Journal of Environmental Modeling and Assessment, to appear

Assessing land use impacts on flood processes in complex terrain

by using GIS and Modeling approach

Y.B. Liu and F. De Smedt

Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, B-1050

Brussels, Belgium, e-mail: yongbliu@vub.ac.be, fdesmedt@vub.ac.be

L. Hoffmann and L. Pfister

Research Unit in Environment and Biotechnologies, Centre de Recherche Public - Gabriel Lippmann,

162 Avenue de la Faïencerie, L-1511 Luxembourg, Grand-Duchy of Luxembourg

A distributed hydrologic modeling and GIS approach is applied for the assessment of land use

impact in the Steinsel sub-basin, Alzette, Grand-Duchy of Luxembourg. The assessment focuses on

the runoff contributions from different land use classes and the potential impact of land use changes

on runoff generation. The results show that the direct runoff from urban areas is dominant for a flood

event compared with runoff from other land use areas in this catchment, and tends to increase for

small floods and for the dry season floods, whereas the interflow from forested, pasture and

agricultural field areas contributes to the recession flow. Significant variations in flood volume, peak

discharge, time to the peak, etc., are found from the model simulation based on the three hypothetical

land use change scenarios.

Keywords: hydrologic modeling, flood prediction, land use impact, GIS, Alzette

1. Introduction

Flood risk is among the most severe risks on human lives and properties, and has become more

frequent and severe along with local economical development. As the watershed becomes more

developed, it also becomes more hydrologically active, changing the flood volume, runoff components

as well as the origin of stream flow. In turn, floods that once occurred infrequently during pre-

development periods have now become more frequent and more severe due to the transformation of

the watershed from rural to urban land uses. The forecast and simulation of floods is therefore

essential for planning and operation of civil protection measures and for early flood warning.

As the earth’s population has been growing rapidly and more stress is put on the land to support

the increased population, hydrologic resources are affected both on local and global scale. One of the

recent thrusts in hydrologic modeling is the assessment of the effects of land use and land cover

changes on water resources, and the influence on storm runoff generation is one of the main research

topics in the last decade. The influence of land use on storm runoff generation is very complicated, as

land use and soil cover have an effect on interception, surface retention, evapotranspiration, and

resistance to overland flow. For instance, cropland and urban land yield more flood volumes, higher

peak discharges and shorter flow travel times than grassland or woodland. Increased runoff from

cropland is mainly due to the removal of native vegetation and soil compaction, which decrease soil

infiltration capacity. Increased runoff from urban areas results from impervious surfaces that prevents

infiltration of water into soils. Urban land uses also reduce the surface roughness and therefore

shorten the overland flow detention time. In contrast, less runoff is produced from undisturbed

grassland and woodland areas. This is due to factors such as interception of precipitation by the

vegetation canopy, the dense network of roots that increase infiltration capacity and soil porosity, as

well as the accumulated organic debris on the surface that increase depression storage capacity and

overland flow detention time. Moreover, dense vegetation causes higher evapotranspiration and

affects the long-term water and energy balance. Evidently, areas with a high percentage of cropland or

urban land use yield more storm runoff than the areas of similar soils and topography with grassland

or woodland.

In this type of analysis, it is desirable that the hydrologic modeling describes the spatio-temporal

variability of anthropogenic effects so that the assessment could reflect the variability of the hydrologic

parameter at the required scales. These kinds of hydrologic models have the advantage of reflecting

the effects of spatially distributed model parameters such as land use on stream flows. Moreover, the

present day availability of spatially distributed data such as digital elevation model (DEM), land use,

and soil information makes the use of distributed models much easier. In addition, recent advances in

computer hardware and GIS software, allow the spatial variation of model parameters and processes

to be considered at a reasonably small scale. Recently, many hydrologic models with a flood

prediction component using information on topography available from DEM have been developed,

whereas models like SHE and TOPMODEL were adapted to a new type of data which can benefit

from the GIS techniques [10, 6]. At the same time, hydrologic models compatible with remotely sensed

data and GIS have been developed or updated from their previous version, such as the model

CASC2D [12], HYDROTEL [7] and so on. These models are either loosely or tightly coupled with GIS

and remote sensed land use data. Olivera and Maidment [3] proposed a method for routing spatially

distributed excess precipitation over a watershed using response functions derived from a digital

terrain model, in which the routing of water from one cell to the next is accomplished by using the first-

passage-time response function derived from the advection-dispersion equation. The flow path

response function is calculated by a convolution integral of the cell response functions along the flow

path, which makes it possible to route excess rainfall from each grid cell to the basin outlet. 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 hydrologic modeling, flood

prediction with distributed models tends to be more advantageous and competent by linking GIS with

hydrologic modeling.

In this paper, a distributed hydrologic modeling and GIS approach for the assessment of land-use

change on flood processes is presented. The model takes into account the spatial heterogeneity of the

basin parameters to predict flood hydrographs and spatially distributed hydrologic characteristics in a

watershed, and therefore making it suitable for analysis of the effect of land use change on stream

flows. The input of the model includes observed data of precipitation and evaporation together with

parameters derived from a combination of a DEM, land-use and soil map in raster format. The model

is validated by comparing calculated and observed hourly discharges for a 52 months period at four

stream flow stations in the Steinsel watershed, located in the upstream part of the Alzette river basin,

Grand-Duchy of Luxembourg. Three land use scenarios are considered for assessment of the effect

on different components of the hydrologic cycle of the basin.

2. Methodology

2.1. Description of the hydrologic model

WetSpa is a grid-based distributed hydrologic model for water and energy transfer between soil,

plants and atmosphere, which was originally developed by Wang et al. [17] and adopted for flood

prediction on hourly time step by De Smedt et al. [2], and Liu et al. [15, 16]. For each grid cell, four

layers are considered in the vertical direction as vegetation zone, root zone, transmission zone and

saturated zone. The hydrologic processes considered in the model are precipitation, interception,

depression, surface runoff, infiltration, evapotranspiration, percolation, interflow, ground water flow,

and water balance in the root zone and the saturated zone (Figure 1). The total water balance for a

raster cell is composed of the water balance for the vegetated, bare-soil, open water and impervious

parts of each cell. This allows accounting for the non-uniformity of the land use per cell, which is

dependent on the resolution of the grid. The processes in each grid cell are set in a cascading way,

which means that an order of occurrence of the processes is assumed after a precipitation event. A

mixture of physical and empirical relationships is used to describe the hydrologic processes in the

model. The model predicts peak discharges and hydrographs, which can be defined for any numbers

and locations in the channel network, and can simulate the spatial distribution of catchment hydrologic

characteristics.

CANOPY

SOIL SURFACE

SOIL

GROUNDWATER

Recharge

Evapotranspiration Precipitation

Interception

Surface runoff

Interflow

Drainage

Infiltration

Depression

Through fallDISCHARGE

CANOPYCANOPY

SOIL SURFACESOIL SURFACE

SOILSOIL

GROUNDWATERGROUNDWATER

Recharge

Evapotranspiration Precipitation

Interception

Surface runoff

Interflow

Drainage

Infiltration

Depression

Through fallDISCHARGE

DISCHARGE

Figure1. Model structure of WetSpa at pixel cell level

The simulated hydrologic system consists of four control stores: the plant canopy, the soil surface,

the root zone, and the saturated groundwater aquifer. Among the process variables, soil moisture

content is a crucial factor in the model as it affects the hydrologic processes of surface runoff, actual

evapotranspiration, interflow and percolation out of the root zone. The precipitation that falls from the

atmosphere before it reaches the ground surface is abstracted by canopy interception storage. The

remaining rainfall reaching to the ground is separated into rainfall excess and infiltration. Rainfall

excess is calculated using a moisture-related modified rational method with potential runoff coefficient

depending on the land cover, soil type, slope, the magnitude of rainfall, and the antecedent moisture

content of the soil. The calculated rainfall excess fills the depression storage at the initial stage and

runs off the land surface simultaneously as overland flow [13]. The infiltrated part of the rainfall may

stay as soil moisture in the root zone, move laterally as interflow or percolate as groundwater recharge

depending on the moisture content of the soil. Both percolation and interflow are assumed to be

gravity driven [8] in the model. Percolation out of the root zone is equated as the hydraulic conductivity

corresponding to the moisture content as a function of the soil pore size distribution index [11].

Interflow is assumed to occur in the root zone after percolation and becomes significant only when the

soil moisture is higher than field capacity. Darcy’s law and a kinematic approximation are used to

estimate the amount of interflow generated from each cell, in function of hydraulic conductivity, the

moisture content, slope angle, and the root depth. The actual evapotranspiration from soil and plant is

calculated for each grid cell using the relationship developed by Thornthwaite and Mather [1] as a

function of potential evapotranspiration, vegetation and stage of growth, and moisture content in the

cell. A percentage of the remaining potential evapotranspiration is taken out from the water content in

the groundwater reservoir as a function of the maximum reservoir storage, giving the effect of a

steeper baseflow recession during dry period. The total evapotranspiration is the sum of evaporation

from intercepted water, depressed water and the bare soil surface, and the transpiration from plants

through the root system and a small part from the groundwater storage.

A simple structure is used in the model because the emphasis here is on developing and testing

parameterizations for the root zone. Excess runoff, infiltration, evapotranspiration, interflow and

percolation estimates are point calculations. Different slope, land use and soil properties in different

grid cells of a watershed result in different amounts of excess runoff when subjected to the same

amount of rainfall. Runoff from different cells in the watershed is routed to the watershed outlet

depending upon flow velocity and wave damping coefficient by using the diffusive wave approximation

method. A two parameter, mean travel time and its variance, approximate solution proposed by De

Smedt et al. [2] in the form of an instantaneous unit hydrograph (IUH) was used in the model relating

the discharge at the end of a flow path to the available runoff at the start of the flow path. The mean

travel time and its variance for each grid cell are spatially distributed, and can be obtained by

integration along the topographic determined flow paths as a function of flow celerity and dispersion

coefficient. Although the spatial variability of land use, soil and topographic properties within a

watershed are considered in the model, the groundwater response is modeled on small GIS-derived

subcatchment scale due to the fact that groundwater flow is much slower than surface flow and little is

known about the bedrock. The simple concept of a linear reservoir is used to estimate groundwater

discharge on a small subwatershed scale, while a non-linear reservoir method is optional in the model

with storage exponent of 2 [4]. The groundwater outflow is added to any runoff generated to produce

the total streamflow at the subwatershed outlet. All model equations are specifically chosen to

maintain a physical basis and well supported by previous studies.

2.2. Description of the study area

The Steinsel watershed covers approximately 407 km² and is located in the upstream part of the

Alzette river basin. The study area is situated in the southern part of the Grand Duchy of Luxembourg,

with a small part in the south located in France, as shown in Figure 2. The elevations in the watershed

range from 450.0 m to a low elevation of 225.5 m at the watershed outlet, with an average basin slope

of 7%. Figure 3 shows the topographic elevation map and measuring stations in the watershed. The

local topography is characterized by a natural sandstone bottleneck, located near Luxembourg-city.

The valley is up to 2.5 km wide upstream of the bottleneck, and only 75 m in the Luxembourg

sandstone, which extends approximately 80 m into the ground (Pfister et al., 2000). The dominant soil

types are loamy sand (29.1%) and silt (37.7%) distributed in the higher terrains, while the rests are silt

clay loam (13.3%), sandy clay loam (10.2%) and clay loam (9.5%) scattered in the river valleys. The

watershed has undergone rapid urbanization and extensive cultivation since the 1950s. Urban areas

cover about 20.5% of the watershed with Luxembourg-city in the downstream and Esch-Alzette city in

the upstream part. Cultivated lands occupy about 22.1% of the total area distributed beside the river

valleys with main crop types of maize and wheat. Forest (28.9%) and grass (24.4%) are predominant

in the river valleys and the high terrain, intermixed with urban areas and cultivated lands. There are

also some former mining areas located in the high terrain of the upstream watershed covering about

2.5% of the total area, where surface runoff is seldom generated. The watershed is well drained with a

dense stream network, open water occupies about 1.6% of the total area.

Stei nsel

Pfaffe nthal

Hesperange

Livange

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Mamer

Reckan ge/Mess

Be lai r

Belvaux

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WaldhaffMull endo rf

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Loren tzweiler

k1050

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Rive rBoundary

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270 - 3 15315 - 3 60360 - 4 05405 - 4 50

225 - 2 70m

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RiverBoundary

# RaingaugeStreamgau

270 - 315315 - 360360 - 405405 - 450

225 - 270

Figure 2. Location map of the study area Figure 3. Topography and gauging stations

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

mean annual temperature is around 10°C, with average temperature of 0.7°C in January and 17.3°C

in July. Rainfall has a relatively uniform distribution throughout the year. High runoff occurs in winter

and low runoff in summer due to the higher evapotranspiration. Winter storms are strongly influenced

by the westerly atmospheric fluxes that bring humid air masses from the Atlantic Ocean (Pfister et al.,

2000), and floods happen frequently because of the saturated soils and low evapotranspiration. The

average annual precipitation in the region varies between 800 mm to 1,000 mm. Precipitation

generally exceeds potential evapotranspiration except for four months in the growing season.

2.3. Data collection

Three digital base maps are prerequisite in the model to define the watershed drainage work and

derive spatial model parameters, i.e. DEM, soil type and land use. A DEM with 50x50 m grid size for

the watershed was constructed using 2-meter resolution elevation contours and the official river

network. Information of soil types was obtained from the digital 1:100,000 Soil Map of the European

Communities, and converted to 12 USDA soil texture classes based on textural properties. The land

use information was obtained from the digital land use map of Luxembourg and France derived from

remote sensed image with respect to the watershed condition in the year 1995. The original land use

map was classified to fourteen classes for use in the WetSpa model, and reclassified to five hydrologic

land use classes for simulation of storm runoff partitions from different land use areas, i.e., crops,

grassland, forest, urban areas, surface water, and mining areas, as shown in Figure 5.

4 stream gauges, namely Steinsel, Pfaffenthal, Hesperange and Livange, as shown in Figure 3,

are located in the study area recording water levels at a 15-minute time step, and 10 rain gauges are

located in and around the watershed recording at an hourly or daily time step. Daily rainfall is

disaggregated into hourly rainfall series according to the nearest hourly reference rain gauges for

being used in the model. Potential evapotranspiration was estimated using the Penman-Monteith

formula with daily meteorological data measured at the Luxembourg airport, and applied to the whole

study area. A total of 52 months of hourly rainfall, discharge and potential evapotranspiration data

from December 1996 to March 2001 are available for model simulation. The average flow at Steinsel

during the monitoring period was 5.6 m³/s, with flows ranging from 0.07 to 45.7 m³/s, and the

measured maximum hourly rainfall intensity was 23 mm/h which occurred on July 2, 2000.

2.4. Model calibration and verification

The WetSpa model was calibrated against the hourly stream flow measurements at the four

stations for the time period of December 1996 to December 1999, while the data for the period of

January 2000 to March 2001 was used for model validation. The calibration was not carried out for all

model parameters, but for three global parameters only, including the evapotranspiration correction

factor in controlling water balance, the interflow scaling factor in controlling the amount of interflow,

and the groundwater flow recession coefficient governing the routing process of baseflow. Other

spatially distributed parameters, such as soil hydraulic conductivity, porosity, field capacity, soil pore

size distribution index, root depth, interception and depression storage capacity, etc., were intuitively

set to values interpolated from the literature representing average conditions, and not calibrated but

fixed to the selected values. Inputs to the model are spatially distributed precipitation interpolated by

the method of Thiessen polygons and potential evapotranspiration, while the outputs are hydrographs

at each gauging site and the simulated spatial distribution of hydrologic characteristics.

Figure 4 gives a graphical comparison between calculated and measured hourly stream flows for

a sequence of floods that occurred in February and March 1997 at Steinsel. The total rainfall was

184.3 mm with an observed runoff of 107.8 mm, while the simulated runoff is 111.0 mm. A small flood

happened in early February, followed by three large floods successively. The simulated hydrographs

of surface runoff, interflow and baseflow are also presented separately in the figure, and have relative

volumes of 38%, 27% and 35% respectively of the total flood volume. The figure shows a very good

agreement between the predicted and measured hydrograph, in which the rising and high water limb

are dominated by surface runoff. Interflow has a few hours delay and forms the flow during recession,

while also partly contributing to the high discharges. Groundwater discharge formed the baseflow of

the total hydrograph. Due to the high antecedent soil moisture content and groundwater storage, the

amount of interflow and groundwater flow was abundant in this flood event, being 62% in total of the

whole flood volume, which was a typical composition for this watershed during winter season.

Figure 4. Measured and calculated hourly flow at Steinsel for the floods in Feb. 1997

For validation of the model, the calibration parameters were applied, and the distribution of initial

soil moisture and groundwater storage was obtained from the simulation results at the end of the

calibration period. With these adjustments to the model setting, the model was run to deliver the

validated hydrographs. Model performance for calibration and validation was evaluated through

qualitative and quantitative measures, involving both graphical comparisons and statistical analysis.

To quantify the importance of the difference between the observed and simulated flow volume, bias or

magnitude of relative mean errors was determined for each observation station. Model efficiency was

evaluated by the Nash-Sutcliffe coefficient [5], for which a value of 1 indicates a perfect fit, while a

negative value means that the prediction is worse than simply using the observed mean. The model

performance is found to be satisfactory as illustrated in Table 1, which shows the watershed

characteristics, the mean observed and simulated stream flow and the evaluation results for both

calibration and validation periods on hourly scale. As can be seen in the table, model biases are within

the range of -0.04 to 0.02 for the four stations. This does not mean that the water balance is perfect for

each individual flood, as it is a measure for the whole simulation period. The Nash-Sutcliffe efficiency

are in the range of 0.75 to 0.85 for hourly stream flow with an average value of 0.81 for the whole

simulation period, which indicates that 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, giving the simulation a fairly high degree of precision, and the general hydrologic

trends being very well captured by the model.

Table 1. Watershed characteristics and model performance for the simulation period

Station Area (km²)

Slope(%)

Urban (%)

Crop (%)

Grass(%)

Forest(%)

Others(%) Period OQ

(m³/s)SQ

(m³/s) Bias Efficiency

Livange 233 7.2 18.6 28.9 22.9 24.7 4.9 Calibration Validation

2.92 3.92

2.86 3.76

-0.02 -0.04

0.78 0.75

Hesperange 291 6.7 17.8 27.4 25.3 25.4 4.1 Calibration Validation

3.71 5.99

3.60 5.87

-0.03 -0.02

0.83 0.79

Pfaffenthal 350 6.5 19.2 25.4 26.8 25.2 3.4 Calibration Validation

3.96 6.34

4.04 6.15

0.02 -0.03

0.81 0.80

Steinsel 407 7.0 20.5 23.2 24.3 29.0 3.0 Calibration Validation

4.69 7.87

4.74 7.63

0.01 -0.03

0.85 0.84

3. Results and Discussion

3.1. Evaluating runoff partitions from different land use classes

Land use has a great influence on the rainfall runoff process, particularly for a complex terrain, as

for instance the study area shown in Figure 5, where the urban areas occupy about 20.5% of the

whole catchment. Since the WetSpa model calculates runoff and flow path response in a spatial way

for each grid cell, it is capable of evaluating storm runoff partitions from different land use areas. By

convolution of the flow responses from the cells belonging to a certain land use category, runoff

partition to the flood hydrograph from this land use category can be estimated. The total flow

hydrograph at the watershed outlet is obtained by the sum of runoff partitions from different land use

areas in the watershed. With the model display options allowing monitoring of various variables during

a simulation run, the model is a good tool for understanding and managing phenomena related to

hydrologic processes. Figure 6 shows the simulated surface runoff distribution for the storm of

February 24-26, 1997, corresponding to the fourth flood in Figure 4. The total storm rainfall was 63

mm with an observed peak discharge at Steinsel of 40.2 m³/s. Due to the very high antecedent soil

moisture content of this storm, almost all areas contribute to the storm runoff but with different

volumes according to their land use types. As can be seen from the map, high surface runoff was

produced on surface water and urban areas, while low surface runoff occurred in the areas with forest

cover and sandy soils. There was very little surface runoff generated from the former mining areas

because of its specific surface characteristics, and most water in these areas recharged to the

groundwater reservoir.

Crops Grassland Forest Urban areas Surface waterMining

CrGrFoUrbSuMi

0 – 5 5 – 1010 – 20 20 – 30 30 – 40 40 – 63

Surface runoff (mm)

Figure 5. Land use map with respect to the Figure 6. Simulated surface runoff distribution

1995 watershed condition for the storm of Feb. 24-26, 1997

Figure 7 gives an hourly graphical presentation for the same flood events presented in the model

calibration but shows storm runoff partitions from different land use areas. Clearly, surface runoff from

urban areas, cropland and grassland formed the high water limb of the hydrograph, representing

39.1%, 11.6% and 9.0% respectively of the storm runoff (excluding baseflow). Interflows from

woodland, grassland and cropland, occupying 16.7%, 11.6% and 8.8% respectively of the storm

runoff, were essential components of the storm runoff for these flood events, contributing not only to

the flow during recession, but also to peak discharges. Other storm runoff components in the figure

were mainly surface runoff from water surface and forested areas, taking about 7.2% of the storm

runoff, while surface runoff and interflow from mining areas and interflow from urban areas were

negligible in the watershed for these flood events.

Figure 7. Runoff partitions at Steinsel from different land use areas for the flood in Feb., 1997

For assessing runoff partitions from different land use areas of the watershed, the calibrated

WetSpa model was performed for the whole simulation period, and the flow components for different

land use areas were calculated for each time step. The estimated runoff contributions are urban

(29.3%), agricultural (22.8%), pasture (22.2%), forested (21.5%), and others (4.2%) for this watershed

over the simulation period. Runoff contribution from urban areas is the highest, contributing mainly to

direct flow. Contributions from agricultural, pasture and forested areas are more or less equal,

contributing to surface runoff, interflow and baseflow. It is also found from model simulation that runoff

partitions from different land use areas vary from one storm event to another and thus are influenced

by antecedent soil moisture, groundwater storage and storm behaviors.

Variations in runoff contribution and runoff partition are directly tied to soil moisture condition and

groundwater storage. For example, winter storms usually encounter high flow coefficients due to high

soil moisture content and high percolation rate into the groundwater storage, causing high baseflow,

interflow and saturation overland flow. Under such conditions, runoff generates partly from

impermeable surfaces and a large amount from natural areas including surface runoff, interflow and

groundwater drainage. This indicates that groundwater drainage plays an important role for the whole

flood hydrograph for the study area in winter season, which might be produced partly from the

previous storms. However, the urban contribution increases greatly if only considering storm runoff

excluding baseflow, and other contributions will be reduced accordingly. On the contrary, summer

storms usually have low flow coefficients due to the low soil moisture content and small groundwater

storage, and runoff from urban areas is dominant in the flood hydrograph for this watershed, while

other contributions are relatively small, especially the runoff from forested areas. Secondly, the storm

behavior, such as volume, duration, intensity and type, has a big influence on the flow coefficient and

the runoff partitions from different land use areas. Practically, large storms with long duration and

higher intensity produce more runoff under similar antecedent soil moisture condition, but the flow

coefficient may not react positively due to the different baseflow contributions and runoff partitions

therefore increase for natural areas and decrease for urban areas. This can be extended for a small

rainfall event for which most runoff is generated from impermeable areas and open water surface,

while runoff from natural areas can be ignored. In addition, runoff varies also with time and storm

types. Flow is due exclusively to direct runoff on urban areas and water surfaces at the beginning of a

storm because of the quick response and the short travel time. Other runoff contributions start to join

the flow, for which the role of soil moisture on runoff production and runoff composition is highly

important. Similar phenomena occur for a compound storm or successive flood events. As for

instance, the case of the flood in February 1997, shown in Figure 7, for which a considerable rainfall

occurred a week before the main storm, causing a small flood in which urban runoff was dominant.

This was no longer the case for the main floods, when the flow from agricultural, pasture and forested

areas was remarkable.

3.2. Assessing the impact of land use changes on flood

Changes in land use may have significant effects on infiltration rates, on the water retention

capacity of soils, on sub-surface transmissivity and thus on the runoff production. It is evident that the

Alzette watershed has undergone rapid urbanization since 1950. In general, the flood potential of a

catchment significantly increases by urbanization. The introduction of impervious surfaces and good

drainage systems increases the volume of runoff and results in flood hydrographs which are faster to

peak, faster to recede, and of increased peak discharge [14]. As the WetSpa model accounts for

spatially distributed hydrologic and geophysical characteristics of the watershed, it is suitable for

assessing the impact of land use changes on hydrologic behaviors in a complex terrain with reliable

land use change scenarios.

A realistic set up of land use scenarios requires scenarios of future regional development. Land

use decision-making is strongly influenced by socio-economic factors. As these particular future land

use policies are complicated, three distinct scenarios are considered in the Steinsel sub-basin of the

Alzette (Figure 8), where urban areas are increased at the expense of crops and grassland for

urbanization, all forests are converted into crops or grassland for deforestation, and forests are

increased at the expense of crops and grassland for afforestation. The urbanisation scenario was

elaborated on the basis of information regarding the changes in land use planned by the government.

The afforestation scenario was meant to recreate in a simple manner the conditions that might have

prevailed some 200 years ago when the Grand-Duchy of Luxembourg was largely covered by forests.

The deforestation scenario was more or less chosen to evaluate what the behaviour of the basin

would be without this important forest cover. Starting from the land use map with respect to the 1995

watershed situation, the first scenario increases urban areas by 31.8%, and decreases agricultural

and grasslands by 11.7% and 15.4% respectively. The second scenario reduces forested areas by

almost 100%, and expands agricultural and grasslands by 108% and 16% accordingly. The third

scenario increases forested areas by 52.6% and decreases agricultural and grasslands by 48.8% and

16% with respect to the present land use situation (Table 2).

Table 2. Land use change scenarios compared with the present situation of Steinsel watershed

Scenarios Urban areas (%) Crops (%) Grass (%) Forest (%) Others (%) Present 20.5 23.2 24.3 29.0 3.0 Urbanization 27.0 20.5 20.5 29.0 3.0 Deforestation 20.5 48.3 28.2 0.0 3.0 Afforestation 20.5 11.9 20.4 44.2 3.0

Urbanization Deforestation Afforestation

Crops Grassland Forest Urban areas Surface water Mining

Figure 8. Land use change scenarios for the Steinsel watershed

Based on these land use change scenarios, model parameters were recalculated and the model

was run to deliver the modified flows. Figure 9 gives the simulated flood hydrographs for the present

and three land use scenarios at Steinsel for a flood event that occurred on December 12, 1999. The

results indicate that the urbanization scenario produces the highest peak flow, followed by the

deforestation and afforestation scenario. The rainfall originated from slow moving westerly

atmospheric fluxes with long duration and low intensity. The 2-day rainfall was 65 mm, but the highest

rainfall intensity was only 4.6 mm/h. The simulated peak discharge for the present land use is 47.6

m3/s, for the urbanization scenario 65 m3/s, the deforestation 53.3 m3/s and the afforestation 43.9

m3/s. Accordingly, the urbanization scenario increases the peak discharge for this storm by 36.6%, the

deforestation scenario increases the peak discharge by 12.0%, while the afforestation scenario

decreases the peak discharge by 7.8%. In addition to the difference in the magnitude of the simulated

peak discharges, differences in time to peak of the modeled discharges are also observed. The peak

discharge occurred around 3 hours after the main rainfall for the present land use condition, after 2

hours for the urbanization and deforestation scenarios, and 3 hours for afforestation scenario.

0

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10

15

20

25

30P

(mm

/h)

Rainfall Urbanisation Deforestation Present Afforestation

20

30

40

50

60

70

20 30 40 50 60 70Qpresent (m3/s)

Qsc

enar

io (m

3 /s)

UrbanisationDeforestationAfforestaion

Present

Figure 9. Simulated hydrographs for each scenario Figure 10. Simulated peak discharges for each

for a storm event in Dec. 1999 scenario over the simulation period

Figure 10 gives the present versus the scenario peak discharges selected from the whole

simulation period. It shows that afforestation has a mild positive effect in reducing the peak discharge

in comparison to the present situation. On the contrary, urbanization and deforestation lead to an

increase of the simulated peak discharges. In addition to the effects of land use change on flood

volume, runoff composition, evapotranspiration and soil moisture were also evaluated quantitatively

from the model results. It was found that urbanization and deforestation result in increasing the flood

volume and the amount of surface runoff, but decreasing the amount of interflow and baseflow, as well

as soil moisture and the amount of evapotranspiration from a long term simulation, while this is the

contrary for afforestation. The magnitudes of changes, however, differed from one storm to another

depending upon the antecedent soil moisture content. This can be explained by the fact that a change

in land cover will alter the leaf area index, the interception storage capacity, the soil infiltration capacity

and thus the evolution of soil moisture. High soil moisture leads to more evapotranspiration,

groundwater recharge and interflow, and vice versa. Investigation of low flows indicates that the effect

on baseflow is not pronounced in summer, due to the fact that most soil water is used for

evapotranspiration, and the baseflow is very small for all the three scenarios. However, considerable

differences in baseflow are found in winter with the afforestation scenario producing the highest

baseflow, while the urbanization scenario producing the lowest baseflow.

4. Conclusions

The widespread availability of digital geographic data and GIS techniques open new opportunities

for using distributed models in assessing land use impact on flood processes. In this paper, a spatially

distributed continuous simulation model, WetSpa, running on hourly time scale and compatible with

GIS and remote sensed information, was presented. The model uses elevation, soil and land use data

in a simple way to predict outflow hydrograph and the spatial distribution of hydrologic characteristics

over the watershed. Three flow components, surface runoff, interflow and groundwater drainage, are

represented in the model, while water and energy balance are maintained for each grid cell. GIS

provides a powerful platform for developing the model, calibrating parameters, and displaying model

results in a spatial way, so that it becomes possible to capture local complexities of a watershed and

compare model results to field measurements. The model was applied to the Steinsel watershed in the

Alzette river basin, Grand-Duchy of Luxembourg, with 52 months of observed hourly rainfall and

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

data was obtained from remote sensed images. Model calibration and validation have shown the

model’s level of representativeness to be quite satisfactory. The outflow at the catchment outlet has

been especially well reproduced.

The uses of the model to predict flood hydrograph, to evaluate storm runoff from different land use

areas, and to assess the impact of land use changes on flood behaviors were discussed. The model is

capable to estimate flood runoff composition and contributions from certain land use classes.

Simulation results of the Steinsel watershed show that the runoff from urban areas is dominant for a

flood event compared to runoff from other land use areas in this catchment, and the partitioning tends

to increase for small floods and for the flood events with low antecedent soil moisture. Other runoff

partitioning tends to increase for large storms and for the storm events with high antecedent soil

moisture. For assessing the hydrologic effects of land use changes on floods, three hypothetical

scenarios, namely urbanization, deforestation and afforestation scenario, were considered based on

the present land use configuration and possible land use trends in the study area. It was found from

the model simulation that the urbanization scenario has a large impact on increasing peak discharge

and flood volume, as well as time to the peak. Likewise, deforestation has a fair negative impact, while

afforestation has a moderate positive impact on the floods.

As discussed in the paper, the model makes full use of the remote sensed data and calculations

are for the most part performed by standard GIS tools, so that the model is especially useful for flood

prediction on complex terrain and analyzing the effects of land use or soil cover on the flood

characteristics.

References

[1] C.W. Thornthwaite and J.R. Mather, The water balance, Laboratory of Climatology, Publ. No. 8,

Centerton NJ. (1955).

[2] F. De Smedt, Y.B. Liu and S. Gebremeskel, Hydrologic modeling on a catchment scale using GIS

and remote sensed land use information, in: Risk Analysis II, ed. C.A. Brebbia (WTI press,

Southampton, Boston, 2000) pp. 295-304.

[3] F. Olivera and D. Maidment, Geographic information systems (GIS)-based spatially distributed

model for runoff routing, Water Resources Research 35(4) (1999) 1155-1164.

[4] H. Wittenberg and M. Sivapalan, Watershed groundwater balance estimation using streamflow

recession analysis and baseflow separation, Journal of Hydrology 219 (1999) 20-33.

[5] J.E. Nash and J.V. Sutcliffe, River flow forecasting through conceptual models, Part 1: A discussion

of principles, Journal of Hydrology 10 (1970) 282-290.

[6] J. Ewen, G. Parkin and P.E. O'Connell, SHETRAN: distributed river basin flow and transport

modeling system, Journal of Hydrologic Engineering (ASCE) 5 (2000) 250-258.

[7] J.P. Fortin, R. Turcotte, S. Massicotte, R. Moussa, J. Fitzback and J.P. Villeneuve, A distributed

watershed model compatible with remote sensing and GIS data, I: Description of the model,

Journal of Hydrologic Engineering (ASCE) 6(2) (2001) 91-99.

[8] J.S. Famiglietti and E.F. Wood, Multiscale modeling of spatially variable water and energy balance

processes, Water Resources Research 30(11) (1994) 3061-3078.

[9] L. Pfister, J. Humbert and L. Hoffmann, Recent trends in rainfall-runoff characteristics in the

Alzette river basin, Luxembourg, Climatic Change 45(2) (2000) 323-337.

[10] P. Quinn, K. Beven, P. Chevallier, and O. Planchon, The prediction of hillslope flow paths for

distributed hydrologic modeling using digital terrain models, Hydrological Processes 5 (1991) 59-

79.

[11] P.S. Eagleson, Climate, Soil, and Vegetation, a simplified model of soil moisture movement in

liquid phase, Water Resources Research 14(5) (1978) 722-730.

[12] P.Y. Julien, B. Saghafian and F.L. Ogden, Raster-based hydrologic modeling of spatially varied

surface runoff, Water Resources Bulletin (AWRA) 31(3) (1995) 523-536.

[13] R.K.J. Linsley, M.A. Kohler and J.L.H. Paulhus, Hydrology for Engineers, (McGraw-Hill, New York,

1982) p. 237.

[14] S. Crooks, H. Davies, and G. Goodsell, Rainfall runoff modeling and the impact of land use

change in the Thams catchment, in: European Conference on Advances in Flood Research, eds.

A. Bronstert, Ch. Bismuth and L. Menzel (2000) pp. 115-130.

[15] Y.B. Liu, S. Gebremeskel, F. De Smedt, L. Hoffmann, and L. Pfister, A diffusive transport

approach for flow routing in GIS-based flood modeling, Journal of Hydrology 283 (2003) 91-106.

[16] Y.B. Liu, F. De Smedt and L. Pfister, Flood prediction with the WetSpa model on catchment scale,

in: Flood Defence ‘2002, ed. Wu et al. (Science Press, New York Ltd., 2002) pp. 499-507.

[17] Z. Wang, O. Batelaan and F. De Smedt, A distributed model for Water and Energy Transfer

between Soil, Plants and Atmosphere (WetSpa), Physics and Chemistry of the Earth 21(3) (1996)

189-193.