A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments.

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

Modelling the influence of short-term climate variability on drinking water quality in tropical developing countries: A case study in Tanzania.

Climate change is expected to increase the prevalence of water-borne diseases especially in developing countries. Climate-resilient drinking water supplies are critical to protect communities from faecal contamination and thus against increasing disease risks. However, no quantitative assessment exists for the impacts of short-term climate variability on faecal contamination at different drinking water sources in developing countries, while existing understanding remains largely conceptual. This critical gap limits the ability to predict drinking water quality under climate change or to recommend climate-resilient water sources for vulnerable communities. This study aims to provide such quantitative understanding by investigating the relationships between faecal contamination and short-term climate variability across different types of water sources. We collected a novel dataset with over 20 months' monitoring of weather, Escherichia coli (E. coli) and total coliforms, at 233 different water sources in three climatically different regions in Tanzania. We then took a rigorous statistical analysis with Bayesian hierarchical models, to relate both contamination occurrence and amount to climate variability. The model results explained the temporal variability in drinking water faecal contamination using climate predictors, and also revealed the climate sensitivity of faecal contamination for individual water sources. We found that: a) short-term climate variability and baseline contamination levels can explain about half the observed variability in faecal contamination (R2 ≥ 0.44); b) increased contamination was most consistently related to recent heavy rainfall and high temperature across different water sources; c) unimproved water sources such as the unprotected dug wells have substantially higher climate sensitivity. Based on these results, we can expect substantial increases in drinking water contamination risks across tropical Sub-Saharan Africa and South-East Asian developing countries under a warmer climate, which highlight the urgent need of protecting vulnerable communities from the severe climate impacts.

A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records.

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15 min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5 years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

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.