Vol. 128, No. 3 * Pages 287–398 * July - September 2024

The standardized precipitation index (SPI) is a fundamental indicator of meteorological, hydrological, and agricultural droughts in the world. This study aims to evaluate different timescales, 3 months (SPI-3), 6 months (SPI-6), 9 months (SPI-9), and 12 months (SPI-12) indices from meteorological data in quantifying drought characterization in lower eastern counties of Kenya from 1990 to 2018 for observed data and from 1986 to 2018 for Climatic Research Unit Time Series (CRU) data. Precipitation in-situ data (annual) and high-resolution (0.5 × 0.5 degree grid) monthly-scale precipitation data were sought from Kenya Meteorological Department and CRU TS, respectively.
Z-Score (SPI) was computed for each year (in-situ data) and month (CRU TS data) using the SPI algorithm, expressed as the departure from the mean in standard deviation units. Quality control of CRU TS data was done by checking outlier values and comparing the data with precipitation data obtained from the meteorological department as well as ERA5 reanalysis data. Results showed that extreme to mild drought was experienced across the Kenyan counties for both annual in-situ and monthly gridded data. Machakos county experienced a year of extreme drought, while Makueni and Taita-Taveta have had 2 and 4 years of severe droughts, respectively. The monthly SPI indices of 3, 6, 9, and 12 months showed a remarkably consistent behavioral pattern detecting extreme droughts across the counties. Considering the uncertainties, unpredictability, and shifting of the long and short rainy seasons in Kenya, results were obtained related to dry and wet episodes and to their relationship with agricultural production as well as water availability and environmental management.

The subject of the analysis were the climatic and bioclimatic conditions of the mountainous areas in Central Europe from 2005 to 2022. The study was conducted based on meteorological data from 4 stations located in 2 mountain ranges in Central Europe, which were obtained from the Ogimet database. The analysis examined the course of mean air temperature, winter days, precipitation, snow cover, wind speed, horizontal visibility, as well as the number of days with thunderstorms and fog. Subsequently, bioclimatic indices were analyzed based on the wind chill index (WCI) and the climate severity index by Osokin (So). The results confirm an increase in mean air temperature and a decrease in the number of winter days. The most significant change in mean annual air temperature was recorded at the Carphatian stations: Varful Omu (0.59 °C/10 years) and Łomnica (0.49 °C/10 years). The largest change in the WCI value was recorded at the Carpathian station Varful Omu (51 W/m²/10 years), while the So index was 1.4/10 years.

Investigation of urban parks is a particular research section in the frame of urban geology, with increasing social importance. Both natural and anthropogenic factors affect these green sites, giving special scientific importance to their investigation. Thus, the behavior of such a ’composite’ system is expected to be quite complex.
Based on the general circumstances, a research project was introduced in 2016 in the former Mining and Geological Survey of Hungary (now: Supervisory Authority for Regulatory Affairs). The ongoing project was focused on the behavior of urban parks. Within the frame of this research, between 2016 and 2019, field studies were started in 4 parks of Budapest. This paper is targeting one of these parks, namely Honvéd square.
Our objective is to understand the behavior of urban parks under special conditions (e.g., during heat island effects) by getting information from well-defined positions. This can be essential to perform more sustainable water management in an urban park.
Within the frame of the field works soil temperature and soil moisture measurements were being recorded manually every week in every park, at four different locations within a park. Observation points were selected to describe the different microclimates of the parks.
A statistical analysis of the data reveals that the urban heat island (UHI) effect is reflected in soil temperatures at a citywide scale and that by moderating urban soil surface temperature extremes, trees and shrubs may help to reduce the adverse impacts of urbanization on microclimate, soil processes, and human health.
It can be stated, that beside the manual measurements, the automated soil temperature detection was significantly influenced by soil depth at the Honvéd square park. At 100 cm below the surface, the soil temperature is relatively constant. It was approved that not only do deeper soil layers undergo less drastic seasonal temperature fluctuations but also the changes taking place lag further behind those of shallower soil layers.

As urbanization continues to increase, changes in the ecological characteristics of urban areas are becoming more reasonable to meet the needs of a growing population. However, the profound impact of human-induced urban pressures on land, often called the “billing impact”, is strongly emphasized in research publications. In many cases, contemporary anthropogenic processes alter the dynamics of environmental functions in complex ways. Urban regions meet a particular climate regime characterized by increased air temperatures compared to peripheral areas and a significant reduction in wind speed, attributable to the interaction of natural and anthropogenic factors. An urban heat island (UHI) occurs when the air above populated areas heats up additionally, causing air to flow from the site’s edges to its center and creating a heat dome. This study reveals the influence of urbanization on microclimatic changes, encompassing increased evapotranspiration, altered vegetation cover, and temperature fluctuations. The results illustrate environmental transformations caused by abrupt and unregulated urbanization in the mountainous area of Zlatibor, Serbia, a trend that has intensified over the past decade.

Due to global warming, precipitation regimes are expected to change, and heavy events are expected to occur more frequently. This study investigates the relative share of heavy daily precipitation events to total precipitation for past and current climates. In this regard, daily precipitation data with a spatial resolution of 0.25° × 0.25° from the APHRODITE and CHIRPS databases are used. We used two nonparametric tests, the Mann–Kendall test and Sen’s slope estimator, to identify the trend. The results showed that the frequency of daily heavy precipitation has been increasing in most regions of Iran. The relative share of heavy daily precipitation events to total precipitation has increased over the southwest and central areas of Iran from a case of the past to the current climate. The rise in the share of heavy precipitation of the total precipitation has led to a decrease in the frequency of rainy days (wet days) and an increase in the intensity and concentration of precipitation in Iran. The amount of increase in the share of heavy rainfall to the total precipitation is more prominent in the middle parts of the Zagros Mountains. These conditions lead to heavy floods in the southwestern plains of Iran.

It is well known that the recent global warming intensifies the magnitude of rainfalls due to the increase in water content in the atmosphere. Therefore, the probability of exceeding the previously observed extreme precipitation values also increases with the experienced climate change, and forecasting extreme weather events is becoming more important. This paper presents a new polynomial regression approach and software (PolReg), where future extreme precipitations exceeding all previous observations are estimated for each month of year by using prediction bounds with a level of certainty at 95%. The presented method determines the degrees and coefficients of best-fitting polynomials for each precipitation station and forecasts the expected extreme value for each month of year by using the determined polynomials. The performance of the method is tested by removing and estimating a total of 792 highest observed monthly total precipitation values of 66 precipitation stations in Turkey (the highest observation for each month of year for each station). The results show that the proposed method and the provided software have a high performance and accuracy in estimating future precipitation extremes and might be applied in many disciplines dealing with forecasting probable extreme values.