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Land Use Patterns and Political Instability as Predictors for the Re-emergence of Malaria in the Caucasus

Posted by Kirsten de Beurs on

This project is in collaboration with: Dr. Katherine Hirschfeld. Others working on the project are PhD student Bradley Brayfield, MS student James Worden, and data researcher Braden Owsley. We also collaborate with Dr. Ani Melkonyan.

Caucasus region during Soviet times.

Malaria was once very prevalent throughout southern Russia and the Caucasus. The World Health Organization (WHO) estimates that approximately 200,000 people were ill with malaria in Armenia in the early 1930s, with another 600,000 in Azerbaijan. 

The Caucasus is naturally hospitable to the malaria mosquito, but large-scale eradication efforts during the Soviet period resulted in an almost total disappearance. The region was first declared malaria free in 1975, but in the 1980s and 1990s, malaria reappeared in the Caucasus, the Central Asian republics, and to a lesser extent in Russia because of the war in Afghanistan, the fall of the Soviet Union, and the appearance of unstable successor states. Outmigration, agricultural development projects and collapse of existing public health prevention activities also contributed to the resurgence of malaria in the region. Past and recent outbreaks in the Trans-Caucasian countries have underlined the fact that all these countries are situated within epidemic-prone areas. In 2015, Europe, Central Asia and the Caucasus were free of malaria transmission once more, for the first time in almost 30 years (World Health Organization 2015).

This project combines spatial and temporal analysis to improve predictive modeling in disease ecology and international health. The overall objective is to apply remotely sensed data for the development of suitability maps for malaria in the Caucasus. Once these maps are created, the goal is to distinguish and isolate the effects of political instability, which include the cessation of malaria prevention, from the land use and land cover impact on malaria transmission by creating precise timelines of each country’s post-Soviet historical trajectory. Institutional failures under unstable governments during periods of conflict could undermine or interrupt public health work so that disease vectors proliferate. This research is based on a solid set of remote sensing methods, which will be expanded by the incorporation of SAR imagery. The development of error surfaces by comparing multiple data streams presents a significant new development.

We investigate trends in land use in the Caucasus between 1984 and 2019 and focus specifically on the change in agriculture from rain-fed to irrigation, forest fragmentation because of overharvesting and natural causes, and changes in open surface water. We also examine the implications of these changes in terms of their impact on the vulnerability of the population (social system) to re-emergence of vector-borne diseases such as P. vivax Malaria.

Malaria prevalence per 100,000 from 1922. Malaria was especially prevalent in the Volga river valley and the South Caucasus. The area shaded with dots are part of the Volga river valley.

Published Project Papers

Worden J, de Beurs KM. 2020. Surface water detection in the Caucasus, International Journal of Applied Earth Observation and Geoinformation 91

Hirschfeld, K. 2020. Microbial insurgency: Theorizing global health in the Anthropocene. The Anthropocene Review.


Cross-Scale Interactions Among Climate, Land-Use, and River-Water Quality – New Zealand

Posted by Kirsten de Beurs on

Relationships between land use and water quality are complex with feedbacks, legacy effects, and cross-scale interactions (CSI). 

CSI occur when processes at one spatial or temporal scale interact with processes at finer or broader scales. In systems where CSI are connected, a change in an environmental driver such as climate or land use can result in positive feedbacks and cascading events that lead to dramatic and widespread changes in system dynamics. This project used sophisticated geospatial tools to analyze multi-resolution environmental datasets over multiple spatio-temporal scales to assess how CSI between changing climate and land use affect river water quality in New Zealand (NZ) from hours to decades, and from small catchments to regional drainage basins. Using MODIS BRDF-adjusted reflectance data (C6), we created a land disturbance dataset for NZ from the year 2000 to 2017 with 8-day temporal resolution and 463-m spatial resolution. This national-scale land disturbance dataset, the first of its kind, revealed that intense land uses such as livestock grazing and forest clear-cuts leave the land bare for considerable periods. When compared with one of the most comprehensive river water quality datasets in the world, we found that these intense land uses have led to excess sediment runoff and nutrient enrichment in almost half of NZ’s rivers. Over time, the CSI among climate, land use, and river water quality have led to nonlinear patterns where landscapes have switched from supply-limited to transport-limited in terms of sediment runoff. Nutrient runoff has been transport-limited (i.e., a function of rainfall frequency-magnitude-duration) for most impacted rivers. Due to best management practices (and possibly from the conversion of sheep farming to cattle farming), sediment concentrations have been declining in 34 of the 77 rivers we measured, while nitrogen concentrations have increased in 33 of 77 rivers. While clearer rivers are seen as an improvement in water quality; when combined with increasing nutrients, warmer water, and lower flows, the perfect recipe for toxic algae blooms is created, which has only recently been brought to the public’s attention. Our results indicate that this problem could worsen given the increasing trends we found in water temperatures, inorganic nutrients, and water clarity.    

Published Papers directly linked with the project:

JP Julian, KM de Beurs, RJ Owsley, B.C., Davies-Colley, AE Ausseil. 2017. River water quality changes in New Zealand over 26 years: response to land use intensity. Hydrology and Earth System Sciences 21, 1149-1171

I Kamarinas, JP Julian, AO Hughes, BC Oswley, KM de Beurs. 2016. Nonlinear Changes in Land Cover and Sediment Runoff in a New Zealand Catchment Dominated by Plantation Forestry and Livestock Grazing. Water 8 (436)

KM de Beurs, BC Owsley, JP Julian. 2016. Disturbance analyses of forests and grasslands with MODIS and Landsat in New Zealand. International Journal of Applied Earth Observation and Geoinformation 45, 42-54

TV Tran, KM de Beurs, JP Julian. 2016. Monitoring forest disturbances in Southeast Oklahoma using Landsat and MODIS images. International Journal of Applied Earth Observation and Geoinformation 44, 42-52

JP Julian, NA Wilgruber, KM de Beurs, PM Mayer, RN Jawarneh. 2015. Long-term impacts of land cover changes on stream channel loss. Science of the Total Environment 537, 399-410

Other Papers derived from methodology developed for this project:

KM de Beurs, NS McThompson, BC Owsley, GM Henebry. 2019. Hurricane damage detection on four major Caribbean islands. Remote Sensing of Environment

D Kingfield, K de Beurs. 2017. Landsat Identification of Tornado Damage by Land Cover and an Evaluation of Damage Recovery in Forests. Journal of Applied Meteorology and Climatology