Projects / P7

Karlsruhe Institute for Technology
Kenya Medical Research

P7: Small-scale hydrometeorological variability and uncertainty quantification in the climate-malaria modeling chain

Further increase of global warming will affect human health. Risks for vector-borne diseases such as Malaria are projected to increase. Weather and climate are driving the geographic extension and the intensity of Malaria transmission, as the spatio-temporal distribution of the Anopheles species vector is sensitive to the small-scale distribution of hydrometeorological variables like temperature, precipitation, and humidity. Despite Malaria is being one of the biggest causes of worldwide mortality, there are still substantial knowledge gaps on the links between environmental variables and transmission intensity. Uncertainty derives also from the limited knowledge of regional and local scale climatology and weather conditions. The latter one is particularly true for Sub Saharan Africa and here Burkina Faso and Kenya, where climate observation networks are extremely coarse and data quality often questionable.

The objectives of this proposal are 1) to quantify and model in high spatial resolution the small scale hydrometeorological variability of Malaria relevant climate variables at the two HDSS (Health and Demographic Surveillance System) site regions in Nouna, Burkina Faso and Kisumu, Kenya, 2) to quantify the sensitivity of agricultural extension on hydrometeorological variability, 3) to validate and analyze the suitability of larger scale climate information for the HDDS site scales and 4) to perform dynamical Malaria transmission modelling and derive uncertainty bounds for the climate-Malaria modelling chain. This will be achieved by six interacting work packages, in cooperation with seven projects of this research unit and in close exchange between the German and African partners. 

We will employ for the first time fully coupled atmosphere and hydrology simulations in 1km spatial resolution in SSA environment, integrate high resolution remote sensing derived land use information into the coupled model system, setup and operate a distributed Malaria transmission model in high spatial resolution and will elaborate feasibility and uncertainty in a climate – Malaria modelling chain for the derivation of expected future malaria risks on regional scale.

Foto von Prof. Dr. Harald Kunstmann

Principle Investigator (PI):
Prof. Dr. Harald Kunstmann
Karlsruhe Institute for Technology (KIT)
Institute for Meteorology and Climate Research
Atmospheric Environmental Research (IMK-IFU)
Garmisch-Partenkirchen, Germany

Foto von Dr. Stephen Munga
Dr. Stephen Munga
Kenya Medical Research Institute (KEMRI)
Centers for Disease Control (CDC) Research & Public Health Collaboration Kisumu, Kenya
Foto von Dr. Ali Sié

Dr. Ali Sié
Centre de Recherche en Santé de Nouna (CRSN)
Nouna, Burkina Faso