Introduction
Humans can exert influence on precipitation through modifications in land use
next to other anthropogenic forcings such as
climate change . Currently, land conversion takes place at a
rapid pace, and this will likely continue in the future
. Therefore, this type of human influence on the
climate system will continue, and will probably become more significant in
the coming decades .
In the Netherlands, the most important land cover changes in the last century
were the conversion of large heather areas into agricultural land or
grassland, and expansion of urban areas . In
addition, almost 1650 km2 of land was reclaimed from the sea in the
former Zuiderzee, now called Lake Yssel . Urban areas
have increased from about 2 % in 1900 to 13 % in 2000, and are
projected to further increase to 24 % in 2040 .
Precipitation in the Netherlands has increased by about 25 % over the
last century, especially along the West coast . The
increase in sea surface temperatures and changes in circulation seem to be
the major causes of this increase . In addition,
there is some evidence that urbanization plays a role .
In contrast to the above, an earlier study using a model to investigate land
surface changes in the Netherlands in spring found that precipitation is in
fact reduced after expansion of urban areas . That study
also tested the sensitivity of precipitation to soil moisture and found a
positive feedback; that is, wet (dry) soils increase (decrease) the amount of
precipitation. The reduction of precipitation after urban expansion was
dominated by the model's response to reduced moisture, overruling the
enhanced triggering of precipitation by boundary layer processes. However,
only a 4-day case study was investigated and questions can therefore be
raised with respect to the climatological representability of the results. In
addition, the simulated land use changes were conceptual, rather than
realistic, and only focused on changes in urban extent, ignoring the
expansion of agricultural areas for example.
The present study aims to improve on both aspects, by (1) sampling a larger
set of meteorological cases, and (2) evaluating the effects of more realistic
land cover changes. Our main interest is the precipitation response to the
altered land surface and the physical processes underlying this response. We
investigate this response in the summer season. The summer months typically
have a larger shower activity, connected to unstable conditions. This
relatively intense type of precipitation, arising from (deep) cumulus
convection, is expected to be most influenced by land surface changes
and is typically expected to increase under
climate change . Also, the largest impact of urban areas
on precipitation along the Dutch West coast was found in summer
.
The current study also aims to put the effects of historic and future land
use changes on precipitation in the perspective of climate change. This will
be done by imposing an increase in overall temperatures as a surrogate
climate change scenario . On a global scale, climate change
is expected to increase both mean and extreme precipitation in response to an
intensification of the hydrological cycle .
Here, the precipitation response to land use changes in the Netherlands, and
climate change, is investigated for multiple summer days. The selection
procedure for the investigated events and the model setup will be described
in the next section, followed by the results, discussion, and conclusions.
Data and methods
Case selection
Selection of days to use as case studies is conducted with the help of a
circulation type classification, similar to . We make use
of the nine-type Jenkinson–Collison types (JCT) classification scheme. This
method was developed by and is intended to provide an
objective scheme that acceptably reproduces the subjective Lamb weather types
. The classification has eight weather types (WTs)
representative of the prevailing wind direction (W, NW, N, NE, E, SE, S, and
SW, where W = 1, etc.) and one that is treated as unclassified (WT9).
Computation of the WTs is done using 12:00 UTC MSLP data from ERA-Interim
at 16 points in the area 47.25 to 57.75∘ N and 3 to
12.75∘ E (Fig. ) with the “cost733class” software
.
Previous work has shown that the downwind effects of urban areas on
precipitation in the Netherlands are largest under WT9 .
Under the light, unclassifiable, flow that occurs in this weather type, the
atmosphere seems to be most susceptible to the land surface. All summer (JJA)
days in the period 2000–2010 are classified with the JCT scheme, but only
days with WT9 and more than 1 mm of precipitation at one station or more are
used for further selection. The remaining 215 days are grouped using a
statistically objective k-means clustering procedure
in R . The k-means clustering partitions n
observations into k clusters, in which each observation belongs to
the cluster with the nearest mean in a principle component space. Clustering
is done to obtain a homogenous set of days with similar meteorological
conditions. The similarity of the cases should result in comparable results
and enable generalization of conclusions.
Seven parameters are used in the clustering procedure: (1) mean
precipitation; (2) total column water; (3) vertical velocity at 700 hPa;
(4) horizontal wind speed at 700 hPa; (5) K-index; (6) land–sea
temperature difference; and (7) a measure of the distribution and
“patchiness” of precipitation, computed as the difference between maximum
precipitation and the 85th percentile. Parameters 2, 3, 4, and 5 are derived
from 12:00 UTC ERA-Interim data averaged over the center of the Netherlands
(4.75 to 5.75∘ E and 51.75 to 52.25∘ N,
Fig. ). Parameter 6 is derived from ERA-Interim data as the
difference between the 2 m temperature over this land area and sea surface
temperature (SST) averaged over a nearby ocean area of similar size
(3–4∘ E and 52.25–52.75∘ N, Fig. ).
Parameter 1 and 7 are computed over the whole of the Netherlands using daily
precipitation data collected at 08:00 UTC from about 320 stations. The
K-index is a linear combination of temperature (T) and
dewpoint (Td) at various levels (T850 - T500 + Td850 - (T700 -
Td700)) and is a measure of the convection used to forecast air mass
thunderstorms. The parameter values are normalized and scaled by subtracting
the mean and dividing by the standard deviation, before being used in the
clustering algorithm.
Map of part of Europe showing the 16 (red) points
used in the circulation type classification, the WRF model domain (black),
and the land (green) and sea (blue) area used for averaging in the selection
procedure.
Boxplots of the seven parameters used in the
procedure to select days to simulate with the WRF model. Boxes of the days
included in the selected cluster are given in orange and boxes of all summer
days classified as WT 9 in the period 2000–2010 are given in white.
Dutch land use maps for 1900, 2000, and 2040 based
on HGN1900, LGN4, and GE2040, respectively.
The k-means clustering algorithm was set to use 12 clusters, repeated 1000
times, and the best, stable, solution is used. A cluster with higher than
mean precipitation was selected (see Fig. ), since sufficient
precipitation is needed to investigate the response to alternative land use
maps. Total column water is about average in the selected cluster, while it
has the most negative vertical velocity (omega), of about 0.3 Pa s-1.
Since omega is positive with increasing pressure, this means the largest
upward speeds are selected. A large upward vertical velocity is associated
with strong hourly precipitation and convective showers .
Low wind speed was found to be favorable for detection of urban effects in
the Netherlands and is therefore desirable. The average
K-index in the selected cluster is over 20, which is the average threshold
for likelihood of thunderstorms. The land–sea temperature difference is
amongst the lowest. High SST is known to cause enhanced precipitation (in the
coastal area), mainly in summer . This could interfere
with our land use experiments and is therefore not sought. Finally, the
selected cluster has quite patchy precipitation, indicative of convective
conditions as desired. The selected cluster consists of 19 days (see
Fig. for the dates), of which 18 will be averaged on an
hourly basis for many of the analyses presented in the results section.
Model setup
We use the non-hydrostatic Advanced Research WRF model (ARW, version 3.4.1)
on a single domain of 1000 × 1000 km (see
Fig. ). The model has a horizontal grid spacing of 2.5 km and
the vertical grid contains 40 sigma levels. Atmospheric and surface boundary
conditions are obtained from ERA-Interim every 6 h. Model output is stored
and analyzed on an hourly basis. The model is run for 48 h, including 12 h
of spin-up from 12:00 to 00:00 UTC the previous day, 24 h of simulation on
the chosen day, and 12 additional hours to be able to compare it to both
radar data (00:00–00:00 UTC) and station data (08:00–08:00 UTC).
Following earlier studies with WRF in the Netherlands
e.g.,, we selected the
following schemes to represent subgrid processes: the YSU PBL scheme
, the WRF Single-Moment 6-Class Microphysics Scheme (WSM6)
, the RRTMG schemes for both longwave and shortwave radiation
, the Grell 3-D cumulus parameterization scheme
, and the Unified Noah Land Surface Model
with the Urban Canopy Model (UCM). The UCM is a
single-layer model that has a simplified urban geometry. Included in the UCM
are shadowing from buildings, reflection of shortwave and longwave radiation,
the wind profile in the canopy layer, and multi-layer heat transfer equations
for roof, wall, and road surfaces .
USGS land use category descriptions and
parameter settings used in WRF, with the national land use map (HGN, LGN, and
GE2040) classes that are reclassified as
such.
USGS land
Land use
z0
Albedo
Green vegetation
Leaf area
Emissivity
HGN/LGN class
GE2040 class
use category
description
(m)
(–)
fraction (%)
index
(%)
description
description
1
Urban and built-up land
0.5
0.15
0.1
1
0.88
Buildings and roads
Urban area, commercial/
industrial, seaport,
building lot,
infrastructure
2
Dryland cropland and pasture
0.15
0.17
0.8
5.68
0.985
Crops and bare soil
Arable land
6
Cropland/woodland mosaic
0.2
0.16
0.8
4
0.985
Other
Recreation – single day,
recreation – stay,
perennial crops
7
Grassland
0.12
0.19
0.8
2.9
0.96
Grassland
Grassland
11
Deciduous broadleaf forest
0.5
0.16
0.8
3.31
0.93
Deciduous forest
Nature – dry
14
Evergreen needle leaf
0.5
0.12
0.7
6.4
0.95
Coniferous forest
Nature – dry
16
Water bodies
0.0001
0.08
0
0.01
0.98
Water
Water
17
Herbaceous wetland
0.2
0.14
0.6
5.65
0.95
Reed swamps
Nature – wet
19
Barren or sparsely vegetated
0.01
0.38
0.01
0.75
0.9
Drifting sands and sandbanks
Greenhouse horticulture,
nature – dry
20
Herbaceous tundra
0.1
0.15
0.6
3.35
0.92
Heath land and raised bogs
Nature – dry
Where possible within the model domain, the European Corine land use map
was used, supplemented with a high-resolution map for the
Netherlands. Corine is not available over the UK, so there the standard USGS
map at 30′ resolution available within WRF is used. Reclassification of the
Corine land use map is done following , but intertidal
flats are classified as water instead of herbaceous wetlands. Three
high-resolution maps were used for the Netherlands: HGN1900
, LGN4 , and GE2040
, representing land use in 1900, 2000, and 2040,
respectively (see Fig. ). The future map is based on the Dutch
Global Economy scenario , a national scenario consistent with
the SRES A2 scenario. The SRES scenarios have been replaced by Representative
Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs). The
SRES A2 scenario is most like SSP3 and between RCP 6.0 and 8.5 in carbon
emissions. Reclassification of the Dutch land use maps is done as specified
in Table . GE2040 unfortunately did not distinguish
between different dry nature classes, so the differentiation was copied from
the LGN map. Therefore, all dry nature in GE2040 was first classified as
herbaceous tundra. Next the newly classified herbaceous tundra was
reclassified to barren or sparsely vegetated areas, evergreen needle leaf,
and deciduous broadleaf forest when it overlapped with the areas classified
as such in the LGN map.
Model simulations
Three model simulations, HIS, REF, and FUT, are done with the land use maps
of, respectively, 1900, 2000, and 2040 in the Netherlands. These simulations
have exactly the same boundary conditions. In 1900 the creation of land in
Lake Yssel had not yet taken place. To test the effect of this conversion
separately from the changes in land use, an additional simulation with the
historic land use map was done, this time with the current land extent
(similar to that in REF). All previously non-existent land is assumed to be
covered with grassland (the most common land cover class). This simulation is
referred to as HIS+Ys.
Furthermore, to be able to put the land cover changes in the perspective of
climate change, simulations with the present and future land use maps and a
temperature perturbation of +1 ∘C are conducted. These will be
referred to as REF+1 and FUT+1. The global surface temperature is predicted
to increase by at least 1 ∘C under all concentration pathways by
2050 . The surrogate climate change scenario is applied to
the initial land and atmospheric conditions of the simulations, as well as to
the driving sea surface temperature following the methodology by
, who suggest a vertically uniform temperature perturbation
is appropriate at mid-latitudes. The relative humidity is unchanged in these
simulations, which implies an absolute surface humidity increase of
6–7 %.
Urban areas outside of the Netherlands are removed in the historic, and
expanded in the future, land cover scenarios, in the same way as in
. 's projections of urban land cover are
used to determine the level of expansion. Across the globe, urban land cover
has increased due to people migrating to urban areas and because the
population density within cities decreased . Within
Europe a population density decline rate of 2 % per annum was reached
between 1990 and 2000 . We assume a conservative increase
with a decline rate of 1 % for the future. Urban areas are therefore less
than doubled in our simulations, consistent with Angel's projection for
Europe and Japan in 2050 with an annual density decline of 1 %.
Precipitation data
In the Netherlands, measurements of precipitation are available from the
national meteorological institute (KNMI). Gauge measurements are available on
a daily basis (08:00–08:00 UTC) at about 320 stations. Gridded observations
of precipitation are available at a 2.4 km resolution on an hourly basis
from (bias-)corrected radar data . Modeled precipitation
amounts are best compared with radar data, because of the similarity in
resolution and spatial extent. Unfortunately for 4 of the 19 selected cases
there are no radar data available, so some averages shown in the results
sections consist of fewer cases.
Results
The focus of this paper is on the sensitivity of precipitation to changes in
land surface conditions in historical and future perspectives. The
precipitation response to the perturbations in the experiments will be
described in the next section. To clarify these responses, the section after
that focusses on the (differences in) atmospheric conditions and processes
leading to the formation of precipitation.
Scatterplot of observed and modeled daily mean
precipitation (mm day-1) by radar (black, 00:00–00:00 UTC) and at
stations (red, 08:00–08:00 UTC) over the Netherlands. The dotted and dashed
lines give a linear regression between precipitation modeled and observed by
radar, respectively, including and excluding the day indicated with an open
dot (30 June 2003). The days with a square (22 July 2007) and triangle
(29 July 2000) are illustrated spatially in Fig. . The solid
1 : 1 line represents a perfect correlation.
Daily mean precipitation (mm day-1) simulated
by the model (top) and observed (bottom) on (from left to right) 29 July 2000
and 22 July 2007 (00:00 to 00:00 UTC), and averaged (08:00 to 08:00 UTC)
over the 18 selected cases.
Relative precipitation difference (%) in each of
the cases for all experiments compared to REF. Here the average is directly
calculated over the 18 selected cases and the mean is calculated using the
mean spatial differences as given in Fig. .
Spatial precipitation differences (mm day-1)
between the HIS, REF+1, and FUT experiments and the reference experiment.
In general, the WRF model overestimates precipitation amounts compared to
both station and radar data (Fig. ). The days marked with red
markers only have station data, and no radar data are available. There is 1
day where precipitation amounts are grossly overestimated, namely for
30 June 2003. This day is marked with an open dot in the scatterplot. This is
the only day in the selection that has easterly winds, and the poor model
performance could therefore be related to the chosen position of the domain.
This day was excluded from further analysis, so only 18 days are used further
on in this paper. The average wind direction on the other days is southwest,
like the year-round dominant wind direction in the Netherlands.
The performance of the model in representing spatial precipitation patterns
is reasonable overall, but shows quite patchy results (Fig. ).
The precipitation pattern of 29 July 2000 for example is well represented by
the model. This day is denoted by a triangle in Fig. . As an
example in which the model does not represent the spatial precipitation
pattern well, the precipitation pattern of 22 July 2007 is given. This day is
denoted by a square in Fig. . Compared to the previous
example, this day is more accurately modeled in terms of amounts, but the
modeled spatial distribution is quite distant from that observed. The average
spatial distribution of all 18 cases overestimates the amount of
precipitation compared to observed station data by almost 50 %.
Nevertheless, the model seems to capture the relatively high precipitation
amounts in the center of the country and lower rainfall amounts in the
northern parts.
The daily evolution of precipitation in observations and in the model is
given in Fig. , which will be discussed more thoroughly in the
next section. Compared to radar data, the phasing of all model runs is 3 h
too early in simulating the intensification of precipitation, and the modeled
precipitation peak is 2 h too early. In addition, the average precipitation
intensity is often higher than in observations. The separation of the model
and observations in the evening is found on only 2 days and is therefore not
a generic feature. The comparison between the radar data and the modeled
amounts in Fig. is not entirely consistent, however, since
the averages are made over a different number of cases (14 vs. 18,
respectively). Repeating the analysis with the lower number of cases leads to
the same results.
Precipitation response
Despite the fact that we select days with similar atmospheric conditions, the
response of precipitation to the land use and climate perturbations is not
uniform and varies strongly between the different cases. In
Fig. the relative difference of precipitation between the
land cover/temperature scenarios and REF is given for each of the 19 cases.
The average precipitation difference given here is calculated over the 18
cases (excluding the 30 June 2003 case) by averaging the relative change per
case. The mean precipitation difference is, on the other hand, directly
calculated from the averaged precipitation amount of the 18 cases as given in
Fig. . Although the strength and sometimes the sign of the
response differs between the days in every simulation, a generic picture of a
decrease in precipitation appears as a response to changes in land use. From
historic to present, and from present to future, land use, the decrease is
about 3–5 and 2–5 %, respectively.
One of the averaging methods shows a difference between HIS and HIS+Ys,
suggesting that the creation of land in Lake Yssel caused a moderate
reduction of precipitation in the last century. The other method gives the
same response for both HIS scenarios, suggesting the creation of land in Lake
Yssel did not influence the total precipitation response. Either way, the
model simulates a reduction of precipitation between HIS(+Ys) and REF.
Similarly, the difference between FUT and REF is negative, so a reduction of
precipitation is simulated by the model after incorporation of future land
use.
On average, the spatial differences between the simulations are quite patchy
(Fig. ). All simulations show small areas of enhancement as
well as areas of reduction in precipitation. The reduction in FUT is seen
over large parts of the Netherlands. Urbanization mainly takes place along
the West coast, where the reduction of precipitation seems to be moderate.
The relatively small reduction might be caused by the downwind enhancement of
precipitation by urban areas, though the patchiness in the rest of the
country does not seem supportive of this hypothesis. In the HIS simulation,
the largest enhancement is located on the eastern side of Lake Yssel. This
increase is not visible in the HIS+Ys simulation, so it might be caused by
the relatively high SST and evaporation over Lake Yssel itself and subsequent
higher moisture content of the air when it reaches the coast. The enhancement
of precipitation in REF+1 and FUT+1 is most pronounced along the southeastern
border of the country. The relatively large spatial changes shown here
average out to the relative changes given before in the order of 2–8 %,
which is only 0.1–0.6 mm. So the average changes between the runs are much
smaller than the patchy spatial differences seen here.
It is interesting to see whether the precipitation response to the
perturbations happens equally throughout the day, or whether it occurs during
a specific moment. In the mean daily evolution of precipitation, the
differences between HIS(+Ys) and REF are hardly distinguishable
(Fig. ). The differences between FUT and REF manifest
themselves in the middle of the day when the intensity of precipitation is
lower in FUT. This reduction of precipitation is also seen in FUT+1 and must
be caused by land use changes, like the expansion of urban areas. The most
pronounced temporal differences are visible in the temperature perturbation
experiments: REF+1 and FUT+1. The differences are most evident in the early
morning between 02:00 and 08:00 UTC. This difference is not significant as
the divergence is mainly caused by the precipitation enhancement on
2000-07-05, the day with the largest response to the temperature
perturbations. So the only systematic differences between REF and other
simulations are seen in FUT and FUT+1 in the middle of the day.
The REF+1 and FUT+1 surrogate climate change experiments are conducted to
allow a comparison between changes in precipitation due to land use changes
and due to climate change. In our simulations, precipitation in the
Netherlands increases in the temperature scenarios. The 7–8 % rainfall
increase in REF+1 (Fig. ) is close to the increase of about
7 % K-1 in near surface humidity that follows from the
Clausius–Clapeyron equation . FUT+1 shows a more moderate
increase in precipitation of 2–6 %. The increase seems to be offset by
the reduction in precipitation from the expected land use change that is
obtained in FUT. Interestingly, it appears that the precipitation response to
land use change and to the climate perturbation can be added linearly. So the
mean and average values in Fig. 6 in REF+1 of, respectively, 8 and 7 %
are reduced with the mean and average values in FUT, of -2 and -5 %,
respectively, to attain the mean and average values in FUT+1, of 6 and
2 %, respectively.
Diurnal cycle of mean precipitation (mm h-1)
over the Netherlands in the different experiments (averaged over 18 cases)
and given by radar data (averaged over 14 cases).
Distribution of hourly precipitation (mm h-1)
for each of the experiments and radar data, averaged over the 14 days that
have radar data available.
Mean relative change (%) over the Netherlands
in latent heat flux (LH), sensible heat flux (HFX), and relative humidity
(RH) in each of the experiments in comparison to REF.
The distribution of precipitation is not well represented by the model, but
is consistent among the scenarios (Fig. ). The extremes of
precipitation are very similar in all of the experiments, except for REF+1.
The REF+1 simulation reveals a considerable increase in precipitation
extremes. In the tail of the distribution the difference with REF is more
than 20 %. For more moderate extremes (> 15 mm) the difference
between REF+1 and REF is about 10 %. Although mean precipitation
increases in FUT+1, the distribution remains similar to REF. Apparently
extreme precipitation is in this case influenced more by land use changes
than mean precipitation. The spatial distribution of the enhanced
precipitation is similar to the pattern of mean precipitation; in other
words, there is more rain in the same locations. Overall, what can be
inferred is that climate change and future land use change have an equal,
though opposed, effect on extreme precipitation. The atmospheric conditions
and relatively little (deep) convection in FUT+1 seem to play a role in the
differences between the simulations.
Mean daily (00:00–00:00 UTC) values of latent
heat flux (LH), sensible heat (HFX), convective available potential energy
(CAPE), precipitation (RAIN), and daytime (06:00–18:00 UTC) values of the
percentage of time and area that the planetary boundary layer top is over the
level of free convection (PBL > LFC), likewise for lifting condensation
level (PBL > LCL), over the Netherlands for the conducted experiments.
Variable
Unit
HIS
HIS+Ys
REF
REF+1
FUT
FUT+1
LH
W m2
88.6
82.5
81.1
83.7
73.0
75.4
HFX
W m2
40.2
38.4
39.4
38.0
43.8
42.6
CAPE
J kg-1
330.1
311.4
301.2
360.6
290.1
346.7
PBL > LCL
%
54.2
54.0
52.7
52.9
51.0
51.2
PBL > LFC
%
45.3
45.0
43.7
44.0
41.7
42.1
RAIN
mm day-1
7.5
7.3
7.2
7.7
6.9
7.5
Surface and atmospheric conditions
To understand the differences between the various simulations, this section
focusses on surface and atmospheric conditions. We first consider changes in
the latent and sensible heat flux and changes in 2 m relative humidity. In
HIS both a higher latent and sensible heat flux are seen in comparison to REF
and to HIS+Ys (Fig. and Table ). This is
largely caused by the inclusion of part of Lake Yssel in the averaging, as
the high lake temperature and low albedo cause both fluxes to be enhanced. In
HIS+Ys the latent heat flux and relative humidity are somewhat higher than in
REF, but the sensible heat flux is lower. Consequently the available moisture
in both historical simulations will be higher, and this boosts precipitation
amounts. In the FUT simulations the reverse effect happens as moisture is
reduced after expansion of urban areas and other land use conversions.
In REF+1 the heat fluxes are not that different from REF. Nevertheless, there
is a large precipitation response. The imposed temperature perturbation with
constant relative humidity increases the amount of moisture at the time of
initialization and the amount that enters the model domain at the boundaries,
causing precipitation to change, but fluxes to remain the same. In FUT and
FUT+1 a reduction of the latent heat flux is simulated in comparison to REF.
Also, in both experiments relative humidity at the surface is lower than in
REF. The expansion of urban areas leads to an increase in the sensible heat
flux and a decrease in the latent heat flux, since potential evaporation is
reduced within urban areas. This decreases overall moisture availability. The
surface responses in FUT and FUT+1 look relatively similar, though the
precipitation response relative to REF is of opposite sign in the experiments
(Fig. ).
We now focus on the possibility of triggering convection by considering the
atmospheric conditions. Figure shows the median of the
diurnal cycle of the planetary boundary layer (PBL), lifting condensation
level (LCL), level of free convection (LFC), and convective available
potential energy (CAPE) calculated at the lowest model level, of the 18 cases
in the REF experiment. We show the median because the mean is influenced more
by outliers from individual cases. For REF+1, FUT, and FUT+1, the average
difference with regards to REF is given for each of these variables. The
differences are normalized with respect to the mean values in REF, so a
relative increase is given at every time. On average, the PBL increases to
about 800 m during daytime and reaches the LCL at around 09:00 UTC. In the
figure, the LFC remains well above the PBL and LCL. In many individual cases,
however, the LFC drops to about 800 m as well, permitting (deep) convection.
The LFC reaches its lowest level at 11:00 UTC. This coincides with the time
of the highest precipitation intensities in the model (Fig. ).
CAPE increases up to 09:00 UTC, while the LFC decreases and then stabilizes
because of the rain and associated temperature and humidity changes. The
early onset and intensification of precipitation in the model
(Fig. ) contributes to the small build-up of CAPE and could
explain the underestimation of extreme precipitation compared to observations
(Fig. ). Also, there are large spatial variations in these
variables. Therefore, we computed the fraction of space and time that the PBL
is higher than the LCL and LFC, respectively (Table ). We
consider this a measure of the amount of triggering that occurs.
Diurnal cycle of the planetary boundary layer
(PBL, solid), lifting condensation level (LCL, dashed), level of free
convection (LFC, dotted) (m), and convective available potential energy
(CAPE, dash-dotted) (J kg-1) in the reference experiment and normalized
mean difference of these variables in the experiments with a temperature
perturbation and reference land cover (REF+1), future land cover (FUT), and a
temperature perturbation and future land cover (FUT+1).
In REF+1 the temperature is higher, while the PBL is quite similar to REF.
During daytime there is little difference between REF and REF+1 regarding the
LCL and LFC, and approximately the same amount of triggering (PBL higher than
LCL/LFC) occurs (Table ). At night the LCL and LFC are
lower in REF+1 than in REF. CAPE is higher throughout the day in REF+1 than
in REF, likely due to the enhanced moisture content above the PBL as a result
of the imposed climate change scenario. This leads to the simulation of
higher precipitation amounts and intensities in REF+1 (Fig. ).
In FUT the large sensible heat flux causes the PBL to grow more during the
day and stay higher during the evening than in REF. The relatively large
sensible heat flux also affects and raises the LCL and LFC. In comparison to
REF, CAPE decreases in FUT from 08:00 UTC onwards when temperatures go up,
and relatively little moisture is available. Consequently, less precipitation
is simulated.
In FUT+1 a combination of atmospheric processes from FUT and REF+1 can be
seen. The LFC remains lower (like in REF+1), while the PBL and LCL are
slightly higher (like in FUT). Accordingly, CAPE is higher than in REF at the
beginning and end of the day (like in REF+1) and drops early in the day (like
in FUT). In FUT+1 in total, precipitation is enhanced by the moisture
availability from the boundary conditions imposed through the climate change
scenario, but high-intensity precipitation is not simulated because there is
little triggering and (deep) convection. Strong precipitation events are
caused by convective instability, which is measured by CAPE, and generally
occur during daytime. In FUT+1, CAPE is mainly enhanced during nighttime, not
during daytime. The relatively low values of CAPE during daytime likely
explain the absence of a response in the tail of the precipitation
distribution in FUT (Fig. ).
Discussion
Although WRF is a widely used atmospheric model, questions regarding the
choice of parameterization schemes and the model's validity for the specific
conditions always remain. The sensitivity to different parameterization
schemes was not specifically investigated in this study, while this is known
to be important
. The
chosen YSU PBL scheme is a first-order nonlocal scheme that is widely used
under convective conditions . Sensitivity to initial conditions
was checked for some of the cases by starting the runs up to 3 h earlier or
later. This had relatively little effect and WRF seems pretty robust in its
predictions, so the sensitivity is small. Previous work
found the largest sensitivity to the initial soil moisture conditions. In the
Netherlands those conditions are generally at field capacity due to the
frequent rain and high groundwater table and can therefore be expected to
have limited influence. The HIS, REF, and FUT experiments were duplicated
without the convection scheme, but this was found to have little effect on
precipitation amounts and is therefore not shown. The utilized and presented
model design is consequently only one version of reality, of which many more
could be simulated.
In this paper our main interest is the response of the model to changes in
land use relative to climate change. The model is very capable of simulating
temperature (changes), as was shown in . Although the
model's representation of precipitation is not perfect for the current
climate, we believe that the current setup can still be useful for exploring
the sensitivities. In addition, the model was used in a slightly different
setup for a 4-day case in spring, and comparable results regarding the
response of precipitation to increased urban areas were found
. A similar reduction in precipitation was also found
with the MM5 model for Europe as a whole ,
which gives confidence in the results. simulated a
0.2 mm day-1 reduction in precipitation in summer after expansion of
urban land by 40 %. They also found that the area in which precipitation
was altered increased nearly linearly with the urban land increment.
The utilized procedure to select cases for simulation was intended to obtain
a homogeneous set of days with similar meteorological conditions that were
thought to favor the land surface impact on precipitation. A large spread
among responses to land use and temperature scenarios was found between the
cases, however, so the intended comparability was not fully accomplished.
This could be a model artefact or a realistic response showing how
differently the atmosphere reacts to similar conditions, thus showing natural
variability. Nevertheless, the majority of cases have a similar sign in their
response. By averaging the results we find a more representable response then
the response of any single case could be. Our estimates could be biased by
the selection procedure that selected cases with rather strong convective
activity. Consequently, convection will always be triggered in the selected
cases and a potential feedback increasing precipitation through enhanced
triggering was excluded. Examples of this feedback can be found in
, , , and others.
The Netherlands is however not located in a region where strong feedbacks of
this type are expected and the influence of
changes in climate, SST, or circulation are likely more important
. If the selection procedure had been more
successful in identifying similar events, we could have made a composite
event by averaging the initial and boundary conditions, similar to
. Their procedure sounds promising, because it could
reduce simulation time and provide a more representative response, but the
selection of cases to average is apparently not straightforward.
In this study reductions in precipitation from historic to present, as well
as from present to future, land use are obtained for selected summer cases in
the Netherlands. Observations show, however, that precipitation has on
average increased by about 25 % in the last century .
So apparently factors other than land use changes have been dominant. The
observed change in precipitation was larger in the winter half-year than the
summer half-year nonetheless, and the trend in the summer months
(June–August) in the period 1951–2009 was only about 5 %
. Hence, land surface changes in the last century might
have mitigated some of the precipitation increase in summer and hereby have
contributed to the relatively low increase observed in summer. The same seems
to happen in the future in the simulations: combining future land use with
the expected temperature rise reduces the precipitation increase in the
model. This might only hold for summer, however, because historical and
theoretical evidence suggests that the precipitation response to land use
changes is lower in cases with non-convective precipitation
. Studies for different types of precipitation,
taking place in other seasons, are therefore desirable as well.
The climate change scenario used here maintains constant relative humidity in
the model. The resulting response in precipitation under current land cover
conditions (REF+1) is close to the expected increase in near surface humidity
of about 7 % estimated with the Clausius–Clapeyron equation. It is
interesting to note that in all simulations, except for REF+1, no differences
in extreme precipitation were simulated. We note that it is not the changes
in mean, but the changes in extreme precipitation that may cause problems for
society, with for example landslides or urban flooding
e.g.,. In REF+1 precipitation
over 15 mm h-1 increases with 10 % or more. This increase is
higher than the average increase in extreme precipitation simulated by global
climate models (GCMs), which is about 6 % per degree global warming
. Mean precipitation also increases more in our
simulations (7–8 %) than in GCMs (3 %) . This can
be explained because we investigate hourly data, while GCM data are generally
daily, and we only simulate 18 cases, while GCMs generate mean climate
simulations. In addition, GCMs generally show a decrease in the occurrence
frequency and an increase in the intensity of precipitation. Because we only
selected cases in which precipitation occurs, there can be no difference in
the occurrence frequency in our simulations. Our estimates are therefore
higher than those made by GCMs, but similar to comparable studies
.
Conclusions
This paper aims to quantify the precipitation response to historic (1900) and
future (2040) land use change in the Netherlands, and to put this response in
the perspective of climate change. To achieve this, historic, present, and
future land use maps are incorporated into the WRF model. In addition,
simulations with a temperature perturbation of +1 ∘C are done as a
surrogate climate change scenario. The investigation is done for 18 summer
days with similar characteristics that were selected with a circulation type
classification and k-means clustering procedure. On average, precipitation
decreases from historic to present land cover by 3–5 %, and decreases by
2–5 % from present to future land cover. Creation of land in Lake Yssel
might have caused a decrease in precipitation, but the evidence is not
exhaustive. Under the present climate, the simulated land use changes hardly
have any influence on extreme precipitation.
Observations of precipitation in the last century show a year-round increase
of 25 %, but only 5 % in summer. The results in this paper suggest
that the relatively low increase in precipitation in summer due to climate
change might have been offset by the effects of land use conversion. The same
land use–climate compensation occurs in our simulations for the future.
Precipitation increases by 7–8 % on average in response to the
temperature perturbation in the climate simulations and has a disproportional
impact on extremes. Expected land use changes, including the expansion of
urban areas, diminish this increase, however. As such an average
precipitation increase of 2–6 % is achieved in the simulation that
combines future land use with climate change. No increase in extreme
precipitation is found in the combined future land use–climate change
simulation. Overall, although the precipitation response to land use changes
is smaller than the response to climate change, it is not negligible in the
summer period in the Netherlands. Our simulations suggest this might be
especially true for precipitation extremes.