Journal of Heat Island Institute International Vol. 12-2 (2017)

Academic Article

Some effects of water bodies on the n environment – numericalexperiments
Günter Gross *1*1 Institute of Meteorology and Climatology, Leibniz University Hannover, Germany
Corresponding author: Günter Gross, gross@muk.uni-hannover.de
ABSTRACTA three-dimensional micro-scale model was used to study some aspects of water bodies on the urban
environment. This is of special importance in the field of urban climate, where ways and means to reduce the heatload produced by city residents need to be found. In a systematic study, the ranges and impacts for different sizes ofwater bodies were estimated and a fitted relation was given. Water temperature, and the resulting temperature
contrast with the surrounding area, seems to be more important during nighttime than during the day. The relevanceof meteorological and morphological parameters on water surface temperature was studied by adding a water modelto an existing numerical framework. The coupled model system was tested against field observations. Besideexternal radiation forcing, wind speed and depth of the water body were identified as the most relevant parameters.
In a numerical study for a realistic urban environment the different impacts of water bodies during day and night
were demonstrated, and the range of the penetration of the water effect into the built-up area was estimated.
Key Words : micro-scale simulation, water model, urban water bodies, range of impact

wide variety of green elements are available, such as green roofs,

1. Introduction

green facades, trees, and lawns. Water bodies are also supposed
A city, as a human environment, is subject to a permanent

to assist in cooling the air above them and by advection from themodification and is continuously developing. Rapid urbanisation

surrounding area. This has been demonstrated by numerous fieldis accompanied by conversion of land, construction activities,

experiments around urban ponds and lakes(6) (7) , rivers(8) (9) andand alteration of the local atmospheric conditions. Caused by

fountains(10) (11) .

changes in the surface energy budget of newly created

Although some data about the daytime cooling impacts fromsurroundings, temperatures in residential areas may occasionallywater bodies exist, general and applicable studies are still needed.

be more than 10 K warmer than the nearby countryside(1) . ThisA primary reason for this deficit is the specificity of conditions

situation will become more extreme, as we expect an overall

for each field experiment and of research questions(2). Völker ettemperature increase of 3–4 K by the end of this century. Theal.(12) summarised the wide variety of uncertainties in estimating

number and frequency of extreme temperatures and heatwaves

the water effect, which include weather conditions, urban

will then increase, with impacts on human comfort, well-being,morphology, the size and shape of the water body, and theand healtharrangement and location of the reference site. The findings ofAgainst this background, it is important for city planers andall experiments indicate a daytime cooling effect by ponds andhealth professionals to develop strategies for the adaptation oflakes on the order of 0.5 to 4.8 K(12), affecting the adjacent area

urban areas to foreseen temperature increases due to climate

up to 400 m away. However, the exact extend to which a specificchange. Particular attention must be paid to measures that arewater body design will impact the wider urban thermalsuitable to cool towns and cities, at least locally. To this end,

environment, and whether the observed effects are due to thegreen and blue design elements are very effective urban

water body alone, is still not clear.

features(2). It is well known that urban greenery offers significant

A very useful method for understanding the impact of blue

potential in moderating the increase of temperatures, especiallyelements on some aspects of local climate in built-up areas isin summer, through evapotranspiration andshading(3) (4) (5).

adopting meteorological models. This type of quantitative toolA-1-can be used very efficiently to estimate the potential effects ofthree-dimensional discrete Poisson equation by Gaussian

water body designs on the urban thermal environment. Whileelimination in the vertical direction, and fast Fourier transforms

numerous numerical experiments concerning the influence ofin the horizontal(17). A grid resolution of 10 m is used in thisvegetation on urban temperature are available, studies reporting

study for both horizontal directions. In the vertical, a 5 m grid

the effectiveness of water in mitigating urban heat are relatively

resolution is adopted near the surface and has been expanded

scarce(13) (14). In the present study, such a numerical model willwith a stretching factor of 1.1 up to 3000 m, resulting in 50 gridbe used to gain additional knowledge about the interactions

levels.

between blue areas and their surroundings. Emphasis will be puton the effects of pond geometry and water temperatures on the

2.2 The water model

impact range.

Urban water bodies are typically of very shallow depths.
Pronounced diurnal temperature variations can be observed withdecreasing amplitudes for increasing depths(18) (19) . There exist a

2. The model system

numberofapplicablenumerical

modelsthat

simulate

A strongly pronounced urban thermal environment is mainly

temperature evolution for different time scales. For weather

determined by an effective exchange of heat and energy between

prediction and climate models, the spatial scale of urban ponds

the various surfaces and the adjacent atmosphere. While great

is too small and detailed information is less relevant, while

efforts have been made to understand and to describe thephysically based parameterisations of different complexity are

relevant physical processes for a large variety of urban surfaces,

sufficient to describe water temperature(20) (21). Numericallike sealed areas, walls, roofs, lawns, and trees, much lessmodels for calculating the detailed distribution of temperaturesattention has been given to urban water bodies, like ponds andin space and time within shallow water bodies have beenlakes.

micro-scale

described, e.g. by Losordo and Piedrahita(22), Jacobs et al.(23),meteorological model and a simple lake model, is used here to

and Boudhiaf(24), with an extension for aquatic ecosystems bystudy some effects of water bodies on the microclimate of ane.g. Strauß(25).

urban environment.

The concept and basic framework of the HyLaM model

A

model

system,consistingofa(Hydrodynamic Lake Model, Strauß(25)) is used here forcalculating diurnal water surface temperature. Excluding

2.1 The meteorological model
Meteorological variables in the atmosphere are calculated using

horizontal exchanges, a water body is considered locally as a

the

und

water column divided into a number of horizontal layers (i) of

StrömungsModell für Urbane Strukturen), which has been used

constant thickness (Δz) between the atmosphere and thefor various numerical studies in urban environments(5) (15) (16).

sediment. For the thin, uppermost water layer, the energy budget

The model is based on the Navier–Stokes equations, theis described as(26) (22) (23):

micro-scale

model

ASMUS

(Ausbreitungs-

continuity equation, the first law of thermodynamics, and an
equationfor

specific

humidity.

These

equations∆Q S + Q Lnet − Q H − QV − QW = cW ∆z

areReynolds-averaged and the resulting correlations of fluctuating

∂TW
∂t

(1)

where TW is the water temperature of the uppermost layer andquantities are parameterised by flux-gradient relationships. The

cW is the volumetric heat capacity. ΔQS is the amount ofeddy exchange coefficient introduced by this approach isshortwave radiation absorbed in the layer depending on angle ofcalculated using the Prandtl-Kolmogorov relation via theincidence, albedo, turbidity of the water, etc. The net longwave

turbulence kinetic energy, which is estimated by an additionalradiation, QLnet, consists of the longwave radiation from theprognostic equation.

atmosphere, parameterised according to Strauß(25), and theBoundary conditions for wind at ground level are zero andoutgoing radiation, calculated by the Stefan–Boltzmann law

turbulence kinetic energy is proportional to local friction

which uses the water surface temperature. Flux gradientvelocity squared. Temperatures at surfaces are calculated by heat

relationships have been used to determine turbulent latent heatbudgets(5), while for specific humidity saturation is assumed for

flux, QV, and turbulent sensible heat flux, QH. This approach

all water surfaces and vanishing fluxes else. At the upper

requires surface and atmospheric temperatures and humidity

boundary an undisturbed situation is adopted with given values

information, as well as eddy diffusivity, a characteristic offor all meteorological variables.

turbulence. QW is the heat exchange with the underlying water

The equations are solved on a numerical staggered grid where all

and depends on water temperature difference and an effective

scalar quantities are arranged in the centre of the grid volume,

diffusion coefficient, which is determined empirically(22)while velocity components are defined at the corresponding side

depending mainly on surface friction velocity and density

walls. Pressure disturbance is calculated by solving astratification of the water column.

-2-The vertical mixing in a water column is caused by surface wind

W Konv ,i =stress and adjustment of density gradients by gravity. In thisstudy, the method published by Strauß(25), which follows strictlygρ W , i − ρ W , i +1∆z i
ρ W ,i(10)

and the convective diffusion coefficient DK results in

Losordo and Piedrahita(22), is adopted and briefly repeated and(11)

DK ,i = WKonv ,i ∆z i

described below. In this highly parameterized approach, anumber of empirical constants and approximations are

The effective diffusion coefficient used in this study is finallynecessary.

calculated by

Turbulent mixing in a neutrally buoyant water column isestimated by

But, as suggested by Strauß(25) and Losordo and Piedrahita(22),u*2exp( −k Z zi )30 u*k Z

N A,i =

(12)

Deff ,i = max( DK ,i , DE ,i )determined by a diffusion coefficient NA,i in depth zi which is
(2)

Deff

with

is limited to a minimum value (here: Deff > 5 10-4 m2 s-1).

For the layers within the water body, the heat energy budget is:
∂TWi
∂t

(13)

u* = τ o / ρW

(3)

∆Q S − ∆QW = cW ∆z

k Z = 6U −1.84(4)where the water temperature of layer i is TWi. The absorbed

τ o = ρ L 0,001U 2(5)

shortwave radiation penetrating into the water is estimated using
the Lambert-Beer law with a light extinction coefficientdepending on the turbidity. ΔQW is the difference in convective

The symbols in the equations above arekZ

empirical decay coefficient [m-1],

heat transport at the top and bottom of the layers, depending onu*surface friction velocity [m s-1],

water temperature difference and the local effective diffusion

τoshear stress at the surface [N m-2]

coefficient.

U is wind speed and ρL and ρW air density and water density

At the bottom of the water column, the heat exchange between

respectively.

the water and the sediment has been considered in the heat

Water density is calculated depending on temperature T byenergy budget. Relevant factors for calculating sedimentρW = 1000 (0,99987088 + 6,314 10 −5 T − 7,78 10 −6 T 2 )temperature with a heat transfer equation are the temperature(6)

difference between sediment and water, and the thermal

Existing density gradients modify turbulent mixing depending

conductivity coefficient for the sediment.

on stratification, which is characterized by the Richardson

Temperatures in the water column are calculated with a grid

number Ri. The formulation

resolution, Δz, of 10 cm for a specific forcing at the waterRii =α W ,i g z ∆Ti
2
i

u*2

surface. The relevant forcing parameter, like shortwave radiation,

(7)

∆z i

near surface wind, atmospheric temperature, or humidity, may
be provided either by observation or by numerical simulation.

is used here withthermal expansion

αW

Input parameters for the water body used in this study are mostly[K-1]

αW = 1,5 10 −5 (Tˆi − 3,85) − 2,0 10 −7 (Tˆi − 3,85) 2

adopted from Strauß(25) and are summarised in Table 1.

(8)

gravitational acceleration g = 9,81 m s-2,

Table 1: Input parameters for water model.

ΔTi temperature difference between adjacent water layers [K]
and

T̂i

mean temperature of adjacent water layers [oC].

Finally, the diffusion coefficient for turbulent mixing in a
stratified water column DE is estimated by
DE .i =N A ,i1 + 0,05 Rii(9)During night time for strong surface cooling of the water layer,water surface albedo

0.05

water heat capacity

4182 J kg-1 K-1Secchi depth

1m

sediment thermal conductivity

0,7 J m-1 s-1 K-1

sediment thickness

0,1 m

sediment heat capacity

2000 J kg-1 K-1

convective transport due to density instabilities become adominant source of intense vertical mixing. The descent velocity
WKonv in case of density instability due to gravity isapproximated by-3-

area is shown for daytime (14:00) and for nighttime (04:00). The

3. Results

strong horizontal pressure gradient at the borders of the water
The model system described above was used to study the effects

area, which is caused by large temperature differences, is theof water bodies on the environment. First, a parameter study was

forcing of a thermally induced local flow system. During day, aperformed to determine the relationship between the size andlake breeze with significant wind speeds, up to 4 m/s, develops

temperature of a small lake and the area affected. The

and advects cooler air from the lake into the surrounding

importance of various parameters that characterise the waterlandscape. For larger water areas, maximum wind speed

body and the effect of spatially variable water surface

increases and the effect of the water body is detectible at greatertemperature will be discussed afterwards.

distances. Even around a small pond of 0.25 ha, significant windspeeds develop, but only at short distances. During nighttime,

3.1 The role of water body size and temperature contrast

the generally more stably stratified atmosphere suppresses

An important issue for urban planners is understanding thevertical diffusion and the horizontal pressure gradient is less

impact and range of a water body with regard to size andpronounced than during daytime. As a consequence, maximum

distance from the water edge. A large number of studies havewind speed is reduced by more than a factor of two.

analysed the effects of open water areas on urban climates.

Diurnal variation of maximum wind speed (windMAX), which is

Detailed reviews are given by Manteghi et al.(2) and Völker etan indicator for important issues in the field of urban climateal.(12). Depending on the size of the lake or river and the time oflike ventilation capability, reduction of heat stress, or transport

the day, urban climate is affected at 30–40 m for small blueof air pollutants, was well pronounced. During the night,

areas(8) up to several hundreds of meters(6) (27) (9). However, allwindMAX increased with the size of the water body but remained

observational studies have been performed in very different

nearly constant for areas larger than 10 ha. windMAX during

surroundings and the analyses are not based on common criteria.

daytime was much larger and a sharp increase for water bodies

The meteorological model introduced above can be used to

up to 10 ha was followed by a smaller, but constant, increase

perform a consistent parameter study on the range of influence

thereafter.

of a water area for different sizes and water temperatures. In the

The model results can be used to estimate the range of the

middle of an area with an edge length of 3 km, a square-shaped

effects of a water body on temperature and wind. The

lake of variable size was arranged. The water area variedobservational findings published in the literature cover a large

between 0.01 ha (= 1 gridpoint of 10 m × 10 m) up to more than

range, from some tens of meters for small rivers up to some42 ha (65 × 65 grid points) and water temperature was fixed at

hundreds of meters for larger ponds. However, the efforts to use

295 K. The temperature of the surrounding bare soil was

the measured values to derive consistent, universal, andcalculated according to a heat balance equation and two diurnaltransferable results face a number of uncertainties like the lackcycles were simulated. The results of the second day were usedof uniform criteria in defining the effects, completely different

for the evaluation.

local landscape conditions, and different large-scale weather
conditions. Here, the numerical results can provide additional
valuable information and insights.
During daytime (14:00), the numerical results showed aconsistent picture of the range R (in m) depending on the area of
the water body A (in ha). Depending on the arbitrary
predetermined threshold, either for wind or for temperature, therange for a 1 ha pond varied between 200 and 400 m (Figure 2).
A good approximation seems to be the fitted relation:(14)

R = a ln( A) + b

with values of a and b for different temperature thresholds given
in Table 2.
Table 2: Regression coefficients.

Figure 1: Simulated 10m wind for 14 hours (day) and 04 hours(night) for different water body areas (blue rectangles indicate

Threshold for temperatureablocation). Maximum wind speed for the two selected times

0.3 K

74

353

depending on size of the pond is given in the inserted graph.

0.5 K

66

289

1.0 K

45

195

In Figure 1, the 10-m wind along the centreline of the simulation-4-

with lower wind speeds. Varying the water temperature, andtherefore the maximum temperature difference with the bare soil
(ΔTMAX), over a wide range reveals that nighttime air flow andventilation are very sensitive to temperature specification (or theaccuracy of available observations), while the response ofdaytime maximum wind speed is comparably small.

Figure 2: Simulated range of impact for various thresholds fordifferent areas of the water body.
During nighttime (04:00), the air flow was directed towards thelake and significant temperature effects on the surrounding were
small. Nevertheless, the moderate wind speeds at this time were
important for the ventilation of the adjacent landscape.

Figure 3: Simulated 10m wind for 14 hours (day) and 04 hours

However, the above-mentioned range for idealised situations(night) for different water surface temperatures. Maximum wind

will be smaller in a real urban environment, where the heat

speed for the two selected times depending on the maximum

island effect modifies the temperature contrast with the water,

temperature difference between water and bare soil is given inwhile buildings and trees decelerate airflow significantly. During

the inserted graph.

daytime, only in broad avenues can the cold air penetrate some
hundred of meters into built-up areas. For narrow streets with

3.2 Results of the water modeltraffic and trees, the cooling effect could not reach further than

Water surface temperature, and the associated temperature

50–150 m(27).

contrast to the surrounding landscape, was the key parameter toData about the water temperatures of the numerous lakes anddescribe the effects of urban blue spaces on urban climate. As

ponds within an urban area, when they exist at all, are very

demonstrated above, the temperature was important inlimited and restricted to samples monitoring water quality(28) or

estimating the wind speed, especially during nighttime, which inswimming suitability. Because of these large uncertainties and,turn was important for nocturnal ventilation. Due to the lack ofat the same time, the great importance of the temperaturesufficient information, a model for water bodies was used tocontrast between water and land as a forcing for the thermallycalculate water temperatures.

induced air flow system, the relevance of water temperature will
be studied in more detail. Numerical simulations were

The model results were verified against measurements that were

performed for a 1 ha pond with a range of fixed surface

collected for a number of field experiments for small ponds andtemperatures as well as a diurnal variation with different

lakes of various sizes and depths in the Hannover urban area(19).

amplitudes. The amplitudes have been prescribed with 10 and

Depending on the depth of the water body, 1-min-mean

30% according to the temperature variation of the undisturbed

temperatures were recorded at 3 to 6 vertical locations with aland surface.

relative accuracy of 0.1 K.

As shown in Figure 3, the effect during nighttime is more

Observed meteorological input data from a university site,

significant than during daytime. The difference in maximum

including radiation, wind, temperature, and humidity with a time

wind speed at 04:00 is about 0.8 m/s for a temperature variation

resolution of ten minutes were used to force the model. These

between 293 and 297 K, while the effect during daytime is only

observations were very comparable to the variables measured

about 0.4 m/s but on a much higher wind speed. Assuming aroutinely by the German Weather Service at the airport weather

diurnal variation in water temperature means a reduction in thestation (Figure 4). As an example, the lake temperatures of thetemperature difference during the day as well as during the night

Maschsee, with an area of 78 ha and a mean depth of around 2 m,

and consequently the system responds to the reduced forcing

were simulated for a one week period and compared to observed-5-data. This weather period was characterised by strong insolation

m below the surface. During daytime a well-stratified waterand a very pronounced diurnal variation in meteorologicalcolumn developed, with warm water near the surface and cooler

variables. The persistent input of solar energy resulted in awater at the bottom. The cooling at night at the interface

continuous increase in water temperature: more than 1 K in one

between the water and atmosphere resulted in cold and denseweek (Figure 4). This warming was not restricted to the water

water in the uppermost part of the water column, causing strong

surface, but affected the entire water body. Temperature

vertical mixing. As a consequence, water temperatures were

amplitudes decreased with depth but were well-apparent even 2

nearly uniform with respect to depth during nighttime hours.

Figure 4: Above: Observed and simulated water temperatures for the centre of the Maschsee (location  in Figure 7).
Meteorological data are given in the upper and lower panel.
below: Observed and simulated water temperatures for the Kiesteich (location  in Figure 7).

-6-

The simulated results follow the observations very satisfactorily.
All main features were captured by the model, including thecontinuous temperature increase over the period for all depths
and the significant diurnal change in water stratification.
However, differences can be identified that might be attributed
to an unfavorable specified input parameter or to uncertainties in
the meteorological input data. As a second example, thetemperature was calculated for a deeper pond of typically 6 m
depth and also compared to observations. Now, the attenuating
effects of the absorption and scattering of solar radiation
penetrating into the water column is large and near the bottom of
the pond temperature variation during the day is small. In theupper part of the water body, the model captures the mainfeatures again quite satisfactorily. Based on the results presented
in this study and additionally by Strauß(25) and Losordo andPiedrahita(22) one can conclude, that the model developed
primary by the latter authors and applied also by Strauß(25) is a

Figure 5: Sensitivity of the surface water temperature onvery promising, valuable and reliable tool to calculate

parameter variation of the lake model.

temperature distribution in ponds and lakes of different size anddepth.

for water bodies with a depth smaller then 1.5 m. Based on these

To identify the importance of different parameters on water

results, a simple method to describe local water surface

surface temperature, model runs were performed for a period oftemperature TS is given by:

30 days in order to allow a steady diurnal behavior to be

TS = −3.3 ln(U ) + TCLIM

established. Different parameters were changed within a(15)

reasonable range compared to a reference simulation. The results

where TCLIM fixes the reference level. This value is either an

are presented in Figure 5 for the daily mean temperature andobservation or a climatological temperature. The variation in TS

maximum diurnal temperature amplitude. The largest effect was

may be modified by the diurnal temperature amplitude byfound for a variation in direct solar radiation, QS, which is not anroughly ±1 K.

unexpected result because this is the main forcing for watertemperature. Wind was also a very important factor, followed byhumidity and temperature of the near-surface atmosphere. Wind
acted in a twofold manner by modifying the turbulent mixing
within the water column and by affecting the turbulent fluxes oflatent and sensible heat. Water and lake characteristics like depth
or extinction coefficient were of minor importance for the dailymean temperature. This picture changed significantly whenevaluating the maximum temperature amplitude over the day.
This caused the depth of the water body to become important.
Also for this temperature measure, the direct solar radiation and

Figure 6: Sensitivity of water surface temperature for variation

the wind speed was of great importance.

of wind forcing and depth of the water body.

While QS controlled the level of the mean temperature of thewater body, wind was also an important factor, modifying this

3.2 Application for an urban setting

value locally by several degrees. For low wind speeds, U, theThe importance of the findings in the previous sections weremean temperature was estimated as around 23°C, which

evaluated by numerical simulations in a complex urban

decreased by four degrees with a moderate wind speed of 4 m/s.

environment. The selected study area was the lake Maschsee inSimultaneously, the diurnal amplitude decreased from 2.2 K to

Hannover and the adjacent urban areas. Input data were provided

around 1.6 K (Figure 6). The strong effect of water depth onon a 10 m grid with detailed specifications of landuse and urban

diurnal temperature amplitude was especially pronounced forvegetation characteristics like building height or stand density.

shallow ponds. While mean daily temperature was very

The vertical grid resolution was changed in these simulations tocomparable, the temperature amplitude increased considerably

2 m for the lowest 10 m of the atmosphere to resolve the urban-7-structures. Numerical simulations were performed for a sunnysummer day with a well-pronounced diurnal variation in surface
temperatures. One main point of this investigation was toevaluate the effect of an inhomogeneous surface watertemperature in space and time compared to the results with aprescribed constant value of 19°C. The surface watertemperature is estimated according to Equation 4 depending on
the local near-surface wind speed.
During nighttime, water temperatures were much warmer than
the surrounding open field and on the same order as theneighbouring urban areas (Figure 7). The rural areas are locatedsouth of the sports area along the river Leine, mainly in the west
and to the south of the Maschsee, while the city is directly to thenortheast. Because of this specific landuse arrangement and theassociated surface temperatures, a well-pronounced land breeze
developed along the western waterside, while the very similar
temperatures

in

the

eastern

part

do

not

produceawell-pronounced thermally-induced local wind system. Wind
speeds across the Maschsee were therefore very inhomogeneous,with higher values in the western part and a calm situation in thenorthern part. Consequently, surface water temperatures are very
different with a range between 17.5 and 19.7°C.
Daytime water temperatures were much lower than thesurrounding land, with large gradients in rural and urban areas.
Significant lake breezes developed to balance these differences.
In the rural area with grass, shrubs, and a sparse stand of trees,
air flow was hardly impeded and penetrated into the surrounding
areas. The built-up area was a major barrier for the lake breeze
and only along broad roads was cooler air advected by the air
flow into neighbouring city quarters.
A similar numerical simulation was performed with a constantwater temperature in order to find out whether detailed

Figure 7: Simulated wind and temperature in 2m a.g. forknowledge of this parameter is relevant for urban studies or not.

daytime (above) and for night time (below).  and  indicate

The Altenbekener Damm is a broad road that directly faces thelocation of the observation sites.

Maschsee and air flow along this street was used for thisanalysis (Figure 8). During the daytime, the temperature contrast
between the water and the urban area was large, and an
additional variation in the water temperature of 1–2 K is ofnegligible importance. For both scenarios, the lake breeze
entered the street and penetrated a certain distance before intense
mixing decreased the forcing and winds slow down. Simulated
results were not significantly different. The depth of penetration
along the street was around 200 m, on the order of magnitude
found by other authors (e.g. Ishii(6)). During nighttime,
temperatures for water and adjacent urban areas were very
similar and the interactions were not as clear as during daytime.

Figure 8: Simulated wind speed in 2m a.g. along the

Local wind depended strongly on the urban morphology and

Altenbekener Damm for 14 hours (day) and 04 hours (night) for

results are difficult to apply to other cities. In general, winddifferent water temperatures.

speeds are lower during the day due to the reduced temperature
contrast. For the scenario adopted here, Hannover city was-8-warmer than the lake and an air flow developed from the lake tothe city centre. Maximum wind speed along the street wasaround 0.7 m/s and the modification of water temperature
affected this local wind, slowing it by about 0.2 m/s. Althoughthese wind speeds are small, especially during the night, evenlight breezes are helpful and valuable for ventilation andreduction of nocturnal heat stress.
As already described in detail by Völker et al.(12), observations
should be treated with caution if universal findings about theeffects of urban water bodies on local climate are to be derived
from these data. Problems arise e.g. due to the specific weather
situation during the experiment, the unique urban morphology,
and

the

particulararrangement

of

the

measurements.

Nevertheless, the wide variety of available data yields very
valuable information about the direction and order of magnitude

Figure 9: Temperature effect of blue spaces during daytime forof the impacts of blue spaces within a city. In order to exclude

grid points with landuse “street”.

the experimental disadvantages, an additional numerical study
for complex Hannover city was performed where temperature

convey a first impression of what can be expected in relation toadvection was excluded. By comparing the results of thewater bodies and urban climate. However, due to the wide

complete simulation with the ones from the idealised simulation

variety of relevant parameters concerning city morphology,

without advection, the effect of temperature advection from theweather, experimental design, and water body geometry, generalblue spaces could be identified. The analysis was performed forand transferable findings are limited. A literature review(12)the urban district of Hannover-Südstadt, to the northeast of afound that during daytime and in the summer the cooling effectlarge urban lake. The temperature differences for the twoof ponds, lakes, and rivers is between 0.5 and 5 K, and the range

scenarios, depending on the distance to the water’s edge, wereof this effect is between some metres up to 400 m.

calculated for all grid points with the landuse “street”. Other

The model introduced here was used for a systematic parameter

urban areas and forested regions were excluded.

study in order to provide additional data to fill in some gaps in

The results for daytime are presented in Figure 9, which shows athe present knowledge. During daytime, a cool water bodyclear decrease in the effect with increasing distance from theproduced a local wind system, resulting in ventilation andwater body. Along the streets, the cold air from the laketemperature advection into the surrounding area. Depending on

penetrated into the city with a strong cooling effect near thethe size of the blue space, a specific maximum wind speed andborder between water and land. For larger distances, this coolingrange was found using a prescribed threshold for temperatureeffect became considerably smaller. Depending on the thresholdchange as an indicator. The results were sufficient to derive afor defining the effect, the range varied for a cooling of larger

relationship between the size of the water body and the range ofthan 1 K from 100–150 m up to 200–300 m for a threshold of

impact.

0.5 K. These values were on the order of magnitude of other

The detailed surface water temperature was found to be more

published experimental findings. Compared to the idealised

important during the night than the day. Because of the strong

situations of the parameter studies presented in section 3.1, thedaytime heating of bare and flat surrounding areas, the pressure

maximum range was only around half of the value.

gradient between the water surface and land was very large andwater temperature variation of some degrees was of minor
importance. To discuss this topic in greater detail, a lake model

4. Conclusions

was included in the model framework to calculate water surface
A three-dimensional micro-scale model was used to study thetemperatures depending on local meteorological conditions. Thiseffects of water bodies on wind and temperature in thelake model was successfully tested against field observations.

surrounding area. Blue spaces in an urban environment have the

The importance of different meteorological parameters and lakepotential to cool down cities, especially during daytime. This ischaracteristics on water surface temperatures was identified. Forof special importance for city planners and health professionals

lakes with a depth of greater than 2 m, which is a typical value

for developing strategies to adapt urban living spaces to climate

for medium to larger urban water bodies, local wind speed was achange.

very important parameter. Based on the numerical results, an

Existing observations and the results of field experiments

empirical relationship was given.

-9-

The findings of the investigations for idealised situations were

Int.J.Environ.Res. Public Health 11 (2014), pp. 1195-1210.

incorporated and considered in a numerical study on the effect of(10) Nishimura, N., T. Nomura, H. Iyota, S. Kimoto, Novelwatera larger water body on a real urban environment. It was foundfacilities

forcreationofcomfortable

urbanthat the multitude of obstacles modified the local wind patterns,

micrometeorology. Solar Energy 64 (1998), pp. 197-207.

making the range of impact much smaller than for a plain.

(11) Xue, F., X. Li, J. Ma, Z. Zhang, Modeling the influence of

However, although there is a large scatter, the daytime coolingfountain on urban mircroclimate. Build.Simul 8 (2015), pp.

effect penetrates up to 300 m into the city, reducing the urban

285-295.
(12) Völker, S., H. Baumeister, T. Classen, C. Hornberg, T.

heat island by 1–2 K. The values calculated here are on the order

Kistemann, Evidence for the temperature-mitigation capacityof magnitude of other published experimental results.
Different blue elements are available to optimise the adaption ofof urban blue space – A health geographic perspective.

our cities to a changing environment. Because of the many

Erdkunde 67 (2013), pp. 355-371.

influencing factors and the wide variety of interactions,(13) Robitu, M., M. Musy, C. Inard, D. Groleau, Modeling themeteorological micro-scale models are very helpful tools for a

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(14) Theeuwes, N.E., A. Solcerova, G.J. Steeneveld, Modeling
Acknowledgements

the influence of open water surfaces on the summertime

The author would like to thank M. Schneider, Department of

temperature and thermal comfort in the city. J.Geophys.Res.
118 (2013), pp. 8881-8896.

Meteorology and Climatology, Leibniz Universität Hannover for(15) Gross,G., On the estimation of wind comfort in a building

providing the water temperature data.

environment by micro-scale simulation. Meteorol. Z. 23(2014), pp. 51-62.

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