Analysis of climate variability and droughts in East Africa using high-resolution climate data products

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F20%3A00524520" target="_blank" >RIV/86652079:_____/20:00524520 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0921818120300199?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0921818120300199?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.gloplacha.2020.103130" target="_blank" >10.1016/j.gloplacha.2020.103130</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of climate variability and droughts in East Africa using high-resolution climate data products

Popis výsledku v původním jazyce

Analysis of climate variability and change as a basis for adaptation and mitigation strategies requires long-term observations. However, the limited availability of ground station data constrains studies focusing on detecting variability and changes in climate and drought monitoring, particularly in developing countries of East Africa. Here, we use high-resolution precipitation (1981-2016) and maximum and minimum temperature (T-max and T-min) (1979-2012) datasets from international databases like the Climate Hazard Group (CHG), representing the most accurate data sources for the region. We assessed seasonal, annual, and decadal variability in rainfall, T-max and T-min and drought conditions using the Standardized Precipitation Index (SPI). The impact of changes in Sea Surface Temperature on rainfall variability and droughts is assessed using the Nino3.4 and Indian Ocean Dipole (IOD) indices. The results show maximum variability in rainfall during October-December (OND, short rainy season) followed by March-May (MAM, long rainy season). Rainfall variability during OND showed a significant correlation with IOD in Ethiopia (69%), Kenya (80%), and Tanzania (63%). In Ethiopia, the period June-September (JJAS) showed a significant negative correlation (-56%) with the Nino3.4. Based on the 12-month SPI, the eastern and western parts of the region are getting drier and wetter, respectively with an average of mild, moderate, and severe droughts of more than 37%, 6%, and 2% of the study period, respectively. The observed severe droughts (e.g., 1999/2000) and extreme floods (e.g., 1997/1998) were found to be linked to respective negative and positive anomalies of the Nino3.4. In general, climate data products with high spatial resolution and accuracy help detect changes and variability in climate at local scale where adaptation is required.

Název v anglickém jazyce

Analysis of climate variability and droughts in East Africa using high-resolution climate data products

Popis výsledku anglicky

Analysis of climate variability and change as a basis for adaptation and mitigation strategies requires long-term observations. However, the limited availability of ground station data constrains studies focusing on detecting variability and changes in climate and drought monitoring, particularly in developing countries of East Africa. Here, we use high-resolution precipitation (1981-2016) and maximum and minimum temperature (T-max and T-min) (1979-2012) datasets from international databases like the Climate Hazard Group (CHG), representing the most accurate data sources for the region. We assessed seasonal, annual, and decadal variability in rainfall, T-max and T-min and drought conditions using the Standardized Precipitation Index (SPI). The impact of changes in Sea Surface Temperature on rainfall variability and droughts is assessed using the Nino3.4 and Indian Ocean Dipole (IOD) indices. The results show maximum variability in rainfall during October-December (OND, short rainy season) followed by March-May (MAM, long rainy season). Rainfall variability during OND showed a significant correlation with IOD in Ethiopia (69%), Kenya (80%), and Tanzania (63%). In Ethiopia, the period June-September (JJAS) showed a significant negative correlation (-56%) with the Nino3.4. Based on the 12-month SPI, the eastern and western parts of the region are getting drier and wetter, respectively with an average of mild, moderate, and severe droughts of more than 37%, 6%, and 2% of the study period, respectively. The observed severe droughts (e.g., 1999/2000) and extreme floods (e.g., 1997/1998) were found to be linked to respective negative and positive anomalies of the Nino3.4. In general, climate data products with high spatial resolution and accuracy help detect changes and variability in climate at local scale where adaptation is required.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Global and Planetary Change

ISSN

0921-8181

e-ISSN

—

Svazek periodika

186

Číslo periodika v rámci svazku

MAR 2020

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

103130

Kód UT WoS článku

000526518600001

EID výsledku v databázi Scopus

2-s2.0-85078201731