xxxxxmodeing the eco hydrologic response of a mediterian type ecosystem to the combined impacts of proects cc and altered fire frequencies

Climatic Change (2009) 93:137–155 DOI 10.1007/s10584-008-9497-7 Modeling the eco-hydrologic response of a Mediterranean type ecosystem to the combined impacts of projected climate change and altered fire frequencies C Tague · L Seaby · A Hope Received: May 2006 / Accepted: 19 August 2008 / Published online: October 2008 © Springer Science + Business Media B.V 2008 Abstract Global Climate Models (GCMs) project moderate warming along with increases in atmospheric CO2 for California Mediterranean type ecosystems (MTEs) In water-limited ecosystems, vegetation acts as an important control on streamflow and responds to soil moisture availability Fires are also key disturbances in semiarid environments, and few studies have explored the potential interactions among changes in climate, vegetation dynamics, hydrology, elevated atmospheric CO2 concentrations and fire We model ecosystem productivity, evapotranspiration, and summer streamflow under a range of temperature and precipitation scenarios using RHESSys, a spatially distributed model of carbon–water interactions We examine the direct impacts of temperature and precipitation on vegetation productivity and impacts associated with higher water-use efficiency under elevated atmospheric CO2 Results suggest that for most climate scenarios, biomass in chaparral-dominated systems is likely to increase, leading to reductions in summer streamflow However, within the range of GCM predictions, there are some scenarios in which vegetation may decrease, leading to higher summer streamflows Changes due to increases in fire frequency will also impact summer streamflow but these will be small relative to changes due to vegetation productivity Results suggest that monitoring vegetation C Tague (B) Bren School of Environmental Science and Management, University of California at Santa Barbara, Santa Barbara, CA 93106, USA e-mail: ctague@bren.ucsb.edu L Seaby Sher Leff, 450 Mission Street, Suite 400, San Francisco, CA 94105, USA e-mail: lseaby@sherleff.com A Hope Department of Geography, San Diego State University, San Diego, CA 92182, USA e-mail: hope1@mail.sdsu.edu 138 Climatic Change (2009) 93:137–155 responses to a changing climate should be a focus of climate change assessment for California MTEs Introduction Recent summaries of GCM climate scenarios predict that the average temperature in Southern California will increase by 1.5–5◦ C in the next century, with small to moderate changes in annual precipitation (Cayan et al 2006; Wilkinson 2002; Goodrich et al 2000) In this paper, we address the potential responses of chaparral ecosystems to these projected changes, focusing on changes to vegetation productivity, carbon cycling and hydrology Chaparral dominated, Mediterranean type ecosystems (MTEs) of southern California are expected to be highly sensitive to climate variability (Moreno and Oechel 1995) MTEs are strongly water limited, and thus potential changes to chaparral ecosystem productivity are tightly linked to vegetation water use Substantial changes in vegetation production in these ecosystems may therefore have important implications for local water resources Changes in vegetation productivity would also have implications for global carbon budgets, with feedbacks to climate change, and may alter ecosystem health and vulnerability to disease, fire and species change (Moreno and Oechel 1995) Models can be useful tools in understanding spatial-temporal controls on ecohydrologic processes (Wigmosta and Lettenmaier 1999) There are numerous modelbased studies that examine the hydrologic impacts of projected climate change in California (e.g Knowles and Cayan 2002) as well as several studies on potential changes in vegetation type (e.g Lenihan et al 2003) Few studies, however, have considered the potential feedbacks between vegetation carbon cycling responses and hydrologic stores and fluxes In general, the contributions of vegetation dynamics to hydrologic sensitivity to climate change have not been well studied in current models of either carbon cycling or hydrologic behaviors in MTEs (Breshears and Allen 2002) Interactions between vegetation and hydrology can be particularly important in the semi-arid ecosystems, such as those in southern California, where ET is a significant component of the water budget and vegetation dynamics are water limited In this paper, we use a process-based model to develop quantitative estimates of chaparral ecosystem responses to climate, that include explicit representation of soil moisture controls on vegetation carbon cycling and growth and concurrently, vegetation controls on ET, soil moisture and ultimately streamflow Given a rise in atmospheric CO2 and associated changes in temperature and precipitation, we are interested how summer streamflow might change We focus in this paper on summer streamflow because it is likely to be highly sensitive to change in vegetation water use and climate forcing in semi arid ecosystems, and because changes to summer streamflow regime are often used as indicators of aquatic ecosystem stress (Poff et al 1997) Hydrologic models typically estimate streamflow as follows: Q = f (ET, P, S) (1) ET = f (V, A, P, S) (2) And Climatic Change (2009) 93:137–155 139 where Q is streamflow, ET is evapotranspiration, P is precipitation V represents vegetation characteristics often specified by LAI (leaf area index) and species type parameters that control vegetation water use LAI is a commonly used surrogate for vegetation biomass and its influence on transpiration, evaporation from canopy interception, and maximum canopy conductance S represents soil characteristics that influence drainage rates and storage and ultimately water availability for ET Atmospheric controls (A) include air temperature (T), radiation, windspeed, and humidity The impact of climate change in hydrologic models is often included by changing temperature—and accounting for corresponding changes in humidity Most hydrologic models used in climate change assessment account for changes in A and P as controls on ET and Q, while holding S and V constant Carbon cycling models typically assume that: V = f (T, CO2, P) (3) In coupled models, V,A,P and CO2 in Eqs and are dynamic It could be argued that there is greater uncertainty in coupled models given the need for additional algorithms and parameters Nonetheless these models support the exploration relationships among different variables and thus may be key tools in the development hypothesis about which interactions or forcing conditions are likely to be important for climate change assessment and monitoring In this study we use a coupled model to examine the relative sensitivity to ET, and ultimately Q to changes in V, T, P, CO2 and interactions among them for chaparral dominated MTEs Wildfire in MTEs is another important driver of land cover change The fire return interval is three to five decades in southern California chaparral (Keeley et al 1999) Wildfire directly alters ecosystem carbon cycling in these watershed and also changes hydrologic response Numerous studies have found that fire in chaparral causes an increase in streamflow, sediment load, and peak discharges (Loaiciga et al 2001; Florsheim et al 1991) Wildfire is likely to become more frequent under a warmer climate Further, frequency and severity of fire can be linked to climate driven changes in both vegetation productivity and hydrology Westerling et al (2003) show that in the Western US, fire severity increased in dry years, and was also higher when the previous year was wet leading to higher biomass and greater fuels Thus, in estimating responses of MTEs to climate change, fire must be included as a driver of vegetation change in Eq (3) We recognize that vegetation composition may change given time and fire frequency but we ignore it here for shorter-term (decadal) analysis In this paper, we use RHESSys (Regional Hydro-Ecologic Simulation System) (Tague and Band 2004), a spatially distributed model of carbon–water interactions, to investigate how vegetation responses to climate change might alter the relationship between watershed hydrology and carbon cycling We use the model to estimate changes in annual productivity, ET, vegetation LAI and summer streamflow under different climate scenarios, and to explore how including fire may alter the resulting patterns of behavior The goal is not necessarily to provide precise quantitative estimates of water and carbon fluxes Instead, this work compares how different drivers of change (temperature, precipitation, atmospheric CO2, within ranges provided by current GCM projections for southern California) contribute to interactions between hydrology and vegetation carbon cycling and to offer insight 140 Climatic Change (2009) 93:137–155 into how important these interactions may be for quantifying future water availability and ecosystem vulnerability Methods 2.1 Study site We focus on chaparral ecosystems in the Santa Ynez mountains of Southern California Modeling scenarios are developed for the Jameson Creek watershed, a 34 km2 watershed in the Santa Ynez Mountains in southern California (Fig 1) Fig Study site Climatic Change (2009) 93:137–155 141 Average annual rainfall is 780mm with most of the rainfall occurring between November and May Elevations range from 677 meters at the watershed outlet to 1771 meters at the highest point The steeper hillslopes have rocky, nutrient poor, sandy-loam soils; while the gentler slopes have deeper more developed sandyloam and loam soils Vegetation cover is predominately evergreen chaparral (e.g Adenostoma fasiculatum, Ceanothus luecodermis, Arctostaphylos glauca) intermixed with summer-deciduous sub-shrubs (e.g Salvia mellifera, Artemisia californica, Eriogonum fasiculatum), oak woodland (e.g Quercus spp.), grass and winter-deciduous riparian trees (e.g Salix spp and Populus spp.) (Stephenson and Calcarone 1999) Daily precipitation and temperature are available from 1952 to 2002 for a nearby National Climate Data Center monitoring site Details on processing and spatial interpolation of precipitation data are provided in Tague et al (2004) Streamflow is recorded at U.S Geolological Survey Gage (no 11121010) at the Jameson Lake Reservoir 2.2 RHESSys RHESSys is a spatially distributed model of watershed scale linkages among water, carbon and nitrogen RHESSys sub-models can be used to estimate the impact of air temperature, humidity and soil moisture on a number of ecosystem processes including evaporation, transpiration, stomatal conductance, photosynthesis and respiration RHESSys models both vertical hydrologic processes (ET, canopy and litter interception, infiltration, drainage) and lateral routing between terrestrial patches The RHESSys carbon cycling sub-model is similar to that used by BIOME_BGC (Thornton et al 2002) and includes estimates of carbon assimilation, respiration and allocation of net photosynthate to leaves, stems and roots as well as soil and litter decomposition Tague and Band (2004) provide a complete description of the RHESSys model For this study, the Jarvis (1976) based stomatal conductance computation in RHESSys was modified following Medlyn et al (2001) to include the impact of increases in atmospheric CO2 RHESSys uses the Farquhar and vonCaemmerer (1982) approach to estimate net photosynthesis, which also responds to increases in atmospheric CO2 concentration Tague et al (2004) describe previous applications of RHESSys to the Jameson watershed that evaluated model predictions of historic streamflow patterns and postfire LAI recovery trajectories, respectively Calibration of RHESSys soil hydrologic parameters for the Jameson watershed is summarized in Tague et al (2004), and show that the model obtained Nash-Sutcliffe efficiency (Nash and Sutcliffe 1970) values of greater than 0.9 for monthly streamflow, and percent error in total flow over a 5-year calibration period of less than 5% for a pareto optimal parameter set For this paper, a single parameter set is selected from optimal parameter space Calibration in this previous work was based on the correspondence between model and observed streamflow at a monthly time step The model is also able to capture inter-annual variation in streamflow, giving an R2 of 0.96 for modeled versus observed annual flow of a 30-year period of record Strong correspondence between observed and modeled flow at the annual time step suggests that the model provides reasonable estimates of ecosystem ET, which is approximately 80% of the mean annual water budget Previous work has also evaluated the carbon cycling 142 Climatic Change (2009) 93:137–155 component of RHESSys by comparing RHESSys modeled and Thematic Mapper remote sensing derived LAI post-fire recovery trajectories and show that the model is able to reproduce post-fire regrowth as well as mature LAI values within ±20% for this watershed (Seaby et al 2006) 2.3 Climate change scenarios Cayan et al (2006) summarize recent GCM climate projections for California While there is consensus that temperatures will increase in California during the next 100 years, the magnitude of this increase depends upon emission scenarios and varies to some extent between different GCMs For Southern California, predicted temperature increases relative to historic (1961–1990 period) conditions range from 0.8–2.3◦ C for the 2035–2064 period and 1.6 to 4.4 for the 2070–2099 period Changes to precipitation show even greater variability across emission scenarios and GCM models, and range from predicted decreases of up to 30% to smaller increases (up to 10%) A key challenge in applying GCM model climate projections is downscaling to local scales, particularly in the complex topography of mountain environments (Ghan et al 2006; Wood et al 2004) Given the uncertainties involved in downscaling GCM data, we choose to develop scenarios based on historic meteorologic records for our site One of the advantages of this approach is that we can consider both the separate and synergistic effects of changes in temperature and precipitation We applied a 2◦ C and 4◦ C degree temperature increases to existing meteorologic station records to generate scenarios consistent with moderate and more extreme warming projected by GCMs Temperature increases assume a uniform temperature increase throughout the year To simulate changes in precipitation, we generated 50-year time series by randomly selecting water years from the historic meteorologic record Note that for meteorologic data used in the Jameson study site, there is no statistically significant temporal correlation in annual precipitation for successive water years We randomly selected water years from the 50-year time series, allowing for repetition of water years, to generate new 50-year climate records that vary in terms of decadal statistics Using this method we were able to generate 50-year time series with mean annual precipitation greater or less than current mean annual precipitation We chose scenarios with ±30, ±10 and no change in precipitation and applied the baseline, 2◦ C and 4◦ C warming to these scenarios to generate the set of climate scenarios for our analysis 2.4 Fire frequency In addition to comparing model predictions of ecosystem function across climate scenarios, we also contrast simulations with and without fire Current fire return interval ranges from 30 to 50 years for southern California chaparral ecosystems (Keeley et al 1999) Fire return intervals of less than 15 years are unlikely given that chaparral reaches reproductive maturity in 5–10 years and becomes most flammable after 15–20 years (Radtke et al 1982; Haidinger and Keeley 1993) We compare simulations run assuming no fire over the 50-year simulation period and simulations with a moderate-high level of fire frequency (return period of 30 years) Because the Climatic Change (2009) 93:137–155 143 region is prone to large fires greater than 120 km2 (Mensing et al 1999; Radtke et al 1982), it is assumed that the entire 34 km2 watershed will burn as a result of each ignition To simulate fire, we set all above-ground carbon and nitrogen model stores, as well as fine root stores to zero Soil carbon and nitrogen stores, however, are not altered 2.5 Scenario analysis RHESSys was run for combinations of the temperature (no increase, +2◦ C, +4◦ C) and precipitation scenarios (−30, −10, 0, +10, +30), with and without fire to produce 30 model realizations In addition, we also consider the sensitivity of model prediction to assumptions made about atmospheric CO2 concentrations For initial runs we assume a relatively modest, CO2 concentration of 400 ppm and compare results with simulations run using 600 and 800 ppm We also perform several addition model runs using only the hydrologic component of RHESSys These simulations are similar to standard hydrologic models that not account for changes in vegetation with a changing climate These static simulations allow us to explore the impact of coupling a hydrologic model with the dynamic ecosystem model and answer the question: Is the additional model complexity warranted? Model outputs for comparison include August streamflow, leaf area index (LAI), ET (ET) and net primary productivity (NPP) Given the Mediterranean climate of our study site, August streamflow typically has the lowest monthly streamflow, and it is likely to be highly sensitive to vegetation water-use As a summer month with the lowest streamflow, August streamflows are also likely to be critical from an aquatic ecosystem perspective For example, maintenance of low flows to support Steelhead habitat in the Santa Ynez region is an ecosystem management goal (EIR Lower Santa Ynez River Fish Management Plan 2004) We examine the impact of climate change on summer streamflow through the probability of obtaining average monthly streamflow values below a threshold We use the low quartile yearly mean August streamflows from baseline climate scenario to define this threshold ET estimates show the direct impacts of climate variability on vegetation water use NPP and LAI demonstrate interactions with chaparral carbon cycling For each scenario, we examine annual mean and inter-annual variation of these ecosystem response variables Results and discussion Figure summarizes annual ET for all climate scenarios Results are shown for dynamic simulations, in which carbon-cycling driven changes in vegetation are included, and static simulations in which vegetation biomass does not change in response to climate forcing For dynamic simulations, we begin by examining results obtained by using atmospheric CO2 concentration of 400 ppm As expected ET increases with increasing precipitation, for all temperature scenarios In this semiarid Mediterranean climate, precipitation increases associated within climate change are likely to be small relative to inter-annual variation precipitation Modeled ET reflects this high inter-annual variation in precipitation such that inter-annual 144 Climatic Change (2009) 93:137–155 400 1000 ET mm/year ET Static dc30 dc10 cczero in10 in30 dc30T2 dc10T2 cczeroT2 in10T2 in30T2 dc30T4 dc10T4 cczeroT4 in10T4 in30T4 in30T2 dc30T4 dc10T4 cczeroT4 in10T4 in30T4 400 1000 ET mm/year ET Dynamic dc30 dc10 cczero in10 in30 dc30T2 dc10T2 cczeroT2 in10T2 400 1000 ET mm/year ET with Fire ● ● ● ● ● ● ● dc30 dc10 cczero in10 ● in30 ● ● ● dc30T2 dc10T2 cczeroT2 in10T2 ● ● ● ● in30T2 dc30T4 dc10T4 cczeroT4 in10T4 in30T4 Fig Annual ET (mm/year) across precipitation and climate scenarios Scenario key is provided in Table Variance within each 50-year climate scenario reflects year to year differences in ET Results are show for a static model (vegetation does not change), a dynamic model in which vegetation responds to climate and a dynamic model that also includes vegetation losses due to fire (30-year return interval) variation in ET within each climate scenario is large relative to differences in mean ET between scenarios (Fig 2) While ET increases with precipitation (for all scenarios), this increase is typically non-linear Figure shows a leveling off of ET with higher annual precipitation for the dynamic simulation with baseline and 4◦ C temperature increase scenario A similar pattern is found for all climate scenarios RHESSys model estimates of mean potential ET for this site are approximately 1,300 mm/year Similarly, Hidalgo et al (2005) estimate potential ET ranging from 1,300 to 1,700 mm/year using data from CIMIS (California Irrigation Management System) stations and pan evaporation from local NCDC (National Climate Data Center) stations within the Santa Ynez region We note that leveling off of ET estimates for years with high annual precipitation occurs at values significantly below these potential ET estimates Thus there is still unmet ET potential even in wet years The ET-precipitation relationship also reflects the within year temporal distribution of precipitation Years with high annual precipitation are typically dominated by one or more large storm events, where most of the additional water is lost as runoff; thus these precipitation increases not lead to large gains in ET The greatest increases in ET are seen in the shift from years with low to moderate precipitation When averaged across precipitation scenarios, the dynamic model predicts decreases in ET with warming, while the static model (no vegetation change) show Table Key for climate change scenario names Precip/temperature 30% decrease 10% decrease No change 10% increase 30% increase Baseline Increase 2◦ C Increase 4◦ C dc30 dc30T2 dc30T4 dc10 dc10T2 dc10T4 cczero cczeroT2 cczeroT4 in10 in10T2 in10T4 in30 in30T2 in30T4 ** ET mm/day 600 800 1000 Fig Annual ET (mm/year) as a function of annual precipitation Results are shown for the baseline climate scenario and a scenario with a 4-degree increase in temperature 145 1200 Climatic Change (2009) 93:137–155 * ● ● ● ●● ● ● ● * *** ● * * ** ● ● 400 ** ** * * * * ** * * * * * *** ***** ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● * *** * ●● ● ● ** ● ● ● ● * ● ● * ** * ● ● ● ● ● ● o ●● ● * 0.5 1.0 baseline T4 1.5 Pcp mm/yr negligible changes in ET (Fig 4) For example mean annual ET for the 4◦ C warming scenario, using the dynamic model, is 10% lower relative to baseline This counter intuitive decrease in ET with warmer temperature using the dynamic model reflects the impact of changes in vegetation Mean LAI for the dynamic model reduces by more than 30% with a 4◦ C warming Mean annual NPP for the dynamic model reduces from 111 gC/m2/year under baseline scenarios to 83 and 60 gC/m2/year with and degree warming respectively Similar reductions in NPP were found in a fieldbased warming experiment in Mediterranean shrublands of Spain, where moderate warming of approximately 3◦ C in a single year, reduced above ground NPP from approximately 160 to 130 gC/m2/year (Penuelas et al 2007) Lower LAI is caused by the higher respiration costs under the warmer temperature The magnitude of changes in LAI for the dynamic model are sensitive to assumptions made about atmospheric CO2 and will be discussed below Results here, however, demonstrate that changes in vegetation can impact model estimates of vegetation water use and its response to climate Including fire in dynamic simulations does reduce ET in specific years, but does not alter overall relationships between decadal average ET and decadal mean climate variables (precipitation and temperature) An overall reduction in ET due to lower LAI with warmer scenarios is also evident and is of similar magnitude to results from the dynamic simulation when fire is not included (Fig 4a) Modeled changes in August streamflow parallel these changes in ET across climate scenarios, such that for dynamic simulations (with and without fire) there is a moderate increase in mean August streamflow with warming (Fig 4b) The frequency of low flow years shows slightly greater sensitivity to climate scenarios (Fig 5) For scenarios with static vegetation, a 30% decrease in decadal precipitation means results in only minor increases in the frequency of low flow conditions (or years with August flow below the baseline scenario lower quartile) Low flows are more sensitive to increases in precipitation With a 10% increase in precipitation, the frequency of low flow (below baseline quartile) reduces by more than 50% For the dynamic model patterns are dominated by the hydrologic effects of vegetation responses to temperature With the dynamic model, baseline climate scenarios show a greater frequency of low flow years relative to results from the static model This 146 Climatic Change (2009) 93:137–155 a 700 600 500 ET (mm/year) 800 baseCO2 base T2 T4 700 600 500 ET (mm/year) 800 with Fire base T2 T4 700 600 500 ET (mm/year) 800 static base T2 T4 Fig Annual ET (a) and Mean August Streamflow (b) for baseline, 2◦ C and 4◦ C warming scenarios Each box-whisker plot shows mean and variance across precipitation scenarios Results are show for a static model (vegetation does not change), a dynamic model in which vegetation responds to climate and a dynamic model that also includes vegetation losses due to fire (30-year return interval) difference in low flow year frequency reflects the hydrologic impact of year-to-year (within the baseline climate scenario) variation in LAI associated with the dynamic simulations For the dynamic model, decreases in vegetation biomass with warmer temperatures (assuming baseline CO2 conditions) leads to reductions in water use and a dramatic decrease in the frequency of low flow years under a warmer climate In contrast to static model, the dynamic model results suggest that variation in the Climatic Change (2009) 93:137–155 b 147 0.6 0.5 0.4 0.3 Aug Str (mm/year) 0.7 baseCO2 base T2 T4 0.6 0.5 0.4 0.3 Aug Str (mm/day) 0.7 with Fire base T2 T4 0.6 0.5 0.4 0.3 Aug Str (mm/day) 0.7 static base T2 T4 Fig (continued) frequency of low flow years is greater across temperature scenarios than across precipitation scenarios Dynamic simulation results, therefore, highlight vegetation responses as a first order control on hydrologic responses Inclusion of fire, in general reduces the frequency of low flow years This is expected, given the reduction in vegetation and associated ET losses The small reduction in the frequency of low flow years, however, suggests that even with the fairly high fire return interval used here, the impact of fire on low flows is relatively minor at decadal time scales Rapid recovery of vegetation, within several years following fire, and high inter-annual variation in precipitation, means that the likelihood of low flow years occurring during the short hydrologically relevant post fire period is small Rapid recovery of chaparral estimated by the model is 148 Climatic Change (2009) 93:137–155 Static Vegetation Relative Frequency 0.10 0.20 0.30 30% dec 10% dec baseline 10% inc 30% inc base T2 T4 base T2 T4 base T2 T4 base T2 T4 base T2 T4 base T2 T4 base T2 T4 T2 T4 T2 T4 Relative Frequency 0.10 0.20 0.30 Dynamic Vegetation base T2 T4 base T2 T4 base T2 T4 Relative Frequency 0.10 0.20 0.30 Dynamic Vegetation with fire base T2 T4 base T2 T4 base T2 T4 base base Fig Frequency of years with August streamflow below a threshold value Threshold is defined as the lower quartile flow from the baseline climate scenario (0.24 mm/day) Relative frequency refers to the proportion of years within a 50-year climate scenario that have August streamflow below the threshold value Results are show for all temperature and precipitation scenarios and for static model (vegetation does not change), a dynamic model in which vegetation responds to climate and a dynamic model that also includes vegetation losses due to fire (30-year return interval) consistent with remote-sensing based estimates of recovery trajectories for this region (McMichael et al 2004) In addition to simulations with baseline atmospheric CO2 concentrations, we also compare dynamic model results across low (400 ppm), moderate (600 ppm) and high (800 ppm) CO2 Increasing CO2 increases plant water use efficiency leading to higher estimates of LAI (Fig 6) Variation in LAI within a given temperature scenario (shown on each box-whisker plot) reflect the range of LAI estimates across different precipitation scenarios, averaged over the 50-year simulation period Variation in LAI due to a change in decadal precipitation means is small relative to variation due to temperature For all scenarios LAI decreases linearly with increasing temperature Higher levels of atmospheric CO2, however, support higher LAI overall, such that the gains due to increased CO2 outweigh losses due to temperature (up to a 4◦ C temperature increase) Differences between scenarios using 600 ppm and 800 ppm are smaller than those between the baseline (400 ppm) and 600 ppm concentration, Climatic Change (2009) 93:137–155 149 with Fire LAI 1.0 1.5 2.0 2.5 3.0 3.5 4.0 LAI 1.0 1.5 2.0 2.5 3.0 3.5 4.0 baseCO2 T2 CO2 600 T4 base T2 CO2 800 T4 LAI 1.0 1.5 2.0 2.5 3.0 3.5 4.0 LAI 1.0 1.5 2.0 2.5 3.0 3.5 4.0 base base T2 T4 base T2 T4 Fig Mean LAI for baseline, 2◦ C and 4◦ C warming scenarios Each box-whisker plot shows mean and variance across precipitation scenarios Results are show for dynamic model runs with baseline (400 ppm), moderate (600 ppm) and high (800 ppm) CO2 atmospheric concentrations Results for dynamic model with fire assume a 400 ppm atmospheric CO2 concentration suggesting that gains due to increased water use efficiency begin to level off at the higher atmospheric CO2 concentrations LAI for scenarios with fire are similar to those without fire The small impact of fire on decadal means reflects rapid post-fire re-growth and associated recovery of vegetation water use Differences in NPP reflect these differences in LAI, and water use efficiency (Fig 7) While NPP and LAI are higher for elevated CO2 scenarios, year-to-year variation in NPP is also greater in elevated CO2 scenarios and suggest greater vulnerability to year-to-year variation in water availability As noted with ET, within scenario variation in NPP tends to be large relative to between scenario variation Summer water use tends to stabilize with increased CO2 concentration such that mean August streamflow no longer varies across temperatures scenarios (Fig 8) Once the ecosystem reaches some threshold LAI, soil water is efficiently used by the system in all years and water that remains for the summer baseflow is deep groundwater bypass flow Frequency of low flow years is substantially higher for scenarios with greater atmospheric CO2 (Fig 9) Changes in frequency of low flow years across temperature are negligible for these higher CO2 concentrations and are not shown With 400 ppm scenario, LAI decreases under warmer years due to increase in water stress, leading to fewer low flow years With greater water use efficiency, however, higher LAIs increase the likelihood of low flow years under a warmer climate 150 Climatic Change (2009) 93:137–155 NPP gC/m2/yr -1000 1000 CO2 400 ● ● ● dc30 dc10 cczero in10 in30 dc30T2 cczeroT2 ● in30T2 dc30T4 cczeroT4 in30T4 dc30T4c6 cczeroT4c6 in30T4c6 dc30T4c8 cczeroT4c8 in30T4c8 NPP gC/m2/yr -1000 1000 CO2 600 ● ● ● dc30c6 cczeroc6 in30c6 dc10T2c6 in10T2c6 ● NPP gC/m2/yr -1000 1000 CO2 800 ● ● dc30c8 cczeroc8 in30c8 dc10T2c8 in10T2c8 Fig Annual net primary productivity across precipitation and climate scenarios Scenario key is provided in Table Variance within each 50-year climate scenario, reflects year-to-year differences in NPP Results are shown for dynamic model runs with baseline (400 ppm), moderate (600 ppm) and high (800 ppm) CO2 atmospheric concentrations Discussion In most semi-arid ecosystems, ET varies strongly with annual precipitation— although the relationship is a non-linear one In years with high precipitation, much of the additional water is lost as runoff and may not increase ET rates Model results for this chaparral dominated ecosystem follow this expected relationship with precipitation Responses to high inter annual variation in precipitation often outweigh climate change effects on ET and NPP Nonetheless, there are changes to decadal mean ET and NPP with temperature increases that can have implications for ecosystem function and water resources, particularly in terms changing the frequency of years with low summer streamflow In many ecosystems, such as those in humid or snow-melt dominated regions, changes in vegetation water use may be small relative to potential changes in water inputs as precipitation or snow-melt Further in many systems, the dominant impact of climate change on ET may be due to changes in temperature or soil moisture availability rather than change in vegetation biomass Recent modeling of hydrologic responses to projected climate warming in California has focused changes in snow accumulation and melt and corresponding reductions in summer streamflow These hydrologic modeling studies not typically incorporate changes to vegetation with climate scenarios (Vicuna et al 2007; Hayhoe et al 2004; Dettinger et al 2004) For snow-dominated regions, vegetation dynamics may play a secondary role and the added complexity of incorporating dynamic vegetation into a hydrologic model is not warranted For more semi-arid, warmer regions in California, however, changes in vegetation may substantially alter annual water balances In this study, modeled changes in vegetation for a range of possible climate scenarios led to both significant increases or decreases in the frequency of low flow conditions Further changes in Climatic Change (2009) 93:137–155 151 0.3 0.3 Aug Str (mm/day) 0.4 0.5 0.6 0.7 with Fire Aug Str (mm/day) 0.4 0.5 0.6 0.7 baseCO2 base T2 T4 base T2 T4 0.3 0.3 Aug Str (mm/day) 0.4 0.5 0.6 0.7 CO2 800 Aug Str (mm/day) 0.4 0.5 0.6 0.7 CO2 600 base T2 T4 base T2 T4 Fig Mean August streamflow for baseline, 2◦ C and 4◦ C warming scenarios Each box-whisker plot shows mean and variance across precipitation scenarios Results are show for dynamic model runs with baseline (400 ppm), moderate (600 ppm) and high (800 ppm) CO2 atmospheric concentrations Results for dynamic model with fire assume a 400 ppm atmospheric CO2 concentration summer streamflow due to changes in ecosystem production were as large as changes due directly to longer term changes in decadal precipitation means Our results argue that for chaparral dominated systems, models that no account for interactions among vegetation growth, hydrology and climate ignore a first order control on hydrologic response to climate change In this environment, the added complexity of a coupled ecohydrologic model is necessary On the other hand, model results also suggest that the effects of fire are small relative to these changes, at least in terms of water availability and summer streamflow For peak flows and associated erosion, hydrologic responses may be more sensitive to fire, particularly given that soils in chaparral environments show hydrophobicity in the first year following fire (Hubbert and Oriol 2005; DeBano 2000) For low flows, however, fast recover of chaparral in the context of high year to year variation in climate mean that the effect of fire frequency on decadal streamflow behavior is small and modeling of post fire recovery trajectories is not essential for estimating decadal patterns in summer streamflow Results from this study also suggest that the response of chaparral ecosystem ET will depend largely on how vegetation productivity responds to the combined effect of CO2 and temperature The current ecosystem is situated such that the magnitude of atmospheric CO2 increases and associated changes in water use efficiency will determine whether increases or decreases in vegetation biomass are likely to occur 0.30 0.25 0.20 0.15 0.10 0.05 0.00 Relative Frequency Fig Frequency of years with August streamflow below a threshold value Threshold is defined as the lower quartile flow from the baseline climate scenario (0.24 mm/day) Relative frequency refers to the proportion of years within a 50-year climate scenario that have August streamflow below this threshold value Results are shown for baseline climate scenarios, with an atmospheric CO2 concentration of 400 ppm and for moderate (2◦ C) warming and baseline (400 ppm), moderate (600 ppm) and high (800 ppm) atmospheric CO2 concentrations Climatic Change (2009) 93:137–155 0.35 152 base T2 C400 T2 C600 T2 C800 Results from this model based study suggest that a CO2 concentrations at the lower end of recent GCM predictions would lead to reductions in LAI (due to increasing temperatures) For higher levels of atmospheric CO2, productivity and biomass would increase, however, these increases diminish for CO2 concentrations at the upper end of GCM scenario ranges Field and other model-based studies have generally shown increases in ecosystem LAI and NPP, under elevated CO2 (Schimel et al 2000; Antle et al 2001) Reductions in LAI predicted here for low levels of elevated CO2, reflect the importance of linking changes due to increased CO2 with temperature impacts on respiration, particularly given the warm, semi-arid Mediterranean climate Both field and model based studies remain uncertain and long-term physiological adaptation with higher levels of atmospheric CO2 may occur and have not been account for Nonetheless, this modeling study provides insight into the balance between different and interacting controls for California chaparral Results here emphasize that CO2 concentrations will be major control on chaparral hydrologic and carbon cycling responses, both in terms magnitude and direction Model results suggest that reducing uncertainty in projections of CO2 concentrations and plant response may be more important than efforts to refine temperature and precipitation estimates Changes in ecosystem productivity and biomass are also important indicators of ecosystem heath Although increases in vegetation biomass (shown here as LAI) suggest a more productive, potentially healthier ecosystem under higher atmospheric CO2 concentration, modeled NPP also show greater year-to-year variation under higher levels of CO2, which may increase ecosystem vulnerability to insect infestation and disease Increases in LAI with elevated CO2 will increase the frequency of years with very low summer streamflow, which may have negative impacts on aquatic ecosystems It is important to note that this study did not account for species change but focused solely on changes in productivity Increased fire frequency in chaparral dominated systems may result in conversion to grass (Oechel et al 1995) Climatic Change (2009) 93:137–155 153 Incorporation of species change responses into model-based analysis will be the focus of future work Conclusion RHESSys, a coupled hydro-ecologic model, was used to tease apart the effect of multiple, interacting controls that influence the sensitivity of chaparral systems and their hydrology to climate change We focus particularly on linkages among ecosystem water use, vegetation growth and atmospheric drivers of hydrologic behavior Simulation results demonstrate that vegetation responses are both uncertain and likely to be particularly important, given our current understanding of system dynamics Results highlight the importance of changes in water-use efficiency in MTEs for summer streamflow, NPP and ET estimates Model results suggest that future hydrologic behavior and ecosystem productivity will depends on the balance between CO2 controls on vegetation water use efficiency and vegetation responses to increasing temperatures It is likely, however, that increases in vegetation biomass will result in a greater frequency of low flow conditions While fire frequency can influence hydrology in individual years, it is less important for understanding decadal hydrologic behavior Resource managers in MTEs must therefore consider ecosystem responses as a major component of changes in water resources within the coming decade The interpretation of model results must be made in the context of model uncertainty (Beven and Freer 2001) Sources of uncertainty in this study include both uncertainty in climate scenarios predictions and uncertainty in RHESSys predictions of hydrologic and ecosystem responses Models such as RHESSys essentially offer quantitative estimates of the implications current understanding of key controls on water and carbon cycling It is worth noting that recent reviews of field studies of ecosystem response show responses that follow our current understanding of physiological controls on plant function (Antle et al 2001) Model estimates of decadal behavior and dominant controls on climate change responses discussed in this paper provide hypothesis that can be used to guide future research and focus adaptive management, assessment and monitoring efforts Results demonstrate the importance of using coupled eco-hydrologic models in estimating climate change impacts when, as is the case with California chaparral, there are multiple, simultaneous controls whose relative influence can vary dramatically from season to season and year to year References Antle J, Apps M, Beamish R (2001) Ecosystems and their goods and services In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001: impacts, adaptation, and vulnerability Cambridge University Press, Cambridge, pp 235–342 Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology J Hydrol 249:11–29 Breshears DD, Allen CD (2002) The importance of rapid, disturbance-induced losses in carbon management and sequestration Glob Ecol Biogeogr 11:1–5 154 Climatic Change (2009) 93:137–155 Cayan D, Maurer E, Dettinger M, Tyree M, Hayhoe K, Bonfils C, Duffy P, Santer B (2006) Climate scenarios for California, white paper, California climate change center, CEC-500–2006– 203-SF DeBano LF (2000) The role of fire and soil heating on water repellency in wildland environments: a review J Hydrol 231–232:195–206 Dettinger MD, Cayan DR, Meyer M, Jeton AE (2004) Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American River basins, Sierra Nevada, California, 1900–2099 Clim Change 62:283–317 Environmental Impact Statement (EIS) Lower Santa Ynez River Fish Management Plan and Cachuma Project Biological Opinion for Southern Steelhead Trout (2004) Prepared by Cachuma Operation and Maintenance Board, Santa Barbara County, California and Depart of the Interior Bureau of Reclamation Farquhar G, vonCaemmerer S (1982) Modeling photosynthetic response to environmental conditions Encyclopedia of Plant Physiology Florsheim JL, Keller EA, Best DW (1991) Fluvial sediment transport in response to moderate storm flows following chaparral wildfire, Ventura County, southern California Geol Soc Amer Bull 103:504–511 Ghan SJ, Shippert T, Fox J (2006) Physically based global downscaling: regional evaluation J Climate 19:429–445 Goodrich DC, Chehbouni A, Goff B, MacNish B, Maddock T, Moran S et al (2000) Preface paper to the Semi-Arid Land-Surface-Atmosphere (SALSA) program special issue Agric For Meteorol 105:3–20 Haidinger TL, Keeley JE (1993) Role of high fire frequency in destruction of mixed chaparral Madroño 40:141–147 Hayhoe K, Cayan D, Field C, Frumhoff P, Maurer E, Miller N, Moser S, Schneider S, Cahill K, Cleland E, Dale L, Drapek R, Hanemann RM, Kalkstein L, Lenihan J, Lunch C, Neilson R, Sheridan S, Verville J (2004) Emissions pathways, climate change, and impacts on California Proc Natl Acad Sci (PNAS) 101(34):12422–12427 Hidalgo HG, Cayan DR, Dettinger MD (2005) Sources of variability of ET in California J Hydrometeorol 6(1):3–19 Hubbert KR, Oriol V (2005) Temporal fluctuations in soil water repellency following wildfire in chaparral steeplands, southern California Int J Wildland Fire 14:439–447 Jarvis PG (1976) The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field Philos Trans R Soc Lond B 273:593–610 Keeley JE, Fotheringham CJ, Morias M (1999) Reexamining fire suppression impacts on brushland fire regimes Science 284(5421):1829–1832 Knowles N, Cayan D (2002) Potential effects of global warming on the Sacramento/San Joaquin watershed and the San Francisco estuary Geophys Res Lett 29:18 Lenihan JM, Drapek R, Bachelet D, Neilson RP (2003) Climate change effects on vegetation distribution, carbon, and fire in Califronia Ecol Appl 13(6):1667–1681 Loaiciga HA, Pedreros D, Roberts D (2001) Wildfire-streamflow interactions in a chaparral watershed Adv Environ Res 5:295–305 McMichael CE, Hope AS, Roberts DA, Anaya M (2004) Post-fire recovery of leaf area index in California chaparral: a remote sensing—chronosequence approach Int J Remote Sens 25(21):4743–4760 Medlyn BE, Barton CVM, Broadmeadow MSJ, Ceulemans R, De Angelis P, Forstreuter M, Freeman M, Jackson SB, Kellomaki S, Laitat E, Ray A, Roberntz P, Sigurdsson BD, Strassemeyer J, Wang K, Curtis PS, Jarvis PG (2001) Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis New Phytol 149:247–264 Mensing SA, Michaelsen J, Byrne R (1999) A 560-year record of Santa Ana fires reconstructed from charcoal deposited in the Santa Ana Basin, California Quat Res 51:295–305 Moreno JM, Oechel WC (1995) Global change and Mediterranean-type ecosystems Springer, New York Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models: Part I—a discussion of principles J Hydrol 10:282–290 Oechel WC, Hastings SJ, Vourlitis GL, Jenkins MA, Hinkson CL (1995) Direct effects of elevated CO2 in Chaparral and Mediterranean-type ecosystem In: Moreno J, Oechel W (eds) Global change and Mediterranean-type ecosystems Springer, New York, pp 58–75 Penuelas J, Prieto P, Beier C, Cesaraccio C, de Angelis P, de Dato G, Emmett BA, Estiarte M, Garadnai J, Gorissen A, Lang EK, Kroel-Dulay G, Llorens L, Pellizzaro G, Riis-Nielsen T, Climatic Change (2009) 93:137–155 155 Schmidt IK, Sirca C, Sowerby A, Spano D, Tietema A (2007) Response of plant species richness and primary productivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: reductions in primary productivity in the heat and drought year of 2003 Glob Chang Biol 12(12):2563–2581 doi:10.1111/j.1365-2486.2007.01464.x Poff NL, Allan JD, Bain MB, Karr JR, Prestegaard KL, Richter BD, Sparks RE, Stromberg JC (1997) The natural flow regime BioScience 47:769–784 Radtke KW-H, Arndt AM, Wakimoto R (1982) Fire history of the Santa Monica Mountains General Technical Report PSW-58: Pacific Southwest Forest and Range Experiment Station, Forest Service: U.S Department of Agriculture, Berkeley, CA, pp 438–443 Schimel DS, Melillo J, Tian H, McGuire AD, Kicklighter D, Kittel T, Rosenbloom N, Running S, Thornton P, Ojima D, Parton W, Kelly R, Sykes M, Neilson R, Rizzo B (2000) Contribution of increasing CO2 and climate to carbon storage by ecosystems in the United States Science 287:2004–2006 Seaby LP, Tague CL, Hope A (2006) Post-fire recovery of eco-hydrologic behavior given historic and projected climate variability in California Mediterranean type environments Eos Transactions, American Geophysical Union, 87(52) Fall Meeting Suppl Abstract H13B–1938 Stephenson JR, Calcarone GM (1999) Southern California Mountains and Foothills assessment: habitat and species conservation issues General Technical Report: PSW-GTR-172, Albany, California: Pacific Southwest Research Station, Forest Service, U.S Department of Agriculture, 402 pp Tague C, Band LE (2004) RHESSys: Regional Hydro-Ecologic Simulation System—an objectoriented approach to spatially distributed modeling of carbon, water, and nutrient cycling Earth Interact 8(19):1–42 Tague C, McMichael C, Hope A, Choate J, Clark R (2004) Application of the RHESSys model to a California semi-arid shrubland watershed J Am Water Resour Assoc 40(3):575–589 Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests Agric For Meteorol 113:185–222 Vicuna S, Maurer E, Yoyce B, Dracup J, Purkey D (2007) The sensitivity of California water resources to climate change scenarios1 J Am Water Resour Assoc 43(2):482–498 doi:10.1111/j.1752-1688.2007.00038.x Westerling AL, Gershunov A, Brown TJ, Cayan DR, Dettinger MD (2003) Climate and wildfire in the Western United States Bull Am Meteorol Soc 84(5):595–604 Wigmosta M, Lettenmaier D (1999) A comparison of simplified methods for routing topographically driven subsurface flow Water Resour Res 35(1):255–264 Wilkinson R (2002) The potential consequences of climate variability and change for California: a report of the California Regional Assessment Group for the U.S Global Change Research Program, September pp 1–432 Wood AW, Leung LR, Sridhar V, Lettenmaier P (2004) Hydrologic implications of dynamical and statistical climate model outputs Clim Change 62:189–216 ... (Thornton et al 2002) and includes estimates of carbon assimilation, respiration and allocation of net photosynthate to leaves, stems and roots as well as soil and litter decomposition Tague and Band... use NPP and LAI demonstrate interactions with chaparral carbon cycling For each scenario, we examine annual mean and inter-annual variation of these ecosystem response variables Results and discussion... sensitive to change in vegetation water use and climate forcing in semi arid ecosystems, and because changes to summer streamflow regime are often used as indicators of aquatic ecosystem stress (Poff