1. Introduction
Vector-borne diseases remain a major public health concern in developing countries. In Ethiopia, more than 75% of the area (elevation < 2000 m asl) of the country is considered to be malarious or potentially malarious, and 68% of the population (>50 million people) live in these malaria-impacted areas [
1,
2]. Malaria transmission in Ethiopia is seasonal and depends on favorable climatic and ecological factors for the growth of mosquito populations and the transmission of malaria parasites. In the Amhara Region, malaria cases typically peak at the end of the rainy season between September and December, and in some areas also have a smaller peak at the beginning of the rainy season in May and June [
3,
4]. Understanding how environmental factors trigger these seasonal outbreaks provides the basis for malaria early warning systems that can forecast changes in malaria transmission risk based on climate variability [
5,
6,
7]. To achieve this goal, it is also essential to have reliable data streams for monitoring variations in climate. This study explores the suitability of several satellite remote-sensing and gridded meteorological datasets for measuring daily temperature and precipitation in the Ethiopian highlands.
Temperature and precipitation are fundamental environmental variables that influence spatial and temporal patterns of malaria risk. Temperature drives mosquito population dynamics and malaria transmission by affecting multiple vital rates, including mosquito egg production, growth, and survival as well as infection probabilities and the rate of parasite development within the mosquito vector [
8]. As a result, the basic reproductive rate (R
0) of malaria is highest at an optimal temperature and decreases at higher and lower temperatures. In the East African highlands, cool temperatures at high elevations historically limited malaria transmission to elevations below approximately 2000 m [
9]. More recently, there is evidence that temperatures have increased in higher-elevation regions of Ethiopia [
10] and that these higher temperatures have resulted in increased malaria cases in the highlands [
11]. Precipitation is the ultimate source of water for larval habitats, and therefore often has a lagged relationship with malaria transmission in drier environments where water for breeding habitats is a limiting factor [
3,
6,
12]. However, the effects of rainfall on the development of pools suitable for mosquito breeding are strongly conditioned by hydrological factors such as terrain and soils [
13]. Also, very heavy or continuous rains can wash out breeding habitats and create large water bodies and rapid flows that are not suitable for the larvae of malaria vectors [
14].
Research on climate–malaria relationships in Ethiopia is important to further our understanding of the complex linkages between climate variation and disease transmission, and this knowledge can be applied to predict the locations of malaria risk hotspots and the timing of malaria outbreaks. Many of these studies have relied on in situ data collected at ground-based weather stations [
12,
15,
16], which are generally considered the gold standard for meteorological data [
17]. However, ground-based data are sparely distributed in space, particularly in Africa, and often have data gaps in their time-series records. [
18]. Therefore, other research studies and applications have used gridded environmental data collected by Earth-observing satellites. Many of these datasets encompass the entire globe and are updated daily, making them suitable for monitoring the environmental factors associated with malaria risk. The satellite remote-sensing data that have been used for malaria research include precipitation estimates [
19], land-surface temperature (LST) [
20], the normalized difference vegetation index (NDVI) and other spectral indices derived from optical-infrared remote sensing [
21]. Gridded meteorological datasets, which are developed using various combinations of interpolated meteorological data, satellite observations, and models, provide another source of spatially continuous environmental data that has been used to assess climate-disease relationships [
22].
The main objective of this study was to explore the accuracy of several types of environmental data derived from satellite remote-sensing and gridded climate data products for measuring temperature and precipitation in the Amhara Region of Ethiopia. This research was motivated by our ongoing work on the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) project, which aims to develop an early-warning system for epidemic human malaria in Ethiopia. The early warning system includes software that integrates temperature, precipitation, and vegetation data from Earth-observing satellites to monitor the environmental risk factors for malaria outbreaks [
23,
24]. Forecasts are made with empirical time series models that use distributed lags to capture the delayed effects of environmental fluctuations on malaria cases [
25]. To make effective forecasts, it is essential that the environmental data used to drive the models is as accurate as possible, and that major sources of error and systematic biases are well understood.
There has been considerable interest in the use of satellite rainfall products for monitoring crop productivity and flooding in Ethiopia. Several previous studies have compared multiple satellite rainfall products with meteorological station data, but these comparisons were made at seasonal or monthly rather than daily time scales [
18,
26]. Another study assessed the relationships between remotely sensed land-surface temperature (LST) measurements and daily air temperature observations from meteorological stations but did not explore other potential sources of geospatial temperature data [
20]. To expand our understanding of remotely sensed climate indices and gridded climate data products in the Ethiopian highlands, we compared multiple spatial precipitation and temperature data products over a 13-year period. with observations from twenty-two meteorological stations in the Amhara Region of Ethiopia. To ensure the results are relevant to the EPIDEMIA project, we selected a subset of the wide range of available data sources that are the most suitable for malaria early warning. These datasets are all have a daily temporal resolution and are available as continuous gridded datasets with latency ranging from a few days to approximately one month. Our main objectives were to: (1) compare the bias, error, and correlation of daily rainfall estimates between spatial climate datasets and meterological station observations; and (2) assess the geographic distribution of these accuracy metrics across elevation gradients within the study area.
4. Conclusions and Recommendations
Vector-borne diseases, such as malaria, are still major public health concerns in developing countries, such as Ethiopia. Forecasting and early warning of such diseases is hindered by scarce and poor-quality meteorological station datasets in the region. This study was aimed at evaluating the accuracy of satellite-based environmental datasets with meteorological station datasets. We found that FLDAS temperature binned scatter plots were closely associated with station temperature, while AMSR temperature displayed an overestimation bias at lower temperature in highland areas. MODIS LST had a consistent overestimation bias, but was more accurate than AMSR temperature. The regression line for the CHIRPS rainfall is close to the 1:1 diagonal line of the binned plot, while that of FLDAS rainfall displayed the largest underestimation bias. The FLDAS interpolated temperature data showed the lowest bias (ME), and error (MAE), and best agreement (COR) with corresponding station temperature data. In contrast, AMSR temperature showed the largest bias and error and weakest correlations. CHIRPS rainfall showed the least bias and error, and best agreement with station rainfall data. FLDAS rainfall displayed the largest bias and error, and weakest correlations.
The FLDAS air temperature and CHIRPS rainfall datasets can provide sources of meteorological data that are strongly associated with daily patterns of station temperature and rainfall data within the study area and potentially in other areas throughout in the world. However, the FLDAS daily products are no longer being produced and FLDAS now provides only a global, monthly product with latency of greater than one month. Similarly, the CHIRPS rainfall dataset has a latency of longer than a month, which limits its utility for early-warning applications. Thus, MODIS LST and AMSR temperature products may be useful in many situations because they have relatively strong day-to-day correlations with station temperatures despite their higher ME and MAE. The TRMM/GPM IMERG daily rainfall data can also provide rainfall estimates with low bias that are only slightly less accurate than CHIRPS and are superior to the FLDAS rainfall product. We also suggest that the development of new, regionally calibrated gridded meteorological datasets that combine satellite observations and stations measurements, such as those developed by the Enhancing National Climate Services (ENACTS) project [
47], will be an important step toward providing better data to support climate and malaria research and applications. Overall, more research will be needed to better understand the strengths and limitations of various sources of meteorological data and to determine how the underlying differences influence climate and health assessments.