Some Meteorological Aspects of Severe Agricultural Drought in the Northern Black Sea Region in 2019–2020

Semenova, Inna

doi:10.3390/ecas2021-10299

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…In Ukraine, the economic impacts of drought are most often seen in the agriculture and water sector, causing reduced or total crop damage, including through impacts on irrigation systems during hydrological drought. The last drought of 2019–2020 in the Northern Black Sea region was caused by long absence of precipitation and warm winter under the predominance of high atmospheric pressure in the region, which led to a critical decrease in soil moisture content and the loss of 80%–100% of crops in the Odessa region in the spring of 2020 (Semenova, 2021).…”

Section: Introductionmentioning

confidence: 99%

Long‐term variability and trends of meteorological droughts in Ukraine

Semenova,

Vicente‐Serrano

2024

Intl Journal of Climatology

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The purpose of this research is to identify changes in the spatiotemporal distribution and severity of meteorological droughts in Ukraine between 1946 and 2020. In order to determine the role of precipitation and atmospheric evaporative demand on drought severity, two drought indices were applied for comparative analysis: the standardized precipitation index (SPI) and the standardized evaporation precipitation index (SPEI). Both indices were calculated on 3‐ and 12‐month timescales. The gridded dataset of monthly air temperature and atmospheric precipitation for Ukraine with high spatial resolution (0.1° × 0.1°), developed in the Ukrainian Hydrometeorological Institute, was used. Results of time series analysis of drought indices showed at least 20 episodes, in which more than 25% of the territory of Ukraine was affected by drought of varying intensity. Trends show an increase in drought severity determined mostly by a strong observed increase in the atmospheric evaporative demand. Tendencies towards an increase in the duration and intensity of droughts were observed mainly in the southwestern, central and northern regions of Ukraine. The main cause of the increase was droughts in spring and summer. The temporal analysis of the spatial distribution of areas affected by drought during the identified main drought episodes showed different patterns in drought propagation across the territory of Ukraine.

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Section: Introductionmentioning

confidence: 99%

Long‐term variability and trends of meteorological droughts in Ukraine

Semenova,

Vicente‐Serrano

2024

Intl Journal of Climatology

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“…A drought is a recurrent event due to a lack of precipitation for an extended period of time in a particular region [1,2]. Like other natural hazards, drought is steady and sometimes considered a creeping phenomenon as it is a gradually evolving natural hazard due to climatic fluctuations [3,4]. Generally, the impacts of drought have effect on agriculture, livestock, ecological system, socio-economic, and energy sectors [5,6].…”

Section: Introductionmentioning

confidence: 99%

Application of Extreme Learning Machine Algorithm for Drought Forecasting

et al. 2022

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Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling-Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI-1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI-1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.

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“…Метеорологічні та сільськогосподарські посухи можуть значно знизити врожайність зернових культур, особливо, якщо трапляються навесні і влітку. Так, посуха 2019-2020 років в Україні, через тривалу відсутність опадів і теплу зиму, була найсильнішою з 1947 р. по зниженню вологи ґрунту, доступної для сільськогосподарських культур, і привела до загибелі до 80 % посівів в південно-західних областях країни навесні 2020 р., що катастрофічно позначилося на регіональній економіці аграрного сектора і призвело до зниження експорту зернових культур [7].…”

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Experimental evaluation of fire weather conditions during periods of wildfires in Ukraine in 2020

Semenova

Mansarliysky

Mishchenko

et al. 2022

Visnyk of V.N. Karazin Kharkiv National University, Series Geology. Geography. Ecology

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Introduction. Almost every year, the territory of Ukraine is affected by drought of different intensity and duration, what is a favorable condition for wildfires. In the forest areas of the country arose about 106.8 thousand fires with a total area of 139.2 thousand hectares during 1990-2017. An analysis of the long-term dynamics shows that wildfires in Ukraine are a sustainable phenomenon. In 2020, under the severe drought conditions, 209 forest fires occurred, the most significant of them were in the Chernobyl zone and in the Lugansk region. Formulation of the problem. There is still no universally accepted method to describe all drought related processes due to complexity of drought phenomena. Although drought indices and fire weather indices usually have different time scales, their combination can be a basis in the prediction scheme in which the drought index acts as a background indicator, which enhances or decreases the current fire weather index. The purpose of this study is evaluate informativeness of a new fire weather index HDW (Hot-Dry-Windy Index) for the territory of Ukraine, which takes into account the main meteorological parameters necessary to describe the degree of wildfire danger due to weather conditions, so this index may become a component of the drought and fire monitoring system. Data and methods. The HDW index has a physical justification as the atmosphere affects the fire, namely, the combination of three atmospheric parameters characterizes the current conditions for the ignition and distribution of fire: wind, temperature, humidity. For the calculation of the HDW index, the data of GFS global numerical model with grid step 0.25 degrees is used. The calculation of the daily fields of the HDW index was carried out for the territory of Ukraine and for local areas. To localize areas of wildfires, in the study were used daily hotspots data provided by the web-resource Firms (https://firms2.modaps.eosdis.nasa.gov/), which are determined using satellite instruments MODIS (AQUA and Terra Satellites) and VIIRS (S-NPP and NOAA-20 satellites). Research results. Comparison of the calculated HDW fields in Ukraine with the position of hotspots showed that the maximum values of the HDW index are generally concentrated in fire areas. Comparison with the synoptic situation showed that the maximum values of the HDW are located in the zones of advection of warm and dry air in the lower troposphere and zones of strong surface winds, which usually correspond to the periphery of anticyclone or the warm sector of cyclone. The analysis of the time courses of the HDW index showed the typical feature, which expressed in the fact that in all cases there was an increase in the index values in the period before the wildfire, maximum of the HDW is fixed at the time of ignition and after that the index values sharply decreases. Scientific novelty and practical significance. The fields of new HDW index reflects the weather conditions typical for wildfires periods in the territory of Ukraine. The presence of specific features and extreme values in the index time course can be used to track the process of increase of fire danger under changing weather conditions to make short- and medium-range fire danger forecasts with evaluation of fire danger classes.

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Some Meteorological Aspects of Severe Agricultural Drought in the Northern Black Sea Region in 2019–2020

Cited by 4 publications

References 2 publications

Long‐term variability and trends of meteorological droughts in Ukraine

Long‐term variability and trends of meteorological droughts in Ukraine

Application of Extreme Learning Machine Algorithm for Drought Forecasting

Experimental evaluation of fire weather conditions during periods of wildfires in Ukraine in 2020

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