Understanding the Impacts of Short-Term Climate Variability on Drinking Water Source Quality: Observations From Three Distinct Climatic Regions in Tanzania

Climate change is expected to increase waterborne diseases especially in developing countries. However, we lack understanding of how different types of water sources (both improved and unimproved) are affected by climate change, and thus, where to prioritize future investments and improvements to maximize health outcomes. This is due to limited knowledge of the relationships between source water quality and the observed variability in climate conditions. To address this gap, a 20-month observational study was conducted in Tanzania, aiming to understand how water quality changes at various types of sources due to short-term climate variability. Nine rounds of microbiological water quality sampling were conducted for Escherichia coli and total coliforms, at three study sites within different climatic regions. Each round included approximately 233 samples from water sources and 632 samples from households. To identify relationships between water quality and short-term climate variability, Bayesian hierarchical modeling was adopted, allowing these relationships to vary with source types and sampling regions to account for potentially different physical processes. Across water sources, increases in E. coli/total coliform levels were most closely related to increases in recent heavy rainfall. Our key recommendations to future longitudinal studies are (a) demonstrated value of high sampling frequency and temporal coverage (a minimum of 3 years) especially during wet seasons; (b) utility of the Bayesian hierarchical models to pool data from multiple sites while allowing for variations across space and water sources; and (c) importance of a multidisciplinary team approach with consistent commitment and sharing of knowledge. Plain Language Summary It is vital to understand how different types of water sources (both improved and unimproved) are influenced by changing climate conditions. This is needed to appreciate the reliability of these water sources in the future. A 20-month observational study was carried out in Tanzania to explore these relationships. Nine rounds of sampling were conducted across three study sites within different climatic regions, with fecal pathogen levels sampled at both water sources and households. A novel statistical model was developed to link water quality change with types of water quality and climate variability. We found that across different source types, the increases in fecal pathogen levels are most closely related to increases in recent heavy rainfall. We recommend that future studies to include at least 3 years of data collection. The successful study design here shows the value of multidisciplinary teams to ensure that appropriate statistical modeling structure can be used to analyze the data and provide new information for climate change adaptation. In the long term, such studies will provide evidence for decision-makers to prioritize future water investments and improvements to maximize public health outcomes.