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Safe drinking water is critical to human health and development. In rural sub‐Saharan Africa, most improved water sources are boreholes with handpumps; studies suggest that up to one third of these handpumps are nonfunctional at any given time. This work presents findings from a secondary analysis of cross‐sectional data from 1509 water sources in 570 communities in the rural Greater Afram Plains (GAP) region of Ghana; one of the largest studies of its kind. 79.4% of enumerated water sources were functional when visited; in multivariable regressions, functionality depended on source age, management, tariff collection, the number of other sources in the community, and the district. A Bayesian network (BN) model developed using the same data set found strong dependencies of functionality on implementer, pump type, management, and the availability of tools, with synergistic effects from management determinants on functionality, increasing the likelihood of a source being functional from a baseline of 72% to more than 97% with optimal management and available tools. We suggest that functionality may be a dynamic equilibrium between regular breakdowns and repairs, with management a key determinant of repair rate. Management variables may interact synergistically in ways better captured by BN analysis than by logistic regressions. These qualitative findings may prove generalizable beyond the study area, and may offer new approaches to understanding and increasing handpump functionality and safe water access.

Global climate change (GCC) has led to increased focus on the occurrence of, and preparation for, climate-related extremes and hazards. Population exposure, the relative likelihood that a person in a given location was exposed to a given hazard event(s) in a given period of time, was the outcome for this analysis. Our objectives were to develop a method for estimating the population exposure at the country level to the climate-related hazards cyclone, drought, and flood; develop a method that readily allows the addition of better datasets to an automated model; differentiate population exposure of urban and rural populations; and calculate and present the results of exposure scores and ranking of countries based on the country-wide, urban, and rural population exposures to cyclone, drought, and flood. Gridded global datasets on cyclone, drought and flood occurrence as well as population density were combined and analysis was carried out using ArcGIS. Results presented include global maps of ranked country-level population exposure to cyclone, drought, flood and multiple hazards. Analyses by geography and human development index (HDI) are also included. The results and analyses of this exposure assessment have implications for country-level adaptation. It can also be used to help prioritize aid decisions and allocation of adaptation resources between countries and within a country. This model is designed to allow flexibility in applying cyclone, drought and flood exposure to a range of outcomes and adaptation measures.

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Climate-related extreme weather events can result in the loss of drinking water access. We assessed the relative vulnerability of 3143 United States (U.S.) counties to loss of drinking water access due to droughts, floods, and cyclones. Five vulnerability assessment models from the literature were compared, each differing in the aggregation method used to combine the three determinants of vulnerability (V) -exposure (E), sensitivity (S), and adaptive capacity (AC). Exposure scores were calculated using historical occurrence data, sensitivity scores were determined from the intrinsic resilience of the drinking water technologies, and adaptive capacity scores were calculated from nine socioeconomic indicators. Our results showed that models V=E+S+AC and V=E+S-AC were the same, as were models V= E×S×AC and V=E×S÷AC. Between these two model forms (form 1: V=E+S+AC and V= E+S-AC; form 2: V=E×S×AC and V=E×S÷AC), scores from one model form could be used to predict scores from the second model form, with R-squared values ranging from 0.61 to 0.82 depending on the extreme weather event type. A fifth model, V=(E-AC)×S was not found to correlate with any of the other four models. We used V=E+S+AC as our reference model as this resulted in a more uniform distribution of counties in each of the five intervals of vulnerability. Comparing the vulnerability scores identified the counties with greatest vulnerability to losing access to drinking water due to floods, droughts, and cyclones. Our results can Climatic Change (2015) 133:665-679