Integration of Hydrological Modeling and Remote Sensing to Identify Potential Malaria Mosquito Breeding Sites
Abstract
Physically based integrated hydrological models are poised to provide surface soil moisture and water ponding information at spatial and temporal scales relevant to assessment and mitigation of malaria health risks associated with mosquito breeding. However, modeling requires a comprehensive set of field data for forcing and parameterization, such as meteorology, land cover, subsurface properties, and data that are often lacking in regions facing malaria risks. This is where remote sensing presents itself as a valuable tool, especially for large scale models as well as rural areas where field data may not be readily available.
In this study, we use ParFlow-CLM to construct a 50m resolution model for an area populated with sugarcane plantations in Arjo, Ethiopia, where malaria is prevalent. Leveraging remotely sensed data for model development, the Advanced World 3D digital terrain model (AW3D DTM) was used to establish terrain attributes. Meteorological data from Version 2 of the Modern-Era Retrospective analysis for Research and Applications (MERRA2), the Global Land Data Assimilation System (GLDAS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification (PERSIANN-CCS) were adopted to force the model. Soil data and bedrock information were downloaded from the SoilGrids system to estimate subsurface parameters. Land use and cover were classified using Landsat 8 data, based on the International Geosphere Biosphere Programme (IGBP), and updated monthly to account for the seasonal variation in the transmission of malaria. Hydrologic simulation was performed over the wet and dry seasons. Different irrigation regimes, including dripping and spray irrigation, were applied over the dry season to understand how human-induced factors can cause conditions favorable for mosquito breeding. Based on the duration of surface water detention and temperature, potential breeding hotspots were identified through soil moisture. Field observations related to the breeding sites in Arjo were used to validate the results.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H33O2232J
- Keywords:
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- 1812 Drought;
- HYDROLOGY;
- 1817 Extreme events;
- HYDROLOGY;
- 1821 Floods;
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY