The following is the established format for referencing this article:Van Schmidt, N. D., T. S. Wilson, L. E. Flint, and R. Langridge. 2023. Trade-offs in adapting to changes in climate, land use, and water availability in California. Ecology and Society 28(4):9.
Changes in land use and land cover, water systems, and climate are inextricably linked, and their combined stresses have had severe impacts in many regions worldwide. Integrated adaptation planning can support adaptive capacity by helping institutions manage land and water resources at regional to local scales. Linkages between these stressors mean that planners are often faced with potential trade-offs, and how to couple social and environmental sustainability remains a key question. We explore these questions in California’s Central Coast, a region that is already experiencing serious water shortages, housing shortages, rapid expansion of perennial agriculture, and severe droughts that are projected to become worse with climate change. Linked models of land use change (the Land Use and Carbon + Water Simulator [LUCAS-W]), water resources (LUCAS-W), and climate (the Basin Characterization Model [BCM]) produced forecasts of exposure to regional changes at 270-m resolution. We worked with regional stakeholders to develop a matrix of nine vulnerability measures that assessed key sensitivities to these changes. Each vulnerability measure combined one of the three exposure projections with spatial datasets representing one of three sensitivity communities (agricultural, domestic, or ecological). We assessed how five scenarios of land-use and water management strategies under consideration by regional planners could provide institutional, top-down adaptive capacity, and whether there were trade-offs in sustainable development goals for these communities. We found that specific land and water management strategies could greatly reduce regional vulnerability, particularly programs to cap water extractions to sustainable levels. The most dramatic trade-off was between the strategy of water demand caps that increased risk of habitat loss and ecosystem preservation that increased water vulnerability. However, trade-offs were usually limited and spatially localized, suggesting local tailoring of the strategies we assessed could reduce them. Trade-offs were more frequent across exposure classes (land use vs. water vs. climate changes) rather than sensitivity classes (agricultural vs. domestic vs. ecological communities), suggesting win-win opportunities for natural resource management. Our vulnerability maps can inform prioritization efforts for local adaptation planning.
Changes in land use and land cover (hereafter, “land use”), water systems, and climate are inextricably linked, and understanding the complex coupled interactions of these processes is a central goal of sustainability science (MacDonald 2010, Kramer et al. 2017). Extensive groundwater depletion has been documented in regions worldwide (Wada et al. 2012, Famiglietti 2014), and future development is likely to stress water supplies (Wilson et al. 2016). Future water shortages are expected in turn to alter patterns of development (Biggs et al. 2010, Venot et al. 2010). Climate change is also projected to increase drought frequency and severity in many regions, worsening water shortages (MacDonald 2010). Future land use will likely be a determining factor in regional resilience to climate change (Purkey et al. 2008, MacDonald 2010, Joyce et al. 2011, Mehta et al. 2013, Johannsen et al. 2016); despite being a potentially powerful tool, however, land-use planning has rarely been applied for climate adaptation (Pyke and Andelman 2007). Likewise, experts note that current rates of groundwater depletion are due to inadequate institutional governance (Foster and Garduño 2013). Integrated adaptation planning by institutions at regional to local scales could therefore direct future management of land and water resources, providing regional adaptive capacity to these interconnected stressors.
However, the linkages between land use, water use, and climate change mean that planners are often faced with potential trade-offs (Okamoto et al. 2020), and how to couple environmental sustainability with socioeconomic sustainability remains a central question of sustainability research (Kramer et al. 2017). For example, programs to limit groundwater pumping of aquifers that are in a state of chronic overdraft (where extraction unsustainably exceeds recharge) have been predicted to cause leakage of development pressures into undeveloped groundwater basins, potentially increasing rates of habitat loss (Priess et al. 2011, Liu et al. 2017). Conservation of undeveloped ecosystems could conversely concentrate development in existing agricultural areas already experiencing substantial water shortages (Van Schmidt et al. 2021, 2022). Decision making about sustainable development strategies is localized in nature and requires accurate data (Thiault et al. 2018a). Few studies have assessed vulnerability trade-offs in a spatially explicit manner, in part because vulnerabilities are often spatially heterogeneous and difficult to quantitatively compare (Okamoto et al. 2020). Climate change data at local scales relevant to land-use decision making are incomplete, and projections of land-use change at multi-ecoregion scales have had limited utility at local scales (Sleeter et al. 2015). Differing on-the-ground environmental or social conditions can also dramatically affect local communities’ ability to cope with stressors (Turner et al. 2003). Understanding an area’s unique exposure to global change processes in conjunction with its social-ecological sensitivity may support establishment of effective adaptation strategies.
Vulnerability assessments are an interdisciplinary subdiscipline of sustainability science focused on understanding patterns of community vulnerability to multiple stressors (Turner et al. 2003). Assessments that synthesize data from different sources are ideal for regional planning because they can directly address priority resources, more comprehensively identify key impacts, and find consensus areas of high vulnerability across measures (Michalak et al. 2022). Vulnerability assessments typically treat vulnerability as having three components (Fig. 1; Thiault et al. 2018a):
- “Exposure” is the degree of stress experienced by a community. We here focus on projected exposure to (1) land-use change, (2) water shortages, and (3) climate change.
- “Sensitivity” is determined by the on-the-ground conditions that define how a community will be impacted by a stressor over the short term. We focus on conditions of (1) agriculture, (2) community demographics, and (3) threatened species.
- “Adaptive capacity” is the degree to which communities are able to anticipate or respond to stress to avoid adverse impacts over the long term. We examine how institutions’ land-use and water management strategies can reduce the degree of exposure and its overlap with sensitive areas, thereby providing adaptive capacity.
We conducted a participatory synthesis vulnerability assessment (Glick et al. 2011) to assess impacts of global change processes at a regional scale on vulnerable social-ecological communities in California’s Central Coast. This region is facing intense coupled pressures from development, chronic groundwater overdraft, and climate change (Fig. 1, inset; Langridge 2018, Wilson et al. 2020). We worked with stakeholders to produce spatial estimates of future exposure to changes in land use, water demand and supply, and climate from two simulation forecast models, and combined outputs of these models with existing spatial datasets on sensitivities in a geospatial overlay analysis (Okamoto et al. 2020) to create a matrix of nine spatial vulnerability measures. We used these measures to assess changes in vulnerability under five scenarios of land-use and water management strategies under consideration by regional planners, in order to examine (1) whether institutional management could improve sustainability, and (2) whether there were sustainability trade-offs between agricultural, domestic, and ecological communities. We hypothesized that trade-offs would be greatest between human versus ecological communities, but that strategies that relied on reciprocal relationships (i.e., water-development linkages) could be effective at producing win-win solutions (Kramer et al. 2017).
Our study region and modeling extent included the five counties of California’s Central Coast: Santa Cruz, Monterey, San Benito, San Luis Obispo, and Santa Barbara (Fig. 1, inset). However, we limited our vulnerability assessment to only areas that overlay groundwater basins or were serviced by a water agency. We chose to limit our assessment to these areas because they were the only regions for which water vulnerability could be assessed, and they contained > 90% of all anthropogenic land uses (Van Schmidt et al. 2022).
The Central Coast is a global biodiversity hotspot with nationally important landscapes, such as the Big Sur Coast (Rundel et al. 2016, Hannah 2018), but it also has major agricultural areas and small- to medium-sized cities. There is a disconnect between prosperous coastal communities and inland agricultural areas, which have communities defined by California as disadvantaged communities (DACs; median annual household incomes < 80% of statewide median; California Water Code, section 79505.5(a)). Historical rates of agricultural and urban development have varied dramatically across the five counties (Wilson et al. 2020, Van Schmidt et al. 2022), which could create divergent stressors for local ecosystems and economies. Agricultural expansion presents challenges to habitat conservation. It also may stress water supplies under climate change, especially coupled with shifts in cropping from annual crops to higher-value perennial orchards and vineyards that cannot be fallowed, removing flexibility in irrigation demand during drought (an important consideration given the region’s highly variable Mediterranean climate; Wilson et al. 2020). The region also has a housing shortage (Johnson et al. 2004) and is projected to add ~300,000 more people by 2060 (California Department of Finance 2018). From 1990 to 2006, most of California’s metropolitan areas adopted policies to limit urban development by restricting housing growth (Alamo and Uhler 2015), and new laws required the demonstration of a sustainable water supply before approval of new housing developments (California Department of Water Resources [CDWR] 2003). Despite the resulting declines in housing construction, water use has continued to grow because of expanding agricultural water usage (Wilson et al. 2020).
Like much of the western United States, the Central Coast is vulnerable to a changing climate, with projected increases in temperatures, extreme droughts, and future water shortages that build on existing over-appropriation of water resources to support substantial development (Barnett et al. 2008, MacDonald 2010, Dettinger et al. 2015, Langridge 2018). Serious water vulnerabilities because of highly variable precipitation are likely to worsen as droughts intensify (Langridge 2018). The Central Coast’s expansive cultivated valleys are almost entirely dependent on groundwater (Martin 2014, CDWR 2015, Langridge 2018). Chronic groundwater overdraft has depleted over 40% of regional groundwater basins, a key water supply during drought, resulting in serious water shortages (Barlow and Reichard 2010, Martin 2014, White and Kaplan 2017). This may disproportionately impact DACs that rely on these resources (Brown 2014) or dry groundwater-dependent ecosystems and eliminate their associated fish and wildlife populations (Kløve et al. 2011). During a severe 2012–2016 drought, reduced surface water increased reliance on groundwater, resulting in unprecedented well failures, water shortages, and emergency water restrictions (Leahy 2016). The 2014 Sustainable Groundwater Management Act (SGMA) could dramatically transform water governance (Leahy 2016). SGMA required 127 groundwater basins in overdraft to form Groundwater Sustainability Agencies (GSAs), which must develop groundwater sustainability plans to manage their basins to eliminate overdraft and associated negative impacts, such as seawater intrusion, land subsidence, and surface water depletions (California Water Code 2015). Planned management options include supply-side strategies that increase surface water for consumption and groundwater recharge using desalinated, imported, and/or recycled water, and demand-side interventions to restrict total water pumping (Langridge and Van Schmidt 2020).
Vulnerability analysis design
We used a stakeholder-driven scenario development approach to create an evidence-based body of research about the impacts of land-use and water management adaptation strategies (Van Schmidt et al. 2022). This included seven stakeholder meetings (supplemented with individual interviews) from 2019 to 2022 with local government agencies and non-governmental organizations (hereafter, “stakeholders”) to identify local priorities and potential adaptations for land and water development (Appendix 1). Meetings were informal discussions, with no quantitative data gathered; we summarize the qualitative feedback we received in this paper and in Van Schmidt et al. (2022). Research partners included the California Climate Change Collaborative (a network of diverse organizations), the City of Salinas (a DAC) and other land-use agencies, and the Elkhorn Slough Foundation (an environmental non-profit). Stakeholders’ key water sustainability goals to address were: (1) sufficient water supplies (especially during drought), (2) reducing or halting groundwater level declines, and (3) reducing water pollution (which was determined to be outside the scope of this study). Key land-use goals were: (1) addressing loss of prime farmland, (2) maintaining healthy ecosystems, and (3) sufficient low- and medium-income housing. We then identified development strategies to quantitatively assess their ability to improve the region’s adaptive capacity. We selected two water management strategies (demand-side interventions to reduce water-dependent development in overdrafted areas and supply-side interventions to increase water supplies) and two land-use management strategies (preserving prime farmlands and recharge areas and conserving priority habitats).
We next worked with stakeholders to design a matrix of nine vulnerability indicators that could assess trade-offs among these goals. We sought a representative set of vulnerability measures to identify vulnerable social and ecological communities and to assess potential trade-offs among development strategies rather than a comprehensive assessment of future vulnerability (Messina et al. 2008, Quinlan et al. 2015, Angeler and Allen 2016, Allen et al. 2018). Quantifying the general resilience of entire social-ecological systems is prohibitively challenging because of their extreme complexity, making it necessary to assess the vulnerability of specific elements to specific stressors (Quinlan et al. 2015, Angeler and Allen 2016, Allen et al. 2018). Specific vulnerabilities of the Central Coast are qualitatively summarized in Appendix 2, of which we selected a tractable subset to analyze quantitatively. Vulnerability indices commonly integrate exposure and sensitivity into single indices to simplify data, assisting in its application by managers (Thiault et al. 2018a). Following the approach of Okamoto et al. (2020) we created a balanced matrix of nine vulnerability measures that captured the specific sensitivity of three communities (agricultural, domestic, and ecological), and expanded upon their framework by also considering the specific exposure to three distinct stressor classes (land use, water, and climate; Table 1). In this two-step process, exposure to changes was forecast from spatial simulation models and post-processed with spatial datasets of differing sensitivity to estimate vulnerability. Our choice of measures was a priori and designed to capture the goals and adaptations reported by our stakeholders (listed above). In Appendix 1 we describe the justification for and parameterization of each measure in detail, and in the next section we provide a concise summary of our approach.
Exposure and sensitivity models
Exposure to future land-use change and water shortages were jointly modeled with the Land Use and Carbon + Water Simulator (LUCAS-W; Van Schmidt et al. 2022). This is a stochastic, spatially explicit (270-m resolution) state-and-transition simulation model in the program SyncroSim’s ST-Sim package (Daniel et al. 2016). Transitions between developed (i.e., domestic-industrial), annual cropland, and perennial cropland (collectively, “development”), as well as natural rangeland, are simulated from 2001 to 2061 on the basis of empirical historical rates (1992–2016; Appendix 1.1.1; Wilson et al. 2020). Our design captured trade-offs by making each land-use transition beneficial in some areas and deleterious in others. For example, urbanization and agricultural contraction increases agricultural land vulnerability if it occurs on areas designated by the state as important farmland to conserve (California Department of Conservation 2016). However, urbanization reduces domestic land vulnerability if it occurs in areas with housing shortages, and agricultural expansion increases ecological land vulnerability if it occurs in critical habitats for endangered species. Outputs included probability of different transitions by, or final land-use state in, 2061 (Table 1).
LUCAS-W estimates total water use in each groundwater basin on the basis of historical data and therefore can project land use–driven water shortages based on conditions of long-term overdraft (Van Schmidt et al. 2022). Importantly, LUCAS-W implicitly incorporates impacts of climate change on water sustainability via a key parameter (total sustainable supply of water in each water agency’s management area) that is derived from local water agency modeling studies that incorporated projected effects of climate change (see Van Schmidt et al. 2022 for details). Although the Central Coast chiefly uses groundwater, some areas also utilize surface water and imported water (Table A2.1), so total sustainable supply included non-groundwater water supplies. We used estimated overdraft (for basins where total sustainable supply was known) as our primary exposure measure; for basins where this was unknown, we estimated it on the basis of percent increase from 2001 levels (Appendix 1.1.2). We paired this single estimate of exposure with all three sensitivity measures to model the degree of overdraft in groundwater basins with sensitive water-intensive crops, DACs, and groundwater-dependent threatened species that were particularly vulnerable to water shortages.
Projected exposure to climate changes was estimated via the Basin Characterization Model (BCM; Flint and Flint 2014), which spatially downscales global climate model projections of temperature and precipitation to 270-m resolution (following methods in Flint and Flint 2012). It develops a rigorous energy balance and integrates spatial data on soils, geology, and monthly climate to estimate change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (an indicator of drought stress on plants and therefore irrigation water demand), among other variables (Tables A2.2, A2.3). We calculated model-averaged outputs from the BCM across five global climate models (Appendix 1.1.3) for Representative Concentration Pathway (RCP) 8.5, a high-greenhouse gas emissions climate change scenario (Riahi et al. 2011). We focused on RCP 8.5 because we sought to assess potential vulnerability and this represented the worst-case emissions scenario. We assessed how land-use patterns could interact with patterns of sensitivity by potentially placing developed land use with at-risk elderly populations in areas of greater increases in heat stress or placing water-intensive crops in areas exposed to increases in climatic water deficit (a proxy for increasing irrigation water needs; Table 1). Our selected measure of ecological climate vulnerability (imperiled freshwater species experiencing surface water declines) was only tenuously linked to land-use change patterns. We elected to not force a linkage to the land use–driven management scenarios and left this measure static across scenarios. We modeled exposure as change in these three metrics between two 30-year windows, historical (1981–2010) and projected (2040–2069).
Sensitivity measures (Table 1) were obtained from diverse datasets (Appendix 1.2). Agricultural sensitivity data were derived from cropland projections from LUCAS-W (Van Schmidt et al. 2022), crop water demand data (CDWR 2014), and farmland importance rankings (California Department of Conservation 2016). Demographic sensitivity maps were derived from 2017 census data (U.S. Census Bureau 2017). Ecological sensitivity data were based on range maps for imperiled species and subspecies (Howard et al. 2015, Thorne et al. 2019). We reviewed species accounts and conservation plans from government agencies to create a supplementary ecological vulnerability report (Appendix 2.5 and citations therein) that classified each of the region’s 25 threatened freshwater-dependent (sub)species according to whether they were endangered because of their habitats drying out (as a result of groundwater overdraft and/or drought; Table 2.4).
Scenario-driven adaptive capacity assessment
The LUCAS-W model was used to assess five scenarios of water and land-use management to estimate adaptive capacity based on regional sustainable development strategies developed with stakeholders. Importantly, each scenario only altered the spatial pattern of where coupled land use and water use changes occurred, whereas overall rates of changes were kept constant. We grouped four policies along two axes: (1) land-use management intensity low (LL) to high (LH), and (2) water management intensity low (WL) to high (WH). A central moderate management scenario (MM) served as the intersection of these axes (Fig. 2). We varied one management axis at a time while the other axis was held constant at the “moderate” policy level, allowing us to assess a tractable subset of the most relevant policy combinations and examine the influence of each by turning each strategy on or off separately. The two “moderate” central strategies were set on the basis of feedback from stakeholders on their current management strategies (Van Schmidt et al. 2022).
For land-use management, the LL scenario had no new land-use strategies implemented (but existing protected areas were included). The MM scenario added the first land management strategy, “urban sprawl limits” that prevented urbanization on any land designated by the county or state as important farmland (California Department of Conservation 2016) or a recharge area (Van Schmidt et al. 2022). Urban sprawl limits were “moderate” management intensity because stakeholders reported such strategies are already usually incorporated into land-use planning. The LH scenario added a more intensive strategy, “ecosystem preservation,” which prevented urbanization and agricultural expansion on federally-listed critical habitats for threatened species or prioritized by the key core areas or corridors for wildlands by the California Essential Habitat Connectivity prioritization effort (Thorne et al. 2019).
For water management, the WL scenario was a continuation of pre-SGMA “business-as-usual” management with no water demand limits. We assessed two water management strategies: (1) demand-side interventions to reduce development in overdrafted areas by adding water demand caps, and (2) supply-side interventions to increase water supplies. The MM scenario added a “water demand caps” strategy that limited new development if total water use overlying an agency’s jurisdictional boundaries exceeded the current total sustainable supply. This strategy simulated a water pumping allocation system, which was included as the moderate management intensity level because this is planned by virtually all GSAs if water sustainability is not reached, whereas only a subset of them propose water supply enhancements. We did not assess water supply enhancements without demand caps in this study because our previous study found this to be ineffective at achieving water sustainability (Van Schmidt et al. 2022). When a water agency’s management area is in overdraft, new development is prohibited if it would increase water demand and fallowing is prioritized (Van Schmidt et al. 2022). This strategy is potentially transformative of system behavior (Walker et al. 2006) because it creates a novel feedback with water demand (i.e., adding the new blue linkage from water supply conditions to land-use change in Fig. 1). In the WH scenario the second strategy, “water supply enhancement,” increased the total sustainable supply value by adding water from projects that GSAs were planning to implement.
Calculating and reporting vulnerability
Each measure of exposure and sensitivity (Table 1) was masked, rescaled 0–1, and resampled to a 270-m resolution raster to allow comparison (Appendix 1; Okamoto et al. 2020). Final measures of each vulnerability were the product of exposure and sensitivity, calculated via raster math. We created maps of overall vulnerability by summing all nine specific vulnerability measures for each cell. We report trends in regional-wide specific vulnerabilities based on mean per-cell values to capture the average vulnerability, and the 75th (land and climate) or 95th (water, due to a very skewed distribution) percentile to capture changes in the number of high-vulnerability areas.
We report spatial patterns in land vulnerability for the LL scenario, treating this as a baseline because it had the least land protections. We report baseline water and climate vulnerability using the WL scenario because this approximated a pre-SGMA trajectory for the region and this strategy had the greatest impact on development patterns (Van Schmidt et al. 2022). We assessed the adaptive capacity of localities and the region overall by determining whether any of the management scenarios were effective at reducing social and ecological vulnerabilities.
Land-use change vulnerability
We first summarize major trends in exposure to projected 2021–2061 land-use change; for a more comprehensive description see Van Schmidt et al. (2022). Region-wide perennial cropland and developed land expanded, outpacing a decline in annual cropland and resulting in a mean net loss of 417 km² (range −167 to −581 km²) of natural areas. Agricultural intensification (annual cropland replaced with perennial cropland) was widespread in all five counties. All five management scenarios assumed these same rates (Van Schmidt et al. 2022).
Without urban sprawl limits protecting prime farmland and groundwater recharge areas from urbanization, new developed land use was most likely to occur around edges of major cities, which resulted in high vulnerability of loss of farmland in these areas (Fig. 3a). This was particularly the case in Santa Cruz, where open land for alternative urban expansion was very limited. San Benito had projected long-term agricultural contraction, which drove high agricultural vulnerability there (Fig. 3a). Agricultural vulnerability was higher where projected water supply shortfalls forced agricultural contraction under water demand caps (Fig. 3a).
Projected domestic vulnerability (the likelihood of no new housing in areas with most housing filled) was high around most cities (Fig. 3b), but particularly in the Monterey Bay region and Santa Barbara. Our stakeholder working group reported that many of these cities are facing serious housing shortages.
Major hotspots of ecological vulnerability (probability of development in or adjacent to critical habitats) were riparian habitats around the Monterey Bay as well as around cities in the southern half of the region (Fig. 3c). Critical habitats of outlying rangelands and forests were low risk.
Despite the overall spread of developed land and perennial cropland, region-wide water use was projected to stay the same from 2021 to 2061 (mean +8916 acre-feet/year, range across simulation replicates −66,354 to +70,563 acre-feet/year) because increased water demand from these land uses was offset by the loss of more water-intensive annual cropland (Van Schmidt et al. 2022). Without water demand caps the Central Coast had widespread water vulnerability projected by 2061 (Fig. 4): nine groundwater agencies were in unsustainable long-term overdraft and an additional four basins for which overdraft could not be calculated roughly doubled their water use (range +93.6% to +141.8%). Several areas with current overdraft issues had low vulnerability across measures because they did not have overdraft with projected 2061 land uses (Table A2.2). Despite all water vulnerability measures sharing this measure of exposure to water shortages, different sensitivities drove divergent patterns of water vulnerability across the region.
Agricultural vulnerability, which represented drought sensitivity as the percent of agriculture that was perennial and therefore could not be fallowed during dry years, was very high in the southern Salinas Valley (Fig. 4a). These areas have experienced recent explosive growth of perennial agricultural that was projected to continue (Van Schmidt et al. 2022). Other vulnerable areas included a basin in southern Salinas Valley that was relatively undeveloped but projected to see significant agricultural expansion (Fig. 4a).
Domestic vulnerability (potential water shortages in DACs) had localized hotspots in cities, many of which were small, urban parcel-block units that represent important higher population density despite their limited spatial extent (Fig. 4b). In some of these communities, up to 100% of households were at risk of future water unaffordability. San Benito County and Santa Barbara County had lower vulnerability (Fig. 4b).
Region-wide there were 980 known freshwater-dependent bird, amphibian, fish, invertebrate, and plant species and subspecies (Howard et al. 2015). Of these, 143 (15%) are species of conservation concern, including 25 federally- or state-listed as threatened. Of these 25 species and subspecies, 18 were threatened by falling water tables drying out their groundwater-dependent habitats (Appendix 2.5 and citations therein; Table A2.4). Up to seven threatened groundwater-dependent species overlapped in the most vulnerable areas (vulnerability = 1.0; Fig. 4c). Around the Monterey Bay, species are currently threatened by high levels of habitat conversion because of urbanization (Appendix 2.5), leading to co-occurrence of high domestic and ecological vulnerability (Fig. 4b, c).
Climate change vulnerability
All five counties are projected to become hotter and to receive increased precipitation on average by the end of the century (Flint and Flint 2014, Langridge 2018). Crucially, although there will be more water on average, individual precipitation events will be more variable and concentrated with worse droughts (Table A2.2). Even with higher rainfall, increased warming will likely increase evaporative losses and subsequent irrigation demand (Langridge 2018).
All areas experienced increases in climatic water deficit (range +44.12 to +156.33) that would likely increase cropland water needs. Agricultural vulnerability was highest in the northern Salinas Valley (Fig. 5a), where major increases in climatic water deficit overlapped areas of annual cropland with higher applied water demand (Van Schmidt et al. 2022). In areas where less water-intensive perennial crops dominated, such as in southern Salinas Valley (Van Schmidt et al. 2022), there was reduced vulnerability to this measure (Fig. 5a).
Maximum summer temperature also increased in all areas (+0.702 to +4.75 ºC). Coastal cities were the greatest hotspots of domestic vulnerability to climate change, which we modeled as increases in maximum temperature in areas with elderly populations that are more at risk of heat-related health impacts (Fig. 5b). The agricultural cities and towns of inland areas tended to have lower vulnerability.
There were 17 threatened freshwater species in the study area that were imperiled by their habitats drying during drought (Appendix 2.5 and citations therein; Table A2.4), which could worsen as recharge and runoff declined under climate change. Declines in water inputs to ecosystems (runoff plus recharge) were highest at high elevations (maximum −88.47 mm), whereas many lowland areas experienced no declines. Santa Cruz County was the area at greatest risk of this, with up to seven species concurrently threatened in some areas (Fig. 5c). Other major at-risk areas were southern Monterey County and southern Santa Barbara County (Fig. 5c). Some areas overlapped where declining groundwater levels also threatened species (Fig. 4b); we identified 14 species as imperiled by both droughts and declining groundwater levels (Appendix 2.5; Table A2.4).
Cumulative vulnerability and adaptive capacity
Water demand caps impacted vulnerability more dramatically than any other measure, significantly decreasing region-wide water vulnerability (Figs. 6 and 7). Mean overall region-wide sum vulnerability was 1.32 (95th percentile = 2.61) without water demand caps (LL scenario; Fig. 6a). Vulnerability was reduced when each water agency capped water demand at current sustainable water supplies (MM scenario mean = 0.69, 95th percentile = 2.14). Reductions were widespread, completely eliminating overdraft in seven of the nine agencies projected to otherwise be in overdraft in 2061; the only exception was in Santa Cruz County, where limited land availability prevented water demand caps from shifting development to less water-stressed basins (Fig. 6a, b). Pairing water demand caps with proposed water supply enhancements resulted in the lowest overall vulnerability (WH scenario mean = 0.68, 95th percentile = 2.03) largely because of reducing overdraft in Santa Cruz County, which could not meet sustainability criteria with water demand caps alone (Fig. 6c, d). Protecting prime farmland and recharge areas from urbanization had little impact (Fig. 7a, b), with regional sum vulnerability close to that in the MM (LL scenario mean = 0.70, 95th percentile = 2.17). Compared to the MM scenario, preserving priority ecosystems resulted in slightly greater average vulnerability (mean = 0.75), but reduced the number of high vulnerability areas (95th percentile = 2.06). This was because the strategy reduced vulnerability in Lockwood Valley (the southeastern-most valley in Monterey County) but increased vulnerability in central Santa Barbara County (Fig. 7b, c).
The areas surrounding cities (i.e., yellow-to-red areas in Fig. 5b) were generally the greatest hotspots of cumulative vulnerability (Figs. 6 and 7) because domestic, agricultural, and ecological communities relied on the same limited land and water resources and experienced significant climate impacts (Figs. 3–5). Santa Cruz County and the southern coast of Santa Barbara County were particularly vulnerable across scenarios, both of which are surrounded by forested mountains that provide little flexibility for alternative development patterns. Agricultural cities were also major hotspots, particularly without water demand caps (Fig. 7a). Although Lockwood Valley is currently sparsely developed and unregulated by SGMA (CDWR 2020), it was projected to be a major vulnerability hotspot because of significant expansion of perennial cropland (Figs. 6 and 7).
Table 2 summarizes region-wide trade-offs in specific vulnerability driven by each management strategy. Appendix 3 maps specific vulnerability (i.e., Figs. 3–5) for all five scenarios.
Capping water demand at current sustainable water supplies resulted in six- to ten-fold reductions in average agricultural (0.35 to 0.03), domestic (0.18 to 0.03), and ecological (0.21 to 0.04) water vulnerability (Fig. 8; compare WL to MM). All other management strategies reduced mean vulnerability by relatively modest amounts in comparison (≤ 0.07). Caps caused leakage of agricultural expansion from major agricultural areas reliant on overdrafted aquifers into unregulated basins that are currently relatively undeveloped (Van Schmidt et al. 2022). This resulted in some trade-offs at local scales, but these were minor compared to the dramatic reduction in water vulnerability. Caps caused slight increases in the number of areas at high vulnerability for agricultural contraction on prime farmland (agricultural land vulnerability; +0.02 mean, +0.07 75th percentile) and development of critical habitats (ecological land vulnerability; +0.01 mean, +0.10 75th percentile; Fig. 8a-c). It did not appear to alter climate change vulnerability, with negligible decreases for agriculture (−0.01 mean, −0.01 75th percentile) and increases for domestic (+0.00 mean, +0.01 75th percentile; Fig. 8g, h) areas.
Water supply enhancement raised the water demand caps, which decreased high-risk areas for domestic (−0.00 mean, −0.08 95th percentile) and ecological (−0.01 mean, −0.05 95th percentile) water vulnerability, but increased agricultural water vulnerability (+0.00 mean, +0.11 95th percentile; Fig. 8d-f, compare MM to WH). This was because they allowed for more development on basins that were at risk (i.e., currently overdrafted). They also slightly increased agricultural (+0.03 mean, +0.02 75th percentile) and domestic (+0.02 mean, +0.02 75th percentile) climate vulnerability by re-concentrating agricultural expansion in areas such as the Salinas Valley (Van Schmidt et al. 2022) that had high climate vulnerability (Fig. 5a, b).
Preventing urbanization of important farmland and areas important for groundwater recharge successfully reduced regional agricultural land vulnerability (−0.03 mean, −0.07 75th percentile; Fig. 8a, compare LL to MM). Although it did not have apparent impacts on water vulnerability (Fig. 8d-f, all changes ≤ 0.01), this may underestimate water sustainability benefits because we could not model increased groundwater recharge from preventing paving of recharge areas. Despite lowering urbanization rates around agricultural cities (Van Schmidt et al. 2022), this strategy had surprisingly minimal impacts on domestic vulnerability to housing shortages (domestic land; +0.01 mean, +0.00 75th percentile; Fig. 8b). However, it did slightly elevate potential health impacts from heat stress under climate change (+0.02 mean, +0.03 75th percentile; Fig. 8h).
Preserving priority ecosystems had some of the most notable trade-offs region-wide (Fig. 8, compare MM to LH). It greatly decreased development of critical habitats (−0.06 mean, −0.30 75th percentile; Fig. 8c), in part by reversing the leakage of development into undeveloped basins caused by water demand caps (Van Schmidt et al. 2022). However, this markedly increased agricultural water vulnerability (+0.03 mean, +0.25 95th percentile), in addition to slight increases in ecological (+0.02 mean, +0.01 95th percentile) and domestic (+0.02 mean, −0.01 95th percentile) water vulnerability. This was because it once again concentrated development in current major agricultural regions where water supplies are more stressed (e.g., in the Salinas Valley; Van Schmidt et al. 2022), which also slightly increased agricultural climate vulnerability (+0.04 mean, +0.02 75th percentile; Fig. 2g).
Trade-offs of development planning strategies
We created a novel coupled approach to modeling land use, water demand, and climate scenarios to assess management trade-offs between nine stakeholder-defined, potentially competing vulnerabilities of agricultural, domestic, and ecological communities in California’s Central Coast. We expanded on the approach of Okamoto et al. (2020) by balancing not only three classes of sensitivities (as they did) but also three classes of exposures. This allows the comparison of which axis—exposure or sensitivity—was more likely to drive trade-offs. Contrary to our hypotheses, we found that trade-offs were more frequent across exposure classes (land use vs. water vs. climate changes) than sensitivity classes (agricultural vs. domestic vs. ecological communities). Water demand caps benefitted all water vulnerability measures for all sensitivity classes (Fig. 8d-f) while increasing land vulnerability for both agriculture and ecosystems (Fig. 8a-c). Conversely, ecosystem preservation policies that reduced ecological land vulnerability did not increase agricultural or domestic land vulnerability (Fig. 8a-c), but increased all three kinds of water vulnerability (Fig. 8d-f). This suggests that trade-offs in social-ecological systems among resource categories (i.e., land vs. water resources) may be more common than trade-offs between social versus ecological communities, which may co-benefit. Future studies should test this hypothesis in other systems.
We found that sustainable development strategies could jointly meet multiple goals with limited trade-offs, which were often spatially localized. This suggests a one-size-fits-all approach to managing land and water resources of the Central Coast may not be optimal, echoing findings of Okamoto et al. (2020). Water supply enhancement increased agricultural water vulnerability (Fig. 8d) by encouraging additional development in overdrafted areas (Van Schmidt et al. 2022), but was necessary to achieve groundwater sustainability in Santa Cruz County (Figs. 6b, c, 8e). Ecosystem preservation decreased vulnerability in Monterey County but increased it in Santa Barbara County (Fig 7b-c). Trade-offs could therefore be reduced by applying management strategies strategically to fit local conditions.
The most notable trade-offs were for demand-based water management interventions, which have been found to be necessary to achieve water sustainability in many semi-arid regions (Purkey et al. 2008, MacDonald 2010, Joyce et al. 2011, Mehta et al. 2013, Johannsen et al. 2016) but have also been predicted to cause leakage of development into undeveloped groundwater basins (Priess et al. 2011, Liu et al. 2017). Previous studies with LUCAS-W likewise showed that water sustainability could be achieved simply by shifting new development outside of overdrafted areas, but speculated this leakage could introduce trade-offs by developing natural regions with high ecological sensitivity (Van Schmidt et al. 2022). In this study, we accounted for differing sensitivities and confirmed that although trade-offs did exist, they appeared minor at broader scales by only slightly increasing the proportion of high-vulnerability ecological and agricultural lands (Fig. 8a-c).
The dramatic benefits of water demand caps on adaptation to climate-mediated water shortages (Fig. 8d-f; Langridge 2018, Van Schmidt et al. 2022) illustrate the importance of development planning for adapting to climate change. Water demand caps co-benefitted all three measures of water vulnerability (agricultural, domestic, and ecological), whereas water supply enhancement increased agricultural water vulnerability. This may be because demand caps transformed the behavior of this socio-ecological system at a fundamental level by adding new feedbacks between development and water sustainability (Fig. 1), rather than simply trying to treat the problem by increasing water supplies in currently overdrafted areas (Van Schmidt et al. 2022). This supports a hypothesis in social-ecological systems research that adding reciprocal couplings between social and ecological processes can help couple environmental sustainability to socio-economic sustainability (Kramer et al. 2017).
Vulnerability assessments can be valuable tools for conservation and climate adaptation planning, and our maps (Van Schmidt et al. 2023) could help local agencies prioritize efforts (Thiault et al. 2018a). Projecting future land-use scenarios allows land managers to visualize alternative futures to optimize best management strategies (Alcamo et al. 2006). Areas where multiple vulnerabilities overlap tended to be in and around major cities (Figs. 6 and 7). Hotspots of vulnerability were otherwise frequently in different areas for different types of vulnerability (Figs. 3–5), in agreement with other assessments (Thiault et al. 2018b).
Complementary patterns of vulnerability resulted from distinct differences between prosperous coastal communities (vulnerable areas in Fig. 5b) and inland agricultural areas with many low-income workers (vulnerable areas in Fig. 6b). Groundwater depletion could drive the drying of wells during drought, which disproportionately impacts DACs (Gleeson et al. 2020). Inland agricultural communities had higher water unaffordability indicators coupled with risk of groundwater depletion (Fig. 4b), and these at-risk areas could be targeted for water affordability programs. Conversely, coastal tourism-based communities with older residents (often retirees) had higher risk of heat-related health impacts among the elderly (Fig. 5b). Programs to improve access to climate-control for low-income elderly residents could be targeted to these communities. Agricultural livelihoods may be at risk in the northern counties (Fig. 3a) as development pressures from major urban centers like the San Francisco Bay Area expand their urban footprint, a concern reported to us by a stakeholder representative for indigenous farmworkers in San Benito County. Lastly, water demand varies significantly across crops (Allan et al. 1998) and was higher for annual cropland in the Central Coast (Van Schmidt et al. 2022). Crop water efficiency programs could be targeted toward water-intensive agricultural areas at risk of increased water demand under climate change (Fig. 5a). Continuation of recent shifts from annual crops to perennial orchards and vineyards, which cannot be fallowed, removes flexibility in irrigation demand during drought (Wilson et al. 2020) that may be useful to account for in drought preparedness strategies in these areas (Fig. 4a).
Ecosystems are also affected by land-use change, climate change, and indirectly by both via reductions in groundwater levels, which often are the main source of water for vegetation in drier regions. Groundwater-dependent ecosystems provide important ecosystem services and support disproportionate amounts of regional biodiversity (Kløve et al. 2014), and their drying can eliminate fish and wildlife populations that depend on them (Kløve et al. 2011). Of the 25 threatened species in the Central Coast, all but three were described as imperiled by either groundwater declines or drought, and over half were imperiled by both (Appendix 2.5 and citations therein; Table A2.4). Investments in wetland hydrology restoration and management could be targeted toward these at-risk ecosystems (Figs. 4c and 5c). We found ecosystem preservation policies could cause trade-offs with agricultural vulnerabilities (Fig. 3a), but our models protected very extensive tracts of land marked as broad habitat conservation priorities (Van Schmidt et al. 2022). More targeted investments in habitat protection in the areas identified as high-risk (Fig. 3c) may preserve key habitats without these trade-offs.
Model uncertainty and limitations
Forecast models have substantial uncertainties, but if uncertainty is taken into account when weighing decisions they can still provide useful information for adaptation planning (Miller et al. 2022). It is difficult to quantify uncertainty for synthesis vulnerability assessments because uncertainty arises from numerous sources, including scenarios, design choices, data collection, and errors in modeling of both the original and the synthesis studies (Evans 2012, Miller et al. 2022). Uncertainty in climate projections is particularly high, arising from choice of global climate model, socioeconomic development scenarios, natural stochasticity and annual variability, and statistical downscaling approaches (Reilly et al. 2001, Gao et al. 2020). Although methods for quantifying uncertainty in synthesis vulnerability assessments are limited, there are guidelines for identifying areas of greater confidence: projections based on different methodologies can identify areas consistently at risk under multiple models (i.e., agreement across Fig. 5a-c), projections that incorporate multiple scenarios can identify areas of agreement (we used a model-averaging approach), and larger areas of consistent high or low vulnerability (i.e., municipalities or counties) are less likely to suffer from random spatial error than individual pixels (Glick et al. 2011, Pacifici et al. 2015, Michalak et al. 2022). In lieu of the impossibility of validating integrated forecast models, researchers have suggested that participatory science can serve as pseudo-validation by having stakeholders “ground-truth” forecasts (Messina et al. 2008, Moss 2008). We followed this tactic, presenting our interim results to regional stakeholders and experts at multiple stages throughout development prior to final results to ensure our effort reasonably captured regional dynamics. Areas that are high risk across scenarios and measures (i.e., the overall vulnerability maps; Figs. 6 and 7) likely have increased confidence because they are more robust to the idiosyncrasies of any one scenario or measure (Michalak et al. 2022).
Our approach may underestimate vulnerability. Our study focused on adaptive capacity derived from institutional decision making about land-use and water management. LUCAS-W is one of the only models that represents top-down institutional feedbacks between land-use change and water resources, but it does not yet have the capacity to model bottom-up feedbacks and synergies from climate (Van Schmidt et al. 2022). In reality, complex linkages between climate change with land use and water are likely to further alter patterns of vulnerability (Michalak et al. 2022). For example, areas of increasing climatic water deficit (Fig. 4a) are likely to increase irrigation water demand (Hayhoe et al. 2004), which could worsen water overdraft (Fig. 3a). Future models could incorporate these and other feedbacks.
The vulnerabilities we assessed can be difficult to quantitatively compare and are not comprehensive. Although we reprocessed, masked, and normalized our data to allow for comparisons of different measures (Appendix 1; Okamoto et al. 2020), quantitative comparison of vulnerability measures could still be affected by measure design choices that might alter the scale of responses (Evans 2012). Notably, water vulnerability had a very skewed distribution in our study, which may complicate comparisons of it with other measures. Similar studies have found scaling and weighting decisions had only limited effects on spatial patterns of social and ecological vulnerability (Thiault et al. 2018a). Nevertheless, when comparing among different measures, the existence of changes in vulnerability (i.e., co-benefits and trade-offs in Table 2) is more reliable than their relative magnitude (i.e., Fig. 8). Vulnerabilities are also value-driven; some impacts could be viewed as categorically unacceptable despite small spatial extents (e.g., the extinction of a rare species; Okamoto et al. 2020).
Last, we sought to create a representative set of measures for representing stakeholder-defined concerns and testing key trade-offs, but we could not comprehensively assess all vulnerabilities. For example, stakeholders ranked water quality as a key concern, but we were unable to model it with our available tools. Many groundwater basins have water quality impairments that we did not account for, including nitrate, arsenic, chloride, and fecal coliform concentrations that exceed regulatory maximum contaminant levels (Table A2.2; CDWR 2003). These contaminants arise from both nonpoint and point sources, including septic systems, former disposal sites, orphaned sites, and stormwater runoff. A critical issue is seawater intrusion into coastal aquifers, which could make groundwater unusable for agriculture (Martin 2014). Our vulnerability measure treated percent overdraft of groundwater as posing an equal threat across areas, but seawater intrusion could arguably make coastal areas more sensitive to overdraft than inland areas.
We therefore stress the importance of holistically viewing our results and map products as “one tool in the toolbox.” Adaptation decision making is more likely to be successful when done in conjunction with local knowledge, stakeholder engagement, and collaboration (Bakker and Morinville 2013, Dobbin et al. 2015). To this end, Appendix 2 provides a complementary qualitative review of additional social and ecological sensitivities for the Central Coast that we compiled in the process of developing our model with stakeholders.
As land use, water systems, and climate feedbacks change over the coming decades, institutions will be challenged to balance the needs of multiple social-ecological communities. There is a substantial need for integrative vulnerability assessments in many regions; however, to be most successful they will need to be intentionally designed to address specific management goals (Michalak et al. 2022). Our approach highlights a way to design vulnerability analyses for systematically identifying benefits and trade-offs to multiple diverse groups. Our results indicate that integrated adaptation planning by institutions could simultaneously achieve multiple goals. The dramatic influence of water demand caps on reducing vulnerability of agricultural systems, human communities, and ecosystems highlights opportunities that potentially transformative laws like SGMA have to transition systems from unsustainable configurations into resilient ones (Walker et al. 2006, Van Schmidt et al. 2022). Our approach was also able to identify trade-offs, which tended to be localized. Spatially explicit vulnerability studies like ours can provide maps to planners that highlight where these trade-offs occur so that managers may better tailor strategies to local conditions and considerations, while still providing consistent regional-scale planning tools.
RESPONSES TO THIS ARTICLE
Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.
Nathan D. Van Schmidt: conceptualization, methodology, software (LUCAS-W, management scenarios), validation, formal analysis, investigation, stakeholder interviews, data curation, writing – original draft, visualization. Tamara S. Wilson: funding acquisition, conceptualization, methodology, software (original LUCAS), writing – review and editing. Lorraine E. Flint: funding acquisition, conceptualization, methodology, software (BCM), writing – review and editing. Ruth Langridge: supervision, project administration, funding acquisition, conceptualization, methodology, workshop and meeting organization, stakeholder interviews, resources, writing – review and editing.
All exposure, sensitivity, and vulnerability maps are available as a data release in the U.S. Geological Survey’s ScienceBase catalog (Van Schmidt et al. 2023; https://doi.org/10.5066/P9XQVEL4). The LUCAS-W model is freely available from ScienceBase (Van Schmidt et al. 2021; https://doi.org/10.5066/P9209XW4); modeling was done using the ST-SIM software application which can be downloaded, free of charge, from APEX Resource Management Solutions (http://apexrms.com). The Basin Characterization Model projections are freely available from the USGS (Flint and Flint 2014; https://doi.org/10.5066/F76T0JPB). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This work was supported by the California Strategic Growth Council Climate Change Research Program (Grant #CCRP0023) and the U.S. Geological Survey’s Ecosystems Land Change Science Program. We thank Paul Selmants and Michelle Stern for additional assistance with exposure models, and our peer reviewers for their feedback.
The data/code that support the findings of this study are openly available in the U.S. Geological Survey’s ScienceBase catalog at https://doi.org/10.5066/P9XQVEL4. The LUCAS-W model is also openly available from ScienceBase at https://doi.org/10.5066/P9209XW4. BCM projections are available from the USGS at https://ca.water.usgs.gov/projects/reg_hydro/basin-characterization-model.html.
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Table 1. Matrix of nine vulnerability measures that were the product of an exposure and a sensitivity measure. To avoid overall vulnerability being skewed by outlier values in any one variable, measures that did not naturally range 0 to 1 (labeled N) were capped to this range after normalization to the range 0 to the 90th percentile, except for domestic land sensitivity, which was normalized for the range 75% to 100%. Data sources were Van Schmidt et al. (2022) for land and water exposure, Flint and Flint (2014) for climate exposure, California Department of Conservation (2016) and Van Schmidt et al. (2022) for agricultural sensitivity, U.S. Census Bureau (2017) for domestic sensitivity, and Thorne et al. (2019) for ecological sensitivity; see Appendix 1 for details.
|Exposure type||Sensitivity type||Vulnerability assessed||Exposure measure||Sensitivity measure|
|Land||Agricultural||Loss of important farmland||Max (Probability of developed land use 2061, probability of ag. contraction 2001–2061)||Important farmland ranking (ordinal)|
|Domestic||Lack of new development in areas with housing needs||1 - probability of new developed land use 2061||Percent of housing units occupied within development zoneN|
|Ecological||Loss of critical habitats for endangered species||Probability of cropland or developed land use 2061||Critical habitat of at least one species (yes/no)|
|Water||Agricultural||Increased water demand that cannot be fallowed (orchards/vineyards)||Percent overdraft 2061 OR ½ the percent increase in total water use 2001–2061N||Percent of water use from perennial cropland in 2061|
|Domestic||Household vulnerability to increased water inaffordability||Percent overdraft 2061 OR ½ the percent increase in total water use 2001–2061N||Percent of population at risk of future water affordability within developed land use|
|Ecological||Drying of groundwater-dependent habitats for endangered species||Percent overdraft 2061 OR ½ the percent increase in total water use 2001–2061N||Percent of endangered freshwater species vulnerable to overdraftN|
|Climate||Agricultural||Increased irrigation water needs of crops||Change in climatic water deficit 1981–2010 to 2040–2069N||Cropland water use (per-cell) in 2061N|
|Domestic||Household vulnerability to heat-related health impacts||Change in mean max temperature (June–August), 1981–2010 to 2040–2069N||Percent of population elderly within developed land use in 2061N|
|Ecological||Loss of runoff and recharge that keeps streams, ponds, and vernal pools wet||Decrease in runoff and recharge 1981–2010 to 2040–2069N||Percent of endangered freshwater species vulnerable to drought|
Table 2. Summary of trade-offs among management strategy assessed via simulated scenarios of coupled land-use change, water demand, and climate change within California’s Central Coast (data in Fig. 8).
|Capping water demand at current sustainable water supply||Major reduction in agricultural, domestic, and ecological water vulnerability||Increased agricultural contraction on prime farmland and development of critical habitats|
|Water supply enhancement (with demand caps)||Reduced domestic and ecological water vulnerability||Increased agricultural water vulnerability, and agricultural and domestic climate vulnerability|
|Urban sprawl limits||Reduced loss of prime farmland||Increased domestic climate vulnerability|
|Ecosystem preservation||Major reduction in loss of critical habitats||Major increase in agricultural water vulnerability, increase in agricultural climate vulnerability|