ORI GIN AL PA PER

Impacts of topography and land cover changeon thunderstorm over the Huangshan (Yellow Mountain)area of China

Die Wang • Junfeng Miao • Zhemin Tan

Received: 28 October 2012 / Accepted: 7 February 2013 / Published online: 27 February 2013� Springer Science+Business Media Dordrecht 2013

Abstract In this study, the Weather Research and Forecasting (WRF) model (version

3.1.1) was used to simulate a thunderstorm event which occurred on August 18, 2010,

over the Yellow Mountain area of China. This event was a typical thunderstorm embedded

in high-pressure systems. First, the development processes of mountain–valley breeze and

convective cells were studied; second, this study focused on revealing the influencing

mechanism of complex topography and heterogeneous land cover on thunderstorm by

removing the Yellow Mountain and changing the land use categories. On flat terrain, the

simulated results displayed that the convection weakened persistently, cloud top decreased

sharply, and intensity of precipitation reduced. Moreover, there was no up-slope valley

breeze, convergence, and lifting of water vapor could be found on the mountaintop. Then,

the role of land use was revealed by changing original land cover into grassland, mixed

forests, and bare soil in the innermost area, respectively. When covered by grassland, there

were less sensible heating and lower moisture, leading to the planet boundary layer height

decreasing and vertical lifting weakening, which tended to cause more stable atmosphere

and less rainfall on the mountaintop; when covered by mixing forests, only small differ-

ences presented in simulated meteorological fields, including wind fields, moisture, cloud

water mixing ratio, precipitation, and other fields; when covered by bare soil, the latent

heating was more important in influencing the process of thunderstorm. There were less

latent heating and lower accumulated water vapor compared to other experiments, causing

vertical lifting weakening, stability of atmosphere increasing, and precipitation decreasing.

Keywords The Yellow Mountain � Thunderstorm � Topography � Land cover change

D. Wang � J. Miao (&)Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University of Information Science and Technology, Nanjing, Chinae-mail: miaoj@nuist.edu.cn

Z. TanKey Laboratory of Mesoscale Severe Weather of Ministry of Education,Nanjing University, Nanjing, China

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Nat Hazards (2013) 67:675–699DOI 10.1007/s11069-013-0595-0

1 Introduction

Orography plays a significant character in influencing weather processes by exchanging

momentum and energy between large-scale and mesoscale weather systems. When

mountains are encountered, many factors, including water vapor, wind shear, atmospheric

stability, and direction of the leading air, will be combined to influence the local weather

situations. Besides, the orographic wave and vortex which may generate torrential flood,

debris flow, cold air damming, exceptional track of storm, and coastally trapped dis-

turbance are also regarded as the important weather phenomena induced by orography

(Lin 2007).

The orographic precipitation is very complex as the primary factor to be considered in

forecasting local mesoscale weather (Hohenegger et al. 2005). When the mountains are

high enough, a large number of moist airs lift forcedly and decrease its temperature to

dewpoint by expansion and adiabatic cooling over the slopes (Wallace and Hobbs 1977).

The condensation of water vapor contained within the air initially forms the orographic

clouds over the mountains. After that, the cloud droplets gradually grow into raindrops and

fall to the ground when large sufficiently. Furthermore, heterogeneous terrain has great

effects on duration, location, scope, quantity, and intensity of rainfall. During the past

several tens of years, the trigger and enhancement mechanisms of orographic rainstorm

have been summed up as thermal force, dynamic factors, and even topographic features

(e.g., shapes and dimensions) (Smith 1979; Roe 2005). To huge mountains, greater pre-

cipitation is related to up-slope condensation, small disturbance, or wind convergence on

the leeward slopes in stable atmospheric conditions (Mass 1981; Chu and Lin 2000); to

monticules, the seeder–feeder mechanism which can lead to intense rainfall is particularly

important, because it helps condensation nucleus grow into droplets (Bergeron 1949;

Robichaud and Austin 1988).

Previously, for understanding the fundamental features of local circulation and devel-

opment processes of orographic precipitation, two-dimensional (2D) hydrostatic models

(e.g., Gallus and Klemp 2000; Chu and Lin 2000) and diagnostic models (e.g., Chris and

Joel 2004; Kunz and Kottmeier 2006) are accessed successfully to be used. Without

considering the static stability of the atmosphere and the dimensions of topography, the

flow dynamics and three-dimensional (3D) structures of wind fields cannot be described

clearly over complex terrains. Therefore, it is crucial to use a 3D non-hydrostatic numerical

model to reveal the 3D structure of convection cells, and the vertical circulation of

the mountain–valley breeze by incorporating an actual terrain and land cover databases

(e.g., Paul and Nikolai 2011).

The Yellow Mountain which located near Huangshan City of Anhui Province in Eastern

China in the subtropics has a typical monsoon humid climate. There are three major peaks

among 72 distinguished peaks with various shapes, namely Lotus Flower Peak, Bright

Summit Peak, and Celestial Capital Peak. Most studies focus on the sea of clouds and

mesoscale rainstorm under the beneficial synoptic-scale circulation background, using

models with simple dynamic frameworks and physical parameterizations in the Yellow

Mountain region (e.g., Zhai et al. 1995; Wu et al. 2005; Chen and Zhao 2006). Not too

many studies pay close attention to the circulation and thunderstorms triggered by inho-

mogeneous heating from the earth’s surface.

Moreover, Land cover over the Yellow Mountain area has been modified significantly

during the past decades. This region will be reshaped continuously by exploiting the

natural scenic spots and expanding urban areas in coming future. Land cover changes have

far-reaching influences on the characteristics of PBL and regional land–atmosphere

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interaction (Pielke 2001; Cui et al. 2006; Mahmood et al. 2006; Ter Maat et al. 2006;

Pielke et al. 2007a, b; Raddatz 2007; Nair et al. 2011). Various numerical models have

been used for studying the impacts of land cover changes on wind fields, temperature, soil

moisture, atmospheric water vapor content, convective cloud, and convective precipitation

(e.g., Chang and Wetzel 1991; Clark and Arritt 1995; Shen 1998; Crawford et al. 2001;

Pielke 2001; Adegoke et al. 2003; Narisma and Pitman 2003; Gero and Pitman 2006; Sen

Roy et al. 2007, 2011). The development and evolution of intense convective weather

processes are influenced profoundly by increasing available energy over vegetated ground

(Pielke et al. 2007a; Pielke 2001). Nevertheless, the most previous studies concentrate on

orographic effects instead of land cover changes in the Yellow Mountain region. Conse-

quently, it is necessary for us to reveal the impacts of different land use types on local

circulation and severe convection deeply, using 3D mesoscale numerical model.

2 Model description and initialization

The 3D non-hydrostatic atmospheric model WRF (Skamarock et al. 2005) is a mesoscale

numerical model designed for a wide range of operational forecasting and academic

research needs. In recent years, it generally gains popularity in simulating the severe

convective weathers. Therefore, in this study, the ARW-WRF version 3.1.1 model with four

nested domains (D1, D2, D3, and D4) and two-way nesting schemes is adopted for use.

Domains are centered on the mountaintop (30.15�N, 118.15�E), and the horizontal grid

resolutions are 27, 9, 3, and 1 km, with 150 9 150, 196 9 196, 184 9 184, and 124 9 124

grid points for D1, D2, D3, and D4, respectively (Fig. 1). D1 covers central and Eastern

China, which is responsible for simulating the large-scale circulation and synoptic-scale

Fig. 1 Map showing the model domains (D1, D2, D3, and D4) and topography (color). Bold solid lines(rectangle) denote the geographic locations of the nested grids. The central point is located on themountaintop (30.15�N, 118.15�E)

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weather systems. The D2 and D3 are setted up to capture mesoscale and local weather

situation, and the D4 is the area of especial concern. The locations of the three inner grids

are far enough away from the boundaries of the outer grids that they would not be greatly

affected by the values at the outer boundaries during a 48-h period. The vertical grids for all

domains consist of 35 levels ranging from the ground to 100 hPa, and the lowest layer is

approximately at 48 m AGL. The topography (Fig. 2a) and land use (Fig. 3) are obtained

from the Moderate Resolution Imaging Spectroradiometer (MODIS) databases in 2001 at

10, 5, 2 arc-min, and 30 arc-sec resolutions for D1, D2, D3, and D4, separately. The physical

parameterizations used in this study are given in Table 1, and the cumulus scheme is not

used in D3 and D4 (Miao et al. 2008, 2009). The final reanalysis (FNL) data from National

Center for Environmental Prediction (NCEP) updated every 6 h with horizontal resolution

of 1� 9 1� was used in this study. Interpolated FNL data in horizontal and vertical direc-

tions were considered as the initial fields and lateral boundary conditions.

3 Numerical experiments

With the purpose of capturing the significant influence of topography and land use changes

on mountain–valley breeze and thunderstorm, a series of sensitivity tests were performed.

The run which used the real topography and land use types was denoted as the control

(CNTL) experiment. In contrast, the remaining three other runs which were identical to the

CNTL run except for land use types were designed to run with uniform grassland

(GRASS), mixed forests (FOREST), and bare soil (DESERT) in D4, respectively

(Table 2). Furthermore, in order to show the dynamic and thermodynamic effects of

Fig. 2 Terrain of D4 (color and with contours every 200 m) in the a CNTL and b TOPO runs, solid line(AB) indicates the location of the vertical cross-section used in this study (along 118.15�E). Location of tenAWSs marked by circle signs: Xiuning (XN), Zhaixi (ZX), Huangshan (HS), Jiuhuashan (JHS), Jingde (JD),Qimen (QM), Yixian (YX), Shexian (SX), Tunxi (TX), and Huangshanqu (HSQ) indicated for verification ofmodel outputs

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terrain, a TOPO experiment was conducted, with the region (29.80–30.35�N,

117.80–118.45�E) being cut down in elevation (to 200 m) (see Fig. 2b). The integral time

period was 48 h (from 0000 UTC 17 August to 0000 UTC August 19, 2010). The first 16 h

of this period was discarded as a part of the model spin-up, and the remaining 32 h formed

the time period of interest for this study.

4 Case description

The period from 0900 to 1400 LST on August 18, 2010, witnessed a thunderstorm over the

Yellow Mountain area. By observation, the relatively heavy precipitation cells were

Fig. 3 Land use categories of D4 with 30 arc-second resolution data from the MODIS (2001) datasets

Table 1 WRF physical parameterizations

Parameterization Reference

Microphysics scheme: Lin et al. Lin et al. (1983)

Longwave radiation: rapid radiative transfer model Mlawer et al. (1997)

Shortwave radiation: Dudhia Dudhia (1989)

Land surface scheme: Noah land surface model Chen and Dudhia (2001)

Surface scheme: Monin–Obukhov (Janjic) Janjic (2002)

Planetary boundary layer scheme: Yonsei University (YSU) Hong et al. (2006)

Cumulus scheme: Kain-Fritsch scheme (only for D1 and D2) Kain (2004)

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situated at Jiuhuashan (JHS), Huangshanqu (HSQ), and Huangshan (HS) automatic

weather stations (AWSs), with the 12-hour-accumulated amount of precipitation reaching

71.7, 68.1, and 58.3 mm, respectively (Fig. 6a). As can be seen clearly from the 500 hPa

height field (Fig. 4a), the southeast of China was subject to the West Pacific subtropical

high (WPSH), and the Yellow Mountain (signed by the red point) located to the top and

back portion of the WPSH. The upper southerly air current would carry the warm and

moist air into the Yellow Mountain area, which could create large-scale background field

for thunderstorm. At 850 hPa (Fig. 4b), the counterpart region was covered by warmer

(above 20 �C) and drier air and prevailed southwest winds without any shear lines. The

large temperature differences between surface and upper air were beneficial to accumulate

convective instability energy and produce thunderstorm. Hence, the selected thunderstorm

mainly developed under weak synoptic pressure gradients. The differences of thermal

properties of non-homogeneous underlying surface layer and the local circulation played a

leading role in triggering this severe convection.

Fig. 4 The synoptic weather pattern at 1000 LST on August 18, 2010, at a 500 hPa and b 850 hPa,showing geopotential height (dam, blue lines), temperature (�C, red lines), and relative humidity (color).The red point shows the position of the mountaintop of the Yellow Mountain

Table 2 Summary of the numerical experiments

Experiment Land use Topography

CNTL Heterogeneous land use (MODIS data) Realistic terrain

TOPO Heterogeneous land use (MODIS data) Flat terrain (200 m)(29.80–30.35�N,117.80–118.45�E)

GRASS Homogeneous land use (grassland) Realistic terrain

FOREST Homogeneous land use (mixed forests) Realistic terrain

DESERT Homogeneous land use (bare soil) Realistic terrain

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5 Results and discussion

5.1 Comparison with observations

There are ten AWSs located within D4, namely Jiuhuashan (JHS), Huangshanqu (HSQ),

Jingde (JD), Huangshan (HS), Qimen (QM), Yixian (YX), Shexian (SX), Tunxi (TX),

Xiuning (XN), and Zhaixi (ZX) (Fig. 2a). The more detailed information of these AWSs

was given in Table 3. In order to evaluate the model performance, four of them (SX, HSQ,

JD, and HS) with different positions, elevations, dominant land use types, and soil

moistures were chosen to perform model-observation comparisons. The analysis of the

outputs in outer domains (D1, D2, and D3) (not shown) indicated that the CNTL run not

only well reproduced the pattern, intensity, position, and evolution of the synoptic-scale

systems, but also showed the similar distribution and variation of meteorological variables

(e.g., temperature, wind, and humidity). Moreover, in D4, the model outputs (e.g., tem-

peratures and wind speeds) were used for comparing with the observation fact at the closest

grid points to AWSs. The hourly observed wind speeds and temperatures are recorded at 10

and 2 m above ground level (AGL), respectively.

Figure 5 showed the diurnal variations of simulated and observed 10-m wind speeds

and 2-m temperatures at SX, HSQ, JD, and HS AWSs, respectively. For 10-m wind speeds,

the WRF model did fairly well in reproducing the variation trends at both SX and HSQ

AWSs (Fig. 5a, c). However, at JD AWS (Fig. 5e), the 10-m wind speed was greatly

overestimated between 0000 and 1600 LST, with the differences reaching about 3 m/s at

0700 LST. At HS AWS (Fig. 5g), although the simulated variation trend was found to be in

good agreement with observations, the maximums (6 and 8 m/s at 0600 and 2100 LST,

respectively) and minimums (0.2 and 1.2 m/s at 1000 and 1400 LST, respectively) were

not successfully simulated. Therefore, it might be inferred that the relatively larger dif-

ferences occurred in more high-elevation regions (e.g., at JD and HS AWSs), where the

stronger turbulent mixing would exist. Simultaneously, the complex terrains, to a great

degree, also caused difficulty on wind forecasts. As for the 2-m temperatures (Fig. 5b, d, f),

they reached their maximums (36, 34, and 35.2 �C) at around 1200 LST in simulation,

while peaking at 35, 33, and 33.2 �C at 1600 LST in observation at SX, HSQ, and JD

AWSs, respectively. The counterparts were overestimated all day at HS AWS (Fig. 5h).

The discrepancies of land cover types and terrains between simulated and real values might

Table 3 Name, location, and elevation for AWSs used in this study (Lat.: latitude, Lon.: longitude, Elev.:elevation), as well as dominant land use (LU) and soil moisture (SM) represented in D4 closest to the AWSs

Name Lat. (�N) Lon. (�E) Elev. (m) LU SM

Xiuning (XN) 29.77 118.17 173.4 Grasslands 0.15

Zhaixi (ZX) 30.06 118.17 601.0 Open shrub-lands 0.15

Jiuhuashan (JHS) 30.48 117.78 643.2 Mixed forests 0.30

Jingde (JD) 30.30 118.53 221.7 Permanent wetlands 0.42

Qimen (QM) 29.85 117.72 138.9 Closed shrub-lands 0.10

Yixian (YX) 29.92 117.92 222.8 Croplands 0.30

Shexian (SX) 29.87 118.42 170.1 Closed shrub-lands 0.10

Tunxi (TX) 29.72 118.27 141.7 Croplands 0.25

Huangshan (HS) 30.13 118.15 1,835.0 Deciduous needle leaf forest 0.30

Huangshanqu (HSQ) 30.30 118.13 192.8 Croplands 0.30

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be attributed to the differences of 2-m temperature. Hence, these analyses confirmed that

the WRF simulation might reasonably capture the dominant regional-scale features and

supplement the observations by providing a broader context in this study.

5.2 Evolution of local circulation

5.2.1 Horizontal structure

First, the diurnal circulation of horizontal 10-m wind fields over the Yellow Mountain area

on August 18, 2010, was analyzed. In Fig. 6, it made clearly that the mountain–valley

breeze was well established, transformed, and developed in this region. There was a

diminishing down-slope wind on the mountaintop and north slope (30.12–30.2�N) between

0000 and 0700 LST, with the wind speed reducing to 2 m/s. After sunrise (0900 LST), the

mountain’s surface heated air quicker than the valley bottom could, causing up-slope flow.

Simultaneously, the convergence zone of wind directions and convective cloud (see

Fig. 11a) appeared on the mountaintop (30.14�N). It might be inferred that the valley wind

played an important role in producing thunderstorm. The valley breeze (up-slope wind)

prevailed from 0900 to 1800 LST when there was an opposite conversion of wind direction

occurred. After that time, the convergence zone moved to the leeward slopes, and there

was a down-slope wind of cooler air along the Yellow Mountain slopes, with the maximum

Fig. 5 Diurnal variations of simulated and observed 10-m wind speed and 2-m temperature at SX, HSQ,JD, and HS AWSs on August 18, 2010

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wind velocity reaching 6 m/s after 2000 LST. Note that the frequency of slight breeze (low

to 0.5 m/s) was higher during the period of valley wind.

Specifically, it could be exhibited clearly in the horizontal cross-sections which

involved the evolution of complex wind fields in D4 (Fig. 7). At 0200 LST (Fig. 7a), an

intense mountain breeze which flowed from the mountaintop to the valley floor was

organized along two sides of the Yellow Mountain, especially on the south slope. How-

ever, with the generally enhanced solar radiation, wind pattern changed dramatically at

0900 (Fig. 7b). The transition from down- to up-slope wind occurred on higher elevations

first, and then, the up-slope flow generally covered the whole valley. A convergence line in

accordance with the orientation of the mountain ridge ranged from northeast to southwest

and displayed a northwest development tendency apparently. Until sunset, being absented

from solar radiation, the mountain breeze prevailed, and wind speed increased rapidly

(Fig. 7d). Thus, through the above analysis, there was obvious and important flow pattern

(mountain–valley breeze) in the Yellow Mountain region on August 18, 2010.

5.2.2 Vertical structure

The vertical cross-sections of wind fields, vertical velocity (W 9 10), and meridional

wind speeds along line AB (see in Fig. 2a) were clearly exhibited in Fig. 8. Although the

thunderstorm produced, the background wind speeds in the Yellow Mountain region

were low under high-pressure systems. The meridional wind speeds less than 4 m/s

during the whole day above 2.5 m. Before accessing to the solar heating (Fig. 8a), cold

air slided down the north slope, and only a slight disturbance of wind cloud be seen at

Fig. 6 N–S and time cross-sections of simulated 10-m wind fields (U and V-components, vector),topography (contours), and 10-m wind speed (color) along line AB on August 18, 2010

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0200 LST, with the vertical velocity reaching 0.35 m/s. Then, the convective layer

started to develop due to enhanced solar radiation, and an up-slope flow flushed out from

the valleys, before converging on the mountaintop. At 0900 LST, the velocity of updraft

increased (up to 1.5 m/s) at 30.15�N in Fig. 8b, which indicated a well-developed valley

circulation. At this time, the initial convection appeared on the mountaintop and

developed sharply in the following hours (Figs. 11a, b). However, the airflow crossed the

mountaintop and formed the down-slope wind on the north slope (30.17–30.3�N) at 1600

LST, because the temperature gradient reversed the background wind rose generally

Fig. 7 Simulated differences between 10-m wind fields and the innermost domain average of 10-m windfields (U and V-components, vector) and V-component (color) at a 0200, b 0900, c 1600, and d 2000 LSTon August 18, 2010, and topography (m, contours)

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(Fig. 8c). The later period experienced that the down-slope wind increased and pooled

down below. At 2000 LST, a downward motion (up to 1.2 m/s) accompanying with

warming might produce to motivate a reversed ascending air current near 30.2�N in

Fig. 8d. Summing up the above, the vertical structure of mountain–valley breeze dis-

played clearly, especially the valley wind in the daytime, providing favorable explanation

for the formation of thunderstorm.

Fig. 8 Vertical cross-sections of simulated wind fields (V and W 9 10-components, vector), verticalvelocity (color), and V-component (ms-1, contours) along line AB at a 0200, b 0900, c 1600, and d 2000LST on August 18, 2010

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5.3 Evolution of cloud and precipitation

In order to evaluate the model performance in simulating the development process of

orographic cloud and precipitation evens, a comparison between observational and sim-

ulated 24-h accumulated rainfall was given. Because of the dispersed AWSs in positions,

the precipitation of every AWS in observation tended to be marked by figures rather than

contours. Figures. 9a, b showed that the WRF model could duplicate the precipitation

process reasonably and integrally, whether in scope or strength of rainfall. More specifi-

cally, the maximum accumulated precipitation centers were highly concentrated on the

mountaintop area in the CNTL run, reaching 50 and 35 mm which was slightly less than

the observation (58.3 and 71.7 mm) at HS and JHS AWSs, respectively. It might be

attributed to the convergence of wind fields and lifting movement in this area (Fig. 10a).

But another maximum precipitation center which located at HSQ AWS (30.3�N, 118.13�E)

was not successfully reproduced. This was likely contributed to terrain height errors

between real (up to 1,864.8 m) and modeled (up to 1,200 m) regions, as well as the system

errors of numerical model. To sum up, it suggested that this should be considered as a

relatively reasonable and effective simulation of this thunderstorm event.

In Fig. 11, the three stages of thunderstorm, including the developing, mature, and

dissipation stages, were well captured by the WRF model on August 18, 2010. Individual

orographic cloud appeared first on the mountaintop (about 30.15�N) at approximately 0900

LST (Figs. 11a, 12a), which was supposed to be the cumulus stage. Weak as it was, the

disturbances in up-flow (1.4 m/s), cloud water mixing ratio (0.7 g/kg), and water vapor

(below 3 km) was discernible. Then, under the influences of topography and mountain–

valley winds, the airflow converged and lifted forcedly on the summit of the Yellow

Mountain, companying with a great amount of moisture perturbation. Being isolation from

the surface heating, the rising air and moisture quickly cooled into water drops, which

could appear as cumulus cloud. Besides, the lower temperature aloft further contributed to

condensate vapor into raindrops, which could lead to latent release and atmosphere

warming. Until 1000 LST (Figs. 11b, 12b), the maximum vertical velocity was 6 m/s, and

the cloud and rain water mixing ratio peaked at 1.1 and 1.9 g/kg, respectively. During this

period, the initial convection barely moved, with the wet tongue deepening and potential

temperature falling sharply. Moreover, as crucial indicators of atmospheric instability, the

convective available potential energy (CAPE) and convective inhibition (CIN) which

could indicate the vertical development of strong convection were considered. The cloud

top height grew rapidly with the increasing CAPE, elevating to 6 km. The CIN decreased

by 11 J/kg in the valley, which created atmospheric instability. With the up-flow, a shallow

convection appeared between 29.9 and 30�N, growing sharply in the following time. Until

1100 LST (Figs. 11c, 12c), The CIN further dropped (to 9 J/kg), while the vapor distur-

bance, vertical velocity, and CAPE rose in the counterpart area. Nevertheless, on the

mountaintop, the potential temperature perturbations only appeared in lower layer (within

2 km), and the velocity of up-flow decreased to 1.2 m/s (Fig. 12c). Then, background wind

increased, causing two convection cells on the south slope merging with each other and

moving to the peak (Figs. 11d, 12d). The stronger downdraft (12 m/s at 3–3.5 m) near

30.2�N caused by the dissipated cells halted the flow traveling north. The local up-flow

transported moist air into the thunderstorm cell which gave rise to a deep moist tongue. An

hour later (at 1300 LST), the convective cells developed after being provided together the

dynamic lifting (e.g., the vertical velocity reached 3.5 m/s) and abundant water vapor

between 30.15 and 30.2�N (see Figs. 11e, 12e). As inferred from Figs. 11f and 12f, the

mountain air was cooled by the less solar radiation after sunset. The momentum

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Fig. 9 Simulated 24-h accumulated precipitation (color), topography (m, contours) on August 18, 2010, fora OBS, b CNTL, c TOPO, d GRASS, e FOREST, and f DESERT. The measured precipitation was markedon every AWS with red numbers

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Fig. 10 Simulated moisture flux divergence (color), wind fields (V and W 9 10-components, vector), andtopography (m, contours) on 1.8 km at 1000 LST on August 18, 2010, for the a CNTL, b TOPO, c GRASS,d FOREST, and e DESERT experiments

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descending and down-slope wind got together to bring high near-surface wind, before

suppressing the development of moisture and potential temperature disturbance. The CIN

increased from 5 to 20 J/kg, which indicated a weakened convection. As the cloud and rain

water mixing ratio dropped to 0.3 and 0.4 g/kg, the thunderstorm had been started to

dissipate.

Consequently, the trigger mechanisms of thunderstorm were likely attributed to the

mountain–valley breeze and topographic feature, including dynamic and thermal factors.

The former could create the up-flow and convergence centers on the mountaintop, and the

latter warmed and humidified the mountain air over the Yellow Mountain area.

5.4 Effect of topography

In Fig. 9c, after the Yellow Mountain was removed, the rainfall sharply reduced to around

5 mm, and the main precipitation cores disappeared in D4. Without orographic forcing, a

sustaining southwesterly prevailed, causing no vertical motion and convergence lines

(convergence centers of wind and moisture) near the mountains (Fig. 10b). It implied that

the thunderstorm was triggered by the climbing airflow on the windward slopes and the

valley winds during the daytime, as the dynamic and thermodynamic effects of the

mountainous area.

Fig. 11 Vertical cross-sections of simulated cloud water mixing ratio (color), rain water mixing ratio(dashed, 10-1g/kg), wind fields (V and W 9 10-components, vector), and water vapor mixing ratio (solid,g/kg) along line AB at a 0900, b 1000, c 1100, d 1200, e 1300, and f 1400 LST on August 18, 2010

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In addition, Fig. 13a, b showed the vertical cross-sections of the wind fields (U- and

W 9 10-components), CIN, CAPE, cloud water mixing ratio, water vapor mixing ratio,

vertical velocity, and potential temperature along line AB at 1000 LST in the TOPO

experiment. It was found that the maximum vertical velocity was only 0.06 m/s, and the

cloud water mixing ratio nearly disappeared. The atmosphere developed into a stable one,

with evenly distributed potential temperature, CIN, CAPE, and water vapor mixing ratio in

north–south direction. This was disadvantage to the vertical transport of moisture and

release of convective instable energy. Hence, the terrain was the main factor of creating the

mountain–valley circulation and convection. Under complex topography, the daytime

period was experienced a strong enough thermally induced up-slope flow, contributing to

transported the warm and moisture into the upper air. Along with the condensation of water

vapor and latent heat release, the potential instable energy accumulated and released,

before causing this thunderstorm event.

5.5 Effect of land cover

In the 1950s, the proportion of land use and land cover in the Yellow Mountain Scenic

Area was 75 %, whereas the percentage rapidly declined to 73.6 % in 1971, by reason of

Fig. 12 Vertical cross-sections of simulated CAPE (color), CIN (blue contours, J/kg), vertical velocity(dashed, 10-1m/s), and potential temperature (solid, K) along line AB at a 0900, b 1000, c 1100, d 1200,e 1300, and f 1400 LST on August 18, 2010

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engineering construction, water and soil erosion, forest fire, and trampling. From then on,

effective management and careful protection were implemented to increase land cover, the

figure reaching 93 % in 2009. From the above, we designed three sensitivity experiments

to study the impacts of land use changes on the development processes of mountain–valley

breeze and thunderstorm. Reducing plant height was considered first, in other words,

inhomogeneous land cover was replaced by uniform grassland (GARSS) to show the

Fig. 13 Vertical cross-sections of simulated cloud water mixing ratio (color), rain water mixing ratio(dashed, 10-1g/kg), water vapor mixing ratio (solid, g/kg), and wind fields (V and W 9 10-components,vector) on the left column; CAPE (color), CIN (blue contours, J/kg), vertical velocity (dashed, 10-1m/s),and potential temperature (solid, K) on the right column along line AB at 1000 LST on August 18, 2010, forthe TOPO, GRASS, FOREST, and DESERT experiments

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effects of vegetation deterioration. Second, a FOREST experiment with homogeneous

mixed forests was designed to reveal the influencing mechanism of natural expansion of

forests in recent years. Finally, the extent of influence which the natural distribution of

vegetation could lead to over the Yellow Mountain area was worthy of consideration.

Hence, it was necessary to give an extreme experiment (DESERT), running without any

vegetation.

Fig. 13 continued

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5.5.1 GRASS experiment

In both GRASS and CNTL (Fig. 9b, d), the precipitation centers had the same locations at

(30.2�N, 118.11�E), (30.19�N, 118.2�E), (30.1�N, 118.4�E), and (30.52�N, 117.81�E),

respectively. When covered by grassland, the precipitations reduced by 20 and 25 mm at

the first two positions, while increased by about 10 and 15 mm at the last two centers. The

convective core on the summit of the Yellow Mountain (30.2�N, 118.11�E) was considered

in more detail. Figure 10c showed that the rainfall decreased according as a slight decrease

in moisture and momentum gathered in the most region of D4, especially on the moun-

taintop in the GRASS experiment.

In Fig. 13c, d, it showed that the vertical velocity dropped to 2.5 m/s at 1000 LST, lead

to less condensation of water vapor in the main body of orographic cumuli. For this reason,

the cloud top height decreased (to around 5 km), and there was an outflow from the cloud

base at 30.16�N, which implied that the deep convection experienced a shorter period and

had been in dissipative stage. The cloud and rain water mixing ratio were 0.88 and 1.1

g/kg, respectively. Moreover, the lesser CAPE and higher CIN were the part factors

of weakening of the convection (Fig. 14a). From Fig. 14b, the perturbation potential

temperature reduced (by 1.2 K) on the mountaintop, causing an increased lapse rate, and

higher static stability. Based on this, relatively less moisture and heat released upward to

maintain the development of thunderstorm.

Covered by grassland, the innermost domain average surface sensible heat flux had

reduced (Fig. 15f), lead to relatively stable atmospheric. Besides, it could be found that the

surface roughness declined (0.12 m), causing the horizontal wind speeds increasing

sharply (Figs. 10c, 15e), and the valley breeze well developing. Therefore, the decrease in

soil moisture could not bring higher evapotranspiration and latent heating (Fig. 15h).

Combining the above, it could not provide advantages in thermal conditions to maintain

the development of thunderstorm. Note that the differences in 2-m temperature, skin

surface temperature, net radiation, PBL height, and latent heat flux were almost negligible

(Fig. 15c, d, g, h).

5.5.2 FOREST experiment

In the FOREST experiment (Figs. 9e, 10d), the differences in simulated wind fields and the

development of convective rainfall were small due to the fact that the most of D4 in the

CNTL run was covered by mix forests. But the rain belt which located near Jiuhua

Mountain expanded and the rain center (30.19�N, 118.2�E) shifted to its southwest.

Figure 13e, f indicated that the vertical velocity dropped to 2.4 m/s, which provided

disadvantage in vertical transporting moisture and energy followed by a decreased cloud

top height (around 4 km) and rain water mixing ratio (0.4 g/kg). Furthermore, it could be

indicated from Fig. 14c, d that the differences between the FOREST and CNTL runs were

small in CAPE, CIN, and perturbation potential temperature. In spite of this, the pertur-

bation potential temperature increased in the lower atmosphere, while had opposite trend in

the higher counterpart. So, the convective cloud could not develop higher and stronger.

Figure 15 revealed that the changes had slight influence on the simulated results of

surface variables. In other words, the diurnal variations were nearly the same and difficult

to distinguish. Due to vegetation height and surface roughness increasing (around 0.5 m),

there was a slight decline in wind speed (Fig. 15e), companying with a weaker conver-

gence and vertical motion (2.4 m/s in Fig. 13f). Along with lower soil moisture, it was

hardly for warm and moisture air near the ground to be delivered to form the deeper

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cumulus convection core (2.8 m in Fig. 13e). Therefore, heterogeneous underlying surface

was propitious to accumulate the water vapor and CAPE, which was likely to continue and

reinforce the thunderstorm.

5.5.3 DESERT experiment

In Fig. 9f, there was almost no center of precipitation in mountainous area without any

vegetation. With the valley wind and orographic forcing, the moisture only gathered on the

Fig. 14 Vertical cross-sections of simulated differences of CAPE (color) and CIN (contours, J/kg) betweenthe a GRASS and CNTL, c FOREST and CNTL, as well as e DESERT and CNTL experiments on the leftcolumn; simulated differences of perturbation potential temperature (color, contours, K) between theb GRASS and CNTL, d FOREST and CNTL, as well as f DESERT and CNTL experiments on the rightcolumn along line AB at 1000 LST on August 18, 2010

694 Nat Hazards (2013) 67:675–699

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top of the Yellow Mountain (Fig. 10e). However, compared to the CNTL run (Fig. 13g, h),

the up-flow aroused by the valley wind and orographic forcing was very weaker at 1000

LST, with lower velocity (0.55 m/s), less range, and lower extended height (3.3 km) on the

mountaintop. The vertical velocity decreased with height rapidly. In Figs. 14e, f, Both the

CAPE and perturbation potential temperature fallen, while the CIN rose. The differences of

three variables between DESERT and CNTL were up to 1,000 J/kg, 0.8 K, and 20 J/kg,

respectively. Therefore, it could be inferred that the bare soil restrained the transportation

of water upward and development of cumulus (below 3 km).

In the CNTL run (Fig. 15b), net radiation increased drastically after 0500 LST with the

results that transpiring water went up, and turbulence of near-surface air enhanced greatly.

In Fig. 15a, before turning into a strong convection at 1000 LST, the turbulent mixing and

ground surface evaporation kept the same level of intensity, causing water vapor mixing

ratio increased slowly. The period between 1600 and 1800 LST witnessed weaker cumulus

convection and decreased turbulent motion, with the near-surface moisture accumulating

quickly and reaching its maximum at 20 g/kg. However, the water vapor mixing ratio

remained steady below 18.4 g/kg for a day in the DESERT experiment. Additionally, the

water vapor content was related to many factors, including convergence and divergence of

airflow, ground surface evaporation, and water–vapor exchange vertically. Partly, due to

the reduction in surface roughness (0.01 m), maximum differences of surface wind speeds

between CNTL and DESERT were up to 2 m/s (Fig. 15e). For lack for vapor source, it was

difficult to transfer the vapor upward and release latent heat flux, despite the wind speed

increasing sharply. The maximum latent heat flux was only 130 W/m2 at 1300 LST

(Fig. 15h). Furthermore, the solar radiation could easily pass through the shallow clouds

and heated the surface of the mountaintop. Hence, as compared to their CNTL counter-

parts, the differences between skin and near-surface temperatures rose because of higher

albedo (0.38), bringing larger sensible heat flux after 1200 LST compared to CNTL

Fig. 14 continued

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(Fig. 15c, d, f). The sensible heat flux rose smoothly and reached its peak at 1300 LST.

From Fig. 15f and g, it was inferred that the sensible heat flux was supposed to be the

primary contributing factors for the diurnal variation of PBL height, which was similar to

the main findings by Zhang et al. (2009). Figure 15g showed that the maximum PBL

height was 1,200 and 1,600 m in CNTL and DESERT, respectively. Therefore, after

desertization, it was adverse for accumulating vapor and heat, which cloud restrain strong

convection within the lower PBL.

6 Summary and conclusions

Using a mesoscale numerical model WRF version 3.1.1, the impacts of topography and

land use changes on thunderstorm over the Yellow Mountain area of China were inves-

tigated. The case we chosen was occurred on August 18, 2010, under weak synoptic

pressure gradients. Detailed analyses and comparative studies lead to primary conclusions

on the research questions posed at the beginning of the study.

First, assessment using observational data of AWSs (e.g., temperatures, wind speeds,

and precipitations) confirmed that WRF could describe the meteorological fields accurately

Fig. 15 Diurnal variations of the innermost domain average modeled a water vapor mixing ratio, b netradiation, c surface skin temperature, d 2-m temperature, e 10-m wind speed, f sensible heat flux, g PBLheight, and h latent heat flux for the CNTL, GRASS, FOREST, and DESERT experiments on August 18,2010

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in spite of missing a precipitation cell in the north valley; second, the mesoscale local

circulation and the development process of thunderstorm were analyzed. Simulation results

indicated that the valley breeze prevailed after sunrise and converged on the mountaintop,

which might provide uplift airflow and water vapor conditions for the formation of initial

convection; third, a TOPO experiment (see Table 2) revealed that the complex topography

(the Yellow Mountain) played a significant role in determining the amount and locations of

the precipitation. On flat terrain, the main effects were local disturbance weakening

without strong topographic convergence and lifting of wind and water vapor associated

with the valley wind. In contrast, the CNTL showed stronger vertical mixing, raising the

moisture, increasing potential temperature disturbance, and accumulating convective

instability energy, which was conducive to the growth of convective clouds; finally, other

three sensitivity tests with uniform grassland (GRASS), mixed forests (FOREST), and bare

soil (DESERT) were conducted. Among the impacts of land use changes, both the thermal

and momentum transport were significant for the localized thunderstorm. When covered by

grassland, there were less sensible heating and lower moisture, leading to the PBL height

decreasing and vertical lifting weakening, which tended to cause more stable atmosphere

and less rainfall on the mountaintop. When covered by mixing forests, only small dif-

ferences presented in simulated meteorological fields (e.g., wind fields, moisture, cloud

water mixing ratio, precipitation, and other fields). In DESERT experiment, the latent

heating was more important in influencing the process of thunderstorm. There were less

latent heating and lower accumulated water vapor compared to other experiments, causing

vertical lifting weakening, stability of atmosphere increasing, and precipitation reducing.

Although the WRF model exhibited reasonable performance and revealed preliminary

results about the impacts of land use changes on thunderstorm, shortcomings should be

pointed out when understanding these conclusions. Because the characteristics of thun-

derstorms cannot give complete investigations in a case, particularly the changes in the

microphysical features of the convection cells which were viewed as an important part of

the simulated thunderstorm. Further investigations in revealing the formation mechanism

of thunderstorm should be worth conducting using advanced numerical models.

Acknowledgments This research was jointly supported by the Special Fund of Scientific Research forPublic Welfare Industry (Meteorology) of the Ministry of Science and Technology of China (Grant No.GYHY201006004), the National Natural Science Foundation of China (Grant No. 41030962), and theNational Key Technology Research and Development Program of the Ministry of Science and Technologyof China (Grant No. 2013BAK05B03).

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