Numerical study of the effect of soil texture and land use distribution on the convective precipitation 3Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. Numerical study of the eff ect of soil texture and land use distribution on the convective precipitation Júlia GÖNDÖCS1, Hajnalka BREUER1*, Ákos HORVÁTH2, Ferenc ÁCS1 and Kálmán RAJKAI3 DOI: 10.15201/hungeobull.64.1.1 Hungarian Geographical Bulletin 64 2015 (1) 3–15. 1 Eötvös Loránd University, Department of Meteorology, H-1117 Budapest, Pázmány P. sétány 1/a. E-mails: kisbucedli@caesar.elte.hu, bhajni@nimbus.elte.hu, acs@caesar.elte.hu 2 Hungarian Meteorological Service, H-8600 Siófok, Vitorlás u. 17. E-mail: horvath.a@met.hu 3 Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, H-1022 Budapest, Herman Ott ó u. 15. E-mail: krajkai@mail.iif.hu * corresponding author Abstract In this study the Weather Research Model is used to analyse the sensitivity of convection to soil texture and land use distribution based on a heavy precipitation event. Both characteristics aff ect the latent heat fl ux and the near surface temperature distribution which are related to buoyancy. The model defaults Food and Agriculture Organization (FAO) soil texture and USGS (United States Geological Survey) land use have been replaced with more accurate databases in Hungary: soil texture based on the Digital Kreybig Soil Information System (DKSIS), land use based on the COoRdination of INformation on the Environment (CORINE). Regarding to soil texture the main changes are related on one hand to clay loam diversifi cation to silty clay, loam, silty loam and sandy loam aff ecting area over 40 percent of Hungary, and on the other hand reclassifi cation of sandy loam to sand. The diff erence between USGS and CORINE land use is sporadic, but signifi cant. It is found that the diurnal latent heat fl ux is the highest at 12 UTC, at this peak the spatial average diff erence in latent heat fl ux is +6.5 W/m2 and –4.3 W/m2 with respect to soil texture and land use change, while the absolute diff erences range from –70 W/m2 to +70 W/m2 in all cases. As a result temperature at 2 m on average increased by 0.1 °C during soil texture and decreased by 0.15 °C during land use database comparison; the absolute diff erences are a magnitude higher. When comparing simulations regarding temperature at 2 m over main soil types and main land use categories results indicate –3 °C to +0.5 °C diff erence. It is found that the modifi cation of both the soil texture and the land use have sometimes a compensating eff ect on latent heat fl ux and temperature change. Decrease in latent heat fl ux results an increase in buoyancy aff ecting convective precipitation. The formation of precipitation is also aff ected by large scale advection, therefore, no systematic changes can be seen on daily precipitation distribution. In spite of this, results indicate shift s in precipitation bands with about 30 km, and formation of new storm cells. Locally the replacement of soil texture and land use information to a more accurate one produced ±8 mm/day diff erences in precipitation. Keywords: convective precipitation, soil texture, land use, numerical weather prediction Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15.4 Depending on the geographical region, the eff ect of soil moisture can even negate the ef- fect of atmospheric lower temperatures and higher moisture content. However, it has to be noted that through advection the precipita- tion occurrence is higher over dry soils when the wind transports additional atmospheric moisture from areas with greater evapotran- spiration (deAngelis, A. et al. 2010). Since soil moisture directly affects the water vapour fl ux to the atmosphere, and it depends on soil texture and land use, the spatial heterogeneity of soil texture and land use can signifi cantly eff ects the precipitation. However, the joint analysis of both surface characteristics is rare. On climatological scale, the land cover change can aff ect the atmos- pheric circulation, especially in South-East Asia, North America and Europe, causing statistically signifi cant changes in the regional distribution of temperature and precipitation without changing globally averaged tempera- ture or rainfall (Chase, T.N. et al. 1996, 2000). Changes in either land use (Collow, T.W. et al. 2014) or soil texture (Khodayar, S. and Schädler, G. 2013) toward more realistic spa- tial distribution result more accurate surface heat fl uxes and near surface temperatures. In turn, this has an eff ect on CAPE and convec- tive precipitation formation. It was shown that a more accurate soil tex- ture distribution can improve the convective precipitation forecast (Breuer, H. 2012) and the eff ect of land use change has a notable ef- fect as well (Drüszler, Á. 2011). To be able to improve model simulations, it is needed to examine the eff ect of the employment of dif- ferent land use and soil texture distribution databases. In this preliminary study using WRF (Weather Research Forecast) model both the land use and the soil texture distribution are replaced with more accurate ones than the commonly used on the global scale. The aim is to assess the magnitude of the eff ects caused by both of the changes, and also to make a comparison to each other. Simulations are cre- ated for a single precipitation event, and are analysed discussing the surface characteris- tics/precipitation relationship. Introduction Land surface characteristics are playing an important role in the exchange of energy, water vapour and moment with the lower atmosphere. These exchange processes make the topic relevant also from a meteorological point of view and became increasingly im- portant with the improvement of numerical weather prediction systems. The upward fl ux of water vapour – essentially the evapotran- spiration – is aff ected by the vegetation and the available soil moisture. Relationships between the lower atmosphere and the soil moisture (Dickinson, R.E. 1984; Pielke, R.A. and Avissar, R. 1990), soil texture (Ek, M. and Cuenca, R.H. 1994; Alapaty, K. et al. 1997), soil parameters (Mölders, N. 2005; Breuer, H. et al. 2012), land surface heterogeneity (Avis- sar, R. and Liu, Y. 1996; Pielke, R.A. 2001) and vegetation (Pielke, R.A. et al. 1997; Adegoke, J.O. et al. 2007) have been extensively analysed in several aspects. Dry soils increase the sensi- ble heat fl ux responsible for creating updraft s, while wet soils add moisture to the boundary layer (lower atmosphere) through evapotran- spiration. Depending on the atmospheric con- ditions, the added moisture can either make the atmosphere more favourable for storms or more stable. These diff erences aff ect thermally driven local atmospheric circulations (Hong, X. et al. 1995) and convective precipitation for- mation (Teuling, A.J. et al. 2009). The feedbacks between soil moisture and precipitation are controversial even in the case of measurements. Some studies have found that higher soil moisture increases the pos- sibility of thunderstorm development by rais- ing the convective available potential energy (CAPE) but the increased water vapour barely aff ects the convective inhibition (CIN) of the atmosphere (Pielke, R.A. and Zeng, Z. 1989; Eltahir, E.A. 1998). While others concluded that the increased vapour amount decreases the atmospheric temperatures creating greater inhibition (Taylor, C.M. and Ellis, R.J. 2006) resulting less precipitation. Taylor, C.M. et al. (2012) described even a higher precipitation formation over dry soils in the Sahel region. 5Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. Free convection The atmospheric buoyancy is responsible for free convection, formed by the sun’s short- wave radiation, which heats the land surface. The nearby atmospheric layers are warmed mostly by sensible heat fl ux. The warmer, as- cending air is also controlled by the humidity and the thermal stratifi cation of the atmos- phere. When the ascending air is warmer than its surroundings the atmosphere is dy- namically unstable (Horváth, Á. 2007). When the rising air contains enough moisture, con- densation occurs, releasing latent heat of va- porization, lift ing air mass higher. There are different measures to esti- mate the instability of the atmosphere, like K-index, CAPE or CIN. CAPE (J/kg) is regard- ed as an indicator of the potential intensity of deep convection, and it is strongly controlled by the properties of the planetary boundary layer. CAPE is calculated from the temperature diff erence between the ascending air particle and its environment at each height level (e.g. model vertical levels) going from LFC (Level of Free convection) to EL (Equilibrium Level). LFC is the level, where the air particle becomes warmer than its surroundings for the fi rst time, due to latent heat release. In the lower part of the atmosphere, under the LFC, the energy is negative as the particle requires this energy, named as the CIN. The CIN’s value shows the atmospheric stability by giving the energy which must be overcome by the air particle to result convection. Neither the CAPE nor the CIN is a measure of possible precipitation, rather they express the potential possibility of free, non-forced (e.g. without the ascent forcing cold front) convective cloud forming if suffi - cient moisture is present in the atmosphere. Model and its sett ings The calculations were made with the WRF 3.4.1 model, developed by NCEP (National Centre for Environmental Prediction) and NCAR (National Centre of Atmospheric Research) (Skamarock, W.C. et al. 2008). This numerical weather prediction model system is a limited area, mesoscale, non-hydrostatical model, which is freely available on the internet, and for this study it was run on the Atlasz cluster of the Eötvös Loránd University. Spatial and temporal distribution sett ings can be varied in a wide range, the horizontal grid scale can be 1,000 km scaling down to 1 km. But with such fi ne resolution as a few kilometres, a nesting technique application is needed in the model area. This means the usage of several encom- passing model domains with decreasing hori- zontal grid size in each nest. In addition to calculating the hydro-ther- modynamic equations governing the dynam- ics of the atmosphere, sub-grid processes (e.g. radiation-transmission, cumulus cloud convection, cloud microphysics, planetary boundary layer processes, soil-atmosphere interactions) were also calculated. The model uses terrain following, hydrostatic pressure vertical coordinate system and a staggered Lambert conformal horizontal grid. The used nesting technique had an external model area with a 9 km horizontal resolution covering the Carpathian Basin, and a nested domain of 3 km covering Hungary (45.3˚– 49.8˚N, 15.6˚–23.6˚E). For the simulations, 34 vertical levels were defi ned. The simulations were run by making changes to the WRF static data, such as soil texture and land use. The model reads the static data as binary fi les, which are used to create the model area. Four simulations were made: one using the origi- nal sett ings (FAO) soil texture and USGS land use (reference), the next by changing the soil type data (DKSIS) inside Hungary, another by modifying the land use cover (CORINE) and in the fourth applying both of the changed datasets (DKSIS&CORINE). Meteorological initial and boundary con- ditions for the simulations were taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) model which has a horizontal resolution of 15’ (approx. 25 km). From the available model levels only the lowest 12 standard pressure levels were selected for initialization, boundary condi- tions were updated in every 3 hours. These Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15.6 fi les contain the horizontal and vertical wind components, specifi c and relative humidity, dew point, geopotential height, surface tem- perature and pressure. Soil moisture and temperature is available in four layers. Surface and soil data Figure 1 shows the soil types over the model area based on the FAO database. The DKSIS database (Szabó, J. et al. 2000; Pásztor, L. et al. 2010) (Figure 2) over Hungary was also used in performing simulations, while outside the coun- try no changes were made in the FAO distribu- tion. In both cases the dominant soil texture of the model area is loam and its variants. The most prominent difference is the appearance of sand in the Danube–Tisza Interfluve, and at the eastern part of the country. Most of the clay loam disappears, resulting in an about 45 percent reduction when changing from FAO to DKSIS. In the new distribution, silt and its variants appear to be scatt ered over Hungary. The used land use database was the USGS (Figure 3) and the CORINE 2000 (European Environmental Agency, 2002) (Figure 4). The previous one was determined using AVHRR measure- ments in 1992–1993, while the latt er one by using Landsat-7 imagery in 2000. In the WRF model the USGS land use is available at a 0.5˚, while the CORINE was implemented at a 30” horizontal resolution. According to USGS, two-thirds of the investi- gated area is “dryland cropland and pasture” and this suff ers the greatest change, around 10 percent, when the USGS is replaced by CORINE. Areas occupied by deciduous broad- leaf forests appear mostly on mountain ridges. The previously scatt ered cropland/woodland areas disappear almost entirely within the Fig. 1. Soil type over the model area based on FAO database 7Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. country borders. The grass- and shrublands are more apparent in Figure 4. The ratio of ur- ban and built-in land areas increased as well as the area of larger cities, such as Budapest and Bratislava, has become more visible. The size of water-covered areas is constant. Weather The convective precipitation was examined for 20 August 2006, when all weather conditions were favourable for its formation. The weather in Europe had been infl uenced by a large, well- developed cyclone, whose cold front has reached Hungary. The convective instability was further increased by the 25 m/s wind and the cold advec- tion at the 500 hPa level. Above the Carpathian Basin a jet stream was fl owing. The thunderstorm line has reached the Carpathian Basin in the late aft ernoon. The cold front reached Hungary at 16 UTC (Coordinated Universal Time) and has left it by 00 UTC on 21 August. Precipitation oc- curred in the northern part of the country, and lo- cal showers occurred Southeast. The majority of the precipitation occurred in the Northwest, the highest measured precipitation in Hungary was 17 mm at Kapuvár. The maximum temperature in Hungary was between 28 C (North-North- west) and 34 ˚C (Southeast) (Horváth, Á. 2006). Results: temperature, latent heat fl ux By changing soil texture map without chang- ing meteorological (initial and boundary) conditions, the water holding capacity will also change, which defi nes the rate of eva- potranspiration. Land use change results changes in the minimum stomatal resist- Fig. 2. Soil type over the model area based on DKSIS database in Hungary. Outside the country FAO database is used Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15.8 Fig. 3. Land use cover over Hungary based on USGS database Fig. 4. Land use cover over Hungary based on CORINE database 9Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. ance also aff ecting the evapotranspiration. Furthermore changes in surface aff ect the albedo. These eff ects have an impact on near surface temperatures and atmospheric mois- ture content. Both temperature and latent heat fl ux reach their daily maximum in the early aft ernoon, fi rst the latent heat, then approximately an hour later the temperature caused by turbu- lent mixing. Figure 5 shows latent heat fl ux (LH) and 2 m temperature (T2) diff erences between simulations obtained at 12 UTC, when the T2 changed from 28 ˚C to 34 ˚C over fl at terrain, and the LH was between 200 W/m2 and 360 W/m2. Aft er changing soil type, the LH increased by 6.5 W/m2 in spatial average (Figure 5, a). The T2 changed inversely with an average of 0.12 ˚C (Figure 5, b). Daytime average change was +3.48 W/m2 and 0.1 ˚C, respectively. At the Danube–Tisza Interfl uve, at the Small Plain (Kisalföld) and at Nyírség area the la- tent heat values increased. The cause of this is that the hydraulic properties of soils altered resulting in a decreased ability to hold mois- ture and therefore prompting an intensifi ed evaporation. At the Kisalföld, clay has been modifi ed to sandy loam or loam, while at Kiskunság sandy loam altered to sand. Over these areas the LH had increased by 20–40 W/m2. In opposition to this, there was an ob- served decrease of 10 W/m2 at Körös region due to more moisture being able to remain in the soil. Evaporation distracts heat from its surroundings leading to cooling, as it was seen at Kiskunság where the T2 change was approximately 1˚C. Areas outside Hungary, where the soil texture is unchanged, differences were caused by advection and especially North to Hungary this had an eff ect on cumulus cloud formation over the mountains resulting great diff erences in LH. Aft er switching the land use cover (Figure 5, c, d), warming of the model area by an aver- age of 0.15 ˚C, and an approximate of 8 W/m2 decrease in the LH was observed (daytime averages: +0.12 ˚C, –4.3 W/m2). Following the modifi cation North to Lake Balaton, the broadleaf forest coverage increased, which caused a decrease in evaporation and in LH, mainly causing the increasing of the mini- mum stomatal resistance from 40 s/m to 100 s/m and the decrease of radiation stress func- tion coeffi cient from 100 to 30. These areas show a decrease in the LH as indicated by red and orange dots. At the Danube–Tisza Interfl uve the T2 rose since the albedo decreased and the minimum stomatal resistance became stronger. The ef- fect of the built-in areas reached its maximum at the early evening, when the T2 was 2.5–3 C higher and the LH was 60–70 W/m2 lower than the reference. The modifi cation of soil types aff ected the results more than changes in the land use cover especially over areas where sandy texture replaced loam or clay. All four simulations show that the location of the changes moved slightly to the East because of the daylong westerly winds at the Hungarian Great Plain (Alföld), while these relocations shifted south at the northern parts of the country due to strong northerly winds. Model used 12 diff erent soil texture and 16 land use categories appearing in Hungary during calculations. In order to make the comparison, simpler groups of land use and soil texture with similar physical properties were created (Table 1. and 2). In each case, the altered land use was com- pared to the reference, shown in Figures 6. and 7. By changing sand and its variants, a warm- ing was caused (average 0.5 C, max. 2 C) be- fore sunrise and aft er the arrival of the front. The initial amount of soil moisture during the simulations remained the same, thus by re- placing e.g. loam with sand the available soil moisture increased. Due to the increment of water resources, a more intensive evaporation was caused and therefore a slight (approx. 0.4 C average) cooling during the day. Table 1. Created groups of soil type categories Sand Loam Clay Sand Sandy clay loam Silt loam Loam Loamy sand Sandy loam Clay Silty clay Clay loam Sandy clay loam Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15.10 Fig. 5. Latent heat fl ux and 2 m temperature diff erence between REF-DKSIS (a, b), REF-CORINE (c, d) and REF-DKSIS&CORINE (e, f) simulations on 20th August 2006 at 12 UTC b e f c a d the entire day, there was less intensive evapo- ration over the appearing grass vegetation type resulting in surface air warming with maximum at approximately 2 °C. In the case of the forested areas a diff erence of 0.5–1 °C can be observed. 11Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. Table 2. Created groups of land use categories Urban, built-up area Woodland Shrubland Grassland Urban and built- up land Deciduous broadleaf forest Evergreen needle leaf forest Mixed forest Wooded wetland Shrubland Mixed shrubland/ grassland Dryland, cropland and pasture Irrigated cropland and pasture Cropland/grassland mosaic Cropland/woodland mosaic Grassland Convective Available Potential Energy (CAPE) Convective Available Potential Energy is used to estimate the instability of the atmosphere through temperature and humidity of the at- mosphere, which is partly controlled by the land surface. Figure 8 shows the CAPE values at 12 UTC. The calculated values were 0–750/2,000 J/kg. The drier and colder air mass preceding the front had a CAPE of 0 J/kg (stable strati- fi cation, less possibility of storm). As the fi g- ures show, the CAPE has increased for DKSIS simulation, while the area by stable stratifi ed air mass has decreased for CORINE. Prior to the front’s passage through the country, the DKSIS values were by 60–80 J/kg greater than the reference, while the CORINE’s values remained below the reference by 120– 160 J/kg. In the case of CORINE, the changes are consistent throughout the entire country, contrary to DKSIS, which concentrates on the sites of modifi cation similarly to Figure 5, b. Due to wind the diff erence formed in band shapes. In the case of the DKSIS&CORINE simulation the absolute values of the changes did not exceed the maxima of the previous simulation diff erences, which was 240 J/kg. At some areas soil texture had a more signifi cant eff ect on CAPE. At some parts of the model area, the processes of the two simulations am- plifi ed each other’s eff ect. The convective inhibition changed parallel as CAPE, the increasing latent heat resulting greater values of CIN. At around noon, the average values of CIN reached in absolute terms 60 J/kg. At the western part of the mod- el area the inhibition was smaller, thus less CAPE was enough to form convective cloudi- Fig. 6. 2 m temperature diff erence between reference and DKSIS in the case of combined soil textures on 20th August 2006 Fig. 7. 2 m temperature diff erence between reference and CORINE in the case of combined vegetation types on 20th August 2006 The steep change at around 16 UTC de- notes the arrival of the cold front. At the loam categories no signifi cant temperature diff er- ence was observed (avg. –0.3 °C), because the spatial ratio and distribution of this type remained approximately constant. The clay type had the biggest impact on the tempera- ture with an average 3 °C warming. During Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15.12 c a b d Fig. 8. Convective available potential energy CAPE (J/kg) for reference run (a), CAPE diff erence between REF- DKSIS (b), CAPE diff erence between REF-CORINE (c), and CAPE diff erence between REF-DKSIS&CORINE (d) at 12 UTC on 20th August 2006 ness. On the other part of the country the modi- fi cation resulted greater CIN in each simulation. In case of breakthrough of CIN, stronger thun- derstorms and precipitation may occur, when the CAPE’s value is suffi ciently large. That process has been observed at the central area, while CIN increased by 20–40 J/kg. Precipitation Figure 9 (a) shows the 24-hour accumulated precipitation. In the western parts of the country, there is a smaller devia- tion between the simulation and the actual measurements (not shown), however, East of the Danube these diff erences became larger, and translocated cells can be observed by the Northeast border. Southern part of the Alföld measurements showed the development of local convective systems, which didn’t appear on the simulation even though the amount of CAPE would have allowed the formation. 24-hour accumulated precipitation was 8.75 mm on average-based on the reference simula- tion. The DKSIS simulation positively deviated from that value by 0.30 percent (Figure 9, b), while the DKSIS&CORINE (Figure 9, d) pre- dicted a value 1.56 percent lower. The modi- fi cation of land use cover (CORINE) caused a temperature increase resulting in higher verti- cal velocities leading to an overall 1.70 percent increase of precipitation (Figure 9, c) compared to the reference. Though this diff erence is neg- 13Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. Fig. 9. 24-hour accumulated precipitation (PREC) reference run (a), PREC diff erence between REF-DKSIS (b), PREC diff erence between REF-CORINE (c) and PREC diff erence between REF-DKSIS&CORINE (d) on 20th August 2006 a c b d ligible, the migration of the precipitation band was faster with 30 minutes, leading to an East- west directional shift . Similar shift was notice- able in case of soil texture modifi cation but it was in the North–South direction. As it could be seen on accumulated pre- cipitation diff erences, even if the total pre- cipitation does not change signifi cantly in the whole domain, locally ±8 mm/day changes can be seen which reaches 50 percent diff er- ence. These are usually related to a shift in precipitation cells with about 30 km; in most cases new precipitation cells appeared which weren’t present in the reference run (e.g. northern border of the Alföld in Figure 9, d). Conclusions This study examined the eff ect of the land use cover and soil texture distribution change on convective precipitation, and on alterations in state variables aff ecting that. In the four simulations, the boundary conditions of the model area were modifi ed. The evaluated data relates to the Hungarian land area over a 24-hour time period. In regard to the exam- ined variables, the diff erences, in comparison to the reference, were larger in the case of the DKSIS than for the CORINE simulation. At the middle of the day, the absolute values of the diff erences in latent heat for the DKSIS Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15.14 were mostly close to 25–50 W/m2, while for the change to CORINE they were around 15– 25 W/m2. Furthermore it has to be noted, that the DKSIS simulation was characterized by both a latent heat fl ux decrease and increase, while the CORINE simulation showed only decrease in latent heat fl ux over the model area. Simultaneously, the latent heat excess, a decrease in temperature was observed, re- sulting in an average of 0.5 °C temperature diff erence between the two simulations. The combined eff ect of soil texture and land use change negated each other over some are- as, but where the soil became more saturated the eff ect of soil texture change was dominant. For CAPE values a similar tendency could be observed. The CAPE values were 50–60 J/kg greater in the case of the soil texture modifi - cation than for the altered land use cover due to the latent heat diff erence. Considering pre- cipitation there were no signifi cant alterations in the 24-hour accumulated precipitation over the whole model area. However, the location of storm cells with the highest intensity shift - ed toward south in case of soil texture change, and an East–West horizontal tilt and shift was observable in case of land use change. As a result locally ±8 mm/day precipitation dif- ferences occurred. Also mostly in the case of land use change, new cells formed as a result of higher temperatures. It can be stated that even though this weather event and the related precipitation was mainly related to cold front passage, the eff ect of switching to a more realistic soil tex- ture and land use distribution is not negligi- ble, not only for near surface temperatures but also for precipitation forecast. To deter- mine whether the realistic distribution results more accurate forecast further analyses, using radar precipitation verifi cation are needed. Acknowledgements: The work was supported by OTKA (Hungarian Scientifi c Research Found) under contract number K 81432. REFERENCES Adegoke, J.O., Pielke, R. and Carleton, A.M. 2007. Observational and modelling studies of the impacts of agriculture related land use change on plan- etary boundary layer processes in the central U.S. Agricultural and Forest Meteorology 142. 203–215. Alapaty, K., Raman, S. and Niyogi, D. 1997. Uncertainty in the specifi cation of surface characteristics: a study of prediction errors in the boundary layer. Boundary–Layer Meteorology 82. 475–502. Asharaf, S., Dobler, A. and Ahrens, B. 2011. Soil moisture initialization eff ects in the Indian mon- soon system. Advances in Science and Research 6. 161–165. doi:10.5194/asr-6-161-2011. Avissar, R. and Liu, Y. 1996. A three-dimensional numerical study of shallow convective clouds and precipitation induced by land-surface forcing. Journal of Geophysical Research 101. 7499–7518. Breuer, H. 2012. A talaj hidrofi zikai tulajdonságainak hatása a konvektív csapadékra és a vízmérleg egyes összetevőire: meteorológiai és klimatológiai vizsgálatok Magyarországon (The eff ect of soil hydro-physical properties on convective precipitation and on components of the water budget: meteorological and climatological investigations in Hungary). PhD Thesis, Budapest, Eötvös Loránd University, 117 p. Breuer, H., Ács, F., Laza, B., Horváth, Á., Matyasovszky, I. and Rajkai, K. 2012. Sensitivity of MM5-simu- lated planetary boundary layer height to soil da- taset: Comparison of soil and atmospheric eff ects. Theoretical and Applied Climatology 109. 577–590. Chase, T.N., Pielke, R.A., Kittel, T.G.F., Nemani, R. and Running, S.W. 1996. Sensitivity of a general cir- culation model to global changes in leaf area index. Journal of Geophysical Research 101. 7393–7408. Chase, T.N., Pielke, R.A., Kittel, T.G.F., Nemani, R. and Running, S.W. 2000. Simulated impacts of historical land cover changes on global climate. Climate Dynamics 16. 93–105. Collow, T. W., Robock, A. and Wu, W. 2014. Infl uences of soil moisture and vegetation on convective pre- cipitation forecasts over the United States Great Plains. Journal of Geophysical Research: Atmospheres 119. 9338–9358, doi:10.1002/2014JD021454. DeAngelis, A., Dominguez, F., Fan, Y., Robock, A., Kustu, M. D. and Robinson, D. 2010. Observational evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. Journal of Geophysical Research 115. D15115, doi:10.1029/ 2010JD013892. Dickinson, R.E. 1984. Modelling evapotranspira- tion for three dimensional global climate models. In Climate Processes and Climate Sensitivity Eds.: Hansen, J.E. and Takahashi, T. Geophysical Monograph Series 29. Washington, DC, AGU, 58–72. 15Göndöcs, J. et al. Hungarian Geographical Bulletin 64 (2015) (1) 3–15. Drüszler, Á. 2011. A 20. századi felszínborítás-változás meteorológiai hatásai Magyarországon (Meteorological eff ects of the land cover types changes during the 20th century in Hungary). PhD Thesis, Sopron, University of West Hungary, 137 p. Ek, M. and Cuenca, R.H. 1994. Variation in soil pa- rameters: implications for modelling surface fl uxes and atmospheric boundary-layer development. Boundary–Layer Meteorology 70. 369–383. Eltahir, E.A. 1998. A soil moisture–rainfall feedback mechanism: 1. Theory and observations. Water Resources Research 34. (4): 765–776, doi:10.1029/ 97WR03499. European Environment Agency 2002. Corine Land Cover 2000 (CLC2000) seamless vector data. Available at: htt p://www.eea.europa.eu/data-and-maps/data/ corine-land-cover-2000-clc2000-seamless-vector- database. Hong, X., Leach, M.J. and Raman, S. 1995. A sensitiv- ity study of convective cloud formation by vegeta- tion forcing with diff erent atmospheric conditions. Journal of Applied Meteorology 34. 2008–2028. Horváth, Á. 2006. A 2006. augusztus 20-i budapesti vihar időjárási hátt ere (Meteorological background of the storm of 20th August, 2006 in Budapest). Légkör 51. (4): 24–27. Horváth, Á. 2007. Atmospheric convection. Budapest, Hungarian Meteorological Service, 64 p. Khodayar, S. and Schädler, G. 2013. The impact of soil moisture variability on seasonal convective precipitation simulations. Part II: sensitivity to land-surface models and prescribed soil type distri- butions. Meteorologische Zeitschrift 22. (4): 507–526. Mölders, N. 2005. Plant – and soil – parameter – caused uncertainty of predicted surface fl uxes. Monthly Weather Review 133. 3498–3516. Pásztor, L., Szabó, J. and Bakacsi, Zs. 2010. Digital processing and upgrading of legacy data collected during the 1:25 000 scale Kreybig soil survey. Acta Geodaetica et Geophysica Hungarica 45. 127–136. Pielke, R. A. and Zeng, X. 1989. Infl uence on severe storm development of irrigated land. National Weather Digest 14. (2): 16–17. Pielke, R.A. 2001. Infl uence of spatial distribution of vegetation and soils on the prediction of cu- mulus convective rainfall. Reviews of Geophysics 39. 151–177. Pielke, R.A. and Avissar, R. 1990. Influence of landscape structure on local and regional climate. Landscape Ecology 4. 133–155. Pielke, R.A., Lee, T.J., Copeland, J.H., Eastman, J.L., Ziegler, C.L. and Finley, C.A. 1997. Use of USGS- provided data to improve weather and climate simulations. Ecological Applications 7. 3–21. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W. and Powers, J.G. 2008. A Description of the Advanced Research WRF Version 3 NCAR/TN–475+STR, June 2008. NCAR Technical Note. Szabó, J., Pásztor, L., Bakacsi, Zs., Zágoni, B. and Csökli, G. 2000. Kreybig Digitális Talajinformatikai Rendszer (Előzmények, térinformatikai meg- alapozás). (Kreybig Digital Soil Information System) (Preliminaries, GIS establishment). Agrokémia és Talajtan 49. 265–276. Taylor, C.M. and Ellis, R.J. 2006. Satellite detec- tion of soil moisture impacts on convection at the mesoscale. Geophysical Research Lett ers 33. L03404, doi:10.1029/2005GL025252. Taylor, C.M., de Jeu, R.A.M., Guichard, F., Harris, P.P. and Dorigo, W.A. 2012. Aft ernoon rain more likely over drier soils. Nature 489. 423–426., doi:10.1038/nature11377. Teuling, A.J., Uij lenhoet, R., van den Hurk, B. and Seneviratne, S.I. 2009. Parameter sensitivity in LSMs: an analysis using stochastic soil moisture models and ELDAS soil parameters. Journal of Hydrometeorology 10. 751–765. Göndöcs, J. et al. 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