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

We model ecosystem productivity, evapotranspiration, and summer streamflow under a range of temperature and precipitation scenarios using RHESSys, a spatially distributed model of carbon

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: 9 May 2006 / Accepted: 19 August 2008 / Published online: 3 October 2008

© Springer Science + Business Media B.V 2008

Abstract Global Climate Models (GCMs) project moderate warming along with

In water-limited ecosystems, vegetation acts as an important control on streamflow and responds to soil moisture availability Fires are also key disturbances in semi-arid 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

1 Introduction

Recent summaries of GCM climate scenarios predict that the average temperature

ecosystems to these projected changes, focusing on changes to vegetation produc-tivity, carbon cycling and hydrology Chaparral dominated, Mediterranean type ecosystems (MTEs) of southern California are expected to be highly sensitive to

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

Models can be useful tools in understanding spatial-temporal controls on

model-based studies that examine the hydrologic impacts of projected climate change in

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

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

Hydrologic models typically estimate streamflow as follows:

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:

that there is greater uncertainty in coupled models given the need for additional algorithms and parameters Nonetheless these models support the exploration rela-tionships 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

Wildfire directly alters ecosystem carbon cycling in these watershed and also changes hydrologic response Numerous studies have found that fire in chaparral causes an

climate Further, frequency and severity of fire can be linked to climate driven

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

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)

to investigate how vegetation responses to climate change might alter the rela-tionship 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

2 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,

Fig 1 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, loam soils; while the gentler slopes have deeper more developed sandy-loam and sandy-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, Eri-ogonum fasiculatum), oak woodland (e.g Quercus spp.), grass and winter-deciduous

Daily precipitation and temperature are available from 1952 to 2002 for a nearby National Climate Data Center monitoring site Details on processing and spatial

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 res-piration 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

and allocation of net photosynthate to leaves, stems and roots as well as soil and

watershed that evaluated model predictions of historic streamflow patterns and post-fire LAI recovery trajectories, respectively Calibration of RHESSys soil hydrologic

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

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

2.3 Climate change scenarios

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

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

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

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

9 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

chaparral reaches reproductive maturity in 5–10 years and becomes most flammable

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

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

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 do 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

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 4 ecosystem response variables

3 Results and discussion

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 semi-arid 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

dc30 dc10 cczero in10 in30 dc30T2 dc10T2 cczeroT2 in10T2 in30T2 dc30T4 dc10T4 cczeroT4 in10T4 in30T4

ET Static

dc30 dc10 cczero in10 in30 dc30T2 dc10T2 cczeroT2 in10T2 in30T2 dc30T4 dc10T4 cczeroT4 in10T4 in30T4

ET Dynamic

dc30 dc10 cczero in10 in30 dc30T2 dc10T2 cczeroT2 in10T2 in30T2 dc30T4 dc10T4 cczeroT4 in10T4 in30T4

ET with Fire

Fig 2 Annual ET (mm/year) across precipitation and climate scenarios Scenario key is provided in

Table 1 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

While ET increases with precipitation (for all scenarios), this increase is typically

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,

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 do 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 de-creases in ET with warming, while the static model (no vegetation change) show

Table 1 Key for climate change scenario names

Precip/temperature 30% decrease 10% decrease No change 10% increase 30% increase

Increase 2 ◦C dc30T2 dc10T2 cczeroT2 in10T2 in30T2

Increase 4 ◦C dc30T4 dc10T4 cczeroT4 in10T4 in30T4

Climatic Change (2009) 93:137–155 145

Fig 3 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

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

Pcp mm/yr

*

*

*

* *

*

*

*

*

**

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

*

**

*

*

*

*

*

*

* *

*

*

*

*

*

*

*

*

o

*

baseline T4

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

reduces from 111 gC/m2/year under baseline scenarios to 83 and 60 gC/m2/year with

2 and 4 degree warming respectively Similar reductions in NPP were found in a field-based warming experiment in Mediterranean shrublands of Spain, where moderate

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

Modeled changes in August streamflow parallel these changes in ET across climate scenarios, such that for dynamic simulations (with and without fire) there

frequency of low flow years shows slightly greater sensitivity to climate scenarios

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

baseCO2

with Fire

static

a

Fig 4 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