Image courtesy Ms. Yu (Jade) Wang

Evaluating the Effects of Land Surface Conditions on Rainfall Patterns of Modeled Landfalling Tropical Cyclones Using Geospatial Methods

Speaker: Ms. Yu (Jade) Wang

PhD Candidate, Department of Geography, University of Florida

Thursday, April 18, 2019

2:50-3:50 PM (Period 8)

Turlington Hall Room 3012

University of Florida

All are welcome to attend.

Image courtesy Dr. Esther Mullens

Will the Extreme Rain Fall Mainly on the Plains? Rainfall Scenarios Under Climate Change for Oklahoma and Texas

Speaker: Dr. Esther Mullens

Assistant Professor, Department of Geography, University of Florida

Thursday, September 20, 2018

2:50-3:50 PM (Period 8)

Turlington Hall Room 3012

University of Florida

All are welcome to attend.

LIPPI, RYANNonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

Rachel Lowe, Antonio Gasparrini, Cédric J. Van Meerbeeck, Catherine A. Lippi, Roché Mahon, Adrian R. Trotman, Leslie Rollock, Avery Q. J. Hinds, Sadie J. Ryan, Anna M. Stewart-Ibarra

Article first published online: 17 JUL 2018 PLOS Medicine

DOI: 10.1371/journal.pmed.1002613

ABSTRACT:

Background
Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016.

Methods and findings
Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure–lag–response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure–lag–response functions of these indicators—rather than linear effects for individual lags—more appropriately described the climate–disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika.

Conclusion
We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.

Read the full publication at PLOS Medicine

 

 

 

 

 

Images courtesy Ms. Carly Muir and Ms. Audrey Smith
Images courtesy Ms. Carly Muir and Ms. Audrey Smith

Geography Colloquium

Assessing the Recovery of Rainfall in Central Ghana

Speaker: Ms. Carly Muir

Graduate Student, Geography

Inter-spatial Relationship of Child Health & Satellite-derived Vegetation in Zambia

Speaker: Ms. Audrey Smith

Graduate Student, Geography

Thursday, April 7, 2016

3:00-3:50 PM (Period 8)

Turlington Hall Room 3012

University of Florida

All are welcome to attend.

Image courtesy Ms. Stephanie Zick
Image courtesy Ms. Stephanie Zick

GAINESVILLE, FL – Geography Ph.D. student Stephanie Zick was awarded one of three $1000 scholarships for best poster at the 5th UF Water Institute Symposium, held on February 16-17, 2016, in Gainesville, FL. In her poster, she quantified the precipitation geometry in tropical cyclones and found significantly different precipitation evolution in storms that make landfall in Florida versus other Gulf of Mexico states.

Blue ribbon poster
Blue ribbon poster

MUIR – Analysis of Rainfall Variability in Relation to Crop Production in Maun, Botswana

Carly Muir

Article first published online: 01 JAN 2016 University of Florida Journal of Undergraduate Research

ABSTRACT: The purpose of the study was to analyze patterns of the average rainfall characteristics during the growing season in Maun before and after a climatic shift that took place in the 1970’s and to assess how this variability affects risks of crop production at specified planting dates. Plots show that the majority of the water year experienced a decrease in the mean of both rainfall total and the count of rainy days. The graph developed can show probabilities of risk within a specified range and could help farmers make decisions about which types of crops to grow and when to plant as they adapt to the new conditions.

Read the full publication at University of Florida Journal of Undergraduate Research