Volume 49, Issue 19 e2022GL099872
Research Letter
Open Access

Evaluation of Extreme Soil Moisture Conditions During the 2020 Sahel Floods and Implications for Disease Outbreaks

N. P. Thomas

Corresponding Author

N. P. Thomas

University of Maryland Baltimore County, Goddard Earth Science Technology and Research II, Greenbelt, MD, USA

NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, USA

Correspondence to:

N. P. Thomas,

[email protected]

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A. Anyamba

A. Anyamba

University of Maryland Baltimore County, Goddard Earth Science Technology and Research II, Greenbelt, MD, USA

NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, USA

Now at Geospatial Science and Human Security Division, National Security Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA

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H. Tubbs

H. Tubbs

University of Maryland Baltimore County, Goddard Earth Science Technology and Research II, Greenbelt, MD, USA

NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, USA

Now at Geospatial Science and Human Security Division, National Security Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA

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B. Bishnoi

B. Bishnoi

University of Maryland Baltimore County, Goddard Earth Science Technology and Research II, Greenbelt, MD, USA

NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, USA

Now at Geospatial Science and Human Security Division, National Security Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA

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First published: 01 October 2022

Abstract

The June-October 2020 growing season was characterized by sustained and extreme flooding across the African Sahel. One consequence of flooding events such as this is outbreaks of vector borne diseases (VBDs) which are often associated with climate anomalies. In this study, data from the soil moisture active passive (SMAP) mission is used with other soil moisture and precipitation data to show that the 2020 Sahelian growing season was the most extreme over the past four decades, ranking first in seasonally accumulated precipitation, which on average exceeded the climatology by around 300 mm. VBD outbreaks of Rift Valley fever and Chikungunya followed in Mauritania/Senegal and Chad, respectively. In some cases, soil moisture is a better indicator of VBD outbreak risk than precipitation, which has so far been more commonly used in studies of VBD outbreaks. It is expected that this finding will inform future monitoring and prediction efforts for VBD risk.

Key Points

  • Above-normal precipitation of up to 600 mm locally led to flooding in the African Sahel during the 2020 growing season (June-October)

  • Precipitation and surface soil moisture data indicate that this event was among the most extreme in the region in recent decades

  • This event is linked with disease outbreaks, and soil moisture is potentially a better indicator of outbreak risk than precipitation alone

Plain Language Summary

Extreme rainfall and flooding can be harmful to society in many ways; this study examines the case study of flooding that affected the African Sahel during the summer of 2020. One consequence of flooding events is the increased risk of vector-borne diseases outbreaks. These outbreaks are often linked with anomalies in climate variables such as precipitation, temperature, and vegetation conditions which prime disease vector habitats. This study uses satellite-measured soil moisture information to assess the 2020 Sahel flooding and its relation to outbreaks of Rift Valley fever and Chikungunya vector-borne disease outbreaks. It is shown that this event was very extreme in the context of the historical data record, and soil moisture is shown to be a valuable information source for indicating the potential risk areas of disease outbreaks.

1 Introduction

The African Sahel is an ecologically and climatically sensitive region, and thus is a valuable test case for examination of climate extremes. Precipitation in the Sahel has experienced various regimes in recent decades (Zhang et al., 2017), with drought in the 1980s followed by recovery in the 1990s and beyond. This has been attributed to oceanic warming (Giannini et al., 2003; Hagos & Cook, 2008), and greenhouse warming is projected to continue to increase Sahel rainfall into the twenty-first century (Haarsma et al., 2005). This “re-greening” of the Sahel in recent decades is not simply a recovery of total annual rainfall, but rather an increase in extreme rainfall events (Ly et al., 2013; Panthou et al., 2014), particularly in the Eastern Sahel (Panthou et al., 2018). Extreme rainfall combined with urbanization and land-cover change has caused flooding events to become more prominent and destructive in the Sahel region (Elagib et al., 2021; Tschakert et al., 2010). This was the case during the summer of 2020, when prolonged and extreme rainfall was observed throughout the African Sahel, leading to flooding in countries throughout the region (Balima, 2020; Elagib et al., 2021; UNHCR, 2020).

Among the many societal consequences of hydrometeorological extremes such as floods is outbreaks of vector-borne diseases (VBDs). Outbreaks of VBDs are often associated with climate anomalies which affect the land surface suitability conditions for survival of disease vector populations. The two prominent diseases relevant during the 2020 Sahel flood period are Rift Valley fever (RVF) and Chikungunya. RVF outbreaks are associated with sustained above-normal rainfall (Anyamba et al., 2009; Linthicum et al., 1999) that is often linked with interannual climate variability patterns such as the El Niño Southern Oscillation (ENSO; Anyamba et al., 2019). ENSO and other modes of climate variability have also been shown to affect the timing of floods in Africa (Ficchì & Stephens, 2019). Chikungunya outbreaks have been correlated with drought conditions (Chretien et al., 2007) and above-normal temperatures (Anyamba et al., 2012), though the explicit relationship with soil moisture conditions has yet to be identified. Understanding how climate variability interacts with VBDs can aid in the forecasting of their outbreaks (e.g., Anyamba et al., 2009).

To date, precipitation, normalized difference vegetation index (NDVI) and land surface temperature have been the primary climatic metric considerations in VBD studies. Soil moisture information has not yet been as widely used in understanding and monitoring of disease outbreaks, despite the fact that surface water availability is an important determinant of vector habitat suitability (Soti et al., 2013). This is in part due to the lack of global consistent soil moisture observations. In this study, we utilize surface soil moisture derived from NASA's Soil Moisture Active Passive (SMAP) satellite mission along with other sources of precipitation and soil moisture information to characterize the floods in the Sahel during the 2020 growing season and the implications for VBD outbreaks. The goals are to determine how extreme the 2020 event was in historical context, and to assess the value of soil moisture information in analyzing the associated disease outbreaks. Data and methods are described in Section 2, while Section 3 outlines the timing and historical context of the floods, and implications for VBD outbreaks. Conclusions follow in Section 4.

2 Data and Methods

2.1 SMAP Data

The Soil Moisture Active Passive (SMAP) Earth satellite mission was launched in 2015 and provides global measurements of soil moisture, with many possible applications (Entekhabi et al., 2010). In this study, we utilize the SMAP Level 4 (L4) data product which provides estimates of land surface properties by assimilating SMAP-measured brightness temperatures into a land data assimilation system to derive soil moisture estimates for the surface and root-zone (R. H. Reichle et al., 2017a2017b). Version 6 of the SMAP L4 product (R. Reichle et al., 2021) is used in this analysis, as it exhibits improved performance over previous versions due to its inclusion of precipitation forcing from the NASA Global Precipitation Monitoring (GPM) mission (R. H. Reichle et al., 2022). Surface soil moisture data is available at 3-hourly temporal resolution and 9 km spatial resolution; these are averaged to daily and monthly means for use in this study. Daily (monthly) anomalies are created by comparing each day (month) to its long-term average over the entire available SMAP period (2015–2021), thereby removing the seasonal cycle. The daily long-term mean is smoothed using a time window of 15 days. For all area-averages, the Sahel is defined as 20°W to 40°E and 10–20°N (Figure S1 in Supporting Information S1).

2.2 MERRA-2 Data

The NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al., 2017) is also used in this analysis as a complementary source of soil moisture information with a longer time series. The land surface in MERRA-2 is forced by observation-corrected precipitation (R. H. Reichle et al., 2017a2017b); over Africa, this is done using the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie & Arkin, 1997). Monthly surface soil moisture data from MERRA-2 are available at a spatial resolution of 0.625° longitude by 0.5° latitude from January 1980 to present (GMAO, 2015). Monthly anomalies are produced by comparing each month to its long-term climatology to remove the seasonal cycle.

2.3 ARC2 Data

The African Rainfall Climatology, version 2 data set sourced from the National Oceanic and Atmospheric Administration (NOAA)–Climate Prediction Center (CPC) archives (ARC2, Novella & Thiaw, 2013) is used as an independent source of precipitation information over Africa. ARC data are derived from several satellites and in situ sources, including: the polar orbiting Special Sensor Microwave/Imager and Advanced Microwave Sounding Unit microwave sensors; infrared bands of the geostationary METEOSAT platforms; and rain gauge measurements from the Global Telecommunications System daily total rainfall product. Daily and monthly data are available at a spatial resolution of 0.1° from January 1983 to present. Monthly anomalies are computed as described in Section 2.1.

2.4 Disease Data

The data on outbreaks of vector borne diseases–RVF and Chikungunya–is compiled from the Program for Monitoring Emerging Diseases (Pro-MED-Mail; https://promedmail.org/) of the International Society for Infectious Diseases (ISID) and the World Organization for Animal Health (OIE–World Animal Health Information System; https://wahis.oie.int/#/home). ProMED uses non-traditional sources of information vetted by a global network of subject matter experts and trusted partners from the region where outbreaks are reported to identify and communicate outbreaks to the international community in near real time, contrasting with significant delays and fragmentation typical of traditional government reports. OIE-WAHIS collects and catalogs data on animal disease outbreaks based on official government reporting. We systematically recorded, and georeferenced outbreaks of Chikungunya from ProMED text reports following the procedures in Anyamba et al. (2019). Already georeferenced outbreak data on RVF was extracted from OIE-WAHIS data base. All these disease outbreaks are gathered on an ongoing basis as part of our global vector borne disease monitoring efforts. Population density data comes from the Gridded Population of the World, Version 4 (GPWv4), Revision 11 data set (CIESIN, 2018).

3 Analysis of 2020 Sahel Floods

3.1 Historical Context

To place the 2020 Sahel floods in historical context, Figure 1 shows the time series of monthly anomalies in precipitation and soil moisture area-averaged over the Sahel (20°W-40 E; 10–20°N). Figure 1a shows surface soil moisture from MERRA-2 and precipitation from ARC2 over 1980–2021 as anomalies with respect to the long-term climatology of 1991–2020. Precipitation and surface soil moisture are positively correlated, with the maximum correlation when soil moisture lags precipitation by 1 month (r = 0.63). Both variables indicate that the 2020 growing season months had the largest positive anomalies over the past 40 years for the area-averaged Sahel region. An apparent positive trend can also be seen from the early 2000s to present, as also identified by Zhang et al. (2017).

Details are in the caption following the image

(a) Monthly anomalies in MERRA-2 surface soil moisture (black line) and ARC2 precipitation (blue line) averaged over the Sahel (20°W–40 E; 10–20 N) with respect to a 1991–2020 climatology. (b) Monthly anomalies in MERRA-2 surface soil moisture (black line), SMAP surface soil moisture (red line), and ARC2 precipitation (blue line) averaged over the Sahel with respect to a 2015–2021 climatology.

To include the SMAP surface soil moisture data, the shorter period starting in 2015 is shown in Figure 1b. Anomalies for all data sets shown in Figure 1b are with respect to the shorter 2015–2021 period, to be consistent with SMAP. Variations among the data sets again show considerable similarity; the correlation between MERRA-2 and SMAP surface soil moisture is 0.73, and the correlation between ARC2 precipitation and SMAP soil moisture is 0.53. Even when using the 2015–2021 climatology period, the precipitation (blue line) and MERRA-2 surface soil moisture (black line) again indicate that the 2020 growing season was the most extreme over this shorter record; both indicate September 2020 to have the strongest positive anomalies in the 2015–2021 period. The SMAP surface soil moisture (red line) also shows positive anomalies in 2020, though the magnitude is smaller than that shown in the other data sets, and is comparable to anomalies in 2019, when extreme flooding affected the Eastern Sahel (Wainwright et al., 2020). Averaged over the whole growing season, though, 2020 ranks first in terms of SMAP soil moisture anomalies (Table S1 in Supporting Information S1) due to the sustained nature of the anomalies as compared to the relatively short jump in 2019.

The 2020 growing season (June-October) was very anomalous with respect to the historical period, with a shift in the distribution of accumulated precipitation toward more extreme values (Figure S2 in Supporting Information S1). For the long-term climatology, the median precipitation across the Sahel is 326 mm, while for 2020, it was 601 mm. Shifts such as this have been shown to be important for disease outbreaks (e.g., Anyamba et al., 2019; Linthicum et al., 1999).

3.2 Spatial Variability and Implications for Disease Outbreaks

Having established that the 2020 floods were anomalous and particularly extreme with respect to the historical record in the area-averaged sense, the next goal is to understand the spatial and temporal variations in the associated anomalies. The seasonal (June-October) standardized anomalies over Africa are shown in Figure 2 for ARC2 precipitation (top) and SMAP surface soil moisture (bottom). The precipitation anomalies (Figure 2a) show widespread above-normal values throughout the Sahel during this five-month period. The soil moisture anomalies (Figure 2b) show a generally similar pattern, with above-normal soil moisture throughout the Sahel, though more localized than the precipitation patterns and with some regions of below-average soil moisture in central Africa. The two maps show similar patterns, but with some notable differences, as expected: while rainfall and soil moisture are undoubtedly linked (Figure S4 in Supporting Information S1), soil moisture contains memory of the recent weather and climate conditions, while precipitation is transient and does not. Convergent evidence in other land surface conditions is shown by concurrent positive anomalies (∼+60%) in the normalized difference index (NDVI) and negative anomalies (∼‒5°C) in land surface temperatures (LST) (Figure S3 in Supporting Information S1).

Details are in the caption following the image

June–October 2020 anomalies in (a) precipitation from ARC2 and (b) surface soil moisture from SMAP. In both cases, anomalies are with respect to the SMAP long-term mean (2015–2021). The red (blue) dots correspond to locations of outbreaks of RVF (Chikungunya) during the June–October 2020 period.

Overlayed on Figure 2 are the locations of outbreaks of RVF and chikungunya during the June–October 2020 period in red and blue dots, respectively. The outbreak of RVF in southern Mauritania occurred in a region with very anomalously positive surface soil moisture and seasonal precipitation. While precipitation is enhanced throughout the entire Sahel, the soil moisture patterns reveal this region of Mauritania to be particularly anomalous. This suggests that, at least for this case, seasonally averaged soil moisture anomalies may be a better indicator of the risk of a RVF outbreak than seasonally averaged precipitation alone. The other outbreak of RVF in the Sahel during this period occurred in Sudan, in a region where the seasonally averaged anomalies in both precipitation and soil moisture were weakly positive.

The outbreak of Chikungunya in Chad (blue dots) occurred in a region with relatively large positive anomalies in both precipitation and surface soil moisture in the seasonal average. This is contrary to previously identified relationships between Chikungunya outbreaks and drought, but it is emphasized that this figure is based on seasonally averaged anomalies.

To further examine the relationships between soil moisture anomalies and specific disease outbreaks, Figure 3 shows daily soil moisture anomalies area-averaged over the regions of disease outbreaks in Mauritania and Chad for 2020. In Mauritania (Figure 3a), soil moisture anomalies were relatively weak until early June, when they started to trend positive. Anomalies became more strongly positive in early July, and largely continued to be positive through mid-October, before weakening again in November and December. The RVF outbreak began in early September, approximately 2 months after the start of the strongest soil moisture anomalies. The outbreak lasted until early November, thus overlapping with the strongest soil moisture anomalies in September and October. The approximate 2 months increase in soil moisture indicates that RVF habitats in the region had sufficient time to flood and generate large populations of mosquitos infected with the virus to trigger an outbreak (Linthicum et al., 1983).

Details are in the caption following the image

Daily SMAP surface soil moisture anomalies (m3 m−3) averaged over the regions of disease outbreaks in (a) Mauritania (8.5–17.29 W; 14.83–21.57 N) and (b) Chad (20.29–23.17 E; 11.54–15.85 N).

In Chad (Figure 3b), soil moisture anomalies were weak until early April, when they became weakly negative. Anomalies were mostly negative through early July, with the strongest and most consistently negative values in June. The Chikungunya outbreak began in early August, about 2 months following these negative anomalies, which aligns with the patterns observed elsewhere (Anyamba et al., 2012; Chretien et al., 2007). The surface soil moisture anomalies became strongly positive around the same time as the start of the outbreak in early August, and remained so until mid-October, when they again were weak in November and December. Evidence indicates that elevated temperatures during drought conditions and temperature induced dehydration have positive effect on blood feeding frequency by Aedes aegypti and albopictus Chikungunya vectors (Hagan et al., 2018) and storage of water containers around households (Chretien et al., 2007) may have facilitated Chikungunya virus transmission and outbreak in the transition period from dry to wet conditions as observed in Chad.

4 Conclusions

The floods in the African Sahel during the growing season (June–October) were extreme and fit in with a recent pattern of increased flooding in the region (Elagib et al., 2021). Multiple data products revealed the 2020 floods to be extreme with respect to recent decades. ARC2 precipitation showed a shift in the seasonally accumulated precipitation with respect to the long-term mean with a longer tail of more extreme values extending. In the area-averaged sense over the Sahel region, both MERRA-2 surface soil moisture and ARC2 precipitation indicate that this event exhibited the most positive anomalies in the record of the last four decades. Considering the SMAP short record (2015-present), the surface soil moisture anomalies also show that 2020 event was anomalous, but comparable to the 2019 event, at least in an area-averaged sense. The meteorological and climatological causes of the flooding was not the focus of this study, but Elagib et al. (2021) attributed flooding in the Eastern Sahel during 2020 to a vertically deeper and northward displaced westerly monsoon, and anomalous westerly moisture flux. Further understanding the causes of this event, as well as its potential predictability, would be a valuable topic of future research.

Flooding such as this can have many societal consequences, among them the outbreak of vector borne diseases. Precipitation, temperature and NDVI are the climate variables typically used in studies of VBD outbreaks. This study represents a first step in employing satellite-based soil moisture data to examine the relationship between hydrometeorological extremes and VBD outbreaks. During 2020, Mauritania and Sudan saw outbreaks of RVF, which has previously been linked with persistent above-normal rainfall. Seasonally averaged soil moisture during June-October 2020 showed strong correspondence with the RVF outbreak in Mauritania, suggesting this may be a better indicator of risk than precipitation alone.

It is crucial to understand the temporal relationships between climate anomalies and disease outbreaks. The outbreak of RVF in Mauritania occurred approximately 2 months after the start of the strongest positive anomalies in the region (Figure 3a). This is in rough agreement with previous work which round RVF outbreaks in eastern and southern African to occur following 2–4 months of above-normal rainfall (Anyamba et al., 2012; Glancey et al., 2015; Linthicum et al., 19831999). The outbreak of Chikungunya in Chad during August 2020 followed about two months of below-normal soil moisture in the region. The outbreak then coincided with strongly positive soil moisture anomalies in the region during the flooding (Figure 3b).

It is important to note that there are many other factors beyond climate conditions that can affect outbreaks of diseases. Figure 4 shows the 2020 population density over the Sahel, indicating that the relevant outbreaks during 2020 occurred in relatively low density population areas (<200 persons/km2). The Sahel is sparsely populated region, except in urban areas, however the impact of disease outbreaks often spreads beyond the source areas of outbreaks. Levels of immunity in the population are also undoubtedly a main factor in disease outbreaks. For example, the outbreak of Chikungunya in Chad during 2020 was very unusual, as there are no previous records of this disease in this country. The plausible explanation is that the virus was introduced into a naïve population, which amplified the magnitude of the outbreak as has been seen in similar outbreaks elsewhere (Staples & Fischer, 2014). Changes in land cover are also important, and can affect suitability for vector habitats, such that even if there is an extreme climate anomaly, it may not lead to an outbreak. Finally, limitations and reporting biases of disease surveillance systems can hinder the ability to detect all outbreaks.

Details are in the caption following the image

Population density (persons/km2) over the Sahel during 2020. The red (blue) dots correspond to locations of outbreaks of RVF (Chikungunya) during the June-October 2020 period.

The present study was primarily focused on one extreme season, and the associated impacts. However, it is important to understand how the identified relationships can be applied more broadly to understand the relationships between soil moisture anomalies and disease outbreaks. Figure 5 shows the monthly soil moisture anomalies over the entire SMAP record, averaged over 10–20°N at all Sahel longitudes. Outbreaks of RVF and Chikungunya are overlayed in red and blue dots. This shows that previous positive soil moisture events in 2015, 2018, and 2019 were also coincident with reported outbreaks of RVF in different locations across the Sahel, reinforcing the notion that such periods of persistent positive soil moisture conditions prime the environment for disease activity. As can be seen, reported outbreaks of Chikungunya in the Sahel have been much rarer, and thus broad patterns are not as obvious. Future work will involve characterizing more extreme events in the soil moisture context to establish a more general understanding of lag time between soil moisture anomalies and VBD outbreaks for different regions. The eventual goal will be to use the identified relationships to employ soil moisture information in projections of risk of VBD outbreaks throughout the world.

Details are in the caption following the image

Monthly anomalies in SMAP surface soil moisture at 20°W–40 E from April 2015–December 2021 averaged over Sahel latitudes (10–20 N). Anomalies are shown as a percent departure from SMAP long-term mean.

Acknowledgments

This work was supported by the NASA Science Mission Directorate's Earth Science Division, Soil Moisture Active-Passive (SMAP) Mission Science Team, Grant 80NSSC21K0777.

    Data Availability Statement

    Datasets used in this analysis can be accessed at the following locations: SMAP L4 v6 surface soil moisture: https://nsidc.org/data/SPL4SMGP/versions/6; ARC2 precipitation: https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/; MERRA-2 surface soil moisture: https://disc.gsfc.nasa.gov/datasets/M2TMNXLND_5.12.4/summary; population density: https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11. Chikungunya outbreak reports are publicly available on ProMED Mail; users can read reports by searching “chikungunya” on the ProMED Search page (https://promedmail.org/promed-posts/). RVF outbreak reports are publicly available through the OIE-WAHIS Dashboard; users can read reports by clicking “by Report” on the OIE-WAHIS front page (https://wahis.woah.org) and filtering the results by disease (“Rift Valley fever virus (Inf. with)”). Users can export outbreak reports or aggregated quantitative outbreak data.