Climate change exacerbates water affordability crisis

Climate change exacerbates water affordability crisis | 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 Article Climate change exacerbates water affordability crisis Jennifer Skerker, Christian Klassert, Baptiste Francois, Aniket Verma, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7603314/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Climate change intensifies water stress globally, necessitating expensive infrastructure interventions to maintain reliable supply. To fund infrastructure, utilities often raise rates, increasing water bills for low-income households. Resulting affordability impacts depend on utility costs and interactions between rate design, financing, climate, and household demands. We develop a city-scale modeling framework to estimate climate change impacts on water affordability, integrating climate, utility adaptation decisions, and demand. In Santa Cruz, California, we find that climate change alone could double water bills by mid-century, leaving an additional 7-16% of Santa Cruz households with unaffordable water. Our results suggest that climate change may lead to greater water affordability challenges than previously estimated in hotspots where supply is vulnerable to climate change. This highlights the need for policy intervention and financing to ensure climate adaptation does not compromise affordability. The magnitude of climate-related affordability challenges depends on local context, requiring city-scale assessments. Scientific community and society/Water resources Scientific community and society/Social sciences/Decision making Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Physical sciences/Engineering/Civil engineering Water affordability climate change infrastructure systems analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Water affordability is a growing challenge in high-income countries across the globe. 1–7 Over the past two decades, water rates have risen three times faster than inflation, 8,9 driven primarily by deferred maintenance and aging infrastructure. If historical trends continue, more than one third of US households could face unaffordable water bills within a decade. 1 However, emerging contaminants requiring advanced treatment and climate change necessitating new supplies are compounding longstanding cost pressures for utilities. 1,10–12 Unaffordable water limits access for drinking, cooking, and hygiene 2 or forces difficult trade-offs with other necessities like food and healthcare. 3,13,14 Inadequate and unaffordable water access exacerbates the risk of disease and negative health impacts, 15 disproportionately impacting low-income and minority communities. 2,16 In many cities around the world, climate change challenges utilities’ ability to provide reliable, affordable water access 17–19 by straining supplies 17,18,20 and driving demands higher as temperatures rise. 21–23 On the supply side, climate change can alter the frequency, severity, and duration of droughts 24–26 and cause drier baseline conditions in some places. 19,27 Utilities respond by either expanding supplies or reducing demands. 28 Utilities increase water supplies by diversifying supply portfolios, often through costly infrastructure projects. 29,30 In the US, new water infrastructure is typically financed by rate increases to customers, 10,31 which can worsen household water affordability challenges. 1,32 Utilities also manage demands by implementing short-term curtailment measures during droughts or water-use efficiency programs. 28,33 Demand-side measures can increase water costs through surcharges needed to pay for lost volumetric revenue or investments in efficiency upgrades and monitoring equipment. 28,33 Recent empirical studies highlight growing affordability challenges 1,2,4,34 but offer limited insight into future trends. Recent work projects rising water affordability challenges by extrapolating historical cost data but does not analyze specific cost drivers like climate that may change in the future. 1 Affordability is driven not only by utility costs but also interactions between social and institutional factors like demand patterns and rate structures, 35 but affordability estimates typically neglect these factors. 5,36 One study highlights cost-demand interactions, demonstrating how low-income rate assistance programs, which decrease household water costs, can increase use. 9 Additionally, most empirical studies assess affordability at a single point in time, 3,5 neglecting seasonal and temporal variability that may exacerbate short-term affordability challenges. 4,35,37,38 High quality projections of future water affordability challenges are essential to developing effective and sustainable policy solutions at the city, state, and federal levels. Drought and climate change in particular may prompt costly water infrastructure investments, but their affordability impacts are poorly understood. Extensive research assesses water supply infrastructure needs due to climate change, but focuses on utility-scale costs and performance 39–42 rather than household-scale water demands and affordability. 43,44 Additionally, infrastructure financing and rate design are typically analyzed from a utility rather than household cost perspective. 43,45,46 One recent paper makes important advances in evaluating how climate-related infrastructure impacts low-income households but does not consider demand feedbacks or financing mechanisms. 47 Other work explicitly models affordability impacts of rate increases due to droughts but focuses on short-term impacts, not climate change. 33,48 To our knowledge, no study has comprehensively assessed the integrated climate, infrastructure, and demand drivers to quantify climate change impacts on low-income water affordability. Our research objective is to quantify the impact of climate change on urban water affordability. We develop a rigorous city-scale modeling framework that captures the integrated climate, utility adaptation decisions, and demand drivers shaping household affordability burdens in US cities. We combine a hydrological and water supply system model with infrastructure financing and rate design models, and an econometric model of household water demands across different demographic groups. We assess the affordability impacts of utility adaptation decisions needed to maintain reliable water supply in future climates. This can include new infrastructure investments, financing mechanisms, rate structures, assistance, curtailment policies, and efficiency programs. We hypothesize that climate change could create water affordability hotspots, where existing affordability challenges are exacerbated in cities with water supply vulnerability and limited demand management opportunities. We complete a city-scale assessment of climate change impacts on water affordability using Santa Cruz, California as a case study and focusing on mid-century impacts. Santa Cruz has low residential water use due to the city’s previous drought experiences, 49 limiting the scope for low-cost adaptation options like demand reduction via curtailment or efficiency policies. It is also reliant on local surface water, making it vulnerable to drought. 50 By examining a city that has largely exhausted lower-cost options for climate adaptation like demand-side management, we focus on costly, long-term infrastructure decisions and highlight challenges likely faced by other cities with similar climate, behavioral, and regulatory conditions. Results Assessing affordability implications of climate change impacts and adaptation We present a modeling framework to quantify the water affordability impacts of climate change effects and related adaptation measures on water supply and demand. Our model captures the key interacting climate, utility adaptation decisions, financing, and demand drivers that determine household affordability (Figure 1a). First, we input future climate scenarios, developed by combining a climate model ensemble with a stochastic weather generator, into water supply systems models, which simulate river flows at the reservoir and diversions, and water allocations from the diversions to the water treatment plants. Second, we model utility adaptation decisions using a risk-of-failure approach in which utilities take action in response to declining storage levels. Third, we assess the water rate impacts of those interventions using an infrastructure financing and rate design model. Using Santa Cruz as a case study, we focus on utility decision-making around infrastructure investments, evaluating multiple planning scenarios that determine when and what new infrastructure is developed (more details in the Methods and SI ). Other utility adaptation decisions (highlighted in yellow) are modeled as fixed assumptions consistent with current Santa Cruz policies and practices, with their effects explored through sensitivity analysis. Third, we simulate household water use patterns as a function of demographics, climate, water costs, and housing characteristics. We capture dynamic feedback across these three analyses that determine how household behaviors impact water supplies and how city decisions affect households. For example, when the city builds new infrastructure, they pay for that infrastructure by increasing water rates; when water bills rise, households use less water, altering how much water the system needs. Finally, the outcome of our framework is estimated water bill and affordability burdens (the percentage of household income spent on water bills) across income groups. See Methods for details. Using this framework, we assess how different climate scenarios drive unequal affordability impacts. Figure 1b-1c presents two illustrative climate simulations selected to visualize the mechanisms through which infrastructure development affects affordability. The simulations show how contrasting climate conditions shape Santa Cruz’s infrastructure needs and affordability burdens for two sample households. In one plausible moderate, cool climate simulation, no new infrastructure is needed, although the low-income household is already spending more than the 2.5 percent affordability threshold recommended by the U.S. Environmental Protection Agency (EPA) (Figure 1c). 1,3,51 In one plausible dry, hot climate simulation, declining reservoir storage levels trigger construction of a four million gallon per day (MGD) desalination plant, leading to rate increases and higher water bills. In this example, the affordability burden for a sample low-income household increases from four to six percent, while a sample high-income household has negligible impacts. These examples demonstrate how climate stress can exacerbate underlying affordability burdens for low-income households. More households face unaffordable water under a drier climate Here, after using an example to illustrate how climate exacerbates affordability, we aggregate results across many stochastic simulations. In Santa Cruz, our scenarios show that climate change could nearly double water bills, which could leave an additional 7-16% of households with unaffordable water (Figure 2). We test four plausible scenarios that combine contrasting climates and infrastructure planning adaptation strategies: first, our “baseline” scenario where no new infrastructure is built and the climate is moderate and cool, similar to the present-day; second, a “moderate climate with adaptation” where new infrastructure is built in response to water stress (“as needed”) under the same moderate, cool climate; third, a “dry climate with adaptation”, where new infrastructure is built as needed under a dry, hot climate; and fourth, an “all climate simulations” scenario, with the entire range of climate simulations. We choose the moderate, cool and dry, hot climates, to be contrasting but within the range of CMIP6 (Figure S1). For each climate, we develop multiple 50-year stochastic climate simulations, developed by using precipitation and temperature changes in the CMIP6 ensemble to inform a stochastic weather generator. In the scenarios with adaptation, new infrastructure is deployed as needed using a risk-of-failure (ROF) approach, in which the utility develops new infrastructure when the projected risk of storage falling below a critical threshold within two years exceeds a defined level, based on current reservoir storage, water demands, and prior investments (see Methods and Figure S2) . The planning strategy utilized first builds a desalination plant when the ROF value surpasses a given threshold, although alternative strategies are compared later in the Results (see Figure 5). We analyze the periods where infrastructure investments lead to the largest rate increases, using these periods to estimate monthly reservoir storage, added water supply costs for new infrastructure, monthly water bills, and affordability burdens across households, resulting in a distribution of outcomes for each scenario. We assess climate impacts on the water system driving the need for new infrastructure, which, without further interventions, then translate to household bills and affordability burdens. Under the moderate climate with adaptation, building more infrastructure leads to greater water availability via higher reservoir storage compared to the baseline scenario (Figure 2a). In contrast, the dry climate with adaptation results in decreased and more variable reservoir storage. Low reservoir levels risk supply shortfalls, triggering new infrastructure investments. Differences in reservoir storage between the scenarios with adaptation drive varying supply costs, impacting household water bills (Figure 2b). Under the moderate climate with adaptation, 60% of months require no new supply costs, whereas over 50% of months under the dry climate with adaptation require at least $1M in additional capital and operating costs to maintain reliable supply. Because we assume these costs are fully passed on to households and that no mitigating policy interventions are implemented, infrastructure investments translate directly into substantial increases in household water bills (Figure 2c). 50th percentile bills increase from $64 to $80 (moderate climate with adaptation) or $120 (dry climate with adaptation). 80th percentile bills rise from $100 to $148 (moderate climate with adaptation) or $204 (dry climate with adaptation). Under the dry climate with adaptation, median water bills could nearly double from current levels. Rising bills increase the proportion of households paying more than the EPA’s affordability threshold (Figure 2d). Currently, 19% of households exceed this threshold, highlighting how Santa Cruz already exhibits affordability challenges. This share could rise to 26% under a moderate climate and to 35% under a dry climate, when additional infrastructure is built for reliability. In the dry climate more than one third of households in Santa Cruz could struggle to afford water, highlighting the scale of potential impacts without any additional policy interventions. Climate change exacerbates low-income unaffordability Next, we analyze how simulated demands, bills, and affordability trends for low-income households compare to the remaining population in Santa Cruz, finding that while the demands and bills of the former are lower, their affordability burdens are substantially higher (Figure 3). First, we compare water demand differences between low-income households and all other households. Differences across income groups and scenarios are small. Across the scenarios, average low-income water use is about 0.4 hundred cubic feet (ccf) less than other households, with 80th percentile demands increasing to around 0.6 ccf lower than all other households (Figure 3a). Small demand differences across income groups align with previous work that finds widespread demand hardening in Santa Cruz due to household efficiency and conservation behaviors. 50 Under the moderate climate with adaptation, building infrastructure decreases demands by about 0.5 ccf, mainly due to increased water prices and household price elasticity responses. Between the moderate and dry climates with adaptation, differences in demands are negligible (<0.1 ccf), as greater temperatures and decreased precipitation counteract cost-driven demand decreases. Small demand differences across scenarios illustrate how the affordability impacts of climate change in Santa Cruz are driven by supply-side factors over demand-side ones. Sensitivity analysis on modeled demand parameters shows that while changing price elasticity and single-family home demands can affect affordability outcomes, climate factors play a larger role in our case study (see Section S3). In comparison to demands, water bill differences across climate scenarios and affordability differences across income groups, are larger (Figure 3b-3c). Differences in water bills between low-income and all other income group bills are small, with median differences ranging from $5/month to $11/month across scenarios. In contrast, when we compare the baseline scenario to the dry climate with adaptation, low-income median bills increase from $60/month to $111/month ($51 increase) and 80th percentile bills increase from $92/month to $186/month ($94 increase). While low-income bills are smaller than other income groups’, low-income affordability impacts are greater. Under the baseline scenario, median low-income affordability burdens are 3.9%, which is already greater than the EPA’s threshold, underscoring existing affordability challenges in Santa Cruz (Figure 3c). Under the moderate climate with adaptation, building infrastructure increases low-income median affordability burdens to 5.1% (0.6 to 0.8% for other incomes). The dry climate with adaptation increases low-income median affordability burdens further to 7.3% (1.1% for other incomes). Additionally, we see that the low-income 90th percentile affordability burdens increase from 16 to 30 percent of income. Likely, households paying this much for water are forced to choose between essential expenditures, 3,13 highlighting the vulnerability of low-income households to water price increases driven by climate change. Precipitation uncertainty impacts low-income households We then assess the relative impact of different climate effects on water affordability, finding that average precipitation decreases lead to the greatest increases in affordability burden (Figure 4). Here, we simulate hundreds of stochastic climate simulations, filtering for different characteristics individually to assess which aspects of climate change most impact affordability. Since precipitation quantity is a primary driver of water availability, declining averages drive new infrastructure needs. Precipitation variability also has a moderate impact on affordability burden. Even if precipitation averages stay constant, greater variability, which leads to wetter wet and drier dry periods, can necessitate additional infrastructure to ensure supply during droughts. This is particularly relevant for Santa Cruz, where the city's single reservoir has limited capacity for interannual storage, limiting its ability to buffer against extreme variability. Lastly, rising temperatures moderately affect affordability by increasing both evapotranspiration, which decreases water availability, 19 and household demands, though supply-side impacts are more pronounced due to demand hardening (see Figure S7). 52 Across all climate factors, where we analyze the minimum and maximum values used throughout the analysis, the largest affordability differences are in the upper percentiles, suggesting that climate change disproportionately impacts households already struggling to pay bills. While median affordability ratios range from 1.0 to 1.5 (difference of 0.5), boxplot upper bounds rise from 5.5 to 8.1 (difference of 2.6). This pattern likely reflects the heightened sensitivity of already burdened households to cost increases. Similar trends appear in water bill costs (Figure S8), likely due to steeper increases for high water users. Low-income households with high water usage—possibly due to larger household sizes 52 —may be especially vulnerable to climate-driven cost changes. Large infrastructure mitigates climate change induced reliability outages An important driver of our results is what and when we choose to build infrastructure in our projected scenarios, and we find that different infrastructure investment strategies shape system reliability and affordability outcomes. Our baseline strategy (“Strategy A”) prioritizes a 4-MGD desalination plant, which we choose because this option has a risk of failure threshold such that reliability declines during major drought events in the model are similar in magnitude to those of the 2013 California drought (see Section S5 ) . Alternative utility strategies could deploy contrasting amounts of new infrastructure, resulting in different reliability impacts. To explore this, we develop optimal planning strategies that minimize new utility supply costs and unmet demands (Figure 5a). Each planning strategy dictates when and which infrastructure is built using ROF thresholds calculated based on water demands, reservoir storage, and prior investments (see Methods) . We choose two additional planning strategies for comparison: one risk-averse strategy where a low ROF threshold builds large infrastructure earlier (“Strategy B”) and one risk-tolerant strategy with a high ROF threshold where smaller-capacity infrastructure is built first (“Strategy C”) (see Table S4). Applying sensitivity analysis to the optimization of our planning strategies, we find the choice of planning strategy to be insensitive to small changes in demand, deployment time, and infrastructure costs. Comparing planning strategies A, B, and C across plausible climate change impacts highlights their substantial influence on utility costs, reliability, and affordability (Figure 5). Strategy B, with the lowest risk tolerance and earliest infrastructure deployment, ensures 98.8% average annual reliability (averaged across each simulation and then across all simulations) but adds at least $9.4M/year in costs, increasing the 80th percentile affordability burden to 5.2%. Strategy C, with delayed, lower-cost investments, limits new spending to $1.4M/year and lowers the 80th percentile affordability burden to 2.7%. While average annual reliability remains relatively high at 96.8%, the average minimum annual reliability is only 61% (i.e., the minimum annual reliability in each simulation, averaged across simulations)--likely unacceptable for most water utilities, which are typically risk averse and concerned with the worst-case outcomes, due to citywide economic risks of water shortages. 53 Santa Cruz, for instance, estimates that a 30% water use reduction during a water shortage could shrink economic output by over $100M (1.1-2.4% loss). 54 Strategy A, our baseline, has a wider cost spread due to its moderate investment approach. However, since it deploys desalination first like Strategy B, both maintain average minimum annual reliability levels above 70% (see Figure S11). Ultimately, under the current US infrastructure financing model, climate change pits affordability against reliability, despite both being essential for water access. Discussion This study develops a city-scale modeling framework to quantify the impacts of climate change on urban water affordability. Previous work documenting affordability challenges does not account for how climate change may fundamentally alter the cost structures, infrastructure needs, and household responses that shape affordability. 1,4,5 By explicitly modeling climate-driven water stress alongside utility adaptation decisions, infrastructure financing, rate design, and household demand and income, this study provides a comprehensive assessment of how climate change alone may exacerbate urban water affordability challenges. A key contribution of this work is to extend the water affordability literature by connecting it to climate adaptation and infrastructure planning processes that have historically been analyzed separately. Much of the water resources literature evaluates adaptation strategies under climate change through reliability–cost tradeoffs, focusing on system performance and aggregate utility expenditures. 39–42 However, cost increases do not translate directly into affordability impacts. Instead, household affordability outcomes depend on how costs are recovered through rates, how households adjust water use in response to price changes, and how income is distributed across the population. 33,48,55 Quantifying reliability–affordability tradeoffs therefore reveals dynamics that are largely invisible in cost-based analyses and highlights distributional consequences that are central to water access but often absent from adaptation planning. 56 Understanding the magnitude of climate-driven affordability impacts is essential for designing effective policy responses. If only a small fraction of households experiences unaffordable water bills, local customer assistance programs may be sufficient to mitigate hardship. In contrast, if a large share of the population faces affordability challenges, utility-funded assistance programs may be financially infeasible, and addressing the problem likely requires state or federal intervention through infrastructure financing, regulatory reform, or direct household support. 51,57 In Santa Cruz, our results suggest that climate change alone could leave an additional 7-16% of households with unaffordable water. This finding underscores the importance of evaluating climate adaptation strategies through an affordability lens. Affordability outcomes are not determined by climate alone, but by interactions between climate stress and a set of hydrological, institutional, and social characteristics that shape how utilities respond and how costs are distributed across households. Table 1 summarizes these dimensions and clarifies the class of urban water systems for which our results are most informative. In this sense, Santa Cruz is not presented as representative of cities in general, but as illustrative of systems where climate stress interacts with constrained adaptation options and existing inequality to amplify affordability impacts. First, climate change–driven water stress increases the need for adaptation to maintain reliable supply. Across California and many semi-arid regions globally, climate projections indicate higher temperatures, altered precipitation patterns, and greater hydrologic variability, increasing the likelihood of supply deficits. 19 Water system characteristics shape how and what utility adaptation decisions are realized. For example, in many supply constrained cities like Santa Cruz, feasible adaptation pathways involve expensive supply expansion alternatives, including desalination, potable reuse, or large-scale transfers. 50 These options are costly relative to conservation or operational measures, leading to larger rate increases. Additionally, limited over-year water storage can further constrain adaptation choices and accelerate the need for capital-intensive investments. Limited conservation opportunities, often the result of prior investments in efficiency and sustained demand hardening, reduce the extent to which utilities can rely on demand-side responses during droughts and shift adaptation pressure toward the supply side. 58,59 Income inequality amplifies these affordability impacts. Where many households already face affordability challenges, even modest bill increases can push a large share of the population beyond common affordability thresholds. 32 In Santa Cruz, high baseline burdens among low-income households mean climate-driven rate increases compound existing inequities. Finally, constraints on rate design and affordability mitigation play a central role in determining distributional outcomes. Regulatory frameworks that limit cross-subsidization or restrict income-based pricing can force utilities to recover infrastructure costs in ways that disproportionately affect low-income households. In California, Proposition 218 exemplifies this constraint, limiting the tools available to utilities even as climate adaptation costs grow. 60,61 Together, these conditions define a class of urban water systems in which climate change is most likely to exacerbate affordability challenges. Cities that share fewer of these characteristics may experience smaller or qualitatively different impacts, even under similar climate stress. However, cities that currently appear less exposed may move toward dynamics similar to those observed in Santa Cruz over time as conservation gains are exhausted, baseline rates rise, and climate stress intensifies. 62 In this sense, Santa Cruz may represent not an outlier, but a plausible future state for water systems that have already utilized lower-cost adaptation options. Beyond the Santa Cruz case study, the modeling framework developed here is designed to be broadly applicable across urban water systems. The framework integrates three components that are common across cities: a water resources systems model that represents climate-sensitive supply and infrastructure decisions, a utility financing and rate design model that translates system costs into household bills, and a household demand model that captures heterogeneity in water use and income. While each component must be parameterized with local data, the structure of the framework and the interactions it captures are not specific to Santa Cruz. As a result, the framework can be used to assess climate-driven affordability risks in other cities, while allowing results to reflect local hydrology, governance, and socio-economic conditions. Our findings highlight a fundamental tension in urban water management. Under prevailing financing and regulatory models, climate adaptation aimed at ensuring reliability can directly undermine affordability. Addressing climate-driven water stress without worsening inequity will likely require interventions beyond the utility scale, including regulatory reform, expanded public financing of adaptation infrastructure, or targeted assistance programs funded outside of water rates. More broadly, our results suggest that evaluations of climate adaptation strategies should routinely assess affordability impacts alongside reliability outcomes. Failing to do so risks shifting the costs of climate adaptation onto households least able to bear them, even when adaptation successfully reduces physical water scarcity. Limitations Several limitations condition the interpretation of our results and clarify the scope of their applicability. First, our results are shaped by hydrologic and infrastructural constraints specific to Santa Cruz, including limited over-year storage and the availability of particular supply expansion options. 50 Cities with larger reservoirs, more interconnected systems, or access to lower-cost water sources may experience delayed or reduced affordability impacts. Although we evaluate climate scenarios that reflect substantially hotter and drier conditions than observed historically, we treat these conditions as stationary, likely understating affordability impacts that would arise under progressively intensifying climate change. For example, non-stationary climate dynamics could produce tipping points in which multiple costly investments are required simultaneously or in rapid succession, sharply worsening affordability outcomes and straining utility finances over a short period. Alternatively, such dynamics may prompt premature or excessive infrastructure expansion, locking in higher utility rates that persist over decades. 63 Second, we represent utility decision-making using a rule-based risk-of-failure framework that captures risk-averse planning behavior in response to supply deficits but abstracts from the political, institutional, and social processes that influence infrastructure implementation. 64 In practice, delays related to permitting, public opposition, or financing challenges could alter both the timing and distribution of costs, potentially increasing short-term affordability shocks or reliability risks relative to modeled outcomes. 65 Third, affordability outcomes are sensitive to rate design and financing rules, which vary widely across jurisdictions. 66,67 Our analysis reflects regulatory constraints typical of California public utilities, limiting cross-subsidization and income-based pricing. 68 In settings with greater rate flexibility or substantial external funding, affordability impacts could be mitigated even under similar infrastructure investments. Fourth, we treat demographic composition, income distributions, and household behavior as static within each scenario. While we test sensitivity to population size, composition, and price elasticity, we do not model long-term demographic change, migration, or endogenous behavioral adaptation. These dynamics could either diffuse or concentrate affordability burdens over time, depending on local housing markets and economic conditions. Finally, our household-level analysis focuses on single-family residential customers due to data limitations. Like in most US cities, multi-family homes in Santa Cruz are not individually metered and billed by the water department and are therefore not represented in our billing data. This omission biases our income distribution upward because multi-family homes tend to house lower-income families. 69 Additionally, multi-family homes may have more inelastic demand, increasing their vulnerability to higher water rates. 33,70 These limitations lead to an underestimate of the fraction of the population experiencing unaffordability both now and in the future. Addressing this gap will require future work using household surveys or other data collection efforts to better assess water use and affordability among multi-family and other hard-to-reach populations. 71 Despite these limitations, the qualitative insights from this study are robust. Where climate stress intersects with expensive adaptation options, constrained rate design, and existing inequality, climate change can substantially worsen water affordability. The framework presented here is intended not to predict exact outcomes for all cities, but to help utilities and policymakers identify when and why climate adaptation may pose risks to equitable water access. Materials and Methods Case Study We apply our model to Santa Cruz, a water-stressed city with high income inequality on California’s central coast. Relying on locally sourced surface water for around 95% of total supply, Santa Cruz is highly vulnerable to climate-driven water stress. 50 The Santa Cruz Water Department (SCWD), the municipal utility, serves 96,000 residents, managing a system anchored by the Loch Lomond Reservoir, designed to store about a year’s supply of water (see Figure S12 with a watershed map). 50 Santa Cruz, with a median household income of about $91,900 in 2020 dollars, has high income inequality with 20% of households below the federal poverty level (compared to 11% nationwide) and 20% earning more than $200,000 annually (14% nationwide). 50,72,73 The presence of the University of California, Santa Cruz skews the population younger with 27% of residents aged 20-29 (15% statewide). 50,74 As a public utility in California, SCWD is limited in how they can structure water rates, fund assistance programs, and increase rates due to Prop. 218. 48,61 Data We use multiple city- and household-level data sources to parameterize the model. We tailor model parameterization to best estimate current conditions and processes in Santa Cruz. At the city scale, we obtain historical data on utility operational costs, financing, and rates from SCWD’s long-range financial reports; 75,76 and historical water use data based on the 2020 Urban Water Management Plan. 50 At the household level, we use SCWD monthly water billing data for every account active between January 2009 and December 2021 (n=2,836,297 bills). Accounts are filtered for adequate length and quality, as detailed in Section S8. We merge the billing data with physical housing characteristics from tax records, block-group-level census data, and hydrological data, listed in Table S5. Water Supply Balance Model We use the Santa Cruz Water System Model (SCWSM) to estimate water allocations. 77 This model uses Pywr, a Python-based water resources simulation modeling library, 78 to simulate daily operations using linear programming and determine how much water should be provided and from which sources. 79 The model includes current and potential future infrastructure, hydrology, and system demands. More details can be found in Section S9. Climate and Hydrological Modeling Climate scenario generation involves two steps: first, developing stochastic weather simulations that reflect historical climate variability; and second, altering these simulations to capture different climate change impacts using the anomalies from climate models. The resulting simulations capture both short-term stochastic variability and long-term climate impacts and are used to force a hydrological model to develop streamflow scenarios. The contrasting climate scenarios differ in the range of anomalies utilized from CMIP6 projections. 80 The moderate, cool climate reflects similar climate conditions to historical data and is used in the baseline and moderate climate with adaptation scenarios, while the dry, hot climate reflects worst-case, plausible climate impacts, used in the dry climate with adaptation (see Figure S1). To test the full range of climate variability, we utilize a scenario called “All Climate Simulations,” where we test all combinations of climate anomalies. See Section S1 for more details on climate scenario generation. 81 The simulations are run through a lumped hydrological model for the San Lorenzo River, which comprises the majority of water within the basin. Then, regression relationships from historical records are used to develop daily flow records at all locations, as further discussed in Section S10. Infrastructure Deployment We simulate infrastructure deployment under water stress using rule-based risk-of-failure (ROF) thresholds. These probability-based triggers incorporate both supply and demand by estimating the likelihood that reservoir storage will fall below a critical threshold (e.g., deadpool levels) within a specified time horizon, as applied in similar urban water supply planning studies. 45,64 One benefit of the ROF approach is that we can easily include deployment time so that infrastructure does not come online instantaneously. The planning strategy utilized first builds a desalination plant when the ROF value surpasses a given threshold, although alternative strategies are compared later in the Results (see Figure 5). For the baseline planning strategy, Figure S2 shows the infrastructure deployment patterns for all scenarios with adaptation as well as the frequency of the number of deployed infrastructure options. We evaluate ROF triggers annually based on three dimensions: current reservoir storage, annual water demand, and planned or implemented infrastructure options. We construct ROF lookup tables by partitioning stochastic climate simulations into two-year segments, running hundreds of two-year simulations, and estimating the probability of storage falling below the critical threshold for each combination of system conditions. Section S11 includes more details on ROF development, including an illustration of system characteristics and thresholds. Infrastructure Options & Planning Strategies We model five infrastructure options, ranging from 0.5 to 4 MGD in capacity, that can be implemented individually or combined: two water transfers, aquifer storage and recovery (ASR), direct potable reuse, and desalination. SCWD is currently considering all included infrastructure options, in various portfolios, in long-term planning efforts. Techno-economic details, including capital and operating costs, based on design studies by SCWD, are included in Table S6. 82 We develop multiple infrastructure planning strategies that include the ROF threshold and infrastructure deployment order (see Table S4). We optimize planning strategies with respect to utility costs and system reliability using a multi-objective approach. 43,44,83 We calculate utility cost as the summation of total utility infrastructure capital and operating costs and reliability using average annual total unmet demand. We average both objectives across twenty climate simulations used in optimization. We use the Borg multiobjective evolutionary algorithm to determine Pareto-approximate infrastructure strategies, detailed in Section S13. 84 We choose one planning strategy as a “baseline” approach determined through conversations with SCWD staff and quantitative analysis comparing reliability declines during historical and simulated drought events, further discussed in Section S5. Modeling Water Demands We use a Discrete/Continuous Choice (DCC) model, an econometric model designed to estimate water demands under increasing block tariffs, to simulate single-family household water demands at the household level. 85,86 We estimate households’ monthly water use (w) as: where p is the marginal price of water; y is home tax value (a proxy for income 87 ); Z is a matrix of housing characteristics and monthly weather data; ɑ, γ, and δ are the estimated model coefficients; W is household water use after including d HH , a direct bias correction term for each household. 85,86 More details on model parameterization and performance are in Section S14 and all model coefficients are listed in Table S7. We achieve a model performance of r 2 =0.36 at the household scale, which is comparable with state-of-the-art applications in other regions, and r 2 =0.78 at the income group scale. 38,86 We use the DCC model estimation results to simulate demands for 21,370 single-family residential accounts. We resample the 21,370 accounts from the household-level data so that our outputs for total single-family residential water use match 2020 water use trends and so that the distribution of household properties matches current distributions across the SCWD service area. For our affordability assessment, we estimate household-level income using an approach that maintains the distributions of household income at the census block group scale, which is our unit of analysis. We use a multivariate regression approach to estimate each household’s income and then apply quantile mapping to assign household income estimates to one of sixteen income bins utilized by the American Community Survey at the block group level, detailed in Section S15. Because our household sample size is large, the effect of error in the regression approach is negligible when aggregating from the household scale to income class scale, which we confirm in Table S8 and Figures S18-S21. We define low-income households as those below the California Poverty Measure income threshold of US$39,900 per year in 2023. 88 We assess affordability burdens using the affordability ratio, which compares simulated monthly household water bill costs to income during periods with the highest water rates. 3,8,51 We also include total water demands for other customer classes, including multi-family residential and non-residential (commercial, institutional, industrial, and irrigation 50 ) classes, and water losses, to simulate realistic total service area demands. For multi-family and non-residential classes, we create three stationary scenarios to capture a range of plausible conditions. We use the 2020 Urban Water Management Plan data to obtain baseline (average), high (10% above maximum), and low (10% below minimum) average total demands, which we overlay with monthly anomalies to model demand seasonality, based on the water billing data (Figure S22). 50 For water losses, we use a constant scenario of 201 MG/year based on the 2020 Urban Water Management Plan. 50 Infrastructure Financing and Rate Design Model The infrastructure financing model translates new infrastructure investment costs to single-family household water rates following three main steps. First, we determine utility revenue requirements related to the portfolio of infrastructure options. We use a cashflow model to calculate the additional required revenue for the utility to recuperate, categorized into three components: pay-as-you-go costs, where the current year’s revenue funds capital expenditures; debt-financed costs, where revenue covers loan repayment; and annual operating costs, which are incurred once infrastructure comes online. 76 General operations and maintenance expenses are modeled exogenously, as further discussed in Section S17. Second, we conduct a cost-of-service analysis based on rate design guidance in the AWWA manual where we determine that single-family households should pay for 38% (78%) of any constructed and operated infrastructure accrued through volumetric rates (fixed fees) based on historical volumetric charges (total number of accounts). 89 Third, we design updated water rates. Santa Cruz uses an increasing block rate (also called an increasing block tariff), where the unit price of water increases above certain thresholds of water use (0-5, 6-9, and 10+ ccf). 48 Volumetric rates include two components: an Infrastructure Reinvestment Fee charge, which funds pay-as-you-go and debt-financed costs; and a volumetric consumption charge, which funds operating costs. We compute rate increases from new infrastructure using tier cost ratios, simulated water demands, and updated revenue requirements. Historically, the ratios between tier 1 and tier 2 or 3 costs, listed in Table S10, have been fairly consistent. 75,76 Since volumetric rates depend on water demands, but water demands are inelastic based on marginal water prices, we model this feedback cycle once through to ensure updated rates reflect updated demands. We update rates when infrastructure is planned to fund the investments, when infrastructure is deployed to fund operational costs, and when investments are paid off. We illustrate the process from updated revenue requirements to rates and demands in Figure S23. Other financing assumptions are detailed in Table S11. Sensitivity Analysis We perform sensitivity analysis on a sample of parameters in our framework. For the optimization, we test one uncertain parameter for each of the three main components of our modeling framework. For the simulation, we perform a fractional factorial analysis, where we analyze high and low values for each parameter, evaluating all parameter combinations. 90,91 We do this separately for (1) infrastructure financing and rate design and (2) demand and demographic parameters, as described in Section S3. Declarations Author Contributions: SF conceptualized the study. SF, JS, CK, BF, and AV designed the methodology. JS, CK, BF, and CB provided software. JS performed the analysis. JS, SF, and CK analyzed data. SF provided supervision. JS and SF drafted the manuscript. JS, SF, BF, and CK revised the manuscript. Competing Interest Statement: The authors do not have any competing interests. Classification: Physical Sciences: Sustainability Science Code Availability All non-proprietary data and software can be found at DOI: 10.5281/zenodo.18752481 (https://zenodo.org/records/18752481) with supplemental published separately at DOI: 10.5281/zenodo.18752509 (https://zenodo.org/records/18752509). Acknowledgments We thank the Santa Cruz Water Department, including Rosemary Menard, Kyle Peterson, Sarah Easley Perez, Heidi Luckenbach, and Taylor Kihoi; Claudia Llerandi from Kennedy Jenks; Aliyah Hamilton and Lesly Rodriguez for their work on the project during their Stanford Undergraduate Research Fellowships; and Lillian Lau for technical assistance. ChatGPT was used to support code development (plotting and debugging) and editing (suggesting language edits for clarity and conciseness) with thorough review by the authors. This material is based upon work supported by the NSF under Grant No. 2337668. Co-author J. Skerker was supported by the TomKat Graduate Fellowship for Translational Research. References Mack, E. A. & Wrase, S. A Burgeoning Crisis? A Nationwide Assessment of the Geography of Water Affordability in the United States. PLOS ONE 12 , e0169488 (2017). Jones, P. A. & Moulton, A. The Invisible Crisis: Water Unaffordability in the United States. 64 (2016). Teodoro, M. P. Measuring Household Affordability for Water and Sewer Utilities. Journal AWWA 110 , 13–24 (2018). Teodoro, M. P. & Thiele, R. Water and Sewer Price and Affordability Trends in the United States, 2017–2023. Journal AWWA 116 , 14–24 (2024). Patterson, L. A., Bryson, S. A. & Doyle, M. W. Affordability of household water services across the United States. 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Vulnerability of Water Systems to the Effects of Climate Change and Urbanization: A Comparison of Phoenix, Arizona and Portland, Oregon (USA) | Environmental Management. https://link.springer.com/article/10.1007/s00267-013-0072-2. Naseri, M. Y., Bernosky, G., Mayer, P. W. & Marston, L. T. Patterns and Predictors of Residential Indoor Water Use Across Major US Cities. Earth’s Future 13 , e2024EF005467 (2025). Fagundes, T. S., Marques, R. C., Ferreira, D. F. da C. & Malheiros, T. F. Exploring water affordability through subsidy policies. Water Research 286 , 124251 (2025). Reibel, M., Glickfeld, M. & Roquemore, P. Disadvantaged communities and drinking water: a case study of Los Angeles County. GeoJournal 86 , 1337–1354 (2021). Tables Table 1. Urban Household Affordability Drivers A list of unaffordability drivers, their household-level impacts, context in Santa Cruz, and other cities exhibiting similar characteristics in California and globally. Unaffordability Driver Household affordability impacts Santa Cruz / California context Cities exhibiting similar characteristics to Santa Cruz Climate change-driven water stress Necessitates investments in supply- or demand-side measures for water supply reliability, which can be expensive and are often paid for through rate increases. Global climate models show much of the region getting hotter and drier, with more extreme dry and wet events. California: Los Angeles, San Diego, San Francisco; Global: Cape Town, South Africa; Melbourne, Australia 92–94 Expensive supply expansion alternatives Larger infrastructure options tend to be more costly, requiring greater rate increases, increasing household water costs and worsening affordability impacts. Santa Cruz is considering one or multiple infrastructure options, including: a desalination plant, direct potable reuse, aquifer storage and recovery, and transfers with two neighboring utilities. California: Los Angeles, San Francisco; Global: Singapore; Tel Aviv, Israel; Melbourne, Australia 95,96 Limited over-year water storage Limits the utility's ability to store water from wet years for use during dry years, limiting what alternative water supply infrastructure can be utilized, potentially necessitating higher investment costs for new infrastructure. Loch Lomond, Santa Cruz's main reservoir, only holds about one year of water supplies. California: East Palo Alto, San Jose; Global: Cape Town, South Africa Limited conservation opportunities When households are already using water efficiently due to water-saving appliances and limited outdoor/discretional use, there are limited opportunities for households to cut back on water use during droughts or rate increases, making households sensitive to rate changes. Santa Cruz has already implemented many conservation measures, leading to low gains from new efficiency investments. California: San Francisco, San Jose, San Diego, Oxnard; Global: Portland, OR; Singapore 97,98 Income inequality More low-income or disadvantaged households can lead to larger affordability impacts across a water system. Santa Cruz has large income equality with about 20% of households below the federal poverty line and another 20% earning more than $200K annually. California: Oakland, San Francisco, Los Angeles; Global: Barcelona, Spain; Rio de Janeiro, Brazil 99,100 Institutional constraints on rate design When utilities are constrained in how they structure rates or provide subsidies, infrastructure costs are passed directly to households, often through higher baseline charges or volumetric rates that do not protect essential use. This limits utilities’ ability to shield low-income households from rate increases associated with climate adaptation. In California, Proposition 218 requires rates to reflect proportional cost of service, limiting cross-subsidization and many forms of income-based assistance. Santa Cruz already exhibits high baseline rates and limited flexibility to design affordability-protective pricing, amplifying the household impacts of new infrastructure investments. California: all public utilities impacted Global: Rio de Janeiro, Brazil 99 Additional Declarations There is NO Competing Interest. Supplementary Files SIJan2026.docx Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7603314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626503441,"identity":"78bc0600-74e3-42d4-b7e3-80cc477a3434","order_by":0,"name":"Jennifer Skerker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3OsWrDMBCA4QsCdVHjNcZDXuGMwWBi+iwqAq3p2PGg0I5aXchzZJYROIshqyBTyZohoVBaMrRJ062geuygfxA6SR8IIBb7h42In1YLkHyP9WWYDCIpsdNGDyAAPwTtUMJMd7u97+t5sV63r3eyno8tazci9LFGu7z3ulp6xbJG6urZcjULk6vHlPYOS88gu353iFaUWZCY1dMH7T+xMI4dhTyT5C1MSHcj8hYRFM8uRPAwabRKqVc48aqcCakxdbyoFgGSmy4/UHeDiWm3GyFrHK8eXvwuROjXEQs8Pzf94z4Wi8ViAF+VXU5cxzw3yQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7022-6337","institution":"Stanford University","correspondingAuthor":true,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Skerker","suffix":""},{"id":626503442,"identity":"ba56cc46-7c76-4127-b7db-5f8b5bd28b75","order_by":1,"name":"Christian Klassert","email":"","orcid":"https://orcid.org/0000-0003-0676-2455","institution":"Helmholtz Centre for Environmental Research (UFZ)","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Klassert","suffix":""},{"id":626503443,"identity":"cbedfa80-fc3e-4d29-a187-af26cfff81b6","order_by":2,"name":"Baptiste Francois","email":"","orcid":"","institution":"University of Massachusetts Amherst","correspondingAuthor":false,"prefix":"","firstName":"Baptiste","middleName":"","lastName":"Francois","suffix":""},{"id":626503444,"identity":"47db70fa-5624-4f54-ab6b-7b419432acbe","order_by":3,"name":"Aniket Verma","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Aniket","middleName":"","lastName":"Verma","suffix":""},{"id":626503445,"identity":"662d0c71-5ada-452e-8ee6-28ba30850879","order_by":4,"name":"Casey Brown","email":"","orcid":"","institution":"University of Massachusetts Amherst","correspondingAuthor":false,"prefix":"","firstName":"Casey","middleName":"","lastName":"Brown","suffix":""},{"id":626503446,"identity":"4e90cff0-578a-4830-88e5-c8732322d728","order_by":5,"name":"Sarah Fletcher","email":"","orcid":"https://orcid.org/0000-0003-3289-2237","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Fletcher","suffix":""}],"badges":[],"createdAt":"2025-09-12 20:20:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7603314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7603314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107973918,"identity":"254e2778-5b6a-48d3-9365-d918886e233b","added_by":"auto","created_at":"2026-04-28 07:19:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":317282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel framework and example simulations\u003c/strong\u003e a) Affordability modeling framework, which integrates the water supply system with infrastructure financing, rate design, and household water demand patterns. Boxes shaded in yellow indicate utility decisions, with patterns indicating: decision variables assessed in this work (solid) and parameters chosen to match the case study and then tested in the sensitivity analysis (stippling). Blue boxes indicate model objectives and gray boxes indicate exogenous and endogenous parameters. An illustrative example to visualize the mechanisms through which infrastructure development impacts affordability, including b) reservoir storage levelsunder sample simulated dry, hot and moderate, cool climates and ROF values for the dry, hot scenario. c) Sample low- and high-income annual household affordability by climate scenario. We highlight the period from 2020-2050 for illustrative purposes, since all climate simulations are stationary. Households were randomly sampled from those below the poverty line and those in the upper quartile for income. The dashed vertical lines indicate infrastructure planning and deployment for the dry, hot climate simulation conditional on reservoir storage, total water demands, and previously-deployed infrastructure. The time between infrastructure planning and deployment shows the construction time, which is incorporated into our modeling framework.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/0da79c69c6112810f4ea0728.png"},{"id":107973913,"identity":"649ca137-9d22-4536-8b3d-1b0eeb1bd3b2","added_by":"auto","created_at":"2026-04-28 07:19:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":562440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjected climate change and adaptation impacts on household affordability\u003c/strong\u003e Cumulative distribution functions (CDFs) across four projected climate scenarios, each with multiple stochastic climate simulations: a baseline similar to the present-day, a moderate climate with adaptation, a dry climate with adaptation, and all climate simulations for a) total city reservoir storage, b) new utility supply costs, c) household water bills and d) household affordability ratios. Horizontal axes for c) and d) are truncated for visual clarity.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/6fbedffdac98e519e21bf2fe.png"},{"id":108007758,"identity":"d3b64aac-17a4-42c3-bab4-8be439f0707f","added_by":"auto","created_at":"2026-04-28 13:01:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLow-income household affordability impacts\u003c/strong\u003e Boxplots comparing low (\u0026lt;$39,900/yr) and all other income classes for a) water demands, b) water bills, and c) affordability ratios across projected climate scenarios. Distributions show monthly values for all households across multiple stochastic climate simulations. Dotted line in c) denotes the EPA affordability threshold of 2.5%.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/d073a011658b4d415bca45b4.png"},{"id":108007523,"identity":"632da81d-4a52-45d6-a61d-6e12ed2c30fd","added_by":"auto","created_at":"2026-04-28 13:00:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClimate uncertainty impacts on affordability\u003c/strong\u003e Boxplots comparing the distribution of monthly affordability ratios under the baseline scenario vs varying climate impacts across the entire analysis range with adaptation, including changes to: average precipitation (multiplier of historical average), annual precipitation variability (multiplier relative to historical coefficient of variation), and temperature (change from historical average in degrees Celsius). Distributions show monthly values for all households across each subset of sampled stochastic climate simulations. The dashed line indicates the EPA affordability threshold of 2.5%.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/144fd5d536ea72cd27945700.png"},{"id":107973917,"identity":"5ada5853-20e2-48ab-94a8-03c525a81bc3","added_by":"auto","created_at":"2026-04-28 07:19:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":501670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfrastructure planning strategy comparison on utilities and households\u003c/strong\u003e a) Scatter plots comparing average added water supply costs and unmet demands for each optimal planning strategy. Shading indicates optimal risk-of-failure (ROF) threshold value and shape indicates the first infrastructure option deployed under that planning strategy. All strategies are optimized over twenty climate simulations representing the range in our analysis. Scatter plots comparing three planning strategies, highlighted in panel 5a, across 500 simulations of plausible climate change impacts for b) added supply cost and average unmet demand, and c) 80th percentile affordability ratios and overall water system reliability. Kernel density estimation plots in b) and c) show the distributions of metrics for each strategy. All scatter points show metric values for a single climate simulation.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/671d2eb73b62ef8524250e76.png"},{"id":108181117,"identity":"6a9093a8-2e15-48b7-92bd-67bd66b1db6b","added_by":"auto","created_at":"2026-04-30 08:57:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1875649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/407aafb1-d236-4e8f-926a-fae43a10f082.pdf"},{"id":108007374,"identity":"efbe5e99-5269-4a5a-a07c-aa4922a7de97","added_by":"auto","created_at":"2026-04-28 12:59:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14690858,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SIJan2026.docx","url":"https://assets-eu.researchsquare.com/files/rs-7603314/v1/3e0719983bdabaa61bda9579.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Climate change exacerbates water affordability crisis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater affordability is a growing challenge\u0026nbsp;in high-income countries across the globe.\u003csup\u003e1–7\u003c/sup\u003e Over the past two decades, water\u0026nbsp;rates have risen three times faster than inflation,\u003csup\u003e8,9\u003c/sup\u003e driven\u0026nbsp;primarily by deferred maintenance and aging infrastructure.\u0026nbsp;If\u0026nbsp;historical trends continue, more than one third of US households could face unaffordable water bills within a decade.\u003csup\u003e1\u003c/sup\u003e However, emerging contaminants requiring advanced treatment and climate change necessitating new supplies are compounding longstanding cost pressures for utilities.\u003csup\u003e1,10–12\u003c/sup\u003e Unaffordable water\u0026nbsp;limits\u0026nbsp;access for drinking, cooking, and hygiene\u003csup\u003e2\u003c/sup\u003e or forces difficult trade-offs with other necessities like food and healthcare.\u003csup\u003e3,13,14\u003c/sup\u003e Inadequate and unaffordable water access exacerbates the risk of disease and negative health impacts,\u003csup\u003e15\u003c/sup\u003e disproportionately impacting low-income and minority communities.\u003csup\u003e2,16\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn many\u0026nbsp;cities around the world, climate change challenges utilities’ ability to provide reliable, affordable water access\u003csup\u003e17–19\u003c/sup\u003e by straining supplies\u003csup\u003e17,18,20\u003c/sup\u003e and driving demands higher as temperatures rise.\u003csup\u003e21–23\u003c/sup\u003e On the supply side, climate change can alter the frequency, severity, and duration of droughts\u003csup\u003e24–26\u003c/sup\u003e and cause drier baseline conditions\u0026nbsp;in some places.\u003csup\u003e19,27\u003c/sup\u003e Utilities respond by either\u0026nbsp;expanding supplies or reducing demands.\u003csup\u003e28\u003c/sup\u003e Utilities increase\u0026nbsp;water supplies by diversifying supply portfolios, often through costly infrastructure projects.\u003csup\u003e29,30\u003c/sup\u003e In the US, new water infrastructure is typically financed by rate increases to customers,\u003csup\u003e10,31\u003c/sup\u003e which can worsen household water affordability challenges.\u003csup\u003e1,32\u003c/sup\u003eUtilities also manage demands by implementing short-term curtailment measures during droughts or water-use efficiency programs.\u003csup\u003e28,33\u003c/sup\u003e Demand-side measures can increase water costs through surcharges needed to pay for lost volumetric revenue or investments in efficiency upgrades and monitoring equipment.\u003csup\u003e28,33\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent empirical studies highlight growing affordability challenges\u003csup\u003e1,2,4,34\u003c/sup\u003e but offer limited insight into future trends.\u0026nbsp;Recent work projects rising water affordability challenges by extrapolating historical cost data but does not analyze specific cost drivers like climate that may change in the future.\u003csup\u003e1\u003c/sup\u003e Affordability is driven not only by utility costs but also interactions between social and institutional factors like demand patterns and rate structures,\u003csup\u003e35\u003c/sup\u003e but affordability estimates typically neglect these factors.\u003csup\u003e5,36\u003c/sup\u003e One study highlights cost-demand interactions, demonstrating how low-income rate assistance programs, which decrease household water costs, can increase use.\u003csup\u003e9\u003c/sup\u003e Additionally, most empirical studies assess affordability at a single point in time,\u003csup\u003e3,5\u003c/sup\u003e neglecting seasonal and temporal variability that may exacerbate short-term affordability challenges.\u003csup\u003e4,35,37,38\u003c/sup\u003e High quality projections of future water affordability challenges are essential to developing effective and sustainable policy solutions at the city, state, and federal levels.\u003c/p\u003e\n\u003cp\u003eDrought and climate change in particular may prompt costly water infrastructure investments, but their affordability impacts are poorly understood. Extensive research assesses water supply infrastructure needs due to climate change, but focuses on utility-scale costs and performance\u003csup\u003e39–42\u003c/sup\u003e rather than household-scale water demands and affordability.\u003csup\u003e43,44\u003c/sup\u003e Additionally, infrastructure financing and rate design are typically analyzed from a utility rather than household cost perspective.\u003csup\u003e43,45,46\u003c/sup\u003e One recent paper makes important advances in evaluating how climate-related infrastructure impacts low-income households but does not consider demand feedbacks or financing mechanisms.\u003csup\u003e47\u003c/sup\u003e Other work explicitly models affordability impacts of rate increases due to droughts but focuses on short-term impacts, not climate change.\u003csup\u003e33,48\u003c/sup\u003e To our knowledge, no study has comprehensively assessed the integrated climate, infrastructure, and\u0026nbsp;demand drivers to quantify climate change impacts on low-income water affordability.\u003c/p\u003e\n\u003cp\u003eOur research objective is\u0026nbsp;to quantify the impact of climate change on urban water affordability.\u0026nbsp;We develop a rigorous city-scale modeling framework that captures the integrated climate,\u0026nbsp;utility adaptation decisions, and\u0026nbsp;demand\u0026nbsp;drivers\u0026nbsp;shaping\u0026nbsp;household affordability burdens in US cities. We combine a hydrological and water supply system model with infrastructure financing and rate design models, and an econometric model of household water demands across different demographic groups. We\u0026nbsp;assess the affordability impacts of utility adaptation decisions needed to maintain reliable water supply in future climates.\u0026nbsp;This can include\u0026nbsp;new infrastructure investments, financing mechanisms, rate structures, assistance, curtailment policies, and efficiency programs. We hypothesize that climate change could create water affordability hotspots, where existing affordability challenges are exacerbated in cities with water supply vulnerability and limited demand management opportunities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe complete a city-scale assessment of climate change impacts on water affordability using Santa Cruz, California as a case study and focusing on mid-century impacts. Santa Cruz has low residential water use due to the city’s previous drought experiences,\u003csup\u003e49\u003c/sup\u003e limiting the scope for low-cost adaptation options like demand reduction via curtailment or efficiency policies. It is also reliant on local surface water, making it vulnerable to drought.\u003csup\u003e50\u003c/sup\u003e By examining a city that has largely exhausted lower-cost options for climate adaptation like demand-side management, we focus on costly, long-term infrastructure decisions and highlight challenges likely faced by other cities with similar climate, behavioral, and regulatory conditions.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAssessing affordability implications of climate change impacts and adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe present a modeling framework to quantify the water affordability impacts of climate change effects and related adaptation measures on water supply and demand. Our model captures the key interacting climate,\u0026nbsp;utility adaptation decisions, financing, and demand drivers that determine household affordability (Figure 1a). First, we input future climate scenarios, developed by combining a climate model ensemble with a stochastic weather generator, into water supply systems models, which simulate river flows at the reservoir and diversions, and water allocations from the diversions to the water treatment plants. Second, we model utility adaptation decisions using a risk-of-failure approach in which utilities take action in response to declining storage levels. Third, we assess the water rate impacts of those interventions using an infrastructure financing and rate design model. Using Santa Cruz as a case study, we focus on utility decision-making around infrastructure investments, evaluating multiple planning scenarios that determine when and what new infrastructure is developed (more details in the \u003cem\u003eMethods\u003c/em\u003e and \u003cem\u003eSI\u003c/em\u003e). Other utility adaptation decisions (highlighted in yellow) are modeled as fixed assumptions consistent with current Santa Cruz policies and practices, with their effects explored through sensitivity analysis. Third, we simulate household water use patterns as a function of demographics, climate, water costs, and housing characteristics. We capture dynamic feedback across these three analyses that determine how household behaviors impact water supplies and how city decisions affect households. For example, when the city builds new infrastructure, they pay for that infrastructure by increasing water rates; when water bills rise, households use less water, altering how much water the system needs. Finally, the outcome of our framework is estimated water bill and affordability burdens (the percentage of household income spent on water bills) across income groups. See \u003cem\u003eMethods\u003c/em\u003e for details.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Using this framework, we assess how different climate scenarios drive unequal affordability impacts. Figure 1b-1c presents two\u0026nbsp;illustrative climate simulations selected to visualize the mechanisms through which infrastructure development affects affordability. The simulations show how contrasting climate conditions shape Santa Cruz’s infrastructure needs and affordability burdens for two sample households. In one plausible moderate, cool climate simulation, no new infrastructure is needed, although the low-income household is already spending more than the 2.5 percent affordability threshold recommended by the U.S. Environmental Protection Agency (EPA) (Figure 1c).\u003csup\u003e1,3,51\u003c/sup\u003eIn\u0026nbsp;one plausible\u0026nbsp;dry, hot climate simulation, declining\u0026nbsp;reservoir storage levels trigger construction of a four million gallon per day (MGD) desalination plant, leading to rate increases and higher water bills.\u0026nbsp;In this example, the affordability burden for a sample low-income household increases from four to six percent, while a sample high-income household has negligible impacts. These examples demonstrate how climate stress can exacerbate underlying affordability burdens for\u0026nbsp;low-income\u0026nbsp;households.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMore households face unaffordable water under a drier climate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, after using an example to illustrate how climate exacerbates affordability, we aggregate results across many stochastic simulations. In Santa Cruz, our scenarios show\u0026nbsp;that climate change\u0026nbsp;could\u0026nbsp;nearly double water bills, which could leave an additional 7-16% of households with unaffordable water (Figure 2).\u0026nbsp;We test\u0026nbsp;four\u0026nbsp;plausible\u0026nbsp;scenarios that combine contrasting climates and infrastructure planning adaptation\u0026nbsp;strategies: first, our “baseline” scenario where no new infrastructure is built and the climate is moderate and cool, similar to the present-day; second, a “moderate climate with adaptation” where new infrastructure is built in response to water stress (“as needed”)\u0026nbsp;under the same moderate, cool climate; third, a “dry climate with adaptation”, where new infrastructure is built as needed under a dry, hot climate; and\u0026nbsp;fourth, an “all climate simulations” scenario, with the entire range of climate simulations. We\u0026nbsp;choose the\u0026nbsp;moderate, cool and dry, hot climates, to be contrasting but within the range of CMIP6 (Figure S1). For each climate, we develop multiple 50-year stochastic climate simulations, developed by using precipitation and temperature changes in the CMIP6 ensemble to inform a stochastic weather generator. In the scenarios with adaptation, new infrastructure is deployed as needed using a risk-of-failure (ROF) approach, in which the utility develops new infrastructure when the projected risk of storage falling below a critical threshold within two years exceeds a defined level, based on current reservoir storage, water demands, and prior investments (see \u003cem\u003eMethods\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Figure S2)\u003c/em\u003e. The planning strategy utilized first builds a desalination plant when the ROF value surpasses a given threshold, although alternative strategies are compared later in the Results (see Figure 5). We\u0026nbsp;analyze\u0026nbsp;the periods where\u0026nbsp;infrastructure\u0026nbsp;investments lead to the\u0026nbsp;largest\u0026nbsp;rate increases,\u0026nbsp;using these\u0026nbsp;periods\u0026nbsp;to estimate monthly reservoir storage, added water supply costs\u0026nbsp;for new infrastructure, monthly water bills, and affordability burdens across households, resulting in a distribution of outcomes for each scenario.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe assess climate impacts on the water system driving the need for new infrastructure, which, without further interventions, then translate to household bills and affordability burdens. Under the moderate climate with adaptation, building more infrastructure leads to greater water availability via higher reservoir storage compared to the baseline scenario (Figure 2a). In contrast, the dry climate with adaptation results in decreased and more variable reservoir storage. Low reservoir levels risk supply shortfalls, triggering new infrastructure investments. Differences in reservoir storage between the scenarios with adaptation drive varying supply costs, impacting household water bills (Figure 2b). Under the moderate climate with adaptation, 60% of months require no new supply costs, whereas over 50% of months under the dry climate with adaptation require at least $1M in additional capital and operating costs to maintain reliable supply. Because we assume these costs are fully passed on to households and that no mitigating policy interventions are implemented, infrastructure investments translate directly into substantial increases in household water bills (Figure 2c). 50th percentile bills increase from $64 to $80\u0026nbsp;(moderate climate with adaptation) or $120\u0026nbsp;(dry climate with adaptation).\u0026nbsp;80th percentile bills rise from $100 to $148\u0026nbsp;(moderate climate with adaptation) or $204\u0026nbsp;(dry climate with adaptation). Under the dry climate with adaptation, median water bills could nearly double from current levels.\u003c/p\u003e\n\u003cp\u003eRising bills increase the proportion of households paying more than the EPA’s affordability threshold (Figure 2d). Currently, 19% of households exceed this threshold, highlighting how\u0026nbsp;Santa Cruz already exhibits affordability challenges. This share could rise to 26% under a moderate climate and to 35% under a dry climate, when additional infrastructure is built for reliability. In the dry climate more than one third of households in Santa Cruz could struggle to afford water, highlighting the scale of potential impacts without any additional policy interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate change exacerbates low-income unaffordability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we analyze how simulated demands, bills, and affordability trends for low-income households compare to the remaining population in Santa Cruz, finding that while the demands and bills of the former are lower, their affordability burdens are substantially higher (Figure 3). First, we compare water demand differences between low-income households and all other households. Differences across income groups and scenarios are small. Across the scenarios, average low-income water use is about 0.4 hundred cubic feet (ccf) less than other households, with 80th percentile demands increasing to around 0.6 ccf lower than all other households (Figure 3a). Small demand differences across income groups align with previous work that finds widespread demand hardening in Santa Cruz due to household efficiency and conservation behaviors.\u003csup\u003e50\u003c/sup\u003e Under the moderate climate with adaptation, building infrastructure decreases demands by about 0.5 ccf, mainly due to increased water prices and household price elasticity responses. Between the moderate and dry climates with adaptation, differences in demands are negligible (\u0026lt;0.1 ccf), as greater temperatures and decreased precipitation counteract cost-driven demand decreases. Small demand differences across scenarios illustrate how the affordability impacts of climate change in Santa Cruz are driven by supply-side factors over demand-side ones. Sensitivity analysis on modeled demand parameters shows that while changing price elasticity and single-family home demands can affect affordability outcomes, climate factors play a larger role in our case study (see Section S3).\u003c/p\u003e\n\u003cp\u003eIn comparison to demands, water bill differences across climate scenarios and affordability differences across income groups, are larger (Figure 3b-3c). Differences in water bills between low-income and all other income group bills are small, with median differences ranging from $5/month to $11/month across scenarios. In contrast, when we compare the baseline scenario to the dry climate with adaptation, low-income median bills increase from $60/month to $111/month ($51 increase) and 80th percentile bills increase from $92/month to $186/month ($94 increase). While low-income bills are smaller than other income groups’, low-income affordability impacts are greater. Under the baseline scenario, median low-income affordability burdens are 3.9%, which is already greater than the EPA’s threshold, underscoring existing affordability challenges in Santa Cruz (Figure 3c). Under the moderate climate with adaptation, building infrastructure increases low-income median affordability burdens to 5.1% (0.6 to 0.8% for other incomes). The dry climate with adaptation increases low-income median affordability burdens further to 7.3% (1.1% for other incomes).\u003c/p\u003e\n\u003cp\u003eAdditionally, we see that the low-income 90th percentile affordability burdens increase from 16 to 30 percent of income. Likely, households paying this much for water are forced to choose between essential expenditures,\u003csup\u003e3,13\u003c/sup\u003e highlighting the vulnerability of low-income households to water price increases driven by climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrecipitation uncertainty impacts low-income households\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe then\u0026nbsp;assess the relative impact of different climate effects on\u0026nbsp;water affordability, finding that average precipitation decreases lead to the greatest increases in affordability burden (Figure 4). Here, we simulate hundreds of stochastic climate simulations, filtering for different characteristics individually to assess which aspects of climate change most impact affordability. Since precipitation quantity is a primary driver of water availability, declining averages\u0026nbsp;drive\u0026nbsp;new infrastructure needs. Precipitation variability also has a moderate impact on affordability burden. Even if precipitation averages stay constant, greater variability, which leads to wetter wet and drier dry periods, can necessitate additional infrastructure to ensure supply during droughts. This is particularly relevant for Santa Cruz, where the city's single reservoir has limited capacity for interannual storage, limiting its ability to buffer against extreme variability. Lastly, rising temperatures moderately affect affordability by increasing both evapotranspiration, which decreases water availability,\u003csup\u003e19\u003c/sup\u003e and household demands, though supply-side impacts are more pronounced due to demand hardening (see Figure S7).\u003csup\u003e52\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Across all climate factors, where we analyze the minimum and maximum values used throughout the analysis, the largest affordability differences are in the upper percentiles, suggesting that climate change disproportionately impacts households already struggling to pay bills. While median affordability ratios range from 1.0 to 1.5 (difference of 0.5),\u0026nbsp;boxplot upper bounds rise from 5.5 to 8.1 (difference of 2.6). This pattern likely reflects the heightened sensitivity of already burdened households to cost increases. Similar trends appear in water bill costs (Figure S8), likely due to steeper increases for high water users. Low-income households with high water usage—possibly due to larger household sizes\u003csup\u003e52\u003c/sup\u003e—may be especially vulnerable to climate-driven cost changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLarge infrastructure mitigates climate change induced reliability outages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn important driver of our results is what and when we choose to build infrastructure in our\u0026nbsp;projected scenarios, and we find that different infrastructure investment strategies shape system reliability and affordability outcomes. Our baseline strategy (“Strategy A”) prioritizes a 4-MGD desalination plant, which we choose because this option has a risk of failure threshold such that reliability declines during major drought events in the model are similar in magnitude to those of the 2013 California drought (see Section S5\u003cem\u003e)\u003c/em\u003e. Alternative utility strategies could deploy contrasting amounts of new infrastructure, resulting in different reliability impacts. To explore this, we develop optimal planning strategies that minimize new utility supply costs and unmet demands (Figure 5a). Each planning strategy dictates when and which infrastructure is built using ROF thresholds calculated based on water demands, reservoir storage, and prior investments (see \u003cem\u003eMethods)\u003c/em\u003e. We choose two additional planning strategies for comparison: one risk-averse strategy where a low ROF threshold builds large infrastructure earlier (“Strategy B”) and one risk-tolerant strategy with a high ROF threshold where smaller-capacity infrastructure is built first (“Strategy C”) (see Table S4). Applying sensitivity analysis to the optimization of our planning strategies, we find the choice of planning strategy to be insensitive to small changes in demand, deployment time, and infrastructure costs.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Comparing planning strategies A, B, and C across plausible climate change impacts highlights their substantial influence on utility costs, reliability, and affordability (Figure 5). Strategy B, with the lowest risk tolerance and earliest infrastructure deployment, ensures 98.8% average annual reliability (averaged across each simulation and then across all simulations) but adds at least $9.4M/year in costs, increasing the 80th percentile affordability burden to 5.2%. Strategy C, with delayed, lower-cost investments, limits new spending to $1.4M/year and lowers the 80th percentile affordability burden to 2.7%. While average annual reliability remains relatively high at 96.8%, the average minimum annual reliability is only 61% (i.e., the minimum annual reliability in each simulation, averaged across simulations)--likely unacceptable for most water utilities, which are typically risk averse and concerned with the worst-case outcomes, due to citywide economic risks of water shortages.\u003csup\u003e53\u003c/sup\u003e Santa Cruz, for instance, estimates that a 30% water use reduction during a water shortage could shrink economic output by over $100M (1.1-2.4% loss).\u003csup\u003e54\u003c/sup\u003e Strategy A, our baseline, has a wider cost spread due to its moderate investment approach. However, since it deploys desalination first like Strategy B, both maintain average minimum annual reliability levels above 70% (see Figure S11). Ultimately, under the current US infrastructure financing model, climate change pits affordability against reliability, despite both being essential for water access.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study develops a city-scale modeling framework to quantify the impacts of climate change on urban water affordability. Previous work documenting affordability challenges does not account for how climate change may fundamentally alter the cost structures, infrastructure needs, and household responses that shape affordability.\u003csup\u003e1,4,5\u003c/sup\u003e By explicitly modeling climate-driven water stress alongside utility adaptation decisions, infrastructure financing, rate design, and household demand and income, this study provides a comprehensive assessment of how climate change alone may exacerbate urban water affordability challenges.\u003c/p\u003e\n\u003cp\u003eA key contribution of this work is to extend the water affordability literature by connecting it to climate adaptation and infrastructure planning processes that have historically been analyzed separately. Much of the water resources literature evaluates adaptation strategies under climate change through reliability–cost tradeoffs, focusing on system performance and aggregate utility expenditures.\u003csup\u003e39–42\u003c/sup\u003e However, cost increases do not translate directly into affordability impacts. Instead, household affordability outcomes depend on how costs are recovered through rates, how households adjust water use in response to price changes, and how income is distributed across the population.\u003csup\u003e33,48,55\u003c/sup\u003e Quantifying reliability–affordability tradeoffs therefore reveals dynamics that are largely invisible in cost-based analyses and highlights distributional consequences that are central to water access but often absent from adaptation planning.\u003csup\u003e56\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the magnitude of climate-driven affordability impacts is essential for designing effective policy responses. If only a small fraction of households experiences unaffordable water bills, local customer assistance programs may be sufficient to mitigate hardship. In contrast, if a large share of the population faces affordability challenges, utility-funded assistance programs may be financially infeasible, and addressing the problem likely requires state or federal intervention through infrastructure financing, regulatory reform, or direct household support.\u003csup\u003e51,57\u003c/sup\u003e In Santa Cruz, our results suggest that climate change alone could leave an additional 7-16% of households with unaffordable water. This finding underscores the importance of evaluating climate adaptation strategies through an affordability lens.\u003c/p\u003e\n\u003cp\u003eAffordability outcomes are not determined by climate alone, but by interactions between climate stress and a set of hydrological, institutional, and social characteristics that shape how utilities respond and how costs are distributed across households. Table 1 summarizes these dimensions and clarifies the class of urban water systems for which our results are most informative. In this sense, Santa Cruz is not presented as representative of cities in general, but as illustrative of systems where climate stress interacts with constrained adaptation options and existing inequality to amplify affordability impacts.\u003c/p\u003e\n\u003cp\u003eFirst, climate change–driven water stress increases the need for adaptation to maintain reliable supply. Across California and many semi-arid regions globally, climate projections indicate higher temperatures, altered precipitation patterns, and greater hydrologic variability, increasing the likelihood of supply deficits.\u003csup\u003e19\u003c/sup\u003e Water system characteristics shape how and what utility adaptation decisions are realized. For example, in many supply constrained cities like Santa Cruz, feasible adaptation pathways involve expensive supply expansion alternatives, including desalination, potable reuse, or large-scale transfers.\u003csup\u003e50\u003c/sup\u003e These options are costly relative to conservation or operational measures, leading to larger rate increases. Additionally, limited over-year water storage can further constrain adaptation choices and accelerate the need for capital-intensive investments. Limited conservation opportunities, often the result of prior investments in efficiency and sustained demand hardening, reduce the extent to which utilities can rely on demand-side responses during droughts and shift adaptation pressure toward the supply\u0026nbsp;side.\u003csup\u003e58,59\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIncome inequality amplifies these affordability impacts. Where many households already face affordability challenges, even modest bill increases can push a large share of the population beyond common affordability thresholds.\u003csup\u003e32\u003c/sup\u003e In Santa Cruz, high baseline burdens among low-income households mean climate-driven rate increases compound existing inequities. Finally, constraints on rate design and affordability mitigation play a central role in determining distributional outcomes. Regulatory frameworks that limit cross-subsidization or restrict income-based pricing can force utilities to recover infrastructure costs in ways that disproportionately affect low-income households. In California, Proposition 218 exemplifies this constraint, limiting the tools available to utilities even as climate adaptation costs grow.\u003csup\u003e60,61\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTogether, these conditions define a class of urban water systems in which climate change is most likely to exacerbate affordability challenges. Cities that share fewer of these characteristics may experience smaller or qualitatively different impacts, even under similar climate stress. However, cities that currently appear less exposed may move toward dynamics similar to those observed in Santa Cruz over time as conservation gains are exhausted, baseline rates rise, and climate stress intensifies.\u003csup\u003e62\u003c/sup\u003e In this sense, Santa Cruz may represent not an outlier, but a plausible future state for water systems that have already utilized lower-cost adaptation options.\u003c/p\u003e\n\u003cp\u003eBeyond the Santa Cruz case study, the modeling framework developed here is designed to be broadly applicable across urban water systems. The framework integrates three components that are common across cities: a water resources systems model that represents climate-sensitive supply and infrastructure decisions, a utility financing and rate design model that translates system costs into household bills, and a household demand model that captures heterogeneity in water use and income. While each component must be parameterized with local data, the structure of the framework and the interactions it captures are not specific to Santa Cruz. As a result, the framework can be used to assess climate-driven affordability risks in other cities, while allowing results to reflect local hydrology, governance, and socio-economic conditions.\u003c/p\u003e\n\u003cp\u003eOur findings highlight a fundamental tension in urban water management. Under prevailing financing and regulatory models, climate adaptation aimed at ensuring reliability can directly undermine affordability. Addressing climate-driven water stress without worsening inequity will likely require interventions beyond the utility scale, including regulatory reform, expanded public financing of adaptation infrastructure, or targeted assistance programs funded outside of water rates. More broadly, our results suggest that evaluations of climate adaptation strategies should routinely assess affordability impacts alongside reliability outcomes. Failing to do so risks shifting the costs of climate adaptation onto households least able to bear them, even when adaptation successfully reduces physical water scarcity.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eSeveral limitations condition the interpretation of our results and clarify the scope of their applicability. First, our results are shaped by hydrologic and infrastructural constraints specific to Santa Cruz, including limited over-year storage and the availability of particular supply expansion options.\u003csup\u003e50\u003c/sup\u003e Cities with larger reservoirs, more interconnected systems, or access to lower-cost water sources may experience delayed or reduced affordability impacts. Although we evaluate climate scenarios that reflect substantially hotter and drier conditions than observed historically, we treat these conditions as stationary, likely understating affordability impacts that would arise under progressively intensifying climate change.\u0026nbsp;For example, non-stationary climate dynamics could produce tipping points in which multiple costly investments are required simultaneously or in rapid succession, sharply worsening affordability outcomes and straining utility finances over a short period. Alternatively, such dynamics may prompt premature or excessive infrastructure expansion, locking in higher utility rates that persist over decades.\u003csup\u003e63\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSecond, we represent utility decision-making using a rule-based risk-of-failure framework that captures risk-averse planning behavior in response to supply deficits but abstracts from the political, institutional, and social processes that influence infrastructure implementation.\u003csup\u003e64\u003c/sup\u003e In practice, delays related to permitting, public opposition, or financing challenges could alter both the timing and distribution of costs, potentially increasing short-term affordability shocks or reliability risks relative to modeled outcomes.\u003csup\u003e65\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThird, affordability outcomes are sensitive to rate design and financing rules, which vary widely across jurisdictions.\u003csup\u003e66,67\u003c/sup\u003e Our analysis reflects regulatory constraints typical of California public utilities, limiting cross-subsidization and income-based pricing.\u003csup\u003e68\u003c/sup\u003e In settings with greater rate flexibility or substantial external funding, affordability impacts could be mitigated even under similar infrastructure investments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, we treat demographic composition, income distributions, and household behavior as static within each scenario. While we test sensitivity to population size, composition, and price elasticity, we do not model long-term demographic change, migration, or endogenous behavioral adaptation. These dynamics could either diffuse or concentrate affordability burdens over time, depending on local housing markets and economic conditions.\u003c/p\u003e\n\u003cp\u003eFinally, our household-level analysis focuses on single-family residential customers due to data limitations. Like in most US cities, multi-family homes in Santa Cruz are not individually metered and billed by the water department and are therefore not represented in our billing data. This omission biases our income distribution upward because multi-family homes tend to house lower-income families.\u0026nbsp;\u003csup\u003e69\u003c/sup\u003e Additionally, multi-family homes may have more inelastic demand, increasing their vulnerability to higher water rates.\u003csup\u003e33,70\u003c/sup\u003e These limitations lead to an underestimate of the fraction of the population experiencing unaffordability both now and in the future. Addressing this gap will require future work using household surveys or other data collection efforts to better assess water use and affordability among multi-family and other hard-to-reach populations.\u003csup\u003e71\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the qualitative insights from this study are robust. Where climate stress intersects with expensive adaptation options, constrained rate design, and existing inequality, climate change can substantially worsen water affordability. The framework presented here is intended not to predict exact outcomes for all cities, but to help utilities and policymakers identify when and why climate adaptation may pose risks to equitable water access.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eCase Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe apply our model to Santa Cruz, a water-stressed city with high income inequality on California\u0026rsquo;s central coast. Relying on locally sourced surface water for around 95% of total supply, Santa Cruz is highly vulnerable to climate-driven water stress.\u003csup\u003e50\u003c/sup\u003e The Santa Cruz Water Department (SCWD), the municipal utility, serves 96,000 residents, managing a system anchored by the Loch Lomond Reservoir, designed to store about a year\u0026rsquo;s supply of water (see Figure S12 with a watershed map).\u003csup\u003e50\u003c/sup\u003e Santa Cruz, with a median household income of about $91,900 in 2020 dollars, has high income inequality with 20% of households below the federal poverty level (compared to 11% nationwide) and 20% earning more than $200,000 annually (14% nationwide).\u003csup\u003e50,72,73\u003c/sup\u003e The presence of the University of California, Santa Cruz skews the population younger with 27% of residents aged 20-29 (15% statewide).\u003csup\u003e50,74\u003c/sup\u003e As a public utility in California, SCWD is limited\u0026nbsp;in how they can structure water rates, fund assistance programs, and increase rates due to Prop. 218.\u003csup\u003e48,61\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use multiple city- and household-level data sources to parameterize the model. We\u0026nbsp;tailor model parameterization to best estimate current conditions and processes in Santa Cruz. At the city scale, we obtain historical data on utility operational costs, financing, and rates from SCWD\u0026rsquo;s long-range financial reports;\u003csup\u003e75,76\u003c/sup\u003e and historical water use data based on the 2020 Urban Water Management Plan.\u003csup\u003e50\u003c/sup\u003e At the household level, we use SCWD monthly water billing data for every account active between January 2009 and December 2021 (n=2,836,297 bills). Accounts are filtered for adequate length and quality, as detailed in Section S8. We merge the billing data with physical housing characteristics from tax records, block-group-level census data, and hydrological data, listed in Table S5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWater Supply Balance Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use the Santa Cruz Water System Model (SCWSM) to estimate water allocations.\u003csup\u003e77\u003c/sup\u003e This model uses Pywr, a Python-based water resources simulation modeling library,\u003csup\u003e78\u003c/sup\u003e to simulate daily operations using linear programming and determine how much water\u0026nbsp;should be provided and from which sources.\u003csup\u003e79\u003c/sup\u003e The model includes current and potential future infrastructure, hydrology, and system demands. More details can be found in Section S9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate and Hydrological Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClimate scenario generation involves two steps: first, developing stochastic weather simulations that reflect historical climate variability; and second, altering these simulations to capture different climate change impacts using the anomalies from climate models. The resulting simulations capture both short-term stochastic variability and long-term climate impacts and are used to force a hydrological model to develop streamflow scenarios.\u003c/p\u003e\n\u003cp\u003eThe contrasting climate scenarios differ in the range of anomalies utilized from CMIP6 projections.\u003csup\u003e80\u003c/sup\u003e The moderate, cool climate reflects similar climate conditions to historical data and is used in the baseline and moderate climate with adaptation scenarios, while the dry, hot climate reflects worst-case, plausible climate impacts, used in the dry climate with adaptation (see Figure S1). To test the full range of climate variability, we utilize a scenario called \u0026ldquo;All Climate Simulations,\u0026rdquo; where we test all combinations of climate anomalies. See Section S1 for more details on climate scenario generation.\u003csup\u003e81\u003c/sup\u003e The simulations are run through a lumped hydrological model for the San Lorenzo River, which comprises the majority of water within the basin. Then, regression relationships from historical records are used to develop daily flow records at all locations, as further discussed in Section S10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Deployment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe simulate infrastructure deployment under water stress using rule-based risk-of-failure (ROF) thresholds. These probability-based triggers incorporate both supply and demand by estimating the likelihood that reservoir storage will fall below a critical threshold (e.g., deadpool levels) within a specified time horizon, as applied in similar urban water supply planning studies.\u003csup\u003e45,64\u003c/sup\u003e One benefit of the ROF approach is that we can easily include deployment time so that infrastructure does not come online instantaneously. The planning strategy utilized first builds a desalination plant when the ROF value surpasses a given threshold, although alternative strategies are compared later in the \u003cem\u003eResults\u003c/em\u003e (see Figure 5). For the baseline planning strategy, Figure S2 shows the infrastructure deployment patterns for all scenarios with adaptation as well as the frequency of the number of deployed infrastructure options. We evaluate ROF triggers annually based on three dimensions: current reservoir storage, annual water demand, and planned or implemented infrastructure options. We construct ROF lookup tables by partitioning stochastic climate simulations into two-year segments, running hundreds of two-year simulations, and estimating the probability of storage falling below the critical threshold for each combination of system conditions. Section S11 includes more details on ROF development, including an illustration of system characteristics and thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Options \u0026amp; Planning Strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe model five infrastructure options, ranging from 0.5 to 4 MGD in capacity, that can be implemented individually or combined: two water transfers, aquifer storage and recovery (ASR), direct potable reuse, and desalination. SCWD is currently considering all included infrastructure options, in various portfolios, in long-term planning efforts. Techno-economic details, including capital and operating costs, based on design studies by SCWD, are included in Table S6.\u003csup\u003e82\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe develop multiple infrastructure planning strategies that include the ROF threshold and infrastructure deployment order (see Table S4). We optimize planning strategies with respect to utility costs and system reliability using a multi-objective approach.\u003csup\u003e43,44,83\u003c/sup\u003e We calculate utility cost as the summation of total utility infrastructure capital and operating costs and reliability using average annual total unmet demand. We average both objectives across twenty climate simulations used in optimization. We use the Borg multiobjective evolutionary algorithm to determine Pareto-approximate infrastructure strategies, detailed in Section S13.\u003csup\u003e84\u003c/sup\u003e We choose one planning strategy as a \u0026ldquo;baseline\u0026rdquo; approach\u0026nbsp;determined through conversations with SCWD staff and quantitative analysis comparing reliability declines during historical and simulated drought events, further discussed in Section S5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModeling Water Demands\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use a Discrete/Continuous Choice (DCC) model, an econometric model designed to estimate water demands under increasing block tariffs, to simulate single-family household water demands at the household level.\u003csup\u003e85,86\u003c/sup\u003e We estimate households\u0026rsquo; monthly water use \u003cem\u003e(w)\u003c/em\u003e as:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"133\" height=\"19\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"88\" height=\"17\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003ep\u003c/em\u003e is the marginal price of water; \u003cem\u003ey\u003c/em\u003e is home tax value (a proxy for income\u003csup\u003e87\u003c/sup\u003e); \u003cem\u003eZ\u003c/em\u003e is a matrix of housing characteristics and monthly weather data; \u003cem\u003eɑ, \u0026gamma;,\u0026nbsp;\u003c/em\u003eand \u003cem\u003e\u0026delta;\u0026nbsp;\u003c/em\u003eare the estimated model coefficients; \u003cem\u003eW\u0026nbsp;\u003c/em\u003eis household water use after including\u003cem\u003e\u0026nbsp;d\u003csub\u003eHH\u003c/sub\u003e\u003c/em\u003e, a direct bias correction term for each household.\u003csup\u003e85,86\u003c/sup\u003e More details on model parameterization and performance are in Section S14 and all model coefficients are listed in Table S7. We achieve a model performance of r\u003csup\u003e2\u003c/sup\u003e=0.36 at the household scale, which is comparable with state-of-the-art applications in other regions, and r\u003csup\u003e2\u003c/sup\u003e=0.78 at the income group scale.\u003csup\u003e38,86\u003c/sup\u003e We use the DCC model estimation results to simulate demands for 21,370 single-family residential accounts. We resample the 21,370 accounts from the household-level data so that our outputs for total single-family residential water use match 2020 water use trends and so that the distribution of household properties matches current distributions across the SCWD service area. For our affordability assessment, we estimate household-level income using\u0026nbsp;an approach that maintains the distributions of household income at the census block group scale, which is our unit of analysis. We use a multivariate regression approach to estimate each household\u0026rsquo;s income and then apply quantile mapping to assign household income estimates to one of sixteen income bins utilized by the American Community Survey at the block group level, detailed in Section S15. Because our household sample size is large, the effect of error in the regression approach is negligible when aggregating from the household scale to income class scale, which we confirm in Table S8 and Figures S18-S21. We define low-income households as those below the California Poverty Measure income threshold of US$39,900 per year in 2023.\u003csup\u003e88\u003c/sup\u003e We assess affordability burdens using the affordability ratio, which compares simulated monthly household water bill costs to income during periods with the highest water rates.\u003csup\u003e3,8,51\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe also include total water demands\u0026nbsp;for other customer classes, including multi-family residential and non-residential (commercial, institutional, industrial, and irrigation\u003csup\u003e50\u003c/sup\u003e)\u0026nbsp;classes, and water losses, to simulate realistic total service area demands. For multi-family and non-residential classes, we create three stationary scenarios to capture a range of plausible conditions. We use the 2020 Urban Water Management Plan data to obtain baseline (average), high (10% above maximum), and low (10% below minimum) average total demands, which we overlay with monthly anomalies to model demand seasonality, based on the water billing data (Figure S22).\u003csup\u003e50\u003c/sup\u003e For water losses, we use a constant scenario of 201 MG/year based on the 2020 Urban Water Management Plan.\u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Financing and Rate Design Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe infrastructure financing model translates new infrastructure investment costs to single-family household water rates following three main steps. First, we determine utility revenue requirements related to the portfolio of infrastructure options. We use a cashflow model to calculate the additional required revenue for the utility to recuperate, categorized into three components: pay-as-you-go costs, where the current year\u0026rsquo;s revenue funds capital expenditures; debt-financed costs, where revenue covers loan repayment; and annual operating costs, which are incurred once infrastructure comes online.\u003csup\u003e76\u003c/sup\u003e General operations and maintenance expenses are modeled exogenously, as further discussed in Section S17. Second, we conduct a cost-of-service analysis based on rate design guidance in the AWWA manual where we determine that single-family households should pay for 38% (78%) of any constructed and operated infrastructure accrued through volumetric rates (fixed fees) based on historical volumetric charges (total number of accounts).\u003csup\u003e89\u003c/sup\u003e Third, we design updated water rates. Santa Cruz uses an increasing block rate (also called an increasing block tariff), where the unit price of water increases above certain thresholds of water use (0-5, 6-9, and 10+ ccf).\u003csup\u003e48\u003c/sup\u003e Volumetric rates include two components: an Infrastructure Reinvestment Fee charge, which funds pay-as-you-go and debt-financed costs; and a volumetric consumption charge, which funds operating costs. We compute rate increases from new infrastructure using tier cost ratios, simulated water demands, and updated revenue requirements. Historically, the ratios between tier 1 and tier 2 or 3 costs, listed in Table S10, have been fairly consistent.\u003csup\u003e75,76\u003c/sup\u003e Since volumetric rates depend on water demands, but water demands are inelastic based on\u0026nbsp;marginal water prices, we model this feedback cycle once through to ensure updated rates reflect updated demands. We update rates when infrastructure is planned to fund the investments, when infrastructure is deployed to fund operational costs, and when investments are paid off. We illustrate the process from updated revenue requirements to rates and demands in Figure S23. Other financing assumptions are detailed in Table S11.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe perform sensitivity analysis on a sample of parameters in our framework. For the optimization, we test one uncertain parameter for each of the three main components of our modeling framework. For the simulation, we perform a fractional factorial analysis, where we analyze high and low values for each parameter, evaluating all parameter combinations.\u003csup\u003e90,91\u003c/sup\u003e We do this separately for (1) infrastructure financing and rate design and (2) demand and demographic parameters, as described in Section S3.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eSF conceptualized the study. SF, JS, CK, BF, and AV designed the methodology. JS, CK, BF, and CB provided software. JS performed the analysis. JS, SF, and CK analyzed data. SF provided supervision. JS and SF drafted the manuscript. JS, SF, BF, and CK revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors do not have any competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification:\u0026nbsp;\u003c/strong\u003ePhysical Sciences: Sustainability Science\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll non-proprietary data and software can be found at DOI: 10.5281/zenodo.18752481 (https://zenodo.org/records/18752481) with supplemental published separately at DOI: 10.5281/zenodo.18752509 (https://zenodo.org/records/18752509).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Santa Cruz Water Department, including Rosemary Menard, Kyle Peterson, Sarah Easley Perez, Heidi Luckenbach, and Taylor Kihoi; Claudia Llerandi from Kennedy Jenks; Aliyah Hamilton and Lesly Rodriguez for their work on the project during their Stanford Undergraduate Research Fellowships; and Lillian Lau for technical assistance. ChatGPT was used to support code development (plotting and debugging) and editing (suggesting language edits for clarity and conciseness) with thorough review by the authors. This material is based upon work supported by the NSF under Grant No. 2337668. Co-author J. Skerker was supported by the TomKat Graduate Fellowship for Translational Research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMack, E. A. \u0026amp; Wrase, S. A Burgeoning Crisis? A Nationwide Assessment of the Geography of Water Affordability in the United States. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e0169488 (2017).\u003c/li\u003e\n\u003cli\u003eJones, P. A. \u0026amp; Moulton, A. The Invisible Crisis: Water Unaffordability in the United States. 64 (2016).\u003c/li\u003e\n\u003cli\u003eTeodoro, M. P. 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Exploring water affordability through subsidy policies. \u003cem\u003eWater Research\u003c/em\u003e \u003cstrong\u003e286\u003c/strong\u003e, 124251 (2025).\u003c/li\u003e\n\u003cli\u003eReibel, M., Glickfeld, M. \u0026amp; Roquemore, P. Disadvantaged communities and drinking water: a case study of Los Angeles County. \u003cem\u003eGeoJournal\u003c/em\u003e \u003cstrong\u003e86\u003c/strong\u003e, 1337\u0026ndash;1354 (2021).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Urban Household Affordability Drivers\u003c/strong\u003e A list of unaffordability drivers, their household-level impacts, context in Santa Cruz, and other cities exhibiting similar characteristics in California and globally.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnaffordability Driver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold affordability impacts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanta Cruz / California context\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCities exhibiting similar characteristics to Santa Cruz\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClimate change-driven water stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNecessitates investments in supply- or demand-side measures for water supply reliability, which can be expensive and are often paid for through rate increases.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal climate models show much of the region getting hotter and drier, with more extreme dry and wet events.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalifornia: Los Angeles, San Diego, San Francisco; Global: Cape Town, South Africa; Melbourne, Australia\u003csup\u003e92–94\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExpensive supply expansion alternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLarger infrastructure options tend to be more costly, requiring greater rate increases, increasing household water costs and worsening affordability impacts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSanta Cruz is considering one or multiple infrastructure options, including: a desalination plant, direct potable reuse, aquifer storage and recovery, and transfers with two neighboring utilities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalifornia: Los Angeles, San Francisco; Global: Singapore; Tel Aviv, Israel; Melbourne, Australia\u003csup\u003e95,96\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited over-year water storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimits the utility's ability to store water from wet years for use during dry years, limiting what alternative water supply infrastructure can be utilized, potentially necessitating higher investment costs for new infrastructure.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLoch Lomond, Santa Cruz's main reservoir, only holds about one year of water supplies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalifornia: East Palo Alto, San Jose; Global: Cape Town, South Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited conservation opportunities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhen households are already using water efficiently due to water-saving appliances and limited outdoor/discretional use, there are limited opportunities for households to cut back on water use during droughts or rate increases, making households sensitive to rate changes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSanta Cruz has already implemented many conservation measures, leading to low gains from new efficiency investments.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalifornia: San Francisco, San Jose, San Diego, Oxnard; Global: Portland, OR; Singapore\u003csup\u003e97,98\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncome inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore low-income or disadvantaged households can lead to larger affordability impacts across a water system.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSanta Cruz has large income equality with about 20% of households below the federal poverty line and another 20% earning more than $200K annually.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalifornia: Oakland, San Francisco, Los Angeles; Global: Barcelona, Spain; Rio de Janeiro, Brazil\u003csup\u003e99,100\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInstitutional constraints on rate design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhen utilities are constrained in how they structure rates or provide subsidies, infrastructure costs are passed directly to households, often through higher baseline charges or volumetric rates that do not protect essential use. This limits utilities’ ability to shield low-income households from rate increases associated with climate adaptation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn California, Proposition 218 requires rates to reflect proportional cost of service, limiting cross-subsidization and many forms of income-based assistance. Santa Cruz already exhibits high baseline rates and limited flexibility to design affordability-protective pricing, amplifying the household impacts of new infrastructure investments.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalifornia: all public utilities impacted\u003c/p\u003e\n \u003cp\u003eGlobal: Rio de Janeiro, Brazil\u003csup\u003e99\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water affordability, climate change, infrastructure, systems analysis","lastPublishedDoi":"10.21203/rs.3.rs-7603314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7603314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change intensifies water stress globally, necessitating expensive infrastructure interventions to maintain reliable supply. To fund infrastructure, utilities often raise rates, increasing water bills for low-income households. Resulting affordability impacts depend on utility costs and interactions between rate design, financing, climate, and household demands. We develop a city-scale modeling framework to estimate climate change impacts on water affordability, integrating climate, utility adaptation decisions, and demand. In Santa Cruz, California, we find that climate change alone could double water bills by mid-century, leaving an additional 7-16% of Santa Cruz households with unaffordable water. Our results suggest that climate change may lead to greater water affordability challenges than previously estimated in hotspots where supply is vulnerable to climate change. This highlights the need for policy intervention and financing to ensure climate adaptation does not compromise affordability. The magnitude of climate-related affordability challenges depends on local context, requiring city-scale assessments.","manuscriptTitle":"Climate change exacerbates water affordability crisis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 07:19:04","doi":"10.21203/rs.3.rs-7603314/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-sustainability","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natsustain","sideBox":"Learn more about [Nature Sustainability](http://www.nature.com/natsustain/)","snPcode":"","submissionUrl":"","title":"Nature Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4e3640bd-1128-4493-a87f-efe8a89969a1","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66678765,"name":"Scientific community and society/Water resources"},{"id":66678766,"name":"Scientific community and society/Social sciences/Decision making"},{"id":66678767,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"},{"id":66678768,"name":"Physical sciences/Engineering/Civil engineering"}],"tags":[],"updatedAt":"2026-04-28T07:19:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 07:19:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7603314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7603314","identity":"rs-7603314","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}