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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

AGRICULTURAL AND FOREST METEOROLOGY

ISSN

0168-1923

e-ISSN

0168-1923

Volume of the periodical

342

Issue of the periodical within the volume

109733

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

13

Pages from-to

1-13

UT code for WoS article

001097564900001

EID of the result in the Scopus database

2-s2.0-85172768465