APPLICATION OF GIS IN FLOOD MODELING FOR...
Transcript of APPLICATION OF GIS IN FLOOD MODELING FOR...
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: [email protected], [email protected]
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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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.
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11/12/99 12/12/99 13/12/99 14/12/99 15/12/99Time (d/m/y)
Q (m
3 /s)
0
5
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.
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