Dataset Information

Observed data of extreme rainfall events over the West African Sahel.

ABSTRACT: The data described in this article are sets of daily rainfall values derived from observed station records. The data was recorded by 72 in-situ rain gauges spread over the West African Sahel. The daily rainfall time series from synoptic, climate, agro-meteorological, and rainfall stations are assessed for quality and consistency before extreme values are extracted based on 90th, 95th, and 99th percentile thresholds. This data is free for use as part of the study "Scales for rating heavy rainfall events in West African Sahel" [1] (Salack et al., 2018). Complementary and up to date time series can be taken from WASCAL data infrastructure (WADI) geoportal https://wascal-dataportal.org/wascal_searchportal2/. This is a derived product (DP), made public in line with WASCAL?s "3rd party data sharing policy" signed by the WASCAL member countries.

SUBMITTER: Salack S

PROVIDER: S-EPMC6143746 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Observed data of extreme rainfall events over the West African Sahel.

Salack Seyni S Saley Inoussa Abdou IA Bliefernicht Jan J

Data in brief 20180906

The data described in this article are sets of daily rainfall values derived from observed station records. The data was recorded by 72 in-situ rain gauges spread over the West African Sahel. The daily rainfall time series from synoptic, climate, agro-meteorological, and rainfall stations are assessed for quality and consistency before extreme values are extracted based on 90th, 95th, and 99th percentile thresholds. This data is free for use as part of the study "Scales for rating heavy rainfall ...[more]

PMID: 30238039

Similar Datasets

Project description:BackgroundThe southeastern United States consistently has high salmonellosis incidence, but disease drivers remain unknown. Salmonella is regularly detected in this region's natural environment, leading to numerous exposure opportunities. Rainfall patterns may impact the survival/transport of environmental Salmonella in ways that can affect disease transmission.ObjectivesThis study investigated associations between short-term precipitation (extreme rainfall events) and longer-term precipitation (rainfall conditions antecedent to these extreme events) on salmonellosis counts in the state of Georgia in the United States.MethodsFor the period 1997-2016, negative binomial models estimated associations between weekly county-level extreme rainfall events (≥90th percentile of daily rainfall) and antecedent conditions (8-week precipitation sums, categorized into tertiles) and weekly county-level salmonellosis counts.ResultsIn Georgia's Coastal Plain counties, extreme and antecedent rainfall were associated with significant differences in salmonellosis counts. In these counties, extreme rainfall was associated with a 5% increase in salmonellosis risk (95% CI: 1%, 10%) compared with weeks with no extreme rainfall. Antecedent dry periods were associated with a 9% risk decrease (95% CI: 5%, 12%), whereas wet periods were associated with a 5% increase (95% CI: 1%, 9%), compared with periods of moderate rainfall. In models considering the interaction between extreme and antecedent rainfall conditions, wet periods were associated with a 13% risk increase (95% CI: 6%, 19%), whereas wet periods followed by extreme events were associated with an 11% increase (95% CI: 5%, 18%). Associations were substantially magnified when analyses were restricted to cases attributed to serovars commonly isolated from wildlife/environment (e.g., Javiana). For example, wet periods followed by extreme rainfall were associated with a 34% risk increase (95% CI: 20%, 49%) in environmental serovar infection.ConclusionsGiven the associations of short-term extreme rainfall events and longer-term rainfall conditions on salmonellosis incidence, our findings suggest that avoiding contact with environmental reservoirs of Salmonella following heavy rainfall events, especially during the rainy season, may reduce the risk of salmonellosis. https://doi.org/10.1289/EHP4621.

Dataset Information

Observed data of extreme rainfall events over the West African Sahel.

Publications

Observed data of extreme rainfall events over the West African Sahel.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets