Increasingly severe hydrological extremes are predicted for the Pannonian Basin as one of the consequences of climate change. The challenges of extreme droughts require the adaptation of agriculture especially during the intense growth phase of crops. For dryland farming, the selections of the optimal land use type and sustainable agricultural land management are potential adaptation tools for facing the challenges posed by increased aridity. To this end, it is indispensable to understand soil moisture (SM) dynamics under different land use types over drought-affected periods. Within the framework of a Slovenian–Hungarian project, soil moisture, matric potential and rainfall time series have been collected at three pilot sites of different land use types (pasture, orchards and a ploughland) in SW Hungary since September 2018. Experiments were carried out in soils of silt, silt loam and clay loam texture. In the summers (June 1 to August 31) of 2019 and 2022, we identified normal and dry conditions, respectively, with regard to differences in water balance. Our results demonstrated that soil moisture is closely controlled by land use. Marked differences of the moisture regime were revealed among the three land use types based on statistical analyses. Soils under pasture had the most balanced regime, whereas ploughland soils indicated the highest amplitude of moisture dynamics. The orchard, however, showed responses to weather conditions in sharp contrast with the other two sites. Our results are applicable for loamy soils under humid and subhumid temperate climates and for periods of extreme droughts, a condition which is expected to be the norm for the future.

Over the past decades, in the mountainous, hilly and/or urban areas of Hungary several high-intensity storms were followed by severe flash flooding and other hydrologic consequences. The overall aim of this paper was to upgrade the national flash flood susceptibility map of Hungary first published by Czigány et al. (2011). One elementary watershed level (FFSIws) and three settlement level flash flood susceptibility maps (FFSIs) were constructed using 13 environmental factors that influence flash flood generation. FFSI maps were verified by 2,677 documented flash flood events. In total, 5,458 watersheds were delineated. Almost exactly 10% of all delineated watersheds were included into the category of extreme susceptibility. While the number of the mean-based FFSIs demonstrated a normal quasi-Gaussian distribution with very low percentages in the quintile of low and extreme categories, the maximum-based FFSIs overemphasized the proportion of settlements of high and extreme susceptibility. These two categories combined accounted for more than 50% of all settlements. The highest accuracy at 59.02% for class 5 (highest susceptibility) was found for the majority based FFSIs. The current map has been improved compared to the former one in terms of (i) a higher number of conditional factors considered, (ii) higher resolution, (iii) being settlement-based and (iv) a higher number of events used for verification.

This paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. “Driving the simulation” means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions.

Climate change is commonly associated with extreme weather phenomena. Extreme weather patterns may bring prolonged drought periods, more intense runoff and increased severity of floods. Rainfall distribution is extremely erratic both in space and time, particularly in areas of rugged topography and heterogeneous land use. Therefore, locating major rainfall events and predicting their hydrological consequences is challenging. Hence, our study aimed at exploring the spatial and temporal patterns of daily rainfall totals of R ≥ 20 mm, R ≥ 30 mm and R ≥ 40 mm (extreme precipitation events, EPE) in Pécs (SW Hungary) by a hydrometeorological network (PHN) of 10 weather stations and the gridded database of the Hungarian Meteorological Service (OMSZ). Our results revealed that (a) OMSZ datasets indicated increasing frequencies of EPEs for the period of 1971–2020 in Pécs, (b) the OMSZ dataset generally underestimated EPE frequencies, particularly for R ≥ 40 mm EPEs, for the period of 2013 to 2020, and (c) PHN indicated a slight orographic effect, demonstrating spatial differences of EPEs between the two datasets both annually and seasonally for 2013–2020. Our results pointed out the adequacy of interpolated datasets for mesoscale detection of EPE distribution. However, topographically representative monitoring networks provide more detailed microscale data for the hydrological management of urban areas. Data from dense rain-gauge networks may complement interpolated datasets, facilitating complex environmental management actions and precautionary measures, particularly during weather-related calamities.

A hybrid bin microphysical scheme is developed in a parcel model framework to study how natural aerosol particles and different types of hygroscopic seeding materials affect the precipitation formation. A novel parameter is introduced to describe the impact of different seeding particles on the evolution of the drop size distribution. The results of more than 100 numerical experiments using the hybrid bin parcel model show that (a) the Ostwald-ripening effect has a substantial contribution to the broadening of the drop size distribution near the cloud base. The efficiency of this effect increases as the updraft velocity decreases. (b) The efficiency of hygroscopic seeding is significant only if the size of the seeding particles is in the coarse particle size range. The presence of the water-soluble background coarse particles reduces the efficiency of the seeding, (c) The efficient broadening of the size distribution due to the seeding depends on the width of the size distribution of water drops in the control cases, but the relation is not as straightforward as in the case of the glaciogenic seeding.