How Tourists ‘Escaping the Heat’ May Drive Future Increases in Municipal Water Demand in Oregon Coastal Communities

How Tourists ‘Escaping the Heat’ May Drive Future Increases in Municipal Water Demand in Oregon Coastal Communities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article How Tourists ‘Escaping the Heat’ May Drive Future Increases in Municipal Water Demand in Oregon Coastal Communities David E. Rupp, Steven J. Dundas, Laura C. Mazaud, Suzanne de Szoeke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3988942/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Discover Water → Version 1 posted 9 You are reading this latest preprint version Abstract Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley – the source of most tourists to the Oregon coast – was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to – if not greater than – the contribution from resident population change alone over the next fifty years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand. Water demand weather climate change tourism coast Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Understanding what determines municipal (i.e., urban and sub-urban) water demand is key to developing resilient drinking water systems. Important drivers are the quantity of consumers (population), types of consumers (e.g., single- or multi-unit residential, commercial, governmental), consumer income, water pricing, water system efficiency, and weather and climate, among others [ 1 , 2 ]. Making long-term (i.e., multi-decadal) projections of water demand is challenging because many of these factors will vary over time in uncertain ways. For example, anthropogenically forced climate change is, and will be for the foreseeable future, occurring at a rate that its impact on future water demand can be important at time horizons of a few decades, which aligns with the lifetime of major water system infrastructure [ 3 – 5 ]. However, the rate of future climate change is highly uncertain, particularly at regional scales [ 6 , 7 ]. Quantifying the potential effect of climate change on future water demand has often been based on empirical analyses of historical sensitivity of demand to changes in meteorological variables, typically air temperature and precipitation, integrated over time intervals of a day to months. Mostly, studies revealed a positive relationship of demand with temperature and/or an inverse relationship to precipitation [ 8 – 13 ]. Temperature affected demand more strongly in some systems [ 13 – 15 ] while precipitation was the larger factor in others [ 10 , 16 , 17 ]. The positive relationship of demand to temperature and negative relationship to precipitation can be partially attributed to the need, whether actual or perceived, for increased watering of lawns, gardens, and parks when soil moisture is low due to high evapotranspiration rates driven by high temperature and due to a paucity of rain [ 1 , 18 ]. Water consumption related to other outdoor activities and water used for cooling can also increase when temperatures are higher [ 19 ]. Given the above relationships, projected increases in temperature across the globe [ 7 ] imply an increase in future municipal water demand - all else being fixed - such that the effect of rising temperature on demand is only a question of magnitude, not direction. In contrast, precipitation projections for summer, the season with typically the highest water demand, range from increases to decreases across the globe [ 7 ], therefore both the sign and magnitude of the effect of precipitation changes will vary regionally. Since as early as Cohen [ 20 ], numerous studies have estimated the effects of climate change on future municipal water demand. Many analyses focused on large cities and metropolitan areas, such as Portland, Oregon [ 4 , 21 , 22 ], Seattle and Eastern Puget Sound Washington [ 23 ], Chicago and northeastern Illinois [ 24 ], Birmingham, UK [ 25 ], Phoenix, Arizona [ 22 ], Bangkok, Thailand [ 26 ], Sydney and the Blue Mountains Region, Australia [ 14 ], and Naples, Italy [ 5 ]. Other analyses were national [ 27 – 30 ] or even global [ 31 ] in extent. Although these latter large-scale studies help with developing regional or national policy, they lack local-scale information needed by water providers and the communities they serve. Some studies estimated the effects of climate change on multiple water sectors including the municipal sector but only reported on total water demand, therefore the impact to municipal water alone is not available to the reader [ 27 – 29 , 32 ], or the contribution of climate change was not given separate from other factors [ 33 ]. Although the above does not provide an exhaustive list of studies of the effects of climate change on future water demand, it is notable that none discussed the role that the interaction of tourism and climate change may have on municipal water demand. In fact, only one study even mentioned tourism [ 26 ] and then only to state that peak demand was generally higher during peak tourism season. One multi-city study of the United States and Canada [ 30 ] even intentionally excluded cities with seasonally varying populations (presumably largely due to tourism) because of their potentially confounding effect. Yet, the relative impact of tourism on water consumption is expected to increase globally; Gössling and Peeters [ 34 ], for example, estimated that both direct and indirect water consumption from tourism would increase by 50% or even 90% in a more extreme scenario, from 2010 to 2050, far outpacing global population growth. Tourism not only increases the number of water consumers, but tourist use differs from residential use. Tourists often can be characterized by more lavish water use related to more or longer showering and bathing, more use of water-intensive leisure and sport facilities such as swimming pools, hot tubs, spas, saunas, and golf courses, more laundering from the frequent changing of bed and bath linens, and more restaurant dining [ 35 – 37 ]. Given the different water demand of tourists compared to residents, and the transient nature of tourists, the sensitivity of water demand to weather and climate may be different for tourists [ 38 , 39 ]. Consequently, tourist and resident water demand may respond differently to future climate change. Despite the recognized role that climate change will have on water demand, climate change impacts have been incorporated into relatively few water management plans in Oregon, United States, outside of the well-resourced Portland metropolitan area. For example, our review of the most recent water master plans and water management and conservation plans of water providers on the middle coast of Oregon (“Mid-Coast”), a region with an important contribution of tourism to the economy, found none that accounted for future climate on water demand. We have little reason to believe the situation is different elsewhere along the Oregon coast and even for small water providers in most places across the globe. Our objective was to address two unanswered questions. The first is how water demand in relatively small, tourist-based communities in the Mid-Coast respond to climate variability. Although Toth et al. [ 38 ] and Mazzoni et al. [ 39 ] addressed this question for a region of northern Italy, the extent to which their results are generalizable is not clear. The second question is how climate change may affect water demand in these communities over the next several decades. To meet our objective, we first estimated sensitivities of historical municipal monthly water demand to air temperature and precipitation for Mid-Coast communities that varied with respect to the relative importance of tourism to their overall economies. Secondly, we used these sensitivities to project the effect of climate change on municipal water demand to the year 2070. We also compared the effect of climate change to the effect of population change alone to consider the relative roles that climate and population may have on water demand over the next several decades. 2. Methods 2.1. Study Area Our study focused on the Oregon Mid-Coast (Fig. 1 ), which shares nearly the same geographical area as Lincoln County, Oregon. Seventy-three water providers serve approximately 60,000 residents in Lincoln County [ 40 ]. The distribution of water providers is skewed towards very small ones: 64% of providers each serve less than 100 people, while the largest ten providers each serve at least 1,250 people, or 89% of the total resident population. We examined water demand from six of the ten largest water providers (Table 1 ), including the two largest providers: the Lincoln City Water District (“Lincoln City”) and the City of Newport (“Newport”). The other four water providers are the City of Toledo (“Toledo”), Seal Rock Water District (“Seal Rock”), City of Waldport (“Waldport”) and City of Yachats (“Yachats”). Water demand in the Mid-Coast varies seasonally and is highest in summer, the season with both the highest irrigation requirements and the largest number of visits of tourists and owners of second homes. On average, overnight tourists increase the population of the Mid-Coast by about 27% above the resident population [ 41 ], though this statistic undoubtedly varies from a low in winter to a high in summer. Single-day tourist visitation also induces a transient increase in the population, but historical numbers of single-day tourists are not well quantified. Relative visitation rates (i.e., visitors per day as a percentage of the resident population) likely vary among cities and towns in the Mid-Coast, but such data have not been published at such fine a scale. Still, we assume that relative visitation rates are higher for the five communities adjacent to the coastline (Lincoln City, Newport, Seal Rock, Waldport, and Yachats) than for Toledo that is located about 13 kilometers inland by car from the ocean shore and has an economy centered on a lumber mill and supporting businesses but a relatively small contribution from tourism. The primary source of tourists to the Oregon coast is the nearby Willamette Valley, though many visitors also come from other parts of Oregon and from Washington and California [ 42 , 43 ]. The Willamette Valley has 3.6 million residents in the largest population centers (Portland metropolitan region, Salem, Eugene-Springfield, Albany, and Corvallis). The climate of the Mid-Coast is classified as temperate, with wet winters and warm and dry summers [ 44 ]. Along the coast, cool and moist marine air dampens both seasonal and diel air temperature fluctuations relative to fluctuations further inland. The north-to-south oriented Coast Range of Oregon serves as a partial barrier to marine air incursions, resulting in high winter precipitation along the windward (western) side of the range and lower precipitation and leeward (eastern) side of Coast Range and in the Willamette Valley. The Willamette Valley also experiences larger seasonal and diel temperature fluctuations because of this barrier. The future climate of the region is expected to be warmer and with more precipitation occurring in winter and less precipitation in summer [ 6 ]. The temperature along the coast is projected to increase less than more inland, such as in the Willamette Valley, due to the moderating influence of the ocean [ 45 ]. 2.2. Data We acquired historical daily or monthly water production for six water providers in the Mid-Coast (Table 1 ). Production is defined as the total water delivered from a water treatment plant to meet the demand of users, plus transmission losses and unmetered water use. Daily production, where available, was aggregated to monthly production. For the City of Waldport (“Waldport”), we also acquired monthly metered water consumption. The water production data record for Waldport was short (3.75 years), whereas the consumption data spanned five years. The length of the water production record varied by provider and ranged from 3.75 to 33 years. We acquired historical meteorological data back to 1981 from the PRISM Climate Group [ 46 , 47 ]: daily precipitation, daily maximum air temperature ( Tmax ), and daily minimum air temperature ( Tmin ). The PRISM data are provided on a 1/24°(~ 4-km)-resolution grid so we extracted data for the grid cells closest to the center of the community served by each water provider. We also extracted data for the cells closest to the Portland International Airport (PDX), Eugene Airport (EUG), and Salem Municipal Airport (SLE). These three locations represent the main population centers (Portland metropolitan region, Eugene-Springfield, and Salem) in the Willamette Valley of Oregon, and we assumed they would be the source of most tourist visits to the Mid-Coast. Table 1 Water production or consumption data used in the analysis Water Provider Period of Record Annual production (ML) 1 Lincoln City Water District Jan-1987 – Dec-2020 2,060 City of Newport Jan-2001 – Dec-2021 2,990 City of Toledo Jan-2010 – Aug-2021 750 Seal Rock Water District Jul-1997 – Dec-2021 410 City of Waldport 2 Jan-2014 – Jun-2019 330 City of Yachats Jan-2017 – Sep-2020 180 1 Average production in millions of liters (ML) of last five years of record or of entire record if record is shorter than five years. 2 The period of record for the City of Waldport is for the consumption data. The production data spans Jan-2014 – Sep-2017. We also acquired simulations of daily precipitation, Tmin , and Tmax under two scenarios of future greenhouse gas concentrations (GHGs): Representative Concentration Pathway (RCP) 4.5 and 8.5 [ 48 ]. RCP 4.5 and RCP 8.5 are generally considered moderate and high-end emissions scenarios, respectively. The GHG concentrations assumed by RCP 8.5 towards the end of the twenty-first century are currently considered unlikely [ 49 ] but still plausible over the next few decades [ 50 ]. The meteorological variables were simulated with twenty of the global climate models (GCMs) (Table S1 ) that were used in the Coupled-Model Intercomparison Project Phase 5 (CMIP5) [ 51 ]. The daily GCM output for the years 1950–2099 was statistically downscaled to 1/24° using the Multivariate Adaptive Constructed Analogs method (MACA) [ 52 ]. We used the data from MACA and PRISM at the same geographic locations. Population forecasts extending to 2070 for cities outside of the Portland metropolitan region were obtained from the Portland State University Population Research Center published in 2021. Population forecasts extending to 2060 for the Portland metropolitan region were obtained from Oregon Metro published in 2016. We extended the Portland metropolitan forecasts to 2070 by extrapolating the linear trend for the last ten years of forecast to the period 2051–2060. 2.3. Historical analysis Water demand is defined here as the total amount of water extracted from the water supply to meet the demand of the municipal water system. Water demand, therefore, includes use by metered consumers (i.e., consumption), unmetered water use, transmission losses, and the relatively small amount of water used in system operations. Transmission losses plus unmetered use vary by water provider and time, but estimates range from 16 (Yachats) to 23% (Waldport) of total water produced by the treatment plants [ 53 , 54 ]. We used treatment plant water production as an estimate of total water demand, except for Waldport, which was the only water provider for which consumption data was acquired. Key challenges in assessing the impacts of weather and climate change on water demand lie in the structure of the data used and how weather enters the estimating equation [ 55 ]. In this application, we have an unbalanced panel of data from six water providers from 1987 to 2021. The panel approach helps mitigate bias from omitted variables and the use of spatial fixed effects control for baseline climate in each location in the model. The identifiable effect of weather comes from the deviations from baseline in observed weather over time. For analyses that use panel model estimates to inform future climate projections, Hsiang’s [ 56 ] marginal treatment comparability assumption, that an estimated response from a weather shock would be similar to the response for a similar change in climate, must hold. The second key choice is how weather enters the model, either linearly or as a non-linear function. Prior work has shown that a non-linear approach, especially for temperature, is more flexible and provides credible estimates when investigating the impacts of weather on outdoor recreation [ 55 , 57 ] and agricultural production [ 58 ]. Our empirical specification estimated the sensitivity of log-transformed monthly water demand to weather and other factors, including a non-linear response to temperature [ 55 , 59 ] as follows: $${\text{ln}\left(water\right)}_{im}= \alpha + \beta {\mathbf{T}}_{im} + \gamma {P}_{im} + \delta {\mathbf{X}}_{im}+{\mu }_{s\left(m\right)} +{\tau }_{y\left(m\right)}+ {\sigma }_{i} + {\epsilon }_{im} \left(1\right)$$ where the dependent variable, \({\text{l}\text{n}\left(water\right)}_{im},\) is the natural log of monthly production in water provider i in month m. This unbalanced panel model uses spatial and temporal fixed effects at the water provider level ( \({\sigma }_{i}\) ), seasonal/quarterly ( \({\mu }_{s\left(m\right)})\) and yearly ( \({\tau }_{y\left(m\right)}\) ) to improve the identification of the impacts of interest on water demand. The spatial fixed effects control for unobservable characteristics common to a specific water provider (e.g., changes in historical water use efficiency, population, or price structure) and, importantly, absorbs the distribution of average weather (climate) at each location. The seasonal fixed effects control for trends that might vary within a given year (e.g., general tourist activity) and the yearly fixed effects control for aggregate shocks common to the study area but that might vary year to year (e.g., recessions). The coefficient vectors of interest ( β , γ , δ ) estimate the impacts of a nonlinear temperature function ( \({\mathbf{T}}_{im})\) , precipitation ( \({P}_{im};\) total monthly (mm) and number of dry days), and holidays and weekends per month ( \({\mathbf{X}}_{im}),\) respectively. When we observe the amount of water produced by each water provider, we can use observed daily Tmin and Tmax during that month that deviate from the underlying distribution (climate) to infer how water demand responds to deviations in temperature relative to mean baseline climate. Model coefficients represented by β directly provide these sensitivities. Daily Tmin and Tmax were transformed into bins by summing the number of days in a month that the variable fell within a bin spanning a range of values. For both Tmin and Tmax , we counted the number of days per month when the temperature fell within specified intervals: <0°C, 0–5°C, 5–10°C, 10–15°C, 15–20°C, 20–25°C, 25–30°C, 30–35°C, and ≥ 35°C. The number of days per month in each interval was treated as a separate variable in the regression, excluding the interval 15–20°C as a reference point. This set of variables are represented in Eq. 1 by \({\mathbf{T}}_{im}\) . Temperature sensitivity, therefore, is defined as the percentage change in monthly demand from an additional day each month in that bin, relative to the omitted reference interval (15–20°C). This method allowed us to consider how daily temperature variability impacts monthly demand, overcoming a weakness of using linear monthly-averaged meteorological variables [ 55 , 60 ]. For precipitation ( \({P}_{im})\) , we summed the number of days with no measurable precipitation (daily total < 0.254 mm) within each month and calculated the total precipitation over each month. The model included holidays and weekends measured as the count of holiday days or weekend days in each month. Multiple interactions of variables with seasonal fixed effects were also considered because the effect of certain factors (e.g., precipitation) may vary seasonally. Our primary specification pools each water provider into an unbalanced panel and was first estimated using meteorological variables that represented local weather climate conditions. We then replaced the local meteorological variables with those representing conditions in the Willamette Valley. For Willamette Valley conditions, we averaged the meteorological variables at PDX, SLE, and EUG. Sensitivity to Willamette Valley weather would suggest that demand is affected by tourists to the coast coming from the Willamette Valley who are responding to the weather at home. We also estimated water provider-specific models (except Yachats due to the shortness of its data record). In the case of Toledo, we subtracted the water demand for the Seal Rock Water District (“Seal Rock”) from Toledo’s water demand because prior to 2023 Toledo production included the sale of water to Seal Rock. Demand data were also pooled over all water providers (including Yachats) except Toledo to build a model that represented the coastal communities in the Mid-Coast in general. Toledo was excluded because, not being located along the coastline, it is less of a tourist destination. 2.4. Demand projections Accounting for the sensitivities of water demand to weather variables as derived from Eq. 1, we projected future monthly total water demand ( D ) as the combined effect climate change and population growth from “current conditions” (~ 2021). We calculated demand representing current conditions by averaging demand over the last five years of record or the length of the available record, whichever was longer. The current demand was calculated separately for each calendar month. To estimate current demand, we used water production for all water providers, including the 3.75 years of production data for Waldport. We assumed no changes in future resident per capita water demand, although reduction in transmission losses, shifts in the relative proportion of different user classes, changes in per capita income, adoption of conservation measures, among other factors, may alter future per capita demand [ 4 ]. To account for climate change, we first applied the sensitivities derived from the historical analysis in Section 2.3 to the simulated weather variables from the MACA dataset. If the 95% confidence intervals on a sensitivity estimate spanned zero, we assumed the sensitivity was zero, though we relaxed this condition for some cases for reasons discussed in Section 3.1 below. The results were monthly time series of “climate-driven-only” water demand from 2001–2070 corresponding to each of 20 GCMs and two RCPs (i.e., 40 time series). Monthly demand anomalies were then calculated as relative differences from a reference demand: $${\stackrel{´}{D}}_{C,m}\left(t\right)=\frac{{D}_{C,m}\left(t\right)- {D}_{ref,m}}{{D}_{ref,m}} \left(2\right)$$ where \(D\) is demand, \(\stackrel{´}{D}\) is the demand anomaly, C indicates a climate-driven effect, m indexes the month of the year (1–12), t is time in yearly intervals, and ref indicates the reference demand. The reference demand was the 2001–2030 climatological average of demand by month. When accounting for both climate change and population growth simultaneously, we considered two scenarios. In the first scenario (Local Constraint), the climate effect over time scaled with the local resident population. Here we implicitly assumed that the tourist response to climate was approximately constrained by the size of the resident population that provides the accommodations and services for tourists. The total monthly demand with time in the Local Constraint scenario was given by: $${D}_{m}\left(t\right)={\left[1+{\stackrel{´}{D}}_{C,m}\left(t\right)\right]D}_{m}\left(0\right)\frac{{P}_{res}\left(t\right)}{{P}_{res}\left(0\right)} \left(3\right)$$ where \({P}_{res}\) is the resident population and t = 0 in the year 2021. In the second scenario (WV Constraint), the climate effect over time scaled with the population in major population centers in the Willamette Valley (WV). In this case, we implicitly assumed that the tourist response to climate was constrained by the number of potential tourists that reside in the Willamette Valley while local accommodation and services would adjust to meet the changes in potential tourists independent of the size of the local resident population. The total monthly demand with time in the WV Constraint scenario was given by: $${D}_{m}\left(t\right)={D}_{m}\left(0\right)\left[\frac{{P}_{res}\left(t\right)}{{P}_{res}\left(0\right)}+{\stackrel{´}{D}}_{C,m}\left(t\right)\frac{{P}_{WV}\left(t\right)}{{P}_{WV}\left(0\right)} \right] \left(4\right)$$ where \({P}_{WV}\) is the population of the major population centers of the Willamette Valley. Equations 3 and 4 can be separated into two contributions. The first is the demand \({(D}_{P,m})\) that scales with resident population and is independent of the climate change effect: $${D}_{P,m}\left(t\right)={D}_{m}\left(0\right)\frac{{P}_{res}\left(t\right)}{{P}_{res}\left(0\right)} \left(5\right)$$ The contribution to demand from climate change \({(D}_{C,m})\) for the Local Constraint and WV Constraint scenarios are given by, respectively: $${D}_{C,m}\left(t\right)={{\stackrel{´}{D}}_{C,m}\left(t\right)D}_{m}\left(0\right)\frac{{P}_{res}\left(t\right)}{{P}_{res}\left(0\right)} \left(6a\right)$$ and $${D}_{C,m}\left(t\right)={{\stackrel{´}{D}}_{C,m}\left(t\right)D}_{m}\left(0\right)\frac{{P}_{WV}\left(t\right)}{{P}_{WV}\left(0\right)} \left(6b\right)$$ Lastly, to summarize the time series of monthly demand, we estimated linear trends in demand over the period 2021–2070 using least squares linear regression. 3. Results 3.1. Historical analysis When describing the results below, we consider five of the six water providers located directly on the coast as “tourist-heavy providers” (Lincoln City, Newport, Seal Rock, Waldport, and Yachats) and Toledo as a “non-tourist provider”. Tourist-heavy provider monthly water demand was largely insensitive to either local Tmax or Tmin (see Fig. 2 a for Tmax ; Tmin not shown). Temperature sensitivity was generally not statistically significant at higher temperatures (> 20°C) nor were the response patterns consistent across water providers (Figs. 2 b, c, e, and f). Only Toledo showed a statistically significant positive response to high temperatures (> 35°C; Fig. 2 d). In contrast, tourist-heavy provider water demand was clearly sensitive to higher Tmax in the Willamette Valley (Fig. 3 a). For instance, an additional day per month above 35°C in the Willamette Valley was associated with a 2.2 percent increase in monthly water demand. For Lincoln City, Newport, and Seal Rock, temperature sensitivities monotonically increased with Willamette Valley Tmax for Tmax ≥ 20°C (Figs. 3 b, c, and e). The model for Waldport suffered from small sample size, but generally showed higher temperature sensitivities at higher temperatures (Fig. 3 f). Only Toledo, the non-tourist provider, did not show sensitivity increasing progressively with higher temperature. While sensitivities remained positive above 20°C for Toledo, they peaked for Tmax in the range 25–30°C and were progressively less positive at higher temperatures (Fig. 3 d). Tourist-heavy provider monthly water demand increased by 0.8% for each additional dry day per month, and the positive response to dry days was statistically significant for three of five water providers (Fig. 4 a). In contrast, water demand was largely insensitive to the number of Willamette Valley dry days per month (Fig. 4 b). The significant effects of Tmax in the models using Willamette Valley weather and the significant effects of precipitation in the models using local weather motivated us to test a model that used Willamette Valley Tmax and local precipitation (as dry days per month). However, the effects of local precipitation in this model were not statistically significant. The relatively small influence of local precipitation may have been subsumed by the stronger influence of temperature. Full model results for all pooled and water-provider specific models are displayed in the SI Tables S2 - S15. The projections summarized in Section 3.2 below rely on the sensitivities to Willamette Valley weather only. For two cases, we relaxed our condition that sensitivities would be set to zero if the 95% confidence intervals spanned zero: at Waldport and Toledo for Tmax > 20°C. We allowed this adjustment because of the seemingly unlikely fluctuations in sensitivity our condition would cause between temperature intervals. Future work could consider expanding the size of temperature bins to narrow confidence intervals and improve estimation, albeit at a loss of resolution across the temperature distribution. 3.2. Demand projections In general, monthly water demand is projected to rise in the tourist-heavy Mid-Coast communities and most so in the high-demand months of summer and early autumn. In these months, the GCM ensemble-mean demand increases by about 11% (Local Constraint) and 14% (WV Constraint) from 2021–2070 under RCP4.5 and somewhat more (15 and 19%, respectively) under RCP8.5 (Fig. 5 a). Under RCP8.5, about 60 and 66% of the total increases are driven by climate change under the Local Constraint and WV Constraint scenario, respectively, and the remaining increases are due to resident population growth. The differences between the Local and WV constraint scenarios result directly from the different population growth projections between the Mid-Coast and the Willamette Valley. The Mid-Coast population was projected to grow by 8% whereas the Willamette Valley population centers were projected to grow by 46% from 2021 to 2070. About the ensemble-mean changes, there is a large spread in projections across the individual GCMs for a given RCP (Fig. 5 a). The spread has two causes: 1) differences in how each GCM simulates the response of the region’s climate to increases in greenhouse gas concentration and 2) internal climate variability occurring on multi-decadal scales [ 6 ]. Although we did not separate the contributions of each cause here, the fact that a small number of GCM simulations produce a negative contribution from climate change indicates that there is a small but non-negligible possibility of internal climate variability driving a regional cooling trend over the next 50-years [ 61 ] that would cause demand to decrease, excluding the effect of resident population growth. Water demand projections vary considerably by water provider during the high demand season (Figs. 5 b-f). Under the WV Constraint-RCP8.5 scenario, for example, ensemble-mean demand changes range from − 17% in Toledo in August to + 35% in Waldport in June. Changes are smaller for the RCP 4.5 scenario and the Local Constraint scenario (Fig. S1 ) but otherwise similar in pattern. The projections differ among water providers in part due to the providers’ different sensitivities to Tmax but also because of differences in population growth. Resident populations are projected to increase in three communities (Lincoln City, Waldport, Yachats) but decrease in two (Newport and Toledo). In the case of Newport under the WV Constraint-RCP8.5 scenario, the effect of climate change on demand is large enough to counter the effect of declining resident population during part of the year (May – June; September – October) but in Toledo the net change in demand is negative throughout the year. For most water providers, the season of higher demand broadens into the shoulder season months of May and October (Figs. 6 and S2). Seal Rock is the exception, where the highest monthly demand becomes yet more concentrated in July – August. 4. Discussion As noted above, we found that monthly water demand was largely insensitive to local temperature but that it increased with the number of local dry days in a month. Similar results have been noted elsewhere [ 10 , 16 , 17 ] although the reasons for the lack of sensitivity to temperature may not be the same in all cases. Along the Oregon coast, daily temperature fluctuations are relatively small, and the temperature is rarely what one would consider hot (e.g., the standard deviation of Tmax in July – August is only 3.8°C and the 99th percentile of Tmax is only 23.5°C, averaging each statistic across the tourist communities). Consequently, water users rarely experience large fluctuations of temperatures, nor extreme temperatures, that might cause a short-term behavioral response. The detectable sensitivity to local dry days suggests that outdoor water application increases when users perceive a greater need for watering lawns and gardens. However, the effect is not large enough that it would substantially influence the demand projections: There would need to be a large change in the future number of dry days per month (i.e., more than 5 days) to change water demand by more than a few percent, yet the number of dry days during high demand season (June – September) is only projected to change by 0.5 and 0.8 days per month from 2021 to 2070 under RCP4.5 and RCP8.5, respectively, in the GCM ensemble average. Our result with respect to local temperature differs from the small number of published studies of tourist water use in coastal communities. Specifically, Toth et al. [ 38 ] and Mazzoni et al. [ 39 ] found that tourist water demand increased with temperature in seaside communities of northern Italy. However, both Toth et al. [ 38 ] and Mazzoni et al. [ 39 ] benefited from tourist visitation data, and it is possible that we would have detected a significant response of tourist use to local temperature had we been able to separate resident and tourist use. In contrast to local temperatures, Willamette Valley temperatures appeared to affect coastal water demand. The relationship between demand and temperature was non-linear, with sensitivity becoming apparent above a threshold temperature of 20°C. A similar non-linear effect was observed by others, though the threshold temperature varied by location [ 8 , 9 , 62 ]. Given that it is unlikely that a coastal resident would think to use more water because it is hot somewhere distant from their residence, we assumed that the temperature effect was driven largely by an influx of tourists and that the tourists were responding to the weather where they permanently reside. In Oregon, the temperature difference between the Willamette Valley and the Coast during a heat wave in the valley can be large (e.g., there was a 26°C temperature difference between Portland and Newport on 28 June 2021). Since travel times from most population centers in the valley to the coast are less than 90 minutes, this travel option represents a climate adaptation strategy for residents of Oregon’s population centers to escape the heat. Tourist visitation data were unavailable for this research but are ultimately needed to confirm the validity of this assumption. It may be possible to estimate variability in tourist visits using proxies, such as cell phone data or monthly revenues from hotel taxes, for cities that can make such records available. Knowing the location of origin of tourists could also better reveal tourist response. Tourists that come from farther away may be less influenced by weather because they tend to make plans further ahead so are less likely to change plans on short notice [ 63 ]. We can look to studies in other locations of the effects of weather and climate on tourism. However, such studies typically consider the weather of the tourists’ destination but not the weather of their location of origin. These studies generally show that tourism increases when the weather at the destination is such that makes it more pleasant to be outdoors [ 64 , 65 ]. In the Mid-Coast, the increased water demand with more local dry days may result from more tourists when it is not rainy at the coast as opposed to the perceived greater need to water outdoors. In a rare example where the weather in the location of origin was considered, Serquet and Rebetez [ 66 ] found that the number and duration of tourist stays in Swiss Alpine resorts increased during heatwaves in the lower elevation surrounding region. Differing sensitivities to Willamette Valley temperature across water providers give some additional evidence that water demand is affected by the weather in the Willamette Valley via tourists’ behavior. Toledo, the water provider with fewest number of tourists relative to the resident population, shows the lowest sensitivity to high Willamette Valley temperatures, with non-significant results above 35°C. Estimated sensitivities to temperature do not appear to be completely explained by the relative proportion of tourists, however. Newport, for example, has the highest sensitivities but is the most economically diverse. Fish processing plants are a major water consumer in Newport and how their consumption might respond to weather and climate is unclear. However, we are reluctant to attribute inter-provider differences to any cause other than estimation error given the large uncertainties on some sensitivity estimates due to short period of record of some water providers’ demand data. These uncertainties in the sensitivities also propagate directly to the demand projections. The low sample size at high temperature in particular, and even the lack of future very high-temperature analogs, means we have low confidence in sensitivity at very high temperatures (> 40°C, 104°F) that are projected to occur with higher frequency. Temperatures in the Willamette Valley have exceeded 40°C in the past, most notably reaching a record high of 47°C (117°F) during the Pacific Northwest Heat Wave of June 2021 [ 67 ] when, anecdotally, large numbers of tourists travelled to the cooler coast to escape the heat. Lastly, we only partially considered how our methodological decisions affect variability in the demand projections. Choices along the modeling chain are made regarding GHG scenario, GCM, meteorological downscaling technique, and water demand model. The effect of these choices can be explored by considering multiple GHG scenarios, GCMs, downscaling techniques, and water demand models [ 68 ]. Though we did include two GHG scenarios and 20 GCMs in our analysis, we only included one downscaling technique. We effectively used two variants of a water demand model (the Local and WV Constraint variants) but only one set of statistical model parameters and one set population projections. Future work could account for uncertainty in demand model coefficients and explore different viable scenarios of population change. 5. Conclusions Using an econometric model of monthly water demand in Oregon Mid-Coast communities, we estimated temperature- and precipitation-response functions for demand. A key result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley was a significant driver. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourism: Tourists were escaping the heat in the Willamette Valley (their home) for cooler conditions on the coast. Applying the temperature response functions to scenarios of future – and warmer – climate leads to projected increases in water demand independent of other factors. Given the assumed pathway between climate change and coastal water demand is through tourism, the magnitude of the climate-driven projected increase in demand becomes a function of tourist visitation. Assuming future tourism is either 1) constrained by the local resident population that serves tourism or is 2) constrained by the potential tourist population provides two scenarios of future demand. Future demand in the second scenario is higher than in the first scenario because the projected resident population growth rate is higher where the tourists come from (e.g., the Willamette Valley) than it is in the Mid-Coast. In either scenario, the climate-change contribution to projected water demand is generally of comparable magnitude to – if not greater than – the contribution from resident coastal population change alone over the next fifty years. In communities where the population is even projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand. The water demand projections assumed no imposed adaptation measures or behavioral changes in response to climate change. This analysis, therefore, provides estimates of what level of water conservation or improvements in system efficiency will be required to offset the effects of climate change. In general, a modest decrease in per capita water consumption and/or transmission losses of no more than a few percent per decade, may be sufficient. Declarations Funding: All authors received financial support from the United States National Ocean and Atmospheric Administration (NOAA) Coastal and Ocean Climate Applications (COCA) and Sectoral Applications Research Program (SARP) Grant #NA19OAR4310306. Conflict of interest/Competing interests: The authors have no relevant financial or non-financial interests to disclose. Data availability: PRISM climate data were obtained from the PRISM Climate Group, Oregon State University, at https://www.prism.oregonstate.edu/. MACA climate projections were obtained from the Climatology Lab, University of California, Merced, at http://www.climatologylab.org/maca.html. Population projections (except Portland metropolitan region) were obtained from the Population Research Center, Portland State University, at https://www.pdx.edu/population-research/. 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Supplementary Files SI20230213.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Discover Water → Version 1 posted Editorial decision: Revision requested 29 May, 2024 Reviews received at journal 14 May, 2024 Reviews received at journal 29 Apr, 2024 Reviewers agreed at journal 18 Apr, 2024 Reviewers agreed at journal 07 Apr, 2024 Reviewers invited by journal 29 Mar, 2024 Editor assigned by journal 19 Mar, 2024 Submission checks completed at journal 19 Mar, 2024 First submitted to journal 25 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Mazaud","email":"","orcid":"","institution":"Department of Applied Economics, Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"C.","lastName":"Mazaud","suffix":""},{"id":281323042,"identity":"82d74cd6-bf5e-48a2-8ace-9c8f76b8b542","order_by":3,"name":"Suzanne de Szoeke","email":"","orcid":"","institution":"GSI Water Solutions, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"de Szoeke","suffix":""}],"badges":[],"createdAt":"2024-02-25 20:34:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3988942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3988942/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43832-024-00133-6","type":"published","date":"2024-09-27T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53143779,"identity":"41960a55-4725-437c-85c7-6e93ed9a1553","added_by":"auto","created_at":"2024-03-21 06:40:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116841,"visible":true,"origin":"","legend":"\u003cp\u003eMap of northwestern Oregon including Mid-Coast cities considered in this study and larger cities in the Willamette Valley. The Seal Rock Water District (not shown) spans the coastal strip between Newport and Waldport.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/31283d2b2b8e71a772b855c6.jpg"},{"id":53143145,"identity":"d057bd6c-9720-4176-930e-1a408e8482d1","added_by":"auto","created_at":"2024-03-21 06:32:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77968,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of monthly water demand to the number of days that the local daily maximum temperature (\u003cem\u003eTmax\u003c/em\u003e) falls within specified ranges of temperature for (a) the pooled tourist-heavy providers (Mid-Coast) and (b – f) five individual water providers. Shaded areas show 95% confidence intervals on the estimates of sensitivity. The y-axis is truncated so does not show the full range of all 95% confidence intervals. Arrows indicate the first and last temperature intervals extend beyond the edges of the panel.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/2f86a91af4241552f0a8e21a.jpg"},{"id":53143144,"identity":"41714cce-55ef-4fbd-be44-f6080df91cc5","added_by":"auto","created_at":"2024-03-21 06:32:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78478,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of monthly water demand to the number of days that the Willamette Valley daily maximum temperature (\u003cem\u003eTmax\u003c/em\u003e) falls within specified ranges of temperature for (a) the pooled tourist-heavy providers (Mid-Coast) and (b – f) five individual water providers. Shaded areas show 95% confidence intervals on the estimates of sensitivity. The y-axis is truncated so does not show the full range of all 95% confidence intervals. Arrows indicate the first and last temperature intervals extend beyond the edges of the panel.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/d4d4315e587e97772bedbdd9.jpg"},{"id":53143149,"identity":"8f55a42b-3279-47b8-bdd5-b0f0d3b845df","added_by":"auto","created_at":"2024-03-21 06:32:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46819,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of monthly water demand to the number of dry days per month for the pooled tourist-heavy providers (Mid-Coast) and for five water providers derived from the model using (a) local weather and (b) Willamette Valley weather. The vertical lines show 95% confidence intervals on the estimates of sensitivity.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/93bac5b0e85837bec26d0702.jpg"},{"id":53143778,"identity":"9e87f0de-f3b4-4cd0-87ab-a86f72e47b6a","added_by":"auto","created_at":"2024-03-21 06:40:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100842,"visible":true,"origin":"","legend":"\u003cp\u003eEnsemble-mean relative linear change in monthly water demand over the period 2021 – 2070 under the WV Constraint scenario for (a) the pooled tourist-heavy providers in the Mid-Coast and (b – f) five water providers. The total change is decomposed into the effects of resident population growth (Eq. 5) and climate change (Eqs. 6a or 6b). The filled circles show the changes from each GCM climate simulation for RCP4.5 (dark yellow) and RCP8.5 (dark red).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/4a20db4c73ef214e6e3b61bd.jpg"},{"id":53143147,"identity":"06a77cd9-36bb-4916-83bb-10ae28a38b1a","added_by":"auto","created_at":"2024-03-21 06:32:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":132048,"visible":true,"origin":"","legend":"\u003cp\u003eEnsemble-mean absolute linear change in monthly water demand over the period 2021 – 2070 under the WV Constraint scenario for (a) the pooled tourist-heavy providers in the Mid-Coast and (b – f) five water providers. The total change is decomposed into the effects of resident population growth (Eq. 5) and climate change (Eqs. 6a or 6b). The filled circles show the changes from each GCM climate simulation for RCP4.5 (dark yellow) and RCP8.5 (dark red).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/2a88349330b9d2e05ed13462.jpg"},{"id":65627145,"identity":"c3a22ef3-d002-4a23-9655-2b798ccbff9f","added_by":"auto","created_at":"2024-09-30 16:12:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/fcf1cd25-726e-4b82-9e91-4522b11da54d.pdf"},{"id":53143150,"identity":"f9fbba04-e845-4bce-a2c0-5a3413827ec8","added_by":"auto","created_at":"2024-03-21 06:32:53","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":618453,"visible":true,"origin":"","legend":"","description":"","filename":"SI20230213.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3988942/v1/7ed22b37920b669dd82e464d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Tourists ‘Escaping the Heat’ May Drive Future Increases in Municipal Water Demand in Oregon Coastal Communities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUnderstanding what determines municipal (i.e., urban and sub-urban) water demand is key to developing resilient drinking water systems. Important drivers are the quantity of consumers (population), types of consumers (e.g., single- or multi-unit residential, commercial, governmental), consumer income, water pricing, water system efficiency, and weather and climate, among others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Making long-term (i.e., multi-decadal) projections of water demand is challenging because many of these factors will vary over time in uncertain ways. For example, anthropogenically forced climate change is, and will be for the foreseeable future, occurring at a rate that its impact on future water demand can be important at time horizons of a few decades, which aligns with the lifetime of major water system infrastructure [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the rate of future climate change is highly uncertain, particularly at regional scales [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQuantifying the potential effect of climate change on future water demand has often been based on empirical analyses of historical sensitivity of demand to changes in meteorological variables, typically air temperature and precipitation, integrated over time intervals of a day to months. Mostly, studies revealed a positive relationship of demand with temperature and/or an inverse relationship to precipitation [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Temperature affected demand more strongly in some systems [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] while precipitation was the larger factor in others [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The positive relationship of demand to temperature and negative relationship to precipitation can be partially attributed to the need, whether actual or perceived, for increased watering of lawns, gardens, and parks when soil moisture is low due to high evapotranspiration rates driven by high temperature and due to a paucity of rain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Water consumption related to other outdoor activities and water used for cooling can also increase when temperatures are higher [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the above relationships, projected increases in temperature across the globe [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] imply an increase in future municipal water demand - all else being fixed - such that the effect of rising temperature on demand is only a question of magnitude, not direction. In contrast, precipitation projections for summer, the season with typically the highest water demand, range from increases to decreases across the globe [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], therefore both the sign and magnitude of the effect of precipitation changes will vary regionally.\u003c/p\u003e \u003cp\u003eSince as early as Cohen [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], numerous studies have estimated the effects of climate change on future municipal water demand. Many analyses focused on large cities and metropolitan areas, such as Portland, Oregon [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Seattle and Eastern Puget Sound Washington [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Chicago and northeastern Illinois [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Birmingham, UK [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Phoenix, Arizona [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Bangkok, Thailand [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Sydney and the Blue Mountains Region, Australia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and Naples, Italy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Other analyses were national [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] or even global [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in extent. Although these latter large-scale studies help with developing regional or national policy, they lack local-scale information needed by water providers and the communities they serve. Some studies estimated the effects of climate change on multiple water sectors including the municipal sector but only reported on total water demand, therefore the impact to municipal water alone is not available to the reader [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], or the contribution of climate change was not given separate from other factors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the above does not provide an exhaustive list of studies of the effects of climate change on future water demand, it is notable that none discussed the role that the interaction of tourism and climate change may have on municipal water demand. In fact, only one study even mentioned tourism [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and then only to state that peak demand was generally higher during peak tourism season. One multi-city study of the United States and Canada [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] even intentionally excluded cities with seasonally varying populations (presumably largely due to tourism) because of their potentially confounding effect. Yet, the relative impact of tourism on water consumption is expected to increase globally; G\u0026ouml;ssling and Peeters [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], for example, estimated that both direct and indirect water consumption from tourism would increase by 50% or even 90% in a more extreme scenario, from 2010 to 2050, far outpacing global population growth.\u003c/p\u003e \u003cp\u003eTourism not only increases the number of water consumers, but tourist use differs from residential use. Tourists often can be characterized by more lavish water use related to more or longer showering and bathing, more use of water-intensive leisure and sport facilities such as swimming pools, hot tubs, spas, saunas, and golf courses, more laundering from the frequent changing of bed and bath linens, and more restaurant dining [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Given the different water demand of tourists compared to residents, and the transient nature of tourists, the sensitivity of water demand to weather and climate may be different for tourists [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Consequently, tourist and resident water demand may respond differently to future climate change.\u003c/p\u003e \u003cp\u003eDespite the recognized role that climate change will have on water demand, climate change impacts have been incorporated into relatively few water management plans in Oregon, United States, outside of the well-resourced Portland metropolitan area. For example, our review of the most recent water master plans and water management and conservation plans of water providers on the middle coast of Oregon (\u0026ldquo;Mid-Coast\u0026rdquo;), a region with an important contribution of tourism to the economy, found none that accounted for future climate on water demand. We have little reason to believe the situation is different elsewhere along the Oregon coast and even for small water providers in most places across the globe.\u003c/p\u003e \u003cp\u003eOur objective was to address two unanswered questions. The first is how water demand in relatively small, tourist-based communities in the Mid-Coast respond to climate variability. Although Toth et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Mazzoni et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] addressed this question for a region of northern Italy, the extent to which their results are generalizable is not clear. The second question is how climate change may affect water demand in these communities over the next several decades.\u003c/p\u003e \u003cp\u003eTo meet our objective, we first estimated sensitivities of historical municipal monthly water demand to air temperature and precipitation for Mid-Coast communities that varied with respect to the relative importance of tourism to their overall economies. Secondly, we used these sensitivities to project the effect of climate change on municipal water demand to the year 2070. We also compared the effect of climate change to the effect of population change alone to consider the relative roles that climate and population may have on water demand over the next several decades.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eOur study focused on the Oregon Mid-Coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which shares nearly the same geographical area as Lincoln County, Oregon. Seventy-three water providers serve approximately 60,000 residents in Lincoln County [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The distribution of water providers is skewed towards very small ones: 64% of providers each serve less than 100 people, while the largest ten providers each serve at least 1,250 people, or 89% of the total resident population. We examined water demand from six of the ten largest water providers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including the two largest providers: the Lincoln City Water District (\u0026ldquo;Lincoln City\u0026rdquo;) and the City of Newport (\u0026ldquo;Newport\u0026rdquo;). The other four water providers are the City of Toledo (\u0026ldquo;Toledo\u0026rdquo;), Seal Rock Water District (\u0026ldquo;Seal Rock\u0026rdquo;), City of Waldport (\u0026ldquo;Waldport\u0026rdquo;) and City of Yachats (\u0026ldquo;Yachats\u0026rdquo;).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWater demand in the Mid-Coast varies seasonally and is highest in summer, the season with both the highest irrigation requirements and the largest number of visits of tourists and owners of second homes. On average, overnight tourists increase the population of the Mid-Coast by about 27% above the resident population [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], though this statistic undoubtedly varies from a low in winter to a high in summer. Single-day tourist visitation also induces a transient increase in the population, but historical numbers of single-day tourists are not well quantified. Relative visitation rates (i.e., visitors per day as a percentage of the resident population) likely vary among cities and towns in the Mid-Coast, but such data have not been published at such fine a scale. Still, we assume that relative visitation rates are higher for the five communities adjacent to the coastline (Lincoln City, Newport, Seal Rock, Waldport, and Yachats) than for Toledo that is located about 13 kilometers inland by car from the ocean shore and has an economy centered on a lumber mill and supporting businesses but a relatively small contribution from tourism. The primary source of tourists to the Oregon coast is the nearby Willamette Valley, though many visitors also come from other parts of Oregon and from Washington and California [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The Willamette Valley has 3.6\u0026nbsp;million residents in the largest population centers (Portland metropolitan region, Salem, Eugene-Springfield, Albany, and Corvallis).\u003c/p\u003e \u003cp\u003eThe climate of the Mid-Coast is classified as temperate, with wet winters and warm and dry summers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Along the coast, cool and moist marine air dampens both seasonal and diel air temperature fluctuations relative to fluctuations further inland. The north-to-south oriented Coast Range of Oregon serves as a partial barrier to marine air incursions, resulting in high winter precipitation along the windward (western) side of the range and lower precipitation and leeward (eastern) side of Coast Range and in the Willamette Valley. The Willamette Valley also experiences larger seasonal and diel temperature fluctuations because of this barrier. The future climate of the region is expected to be warmer and with more precipitation occurring in winter and less precipitation in summer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The temperature along the coast is projected to increase less than more inland, such as in the Willamette Valley, due to the moderating influence of the ocean [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data\u003c/h2\u003e \u003cp\u003eWe acquired historical daily or monthly water production for six water providers in the Mid-Coast (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Production is defined as the total water delivered from a water treatment plant to meet the demand of users, plus transmission losses and unmetered water use. Daily production, where available, was aggregated to monthly production. For the City of Waldport (\u0026ldquo;Waldport\u0026rdquo;), we also acquired monthly metered water consumption. The water production data record for Waldport was short (3.75 years), whereas the consumption data spanned five years. The length of the water production record varied by provider and ranged from 3.75 to 33 years.\u003c/p\u003e \u003cp\u003eWe acquired historical meteorological data back to 1981 from the PRISM Climate Group [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]: daily precipitation, daily maximum air temperature (\u003cem\u003eTmax\u003c/em\u003e), and daily minimum air temperature (\u003cem\u003eTmin\u003c/em\u003e). The PRISM data are provided on a 1/24\u0026deg;(~\u0026thinsp;4-km)-resolution grid so we extracted data for the grid cells closest to the center of the community served by each water provider. We also extracted data for the cells closest to the Portland International Airport (PDX), Eugene Airport (EUG), and Salem Municipal Airport (SLE). These three locations represent the main population centers (Portland metropolitan region, Eugene-Springfield, and Salem) in the Willamette Valley of Oregon, and we assumed they would be the source of most tourist visits to the Mid-Coast.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater production or consumption data used in the analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Provider\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod of Record\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual production (ML)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLincoln City Water District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan-1987 \u0026ndash; Dec-2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity of Newport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan-2001 \u0026ndash; Dec-2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity of Toledo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan-2010 \u0026ndash; Aug-2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeal Rock Water District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul-1997 \u0026ndash; Dec-2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity of Waldport\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan-2014 \u0026ndash; Jun-2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity of Yachats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan-2017 \u0026ndash; Sep-2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAverage production in millions of liters (ML) of last five years of record or of entire record if record is shorter than five years.\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eThe period of record for the City of Waldport is for the consumption data. The production data spans Jan-2014 \u0026ndash; Sep-2017.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also acquired simulations of daily precipitation, \u003cem\u003eTmin\u003c/em\u003e, and \u003cem\u003eTmax\u003c/em\u003e under two scenarios of future greenhouse gas concentrations (GHGs): Representative Concentration Pathway (RCP) 4.5 and 8.5 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. RCP 4.5 and RCP 8.5 are generally considered moderate and high-end emissions scenarios, respectively. The GHG concentrations assumed by RCP 8.5 towards the end of the twenty-first century are currently considered unlikely [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] but still plausible over the next few decades [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The meteorological variables were simulated with twenty of the global climate models (GCMs) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) that were used in the Coupled-Model Intercomparison Project Phase 5 (CMIP5) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The daily GCM output for the years 1950\u0026ndash;2099 was statistically downscaled to 1/24\u0026deg; using the Multivariate Adaptive Constructed Analogs method (MACA) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. We used the data from MACA and PRISM at the same geographic locations.\u003c/p\u003e \u003cp\u003ePopulation forecasts extending to 2070 for cities outside of the Portland metropolitan region were obtained from the Portland State University Population Research Center published in 2021. Population forecasts extending to 2060 for the Portland metropolitan region were obtained from Oregon Metro published in 2016. We extended the Portland metropolitan forecasts to 2070 by extrapolating the linear trend for the last ten years of forecast to the period 2051\u0026ndash;2060.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Historical analysis\u003c/h2\u003e \u003cp\u003eWater demand is defined here as the total amount of water extracted from the water supply to meet the demand of the municipal water system. Water demand, therefore, includes use by metered consumers (i.e., consumption), unmetered water use, transmission losses, and the relatively small amount of water used in system operations. Transmission losses plus unmetered use vary by water provider and time, but estimates range from 16 (Yachats) to 23% (Waldport) of total water produced by the treatment plants [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. We used treatment plant water production as an estimate of total water demand, except for Waldport, which was the only water provider for which consumption data was acquired.\u003c/p\u003e \u003cp\u003eKey challenges in assessing the impacts of weather and climate change on water demand lie in the structure of the data used and how weather enters the estimating equation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this application, we have an unbalanced panel of data from six water providers from 1987 to 2021. The panel approach helps mitigate bias from omitted variables and the use of spatial fixed effects control for baseline climate in each location in the model. The identifiable effect of weather comes from the deviations from baseline in observed weather over time. For analyses that use panel model estimates to inform future climate projections, Hsiang\u0026rsquo;s [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] marginal treatment comparability assumption, that an estimated response from a weather shock would be similar to the response for a similar change in climate, must hold. The second key choice is how weather enters the model, either linearly or as a non-linear function. Prior work has shown that a non-linear approach, especially for temperature, is more flexible and provides credible estimates when investigating the impacts of weather on outdoor recreation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and agricultural production [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur empirical specification estimated the sensitivity of log-transformed monthly water demand to weather and other factors, including a non-linear response to temperature [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{ln}\\left(water\\right)}_{im}= \\alpha + \\beta {\\mathbf{T}}_{im} + \\gamma {P}_{im} + \\delta {\\mathbf{X}}_{im}+{\\mu }_{s\\left(m\\right)} +{\\tau }_{y\\left(m\\right)}+ {\\sigma }_{i} + {\\epsilon }_{im} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the dependent variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\left(water\\right)}_{im},\\)\u003c/span\u003e\u003c/span\u003e is the natural log of monthly production in water provider \u003cem\u003ei\u003c/em\u003e in month \u003cem\u003em.\u003c/em\u003e This unbalanced panel model uses spatial and temporal fixed effects at the water provider level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{i}\\)\u003c/span\u003e\u003c/span\u003e), seasonal/quarterly (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{s\\left(m\\right)})\\)\u003c/span\u003e\u003c/span\u003eand yearly (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau }_{y\\left(m\\right)}\\)\u003c/span\u003e\u003c/span\u003e) to improve the identification of the impacts of interest on water demand. The spatial fixed effects control for unobservable characteristics common to a specific water provider (e.g., changes in historical water use efficiency, population, or price structure) and, importantly, absorbs the distribution of average weather (climate) at each location. The seasonal fixed effects control for trends that might vary within a given year (e.g., general tourist activity) and the yearly fixed effects control for aggregate shocks common to the study area but that might vary year to year (e.g., recessions). The coefficient vectors of interest (\u003cem\u003eβ\u003c/em\u003e, \u003cem\u003eγ\u003c/em\u003e, \u003cem\u003eδ\u003c/em\u003e) estimate the impacts of a nonlinear temperature function (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{T}}_{im})\\)\u003c/span\u003e\u003c/span\u003e, precipitation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{im};\\)\u003c/span\u003e\u003c/span\u003e total monthly (mm) and number of dry days), and holidays and weekends per month (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{X}}_{im}),\\)\u003c/span\u003e\u003c/span\u003e respectively.\u003c/p\u003e \u003cp\u003eWhen we observe the amount of water produced by each water provider, we can use observed daily \u003cem\u003eTmin\u003c/em\u003e and \u003cem\u003eTmax\u003c/em\u003e during that month that deviate from the underlying distribution (climate) to infer how water demand responds to deviations in temperature relative to mean baseline climate. Model coefficients represented by \u003cem\u003eβ\u003c/em\u003e directly provide these sensitivities. Daily \u003cem\u003eTmin\u003c/em\u003e and \u003cem\u003eTmax\u003c/em\u003e were transformed into bins by summing the number of days in a month that the variable fell within a bin spanning a range of values. For both \u003cem\u003eTmin\u003c/em\u003e and \u003cem\u003eTmax\u003c/em\u003e, we counted the number of days per month when the temperature fell within specified intervals: \u0026lt;0\u0026deg;C, 0\u0026ndash;5\u0026deg;C, 5\u0026ndash;10\u0026deg;C, 10\u0026ndash;15\u0026deg;C, 15\u0026ndash;20\u0026deg;C, 20\u0026ndash;25\u0026deg;C, 25\u0026ndash;30\u0026deg;C, 30\u0026ndash;35\u0026deg;C, and \u0026ge;\u0026thinsp;35\u0026deg;C. The number of days per month in each interval was treated as a separate variable in the regression, excluding the interval 15\u0026ndash;20\u0026deg;C as a reference point. This set of variables are represented in Eq.\u0026nbsp;1 by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{T}}_{im}\\)\u003c/span\u003e\u003c/span\u003e. Temperature sensitivity, therefore, is defined as the percentage change in monthly demand from an additional day each month in that bin, relative to the omitted reference interval (15\u0026ndash;20\u0026deg;C). This method allowed us to consider how daily temperature variability impacts monthly demand, overcoming a weakness of using linear monthly-averaged meteorological variables [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. For precipitation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{im})\\)\u003c/span\u003e\u003c/span\u003e, we summed the number of days with no measurable precipitation (daily total\u0026thinsp;\u0026lt;\u0026thinsp;0.254 mm) within each month and calculated the total precipitation over each month. The model included holidays and weekends measured as the count of holiday days or weekend days in each month. Multiple interactions of variables with seasonal fixed effects were also considered because the effect of certain factors (e.g., precipitation) may vary seasonally.\u003c/p\u003e \u003cp\u003eOur primary specification pools each water provider into an unbalanced panel and was first estimated using meteorological variables that represented local weather climate conditions. We then replaced the local meteorological variables with those representing conditions in the Willamette Valley. For Willamette Valley conditions, we averaged the meteorological variables at PDX, SLE, and EUG. Sensitivity to Willamette Valley weather would suggest that demand is affected by tourists to the coast coming from the Willamette Valley who are responding to the weather at home. We also estimated water provider-specific models (except Yachats due to the shortness of its data record). In the case of Toledo, we subtracted the water demand for the Seal Rock Water District (\u0026ldquo;Seal Rock\u0026rdquo;) from Toledo\u0026rsquo;s water demand because prior to 2023 Toledo production included the sale of water to Seal Rock. Demand data were also pooled over all water providers (including Yachats) except Toledo to build a model that represented the coastal communities in the Mid-Coast in general. Toledo was excluded because, not being located along the coastline, it is less of a tourist destination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Demand projections\u003c/h2\u003e \u003cp\u003eAccounting for the sensitivities of water demand to weather variables as derived from Eq.\u0026nbsp;1, we projected future monthly total water demand (\u003cem\u003eD\u003c/em\u003e) as the combined effect climate change and population growth from \u0026ldquo;current conditions\u0026rdquo; (~\u0026thinsp;2021). We calculated demand representing current conditions by averaging demand over the last five years of record or the length of the available record, whichever was longer. The current demand was calculated separately for each calendar month. To estimate current demand, we used water production for all water providers, including the 3.75 years of production data for Waldport.\u003c/p\u003e \u003cp\u003eWe assumed no changes in future resident \u003cem\u003eper capita\u003c/em\u003e water demand, although reduction in transmission losses, shifts in the relative proportion of different user classes, changes in per capita income, adoption of conservation measures, among other factors, may alter future per capita demand [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo account for climate change, we first applied the sensitivities derived from the historical analysis in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e to the simulated weather variables from the MACA dataset. If the 95% confidence intervals on a sensitivity estimate spanned zero, we assumed the sensitivity was zero, though we relaxed this condition for some cases for reasons discussed in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e below. The results were monthly time series of \u0026ldquo;climate-driven-only\u0026rdquo; water demand from 2001\u0026ndash;2070 corresponding to each of 20 GCMs and two RCPs (i.e., 40 time series). Monthly demand anomalies were then calculated as relative differences from a reference demand:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${\\stackrel{\u0026acute;}{D}}_{C,m}\\left(t\\right)=\\frac{{D}_{C,m}\\left(t\\right)- {D}_{ref,m}}{{D}_{ref,m}} \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(D\\)\u003c/span\u003e\u003c/span\u003e is demand, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{\u0026acute;}{D}\\)\u003c/span\u003e\u003c/span\u003e is the demand anomaly, \u003cem\u003eC\u003c/em\u003e indicates a climate-driven effect, \u003cem\u003em\u003c/em\u003e indexes the month of the year (1\u0026ndash;12), \u003cem\u003et\u003c/em\u003e is time in yearly intervals, and \u003cem\u003eref\u003c/em\u003e indicates the reference demand. The reference demand was the 2001\u0026ndash;2030 climatological average of demand by month.\u003c/p\u003e \u003cp\u003eWhen accounting for both climate change and population growth simultaneously, we considered two scenarios. In the first scenario (Local Constraint), the climate effect over time scaled with the local resident population. Here we implicitly assumed that the tourist response to climate was approximately constrained by the size of the resident population that provides the accommodations and services for tourists. The total monthly demand with time in the Local Constraint scenario was given by:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${D}_{m}\\left(t\\right)={\\left[1+{\\stackrel{\u0026acute;}{D}}_{C,m}\\left(t\\right)\\right]D}_{m}\\left(0\\right)\\frac{{P}_{res}\\left(t\\right)}{{P}_{res}\\left(0\\right)} \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{res}\\)\u003c/span\u003e\u003c/span\u003e is the resident population and \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 in the year 2021.\u003c/p\u003e \u003cp\u003eIn the second scenario (WV Constraint), the climate effect over time scaled with the population in major population centers in the Willamette Valley (WV). In this case, we implicitly assumed that the tourist response to climate was constrained by the number of potential tourists that reside in the Willamette Valley while local accommodation and services would adjust to meet the changes in potential tourists independent of the size of the local resident population. The total monthly demand with time in the WV Constraint scenario was given by:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${D}_{m}\\left(t\\right)={D}_{m}\\left(0\\right)\\left[\\frac{{P}_{res}\\left(t\\right)}{{P}_{res}\\left(0\\right)}+{\\stackrel{\u0026acute;}{D}}_{C,m}\\left(t\\right)\\frac{{P}_{WV}\\left(t\\right)}{{P}_{WV}\\left(0\\right)} \\right] \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{WV}\\)\u003c/span\u003e\u003c/span\u003e is the population of the major population centers of the Willamette Valley.\u003c/p\u003e \u003cp\u003eEquations\u0026nbsp;3 and 4 can be separated into two contributions. The first is the demand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(D}_{P,m})\\)\u003c/span\u003e\u003c/span\u003ethat scales with resident population and is independent of the climate change effect:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${D}_{P,m}\\left(t\\right)={D}_{m}\\left(0\\right)\\frac{{P}_{res}\\left(t\\right)}{{P}_{res}\\left(0\\right)} \\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe contribution to demand from climate change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(D}_{C,m})\\)\u003c/span\u003e\u003c/span\u003e for the Local Constraint and WV Constraint scenarios are given by, respectively:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${D}_{C,m}\\left(t\\right)={{\\stackrel{\u0026acute;}{D}}_{C,m}\\left(t\\right)D}_{m}\\left(0\\right)\\frac{{P}_{res}\\left(t\\right)}{{P}_{res}\\left(0\\right)} \\left(6a\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eand\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$${D}_{C,m}\\left(t\\right)={{\\stackrel{\u0026acute;}{D}}_{C,m}\\left(t\\right)D}_{m}\\left(0\\right)\\frac{{P}_{WV}\\left(t\\right)}{{P}_{WV}\\left(0\\right)} \\left(6b\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLastly, to summarize the time series of monthly demand, we estimated linear trends in demand over the period 2021\u0026ndash;2070 using least squares linear regression.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Historical analysis\u003c/h2\u003e \u003cp\u003eWhen describing the results below, we consider five of the six water providers located directly on the coast as \u0026ldquo;tourist-heavy providers\u0026rdquo; (Lincoln City, Newport, Seal Rock, Waldport, and Yachats) and Toledo as a \u0026ldquo;non-tourist provider\u0026rdquo;. Tourist-heavy provider monthly water demand was largely insensitive to either local \u003cem\u003eTmax\u003c/em\u003e or \u003cem\u003eTmin\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea for \u003cem\u003eTmax\u003c/em\u003e; \u003cem\u003eTmin\u003c/em\u003e not shown). Temperature sensitivity was generally not statistically significant at higher temperatures (\u0026gt;\u0026thinsp;20\u0026deg;C) nor were the response patterns consistent across water providers (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c, e, and f). Only Toledo showed a statistically significant positive response to high temperatures (\u0026gt;\u0026thinsp;35\u0026deg;C; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eIn contrast, tourist-heavy provider water demand was clearly sensitive to higher \u003cem\u003eTmax\u003c/em\u003e in the Willamette Valley (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For instance, an additional day per month above 35\u0026deg;C in the Willamette Valley was associated with a 2.2 percent increase in monthly water demand. For Lincoln City, Newport, and Seal Rock, temperature sensitivities monotonically increased with Willamette Valley \u003cem\u003eTmax\u003c/em\u003e for \u003cem\u003eTmax\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;20\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, c, and e). The model for Waldport suffered from small sample size, but generally showed higher temperature sensitivities at higher temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Only Toledo, the non-tourist provider, did not show sensitivity increasing progressively with higher temperature. While sensitivities remained positive above 20\u0026deg;C for Toledo, they peaked for \u003cem\u003eTmax\u003c/em\u003e in the range 25\u0026ndash;30\u0026deg;C and were progressively less positive at higher temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eTourist-heavy provider monthly water demand increased by 0.8% for each additional dry day per month, and the positive response to dry days was statistically significant for three of five water providers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In contrast, water demand was largely insensitive to the number of Willamette Valley dry days per month (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe significant effects of \u003cem\u003eTmax\u003c/em\u003e in the models using Willamette Valley weather and the significant effects of precipitation in the models using local weather motivated us to test a model that used Willamette Valley \u003cem\u003eTmax\u003c/em\u003e and local precipitation (as dry days per month). However, the effects of local precipitation in this model were not statistically significant. The relatively small influence of local precipitation may have been subsumed by the stronger influence of temperature.\u003c/p\u003e \u003cp\u003eFull model results for all pooled and water-provider specific models are displayed in the SI Tables S2 - S15. The projections summarized in Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e below rely on the sensitivities to Willamette Valley weather only. For two cases, we relaxed our condition that sensitivities would be set to zero if the 95% confidence intervals spanned zero: at Waldport and Toledo for \u003cem\u003eTmax\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;20\u0026deg;C. We allowed this adjustment because of the seemingly unlikely fluctuations in sensitivity our condition would cause between temperature intervals. Future work could consider expanding the size of temperature bins to narrow confidence intervals and improve estimation, albeit at a loss of resolution across the temperature distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Demand projections\u003c/h2\u003e \u003cp\u003eIn general, monthly water demand is projected to rise in the tourist-heavy Mid-Coast communities and most so in the high-demand months of summer and early autumn. In these months, the GCM ensemble-mean demand increases by about 11% (Local Constraint) and 14% (WV Constraint) from 2021\u0026ndash;2070 under RCP4.5 and somewhat more (15 and 19%, respectively) under RCP8.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Under RCP8.5, about 60 and 66% of the total increases are driven by climate change under the Local Constraint and WV Constraint scenario, respectively, and the remaining increases are due to resident population growth.\u003c/p\u003e \u003cp\u003eThe differences between the Local and WV constraint scenarios result directly from the different population growth projections between the Mid-Coast and the Willamette Valley. The Mid-Coast population was projected to grow by 8% whereas the Willamette Valley population centers were projected to grow by 46% from 2021 to 2070.\u003c/p\u003e \u003cp\u003eAbout the ensemble-mean changes, there is a large spread in projections across the individual GCMs for a given RCP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The spread has two causes: 1) differences in how each GCM simulates the response of the region\u0026rsquo;s climate to increases in greenhouse gas concentration and 2) internal climate variability occurring on multi-decadal scales [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although we did not separate the contributions of each cause here, the fact that a small number of GCM simulations produce a negative contribution from climate change indicates that there is a small but non-negligible possibility of internal climate variability driving a regional cooling trend over the next 50-years [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] that would cause demand to decrease, excluding the effect of resident population growth.\u003c/p\u003e \u003cp\u003eWater demand projections vary considerably by water provider during the high demand season (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-f). Under the WV Constraint-RCP8.5 scenario, for example, ensemble-mean demand changes range from \u0026minus;\u0026thinsp;17% in Toledo in August to +\u0026thinsp;35% in Waldport in June. Changes are smaller for the RCP 4.5 scenario and the Local Constraint scenario (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) but otherwise similar in pattern. The projections differ among water providers in part due to the providers\u0026rsquo; different sensitivities to \u003cem\u003eTmax\u003c/em\u003e but also because of differences in population growth. Resident populations are projected to increase in three communities (Lincoln City, Waldport, Yachats) but decrease in two (Newport and Toledo). In the case of Newport under the WV Constraint-RCP8.5 scenario, the effect of climate change on demand is large enough to counter the effect of declining resident population during part of the year (May \u0026ndash; June; September \u0026ndash; October) but in Toledo the net change in demand is negative throughout the year.\u003c/p\u003e \u003cp\u003eFor most water providers, the season of higher demand broadens into the shoulder season months of May and October (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and S2). Seal Rock is the exception, where the highest monthly demand becomes yet more concentrated in July \u0026ndash; August.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs noted above, we found that monthly water demand was largely insensitive to local temperature but that it increased with the number of local dry days in a month. Similar results have been noted elsewhere [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] although the reasons for the lack of sensitivity to temperature may not be the same in all cases. Along the Oregon coast, daily temperature fluctuations are relatively small, and the temperature is rarely what one would consider hot (e.g., the standard deviation of \u003cem\u003eTmax\u003c/em\u003e in July \u0026ndash; August is only 3.8\u0026deg;C and the 99th percentile of \u003cem\u003eTmax\u003c/em\u003e is only 23.5\u0026deg;C, averaging each statistic across the tourist communities). Consequently, water users rarely experience large fluctuations of temperatures, nor extreme temperatures, that might cause a short-term behavioral response.\u003c/p\u003e \u003cp\u003eThe detectable sensitivity to local dry days suggests that outdoor water application increases when users perceive a greater need for watering lawns and gardens. However, the effect is not large enough that it would substantially influence the demand projections: There would need to be a large change in the future number of dry days per month (i.e., more than 5 days) to change water demand by more than a few percent, yet the number of dry days during high demand season (June \u0026ndash; September) is only projected to change by 0.5 and 0.8 days per month from 2021 to 2070 under RCP4.5 and RCP8.5, respectively, in the GCM ensemble average.\u003c/p\u003e \u003cp\u003eOur result with respect to local temperature differs from the small number of published studies of tourist water use in coastal communities. Specifically, Toth et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Mazzoni et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found that tourist water demand increased with temperature in seaside communities of northern Italy. However, both Toth et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Mazzoni et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] benefited from tourist visitation data, and it is possible that we would have detected a significant response of tourist use to local temperature had we been able to separate resident and tourist use.\u003c/p\u003e \u003cp\u003eIn contrast to local temperatures, Willamette Valley temperatures appeared to affect coastal water demand. The relationship between demand and temperature was non-linear, with sensitivity becoming apparent above a threshold temperature of 20\u0026deg;C. A similar non-linear effect was observed by others, though the threshold temperature varied by location [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven that it is unlikely that a coastal resident would think to use more water because it is hot somewhere distant from their residence, we assumed that the temperature effect was driven largely by an influx of tourists and that the tourists were responding to the weather where they permanently reside. In Oregon, the temperature difference between the Willamette Valley and the Coast during a heat wave in the valley can be large (e.g., there was a 26\u0026deg;C temperature difference between Portland and Newport on 28 June 2021). Since travel times from most population centers in the valley to the coast are less than 90 minutes, this travel option represents a climate adaptation strategy for residents of Oregon\u0026rsquo;s population centers to escape the heat.\u003c/p\u003e \u003cp\u003eTourist visitation data were unavailable for this research but are ultimately needed to confirm the validity of this assumption. It may be possible to estimate variability in tourist visits using proxies, such as cell phone data or monthly revenues from hotel taxes, for cities that can make such records available. Knowing the location of origin of tourists could also better reveal tourist response. Tourists that come from farther away may be less influenced by weather because they tend to make plans further ahead so are less likely to change plans on short notice [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe can look to studies in other locations of the effects of weather and climate on tourism. However, such studies typically consider the weather of the tourists\u0026rsquo; destination but not the weather of their location of origin. These studies generally show that tourism increases when the weather at the destination is such that makes it more pleasant to be outdoors [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In the Mid-Coast, the increased water demand with more local dry days may result from more tourists when it is not rainy at the coast as opposed to the perceived greater need to water outdoors. In a rare example where the weather in the location of origin was considered, Serquet and Rebetez [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] found that the number and duration of tourist stays in Swiss Alpine resorts increased during heatwaves in the lower elevation surrounding region.\u003c/p\u003e \u003cp\u003eDiffering sensitivities to Willamette Valley temperature across water providers give some additional evidence that water demand is affected by the weather in the Willamette Valley via tourists\u0026rsquo; behavior. Toledo, the water provider with fewest number of tourists relative to the resident population, shows the lowest sensitivity to high Willamette Valley temperatures, with non-significant results above 35\u0026deg;C. Estimated sensitivities to temperature do not appear to be completely explained by the relative proportion of tourists, however. Newport, for example, has the highest sensitivities but is the most economically diverse. Fish processing plants are a major water consumer in Newport and how their consumption might respond to weather and climate is unclear. However, we are reluctant to attribute inter-provider differences to any cause other than estimation error given the large uncertainties on some sensitivity estimates due to short period of record of some water providers\u0026rsquo; demand data.\u003c/p\u003e \u003cp\u003eThese uncertainties in the sensitivities also propagate directly to the demand projections. The low sample size at high temperature in particular, and even the lack of future very high-temperature analogs, means we have low confidence in sensitivity at very high temperatures (\u0026gt;\u0026thinsp;40\u0026deg;C, 104\u0026deg;F) that are projected to occur with higher frequency. Temperatures in the Willamette Valley have exceeded 40\u0026deg;C in the past, most notably reaching a record high of 47\u0026deg;C (117\u0026deg;F) during the Pacific Northwest Heat Wave of June 2021 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] when, anecdotally, large numbers of tourists travelled to the cooler coast to escape the heat.\u003c/p\u003e \u003cp\u003eLastly, we only partially considered how our methodological decisions affect variability in the demand projections. Choices along the modeling chain are made regarding GHG scenario, GCM, meteorological downscaling technique, and water demand model. The effect of these choices can be explored by considering multiple GHG scenarios, GCMs, downscaling techniques, and water demand models [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Though we did include two GHG scenarios and 20 GCMs in our analysis, we only included one downscaling technique. We effectively used two variants of a water demand model (the Local and WV Constraint variants) but only one set of statistical model parameters and one set population projections. Future work could account for uncertainty in demand model coefficients and explore different viable scenarios of population change.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eUsing an econometric model of monthly water demand in Oregon Mid-Coast communities, we estimated temperature- and precipitation-response functions for demand. A key result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley was a significant driver. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourism: Tourists were escaping the heat in the Willamette Valley (their home) for cooler conditions on the coast.\u003c/p\u003e \u003cp\u003eApplying the temperature response functions to scenarios of future \u0026ndash; and warmer \u0026ndash; climate leads to projected increases in water demand independent of other factors. Given the assumed pathway between climate change and coastal water demand is through tourism, the magnitude of the climate-driven projected increase in demand becomes a function of tourist visitation. Assuming future tourism is either 1) constrained by the local resident population that serves tourism or is 2) constrained by the potential tourist population provides two scenarios of future demand. Future demand in the second scenario is higher than in the first scenario because the projected resident population growth rate is higher where the tourists come from (e.g., the Willamette Valley) than it is in the Mid-Coast. In either scenario, the climate-change contribution to projected water demand is generally of comparable magnitude to \u0026ndash; if not greater than \u0026ndash; the contribution from resident coastal population change alone over the next fifty years. In communities where the population is even projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand.\u003c/p\u003e \u003cp\u003eThe water demand projections assumed no imposed adaptation measures or behavioral changes in response to climate change. This analysis, therefore, provides estimates of what level of water conservation or improvements in system efficiency will be required to offset the effects of climate change. In general, a modest decrease in per capita water consumption and/or transmission losses of no more than a few percent per decade, may be sufficient.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding: \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors received financial support from the United States National Ocean and Atmospheric Administration (NOAA) Coastal and Ocean Climate Applications (COCA) and Sectoral Applications Research Program (SARP) Grant #NA19OAR4310306.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConflict of interest/Competing interests:\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePRISM climate data were obtained from the PRISM Climate Group, Oregon State University, at https://www.prism.oregonstate.edu/. MACA climate projections were obtained from the Climatology Lab, University of California, Merced, at http://www.climatologylab.org/maca.html. Population projections (except Portland metropolitan region) were obtained from the Population Research Center, Portland State University, at \u0026nbsp;https://www.pdx.edu/population-research/. Population projections for Portland metropolitan region were obtained from Oregon Metro at https://www.oregonmetro.gov/2060-growth-forecast. Water demand data were not available on-line and were obtained by request from each water provider listed in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe econometric analysis of water demand was done with Stata, v.18. Future water demand projections and all figures were made using R v.4.3.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eD.R. and S.D. conceived and designed the study. D.R., S.D., and L.M. performed the data analysis. S.de S. led the data collection. D.R. and S.D. wrote the first draft of the manuscript. All authors commented on and revised previous versions of the manuscript and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHouse-Peters LA, Chang H. Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resour Res. 2011;47:W05401. https://doi.org/10.1029/2010WR009624.\u003c/li\u003e\n\u003cli\u003eDonkor EA, Mazzuchi TA, Soyer R, Alan Roberson J. Urban water demand forecasting: review of methods and models. 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Water Resour Manag. 2021;35:1449\u0026ndash;62. https://doi.org/10.1007/s11269-021-02789-4.\u003c/li\u003e\n\u003cli\u003eRupp DE, Abatzoglou JT, Mote PW. Projections of 21st century climate of the Columbia River Basin. Clim Dyn. 2017;49:1783\u0026ndash;99. https://doi.org/10.1007/s00382-016-3418-7.\u003c/li\u003e\n\u003cli\u003eLee J-Y, Marotzke J, Bala G, Cao L, Corti S, Dunne JP, et al. Future global climate: scenario-based projections and near-term information. Clim. Change 2021 Phys. Sci. Basis Contrib. Work. Group Sixth Assess. Rep. Intergov. Panel Clim. Change. Cambridge University Press; 2021. pp. 553\u0026ndash;672. https://doi.org/10.1017/9781009157896.006.\u003c/li\u003e\n\u003cli\u003eMaidment DR, Miaou S-P. Daily water use in nine cities. Water Resour Res. 1986;22:845\u0026ndash;51. https://doi.org/10.1029/WR022i006p00845.\u003c/li\u003e\n\u003cli\u003eAkuoko-Asibey A, Nkemdirim LC, Draper DL. 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Tour Recreat Res. 2022;0:1\u0026ndash;13. https://doi.org/10.1080/02508281.2022.2148076.\u003c/li\u003e\n\u003cli\u003eSerquet G, Rebetez M. Relationship between tourism demand in the Swiss Alps and hot summer air temperatures associated with climate change. Clim Change. 2011;108:291\u0026ndash;300. https://doi.org/10.1007/s10584-010-0012-6.\u003c/li\u003e\n\u003cli\u003eFleishman E, editor. Sixth Oregon Climate Assessment. Corvallis, Oregon, U.S.A.: Oregon Climate Change Research Institute, Oregon State University; 2023. https://doi.org/10.5399/osu/1161.\u003c/li\u003e\n\u003cli\u003eChegwidden OS, Nijssen B, Rupp DE, Arnold JR, Clark MP, Hamman JJ, et al. How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates. Earths Future. 2019;7:623\u0026ndash;37. https://doi.org/10.1029/2018EF001047.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-water","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diwa","sideBox":"Learn more about [Discover Water](https://www.springer.com/43832)","snPcode":"","submissionUrl":"","title":"Discover Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Water demand, weather, climate change, tourism, coast","lastPublishedDoi":"10.21203/rs.3.rs-3988942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3988942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLittle is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley \u0026ndash; the source of most tourists to the Oregon coast \u0026ndash; was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to \u0026ndash; if not greater than \u0026ndash; the contribution from resident population change alone over the next fifty years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand.\u003c/p\u003e","manuscriptTitle":"How Tourists ‘Escaping the Heat’ May Drive Future Increases in Municipal Water Demand in Oregon Coastal Communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 06:32:48","doi":"10.21203/rs.3.rs-3988942/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-29T05:05:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-14T12:57:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-29T15:05:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51d8928a-2d22-41f0-81c0-a8288b5d4907","date":"2024-04-18T13:07:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7f4f59f9-9056-43da-b350-5cf3b086d256","date":"2024-04-07T17:50:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-29T13:01:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-19T10:49:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-19T10:46:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Water","date":"2024-02-25T20:30:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-water","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diwa","sideBox":"Learn more about [Discover Water](https://www.springer.com/43832)","snPcode":"","submissionUrl":"","title":"Discover Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"036865f0-bfac-414d-8e23-7d81f69d0481","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:01:06+00:00","versionOfRecord":{"articleIdentity":"rs-3988942","link":"https://doi.org/10.1007/s43832-024-00133-6","journal":{"identity":"discover-water","isVorOnly":false,"title":"Discover Water"},"publishedOn":"2024-09-27 15:57:16","publishedOnDateReadable":"September 27th, 2024"},"versionCreatedAt":"2024-03-21 06:32:48","video":"","vorDoi":"10.1007/s43832-024-00133-6","vorDoiUrl":"https://doi.org/10.1007/s43832-024-00133-6","workflowStages":[]},"version":"v1","identity":"rs-3988942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3988942","identity":"rs-3988942","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}