observed hourly rainfall

and stream flow data. Results from the integrated output show that it adequately

predicts stream flow at the basin outlet and the flow gauging stations with a relatively

large drained area inside the catchment. Likewise, this methodology can also be

applied over large watersheds (e.g. drainage areas are >1500 km2) using smaller sub-

basins within the large watershed as model elements, so as to account for spatial

differences in soils, land use, topography, channel morphology, and weather conditions

in a semi-distributed manner. However, application of this method for a large area may

introduce some errors, either due to local variations which are not taken into account,

or regional variations for model parameters.

The use of the modified WetSpa model with its physiographic parameters provided

by ArcView GIS has the attraction of reducing the cost and time required for

parameterization and model simulation. This advantage is more outstanding when

modelling a large watershed with long data series and short time intervals. The

implementation of the model entirely within ArcView GIS enables the integration ofmodelling capabilities within a GIS environment. The method makes full use of the

spatially distributed hydrological and geophysical characteristics of the catchment, and

is therefore suitable for stream flow forecasting and scenario simulations.

REFERENCES

Beven, K. J. (1995) Linking parameters across scales—subgrid parameterizations and scale dependent hydrologicalmodels. Hydrol. Process. 9, 507–525.

De Smedt, F., Liu, Y.B. & Gebremeskel, S. (2000) Hydrologic modelling on a catchment scale using GIS and remotesensed land use information. In: Risk Analysis II (ed. by C. A. Brebbia), 295–304. WTI Press, Southampton, Boston,USA.

Drogue, G., Leviandier, T., Pfister, L., El Idrissi, A., Iffly, J. F., Hoffmann, L., Guex, F., Hingray, B. & Humbert, J. (2002)The applicability of a parsimonious model for local and regional prediction of runoff. Hydol. Sci. J. 47(6), 905–920.

Henderson, F. M. (1966) Open Channel Flow. McMillan Company, New York, USA.



Y. B. Liu et al.58

Liu, Y.B., De Smedt, F. & Pfister, L. (2002) Flood prediction with the WetSpa model on catchment scale. In: Flood Defence ‘2002 (ed. by B. S. Wu, Z. Y. Wang, G. Q. Wang, G. H. Huang, H. W. Fang, and J. C. Huang), 499–507.Science Press, New York, USA.

Molnar, D. K. & Julien, P. Y. (2000) Grid-size effects on surface runoff modelling. J. Hydrol. Eng. 5, 8–16.

Molnar P. & Ramirez, J. A. (1998) Energy dissipation theories and optimal channel characteristics of river networks.Water Resour. Res. 34(7), 1809–1818.

Nash, J. E. & Sutcliffe, J. V. (1970) River flow forecasting through conceptual models, Part 1: A discussion of principles. J. Hydrol. 10, 282–290.

Wang, Z., Batelaan, O. & De Smedt, F. (1996) A distributed model for water and energy transfer between soil, plants andatmosphere (WetSpa). Phys. Chem. Earth 21, 189–193.

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Parameterization Using ArcView GIS in Medium