Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Ai-Ling Jiang,Kuolin Hsu,Brett F Sanders,Dawit Hawaria,Delenasaw Yewhalaw,Daibin Zhong,Solomon Kibret,Ming-Chieh Lee,Guofa Zhou,Guiyun Yan

doi:10.1038/s41598-021-89576-8

Ai-Ling Jiang, Kuolin Hsu + Show 8 more

Open Access

https://doi.org/10.1038/s41598-021-89576-8

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Abstract

Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

Highlights

The larval source management (LSM) program requires identification of aquatic habitats for malaria vectors
We seek to answer the following: (1) Where are the potential larval habitats located and what is the probability of occurrence? (2) How long can the larval habitats be sustained? (3) Is there a cyclical pattern in the extent of the larval habitats? (4) What is the impact of irrigation on each of the above? The uniqueness of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats such as groundwater-surface water interactions[24]
Anopheles arabiensis mosquito takes around 15 days to develop from egg to adult, but the duration can be as short as 10 days due to selection pressures from the stressed environment such as drought, temperature anomaly, or limited food resources[48,49]

Summary

Introduction

The LSM program requires identification of aquatic habitats for malaria vectors. Past studies have attempted to use field-based surveys or harness remotely sensed data for larval habitat identification[6,7]. The larvae of the major malaria vector in Ethiopia, Anopheles arabiensis[17], have been associated with transient p ools[18] and our approach allows the larval habitats to be resolved down to sub-daily frequencies and tens of meters resolutions necessary to capture the dynamic nature of the habitats. It can be scaled up in coverage if required. To take into account irrigation and land cover characteristics, ParFlow was coupled with the Community Land Model (CLM)[30] to simulate soil moisture for the identification of malaria larval habitats in a sugarcane plantation and its vicinity in Arjo, Ethiopia

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