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Detecting drought events over a region in Central Europe using a regional and two satellite-based precipitation datasets

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F23%3A97520" target="_blank" >RIV/60460709:41330/23:97520 - isvavai.cz</a>

  • Výsledek na webu

    <a href="http://dx.doi.org/10.1016/j.agrformet.2023.109733" target="_blank" >http://dx.doi.org/10.1016/j.agrformet.2023.109733</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.agrformet.2023.109733" target="_blank" >10.1016/j.agrformet.2023.109733</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Detecting drought events over a region in Central Europe using a regional and two satellite-based precipitation datasets

  • Popis výsledku v původním jazyce

    In this study, the accuracy of two satellite-based datasets is evaluated. The evaluation includes monthly precipitation estimates, spatial detection of precipitation, and drought monitoring against a regional gridded dataset spanning 2007-2019. A study area covering Poland and parts of the neighboring countries in Central Europe was selected for this evaluation. The Standardized Precipitation Index (SPI) at multi-time scales was employed to monitor meteorological (SPI-3), agricultural (SPI-6, SPI-9), and hydrological (SPI-12) droughts over the study region. This study selected PERSIANN-CDR as a top-down precipitation dataset and SM2RAIN-ASCAT as a bottom-up dataset. According to the results, both datasets exhibit good accuracy for precipitation estimations, but PERSIANN-CDR shows higher accuracy based on the R (coefficient of correlation) and KGE (Kling-Gupta Efficiency) performance indicators. However, SM2RAIN-ASCAT has a lower bias according to PBIAS(%) (percent bias). The reference dataset indicates that the study area experienced dry conditions over 50% of the months. Specifically, based on the reference dataset, 12 (SPI-6) and 16 (SPI-9) severe agricultural droughts were detected. Twenty-four severe agricultural drought events were identified via SPI-6, while the longer SPI window (SPI-9) demonstrated that PERSIANN-CDR assessed 20 severe droughts over the study area. SM2RAIN-ASCAT detected 11 severe agricultural droughts via SPI-6 and SPI-9. Furthermore, based on SPI-12, the reference dataset identified 75 hydrological droughts, while the top-down dataset indicated a lower number of hydrological droughts (67 events) than the reference dataset over the studied period. In contrast, the bottom-up dataset detected 84 hydrological droughts. The spatial distribution of severe meteorological droughts showed a clear pattern with predominant occurrence in eastern parts (Vistula River Basin), as shown by the reference dataset, while this pattern changed for agricultural and hydrological droughts (Odra River Basin). Additionally, the results reveal that meteorological drought does not have a similar spatial distribution to agricultural and hydrological droughts.

  • Název v anglickém jazyce

    Detecting drought events over a region in Central Europe using a regional and two satellite-based precipitation datasets

  • Popis výsledku anglicky

    In this study, the accuracy of two satellite-based datasets is evaluated. The evaluation includes monthly precipitation estimates, spatial detection of precipitation, and drought monitoring against a regional gridded dataset spanning 2007-2019. A study area covering Poland and parts of the neighboring countries in Central Europe was selected for this evaluation. The Standardized Precipitation Index (SPI) at multi-time scales was employed to monitor meteorological (SPI-3), agricultural (SPI-6, SPI-9), and hydrological (SPI-12) droughts over the study region. This study selected PERSIANN-CDR as a top-down precipitation dataset and SM2RAIN-ASCAT as a bottom-up dataset. According to the results, both datasets exhibit good accuracy for precipitation estimations, but PERSIANN-CDR shows higher accuracy based on the R (coefficient of correlation) and KGE (Kling-Gupta Efficiency) performance indicators. However, SM2RAIN-ASCAT has a lower bias according to PBIAS(%) (percent bias). The reference dataset indicates that the study area experienced dry conditions over 50% of the months. Specifically, based on the reference dataset, 12 (SPI-6) and 16 (SPI-9) severe agricultural droughts were detected. Twenty-four severe agricultural drought events were identified via SPI-6, while the longer SPI window (SPI-9) demonstrated that PERSIANN-CDR assessed 20 severe droughts over the study area. SM2RAIN-ASCAT detected 11 severe agricultural droughts via SPI-6 and SPI-9. Furthermore, based on SPI-12, the reference dataset identified 75 hydrological droughts, while the top-down dataset indicated a lower number of hydrological droughts (67 events) than the reference dataset over the studied period. In contrast, the bottom-up dataset detected 84 hydrological droughts. The spatial distribution of severe meteorological droughts showed a clear pattern with predominant occurrence in eastern parts (Vistula River Basin), as shown by the reference dataset, while this pattern changed for agricultural and hydrological droughts (Odra River Basin). Additionally, the results reveal that meteorological drought does not have a similar spatial distribution to agricultural and hydrological droughts.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Návaznosti výsledku

  • Projekt

  • Návaznosti

    S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    AGRICULTURAL AND FOREST METEOROLOGY

  • ISSN

    0168-1923

  • e-ISSN

    0168-1923

  • Svazek periodika

    342

  • Číslo periodika v rámci svazku

    109733

  • Stát vydavatele periodika

    CZ - Česká republika

  • Počet stran výsledku

    13

  • Strana od-do

    1-13

  • Kód UT WoS článku

    001097564900001

  • EID výsledku v databázi Scopus

    2-s2.0-85172768465