Search

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Extreme rain events have caused numerous issues and have had a significant impact on agriculture, human activities, ecology, infrastructure, and casualties. The theory of extreme values has been widely applied in extreme precipitation modelling and a variety of other fields. This paper employs the generalized Pareto distribution, which has been widely used to analyse extreme climates, in conjunction with the peak over thresholds approach to investigate exceedances. The occurrence of intense rainfall events in Kigali city causes severe damage to human properties, infrastructure damage, people injuries, loss of life, and other various harmful consequences. Early detection of extreme rainfall in Kigali aids in the development and implementation of strategies and measures to mitigate the negative effects of extreme rainfall before it occurs. The aim of this research is to estimate the frequency and magnitude of intense rainfall events in Kigali. The daily rainfall data from Kigali Airport station collected by Rwanda Meteorological Agency from 1990 to 2019 were applied. The results showed that as the return periods increased, so did the return levels, implying that the intensity and frequency of rainfall in Kigali will increase in the future. The model's goodness was tested, and the study suggests a model that has a non‐negative shape parameter (ξ) to be good. The study's findings are extremely important for understanding the occurrence of these events and also serve as a tool for decision‐making and the development of policies aimed at mitigating the effects of such events. [ABSTRACT FROM AUTHOR]

In climate change research, accurate temperature data are often demanded. However, affected by many factors, especially solar radiation, the accuracy of environmental air temperature measurement can be greatly reduced, since there is a difference in temperature between the environmental air and the related temperature measured by the sensor accommodated inside the radiation shield. In the paper, the term “temperature error” refers to the temperature difference described above. To improve the accuracy of the temperature data, a temperature errorcorrection method is proposed. First, a computational fluid dynamics (CFD) method is adopted to quantify the temperature errors accurately. A neural network algorithm is then applied to form a universal correction equation by fitting temperature errors calculated using the CFD method. Finally, to validate the correction equation, field observation experiments are performed. The root mean square error (RMSE) and the mean absolute error (MAE) between the temperature errors obtained experimentally using a sensor inside the DTR503A shield and the corresponding temperature errors determined by using the proposed correction method are 0.043 and 0.038°C, respectively. The RMSE and MAE for the DTR13 radiation shield are 0.049 and 0.044°C, respectively. This method may reduce the error of the temperature data to 0.05°C. If the environmental factors corresponding to the temperature data can be quantified accurately, the factors influencing the temperature error can be added to the correction method continuously. The accuracy of this correction method may be furtherly improved. [ABSTRACT FROM AUTHOR]

In an average year (1979–2016), the United States experiences nearly 1,100 tornadoes, which cause a total of 68 fatalities. Annual fatality rates have decreased since the peak in the 1920s, but there is a concern that they could start to rise again with increases in vulnerable populations and the impacts of climate change. It is possible to assess the risk of tornado fatalities using the historical record. However, the rarity of tornadoes and the short period of record may not capture the true risk. One way around this problem is to simulate thousands of years' worth of tornadoes to obtain a broader picture of risk. Previous tornado risk models have distributed tornadoes randomly or used climatology to generate realistic tornado patterns on an annual (or longer) time scale. From an operational standpoint, it would be useful to have a model that distributes tornadoes on a daily time step to enable the forecasting of potential tornado impacts on a given day. The present study introduces one such model that distributes tornadoes using information about the favourability of the atmospheric environment for tornado development: The Tornado Daily Impacts Simulator (TorDIS). The paper demonstrates model utility through 1,000 year simulations over several metropolitan areas and with a comparison between modelled and observed impacts for several high‐impact tornado days. Forecasting potential tornado impacts on a daily time step could allow emergency managers to plan ahead for high‐risk days to prioritize their resources and save lives. [ABSTRACT FROM AUTHOR]