Specific Objectives of the Project:

• Assess water utilities wildfire risk, with a specific focus on rural and agricultural communities;
• Establish an association between water utilities’ vulnerability level to wildfires with water utilities’ and communities’ characteristics; and
• Estimate short- and long-run effects of wildfires on households’ bottled water and water purifying products purchases, with a specific focus on rural and agricultural communities.

Summary of Results:
The project is completed for the first and second objectives, and a paper is submitted to the Journal of Water Resources Management. For the third objective, data is collected and merged/cleaned, but we are still working on refining the results. We do not have a working paper for this objective yet. Summary of the results from the first and second objectives are listed below.

Wildfires have occurred more frequently and are more devastating in California, and quantifying their impact on water utilities, which potentially may lead to water availability and water quality threats, is essential. This is especially important for water utilities whose characteristics are susceptible to wildfires. Due to the unpredictable nature of wildfires, drinking-water utilities face a considerable challenge in developing plans and strategies for managing floods and treating polluted water. Information and tools are needed to help water storage and treatment managers better prepare for the impacts of wildfires.

Our study quantifies these impacts by measuring the effects of wildfire on each water utility service area, based on the exposure frequency and the extent of acres burned by wildfires that occurred in each water utility service area, and by calculating the severity of the wildfire. Our quantitative models take into account the nature of the censoring and selection biases on wildfire data and show an association between water utility characteristics and the level of vulnerability to wildfire risks.

As a result of the cross-sectional estimation of the OLS, Tobit, and Heckman models, we found that wildfire severity increased in areas of (1) government-owned utilities vs. private-owned utilities; (2) utilities relying on surface water vs. those relying on groundwater; (3) utilities relying on local water sources vs. those relying on purchased water; (4) utilities located in Southern vs. Northern California; (5) utilities located on the coast vs. inland California; and (6) utilities in highly populated areas vs. non-populated areas.

Our findings can potentially inform public land managers and water utilities by identifying which water utilities are most vulnerable to wildfires based on their characteristics. They also show the potential characteristics of water utilities that are highly likely to experience changes in potential water availability and quality degradation immediately following a wildfire. Our research reveals that utilities more vulnerable to wildfires will require more strategic management decisions. It also suggests that the countermeasures on wildfires should be different depending on the characteristics the utility has. For example, based on an assessment of wildfire severity, by identifying the utilities and their locations of greatest risk, the relevant institutes could set costs associated with wildfire damage and mitigation activities.

Wildfire management and reducing the risk of wildfires are problems not only for the U.S. Forest Service or other public land management utilities but many other entities, such as water utilities. To help solve this pressing issue, partnerships are needed to help identify landscapes with hazardous vegetation and implement effective land management strategies by federal, state, local municipalities, communities, and nongovernment utilities. From this point of view, our study findings may help inform public land managers about possible shared stewardship partnerships with water utilities to leverage resources and expertise to reduce hazardous vegetation on shared landscapes, thus reducing wildfire risk.

Specific Objectives of the Project:
1. Disaggregate residential water consumption to indoor and outdoor usage using machine learning methods.
2. Estimate effect of HWURs on indoor and outdoor water consumption (obtained in the first objective).
3. Estimate the impact of HWURs on peak hour and day water consumption.
Analysis of the data revealed that we could not analyze objectives two and three using hourly data (the hourly data started around the same time the HWURs program launched). Instead, we use daily data to perform the analysis for objectives 2 and 3. Additional analysis is done to identify rebound effects and it is heterogeneity after the CA water mandate in 2015.

Project Report/Summary of Results:
Increased frequency and severity of droughts and rapidly growing populations increase the stress on water resources in many arid and semi-arid regions worldwide, such as the Western United States. In response to these evolving realities and their associated challenges, water providers often use demand-side management via conservation and efficiency to buffer against short-term water supply shortfalls. The implementation of a smart water metering system in the medium-size water utility in Northern California in 2014 allowed the water utility to record the hourly water consumption of all its customers. This data availability has enabled a large-scale research project to proceed with the aim to disaggregate residential daily water consumption to indoor (e.g., shower, washing machine, etc.) and outdoor (e.g., irrigation) components. Such information can guide the development of alternative tariff structures and other demand management initiatives to reduce peak demand which is a critical parameter for water infrastructure planning and design. We also contribute to the literature on social and economic patterns of water use rebound after the 2015-2016 CA water mandate.

We use hourly residential water consumption data (more than 500 million data points) between 2015-2019 medium-size water utility in Northern California to identify the peak/off-peak use hours and gain insights into how it changed once mandatory restrictions were lifted in June 2016.

Our results illustrate the peak use hours are between 1-5 am, but its distribution changed dramatically after the drought in 2017-2019. There was a shift in the peak hour of consumption from morning (6 am) to the early mornings (4-5 am). Water use distribution became narrower, with decreases in standard deviations while increases in means. Our results also indicate that the water use rebound from the mandate period was 2.8 gallons/hour, which equals 31% of average hourly water use during the mandate period (8.97 gallons/hour). The rebound varies considerably by the hour, season, and consumption, and income levels. This was the highest level of consumption of a day. The highest rebound at 5 am was 11.53 gallons/hour, followed by a rebound at 4 am of 11.40 gallons/hour. The rebounds were noticeably flat during 4 pm-1 am, with a range of 1.08-3.23 gallons/hour. Interestingly, we found that the rebounds were negative during 8 am-2 pm, with the largest rebound of -0.88 gallons/hour. The results showed that the rebound in summer was four times higher than that in Winter. Rebound in quintile five of consumption level was 6.34 gallons/hour while the rebound in quintile one was ten times lower than that.

The second part of our analysis is based on daily data from the same agency. This part estimates how web-based Home Water Use Reports (HWURs) affect household-level water consumption in a medium-size water utility in Northern California. The HWURs under the study share social comparisons, consumption analytics, and conservation information to residential accounts, primarily through digital communications. The data utilized in this part is a daily panel dataset that tracks single-family residential households from January-2013 to September-2019. We found that there is a 6.2% reduction in average daily household water consumption for a typical household who enrolled in the program. We estimate heterogeneous treatment effects by the day of the week, the content of push notifications, and baseline consumption quintile. For the latter, we provide an illustrative test to emphasize how mean reversion can severely bias a naïve panel data estimator for heterogeneous treatment effects when the source of heterogeneity is the outcome variable (e.g., consumption or expenditures). We find evidence that leak alerts are effective in reducing consumption immediately following the alert.