## Materials and Methods
### Statistical Analysis  
To characterize the factors that influenced motorized borehole use, we examined sensor and water point data between September 2016 and January 2019. Water point data consisted of site characteristics that were recorded using a mobile survey at the time of sensor installation. Sensors were installed and replaced over the course of this study, resulting in an unbalanced number of daily observations for each pump site. Given that daily observations were nested within each pump site, we used a two-level multilevel linear regression model with random intercepts and an AR-1 correlation structure to account for autocorrelation and non-independence of the errors. Individual observations were nested within each site, and one time-varying predictor was incorporated: a rainy vs. dry season indicator, where the rainy season corresponded to the primary and secondary rainfall periods of March through May and September through November [30]. In addition, several site-level predictors were examined, including borehole status (strategic vs. non-strategic), number of households served, number of livestock served, power source (hybrid, solar, generator, or utility power), alternative sources of water (a surface water source or a different water tap), whether the water point was used for filling water trucks, and urban vs. rural location. These variables were specified using multilevel regression with the hours of use as the outcome variable (see Equation (1)), where \(\beta_{00}\) represents average use across all clusters when controlling for all covariates, \(U_{0i}\) represents the random effect that is allowed to vary for each pump site, and \(\varepsilon_{it}\) is the residual that varies for each observation within each pump site.  
\[\begin{split} Hours_{it}=\beta_{00}+\beta_{01-04}(County_{i})+ \beta_{05}(Borelhole\_Status_{i})+\beta_{06}(Households_{i})+\beta_{07}(Livestock_{i})+\\ \beta_{08-010}(Power\_Source_{i})+\beta_{011}(Seasonal\_Pump_{i})+ \beta_{012}(Alternative\_Water\_Source_{i})+\\ \beta_{013}(Water\_Trucking_{i})+\beta_{014}(Location_{i})+U_{0i}+ \beta_{10}(Rainy\_Season_{it})+\varepsilon_{it}\end{split} \tag{1}\]  
As there was almost half the amount of precipitation in Northern Kenya during 2017 compared to 2018, we also conducted a subgroup analysis to compare overall site use under two different drought conditions (see Equation (2)). In 2017, a state of emergency was declared due to reduced rainfall during the primary rainy season (March through May), catalyzing significant resources and support from organizations such as the NDMA, non-profits, county governments, and the federal government. Since only 25 sensors were installed at the beginning of the 2017 drought season--eight of those sensors were installed on strategic boreholes--we compared how use differed between 2017 and 2018 for this subset of sites. All statistical analyses were conducted using R, and the multilevel models were evaluated using the nlme package [31].  
\[\begin{split}& Hours_{it}=\beta_{00}+\beta_{01}(Borehole\_Status_{i})+ \beta_{02}(Households_{i})+\beta_{03}(Livestock_{i})+\\ &\beta_{04-07}(Power\_Source_{i})+\beta_{08}(Seasonal\_Pump_{i})+ \beta_{09}(Alternative\_Water\_Source_{i})+\\ &\beta_{010}(Water\_Trucking_{i})+\beta_{011}(Location_{i})+U_{0i}+ \beta_{10}(Drought\_Period_{it})+\varepsilon_{it}\end{split} \tag{2}\]