Drought – how the western U.S. is transformed from

energy-limited to water-limited landscapes.

Hidalgo H.G.1, Cayan D.R.1,2 and Dettinger M.D.2,1

1Scripps Institution of Oceanography

2United States Geological Survey

SUBMITTED TO:

Journal of Hydrometeorology (?)

MAY 2006

Abstract

Keywords: Evapotranspiration, drought, climate change, desiccation, desertification, Variable Infiltration

Capacity, VIC, regional hydrology.

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ABSTRACT

Simulations of the western United States’ (US) hydrology using the Variable Infiltration

Capacity were used to quantify the effects of drought and climate change on the

landscape’s aridity. An index of aridity based on the ratio of actual to potential

evapotranspiration (AET/PET) was calculated for years of extreme droughts and pluvials

and for altered conditions of precipitation (P) and average temperature (Tavg). In the

high elevations increases in aridity are related to reductions in the areas where AET rates

can be considered energy-limited, while in the low elevations increases in aridity are

associated with increases in aridity that in the long-term can lead to desertification. In

terms of the shifts in the climatological aridity, the overall changes in the arid regions are

on the order of 3-4% of the area of the West that would switch from semiarid to arid for a

change in 3oC or 5% decrease in P. This is significant, as it constitutes an expansion of

around 18% of the current arid areas of the west. The reduction of the energy-limited

areas to water limited for the warming scenario represents a reduction of 17% of the

available energy-limited (humid and semi-humid) areas. This represents an important

reduction in the mean moisture availability in the high-elevation regions. Drought can

impose large year-to-year variability in the aridity of the West landscape, so we expect

that it may be a challenge to distinguish the climate change signal in aridity from natural

variability unless the anthropogenic signal takes the form of significant climatic trends.

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1) INTRODUCTION.

Actual evapotranspiration (AET) consumes approximately 70% of precipitation (P) that

is delivered annually to the western United States (the West; Table 1). The amount of

annual AET is determined by a) the amount of P, which represents the upper limit of

AET; and b) the amount of energy available to convert water to vapor. This water

volume is stored in the soil, in the snow or intercepted by the canopies, and exchanged to

vapor phase through the processes of evaporation, transpiration and sublimation. In cases

where the supply of water is exceeded by the demand to evapotranspire it, the

evapotranspiration may be thought of as “water limited”. In cases where there is ample

water and not sufficient of energy to consume all of it, AET can be thought of as “energy

limited”.

Potential evapotranspiration (PET) represents the atmospheric demand of water from the

soil and free water surfaces. PET is estimated from the energy available for the

vaporization of water with no regard for the actual availability of water to evaporate.

Data from a meteorological network in California, has shown that PET is strongly

controlled by the variability of net radiation (Rn = shortwave + longwave), more than

Tavgs (Hidalgo et al. 2005). The ratio of AET/PET, known as the evaporative efficiency

or (Equation 1) will be used here as an indicator of aridity. Semi-humid and humid

regions in the high elevations of the West present the highest s. Those same regions

commonly present winter snowpack and energy-limited AET rates during large part of

the year. Very low values are associated with deserts, or regions in which the AET rates

are extremely water-limited. Climatological aridity is a critical environmental factor

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that influences the character and sustainability of natural vegetation and terrestrial

ecosystems (Rind et al. 1990).

(1)

As such, is an index that links several aspects of the soil-vegetation-atmosphere

continuum. For example, it is thought that plays a role in indexing surface soil-

vegetation feedbacks (Rind et al. 1990). That is, an initial P deficit would result in a

reduction in that would cause plant desiccation, which will cause further reductions in

the values (Rind et al. 1990). Also, has been associated with vegetation conditions,

or as a proxi indicator of vegetation distribution (Specht 1972; 1981). The AET

efficiency is also used as an index for vegetation conditions in the Idso-Jackson Crop

Water Stress Index (Idso et al. 1981; Jackson et al. 1981), as well as in the Water Deficit

Index by Moran et al. (1994).

Drought can stress different aspects of the water resources, agricultural and ecological

systems of a region. Therefore several conceptual definitions of drought are commonly

used to aid in the quantification of the impacts. For example, deficits in runoff (or

streamflow) have been associated with hydrological drought, soil moisture deficits are

usually associated with agricultural drought and P deficits are linked to meteorological

drought (Dracup et al. 1980). In our particular case, we are interested in the variability of

the landscape’s aridity and its relation to the variability of drought over a large part of the

20th century. One of the most noticeable consequences of drought is the temporary

increase in the aridity in the low-elevations. As it will be seen in following sections,

years with P deficits also result in reduction of the areas presenting energy-limited AET

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rates in the montane regions, and there is although there is a buffer of moisture in the

high-elevations, severe and sustained droughts can have severely impact the ecosystem’s

balance in those regions. For example, the reductions of energy-limited AET regions can

produce negative consequences for the vegetation, ecosystems in general, wildfires and

land erosion in the high-elevations.

Climate change can potentially bring (more permanent) shifts in the mean aridity of the

West across its full range of elevations and climates. Average temperature (Tavg)

projections from General Circulation Models for the 21st century consistently suggest

warming trends for the region, while the sign and magnitude of the P trends are less

unanimous across the different models and greenhouse gases emission scenarios

(Dettinger 2005). There is evidence from historic observations of the second half of the

20th century that warming in snow-covered areas translates in a greater fraction of winter

P falling as rain (instead of snow), earlier start of the spring-summer evapotranspiration

season, earlier timing of spring streamflow peaks, as well as reductions in summer soil

moisture (Aguado et al. 1992; Dettinger and Cayan 1995; Stewart et al. 2004; 2005;

Manabe and Wetherald 1987; Findell and Delworth 2005). The hydrological impacts due

to warming are mainly confined to the high elevations since these areas have the highest

yield, and also because the processes related with snow accumulation and melting are

quite sensitive to temperature changes. Conversely, even though the hydrological

impacts under warm scenarios in the lower-elevations are generally smaller, it will be

shown here that there are significant increases on the aridity of these regions under warm

scenarios.

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The objectives of this study are: 1) to examine the spatial structure of energy and water

limited regions (at the same time incorporating extremely water-limited (arid) category),

within the West; 2) to describe temporal variability of energy-limited, water-limited and

arid regions, and consider changes from years that are relatively wet to those that are

relatively dry; and 3) to investigate changes in these hydrologic characteristics when

Tavg is increased and P is decreased.

2) DATA SOURCES AND HYDROLOGICAL MODEL

The main hydroclimatic dataset used in this analysis was produced using the Variable

Infiltration Capacity (VIC) model (Liang et al. 1994) macroscale land-surface

hydrological model originally developed at the University of Washington and Princeton.

A variety of hydrological parameters such as soil moisture, snow water equivalence

(SWE) and runoff can be estimated with the model using as input daily meteorological

data along with soil and vegetation properties. The land-surface is modeled using a tiled

configuration of vegetation covers, while the subsurface flow is modeled using three soil

layers of different thicknesses (Sheffield et al. 2004). Defining characteristics of VIC

are the probabilistic treatment of sub-grid soil moisture capacity distribution, the

parameterization of baseflow as a nonlinear recession from the lower soil layer, and that

the unsaturated hydraulic conductivity at each particular time step is a function of the

degree of saturation of the soil (Sheffield et al. 2004; Campbell 1974; Liang et al. 1994).

Details on the characteristics of the model can be found elsewhere (Liang et al. 1994;

Cherkauer et al. 2002). VIC has been used extensively in a variety of water resources

applications, from studies of climate variability, forecasting and climate change studies

(e.g. Wood et al. 1997; 2004; Hamlet et al. 1999; Nijssen et al. 1997; 2001). We ran

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the model using soil and vegetation properties information at 1/8 x 1/8 degree resolution

obtained from the North American Land Data Assimilation Systems (NLDAS;

http://ldas.gsfc.nasa.gov), along with the daily gridded meteorological data (maximum

and minimum temperature, P and windspeed) for the West from 1950 to 1999 obtained

from the Surface Water Modeling group at the University of Washington from their web

site (http://www.hydro.washington.edu/Lettenmaier/gridded_data/index_maurer.html),

the development of which is described in Maurer et al. (2002). We also used a separate

meteorological dataset from 1915 to 2003 from Hamlet and Lettenmaier (2005) and

Hamlet (Alan Hamlet, University of Washington personal communication 2005). This

longer dataset is used to provide a longer term variability perspective of the West’s

hydrology, even though the spatial extend of the data only covers the four main major

water resources regions: the Colorado, the Columbia river basin, the state of California

and the Great Basin. The longer dataset is mainly used in Section 5, in the rest of the

sections the VIC simulations are computed using the data from Maurer et al. (2002).

The daily output from the model for each grid-point was averaged (or aggregated) to

produce monthly averages (or totals). The daily soil moisture for each layer was divided

by their respective local soil capacity (obtained from NLDAS) and integrated to produce

monthly time-series of a fractional index for each layer. The average fractional index for

all three layers was averaged to produce a soil moisture index that will be used

subsequently in this study as a drought index time-series at every grid-point. Thus, this

index represents the variability of moisture in the subsurface without considering

infiltration to the deep aquifers (i.e. all water from the three layers can be used for

transpiration and therefore available for returning to the atmosphere).

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PET was computed using Rn and relative humidity from VIC, along with Tavg and

windspeed obtained from the Maurer et al. (2002) or Hamlet and Lettenmaier (2005)

dataset using a Penman-Monteith equation (Penman 1948; Monteith 1965) as described

in Shuttleworth (1993). For each gridpoint, PET was estimated as the weighted sum of

the contributions from all vegetation types -as well as bare soil- at daily time-scales. The

daily PET values were then aggregated to produce monthly totals.

A PDSI dataset for the contiguous US was computed using the 1/8 degree P and Tavg

data from the Maurer et al. (2002) dataset using a computer program from the National

Climatic Data Center (NCDC). The Thornwaite coefficients (Thornwaite and Mather

1955), needed for estimation of PET by the program, were fitted using the Penman-

Monteith based PET data calculated from VIC along with the Tavg and P data from

Maurer et al. (2002). The soil capacities were obtained from NLDAS. The resulting

monthly PDSI data at 1/8 degree cover the period from 1950 to 1999, and compared

fairly well the PDSI patterns obtained from Climate Division data from NCDC.

Monthly remotely sensed Normalized Difference Vegetation Index (NDVI) data at ¼ x ¼

degree resolution from 1981 to 1999 were obtained from the Global Inventory Modeling

and Mapping Studies (GIMMS) data set (Pinzon and Tucker 2004; Tucker et al. 2005).

The GIMMS data form part of the International Satellite Land-Surface Climatology

Project (initiative II) data archive (Hall et al. 2005). An alternative VIC dataset was

interpolated to the ¼ x ¼ degree resolution from the original 1/8 degree resolution to

match the NDVI data in cases when the interaction between the VIC variables and NDVI

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was needed. The NDVI is a proxy indicator for vegetation greenness. It is based from

satellite-measurements of surface light absorption that are later used to estimate an

standardized index of the fraction of absorbed photosynthetically active radiation or

photosynthetic capacity of vegetation. NDVI data are generally useful only during the

warm season, as the snow cover can obstruct satellite sensors. The NDVI data will be

used in section 3.3 to provide a linkage between aridity and vegetation conditions.

3) THE EVAPORATIVE EFFICIENCY (β)

3.1 Relationship of the evaporative efficiency the a commonly used aridity index

The AET /PET ratio (evaporative efficiency or β; Equation 1) will be used here as a way

to differentiate between energy-limited and water-limited regions and also to measure the

extension of the deserts (extremely water-limited limited regions). The advantage of

using β to delimit regions compared to the -more commonly used- aridity index ,

defined as the ratio of PET and P (Equation 2) is that β could be calculated at monthly

time-scales allowing to track seasonal changes in the soil moisture footprint of snowpack

and to look at intraseasonal changes in aridity. In addition, β is more intrinsically related

to physiological plant processes, as transpiration is a large fraction of AET (Table 1), and

therefore its variations are more directly associated with ecosystem health.

(2)

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At annual time-scales, β for snow-and-ice free regions is approximately related to the

aridity index , through the following empirical formulas (see also Arora 2002 and Sharif

and Miller 2005; Figure 1):

(3) Schreiber (1904)

(4) Ol’dekop (1911)

(5) Budyko (1948)

(6) Turc-Pike (1954; 1964)

(7) Zhang et al. (2001)

Where all the symbols were previously defined, except for w, the plant available water

coefficient (2.0 for forests, 0.5 for short grass or crops). The threshold between water-

limited and energy limited AET is more intuitively defined using than β, as it would

correspond to the points were the annual demand of water represented by PET is equal to

the annual supply represented by P (and therefore =1). The corresponding β for =1, for

each of the formulas 3 to 7 are shown in Table 2. According to these formulas the

corresponding threshold AET efficiencies range from 0.60 to 0.76.

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By definition is calculated using annual totals. For consistency, β will be defined using

annual totals here too. Therefore index represents the ratio of the total accumulated

water demand divided by the total accumulated annual supply of water irrespective of

their seasonality. The analysis of the effects of seasonality on aridity is out of the scope

of this study, but it should be noted, that the peaks of the seasonal cycles of P and PET in

the coastal regions of the West are out of phase by almost six months, while in the more

continental regions their peaks tend to be more synchronous.

AET rates with efficiencies higher that a certain threshold β, are limited by the

availability of energy, suggesting that there is ample water in the surface or in the

subsurface to sustain such high efficiencies. From all equations tested relating β and

(equations 3 to 7), the equations that fit better the western US hydrology computed using

VIC are Schreiber’s (Equation 3) in the coastal West regions (including the mountains)

and parts of the northern Rockies and Ol’dekop’s (Equation 4) in most of the arid and

semiarid regions (not shown). Therefore an initial general limit of β=0.63 (Table 2) will

be used (according to as Schreiber’s formula) as the defining point between energy

limited regions and water-limited regions (Figure 1). However, the empirical equations

(3 to 7) seem to generally overestimate β for <2 in the coastal regions, and in particular

the Sierra Nevada mountain range (not shown). A final adjustment was performed on the

β limits so that the classification of regions defined by β matched more closely the

classification. The reasons for these discrepancies seem to be related to relatively high

climatological PET rates in these southern coastal regions, related to high Rn estimations

from VIC. (Rn is estimated by VIC from daily P, maximum and minimum temperature).

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Even in those experiments when PET was approximated using the Rn divided by the

latent heat of vaporization (Budyko’s approximation), the calculated βs are still lower

than predicted by the empirical formulas 3 to 7 (not shown). It is unknown whether there

are particular characteristics of the climate and hydrology of the coastal western regions,

that make the empirical equations 3 to 7 unsuitable for describing the relationship

between β and or if this is a characteristic of the generation of Rn from this particular

hydrological model. Note that strictly speaking the relationships from equations 3 to 7

should not be applied to ice/snow regions, and therefore the equations may not provide a

good fit in the very high elevation regions such as the gridpoint in Figure 1a for example.

This may affect the determination of the boundary between energy-limited and water-

limited regions. This, however, does not completely explain why the equations 3 to 7 are

still inadequate for the gridpoint shown in Figure 1b, which has little to almost no snow,

as the βs computed from the VIC data (points) were still underestimated (at low aridity

values) compared to the values predicted from the equations (lines).

The threshold between water-limited and arid regions is more consistently defined using

any of the equations 3 to 7, as there is large similitude between the extent of the desert

areas defined using either β or (not shown), and to other sources (e.g. Goodall et al.

1979). A threshold of =0.20 will be used here to define the extremely water limited

regions (arid regions), obtained from the classification from Ponce et al. (2000),

converted to a β limit using the Schreiber equation (Table 3). This limit is also consistent

with the value suggested by Rind et al. (1990). The resulting classifications of regions

using the 1950 to 1999 data are shown in Figure 2.

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3.2 Relationship of β and the Bowen’s ratio

In a similar way as , β can be interpreted in terms of balance berween the demand and

supply of water. However as will be seen next, in many cases β also is related to the

energy balance near the surface, and in particular it indexes the partition of energy into

latent heat and sensible heat components (Bowen’s ratio):

From the energy-balance near the surface for an ice/snow free gridpoint:

(7)

Where λ is the latent heat of vaporization, H is the sensible heat flux and ΔG is change in

the net ground heat flux.

Dividing by λ:

(8)

Assuming that the ground flux is small and that PET is approximated by the left hand

term of equation (8) according to Budyko’s approximation.

(9)

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Defining the Bowen ratio as:

(10)

From 9 and 10:

(11)

Scatterplots of versus 1/(1+) result in the expected linear relationship suggested by

Equation 11 for snow/ice free gridpoints even at monthly time-scales (not shown).

Therefore indexes the partition of energy near the surface in the same way as for

these gridpoints below snowline and at monthly and longer time-scales. For gridpoints

with significant winter snowpack, Equation 7 needs to incorporate extra terms related to

the exchanges of energy between the snowpack and the environment and therefore

equation 11 is not strictly valid for those cases. High s are associated with small

Bowen ratios and vice versa according to (11). Small Bowen ratios are associated with

relatively shallow boundary layers (Schar et al. 1999). Therefore the surface fluxes of

heat and moisture in the energy-limited regions are thus concentrated in a relatively small

volume of air. In a similar way as , Bowen ratios are used to provide an index for

vegetation condition and distribution (Mahrt et al. 2001; Wilson et al. 2002).

3.3) Relationships of β with vegetation conditions

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During the warm-season, the changes in the extension of the water-limited regions seem

to be related to the vegetation greenness indexed by the NDVI (Figure 3). It is interesting

to note that for β values higher than a certain threshold, there is not much change in the

NDVI anomalies. This suggests that the primary control of vegetation greenness in the

West is water (Nemani et al. 2002), and that decreasing AET rates below the β threshold

can have consequences for vegetation conditions (Figure 3).

According to Figure 3, during droughts or altered climatic conditions imposed by climate

change, the vegetation in the highest regions will not be affected as much, as long as

these regions hold sufficient moisture to maintain energy limited AET rates. However,

the vegetation in regions around the transitional zone between energy-limited and water-

limited, and all the way down to the arid regions would be vulnerable to be affected by

climate alterations. The specific impacts on vegetation would depend on the type of

vegetation and biological resilience to drought at each particular elevation.

4) DIFFERENCES IN THE HYDROLOGICAL VARIABILITY OF ENERGY

AND WATER LIMITED REGIONS

In water limited (and arid) regions soil moisture, PDSI and AET tend to present a strong

seasonal correlation with P throughout the year, and generally weak correlations with

Tavg (Figures 4 and 5). These indices are therefore intercorrelated to each other and each

of them provides similar characterization of drought variability (in this case P deficits or

surpluses). With the exception of the significant negative correlations between soil

moisture and AET during the summer (that are partially associated with significant

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intercorrelations between P and Tavg during this season), the strongest control of the

intraseasonal variability of many important hydrological variables in water limited (and

arid) regions is precisely P.

In contrast the presence of snowpack on the energy limited regions makes similar

correlation plots more complex (Figure 6). For example the significant positive

correlation between Tavg and AET is an indication of the availability of water during the

spring-summer, and therefore of the presence of energy-limited AET rates. In the

high AET efficiency regions, the effect of the snow accumulation produces a distinct

correlation lag between winter P and spring-summer soil moisture. This contrast with the

results of the simpler PDSI model that does not consider snow physics, but that tends to

have a stronger autocorrelation (stronger soil memory). The PDSI model is evidently

inadequate to describe drought variability in snow-covered (energy-limited) regions.

VIC’s soil moisture presents a more realistic version of the correlations between winter P

and spring summer drought due to the explicit consideration of the physics of snow

processes (Figure 6).

The connection between winter P and soil moisture in the energy-limited regions is

delayed compared to the water-limited regions because of the effect of the snowpack.

Significant correlations were found in the energy-limited regions between OND P and

JJA soil moisture, (although the strongest connection is between DJF P and MJJ soil

moisture). Winter P is still an important factor in the water-limited regions, but at shorter

lags. Significant correlations were found between early winter (NDJ) P and AMJ soil

moisture in the water limited regions (Figures 4 and 5). Therefore spring-summer (MJJ)

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drought is controlled by spring-summer P and Tavg, but there are significant influences

from previous’ winter P, particularly in the energy-limited-regions.

5) VARIABILITY OF LAND SURFACE ARIDITY

In this section the changes aridity of the West from water years 1916 to 2003 will be

explored. Aridity in the west was very high from 1916 to 1940 and then decreased slowly

until 1999 (Figure 7). Then it increased sharply up to 2002, the year with the largest

percentage of land considered under arid conditions. In 2002, more than 35% of the

West was considered arid. This contrasts with the wettest year on record: 1983, when

only 10% would be considered arid (figures 8 and 9).

Due to the presence of snow, the extent of the energy limited areas should be more

sensitive to the warming during the last half of the 20th century than the arid regions.

That is, it would be expected that changes in the timing of the spring associated with

earlier snowmelt would result in less total annual AET and therefore there would be

decreasing β trends from 1950 to 1999. However other processes, such as the increasing

P trends in from 1950 to 1999 may have compensated for the effect of Tavg, causing

increasing AET rates and decreasing aridity of the West during that period (Sheffield et

al. 2004).

Notice also that the soil moisture anomaly patterns for the same years (Figure 9) tend to

be consistent in certain aspects with the aridity patterns, but there are differences. The

variability of soil moisture is not only related to climate, but also to soil properties. For

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that reason gridpoints in different regions would have different mean states for a constant

degree of aridity (Figure 10). The index does not depend on soil properties (only on

climate) and therefore is better suited for climate classification. This classification shifts

from year-to-year according to alterations in climate around its climatogical mean. For

example, the desert areas would extend northwards during drought from their original

positions in the southwest. The sensitivity of these aridity patterns and their shape is

different from a generalized anomalous pattern of soil moisture during droughts,

therefore both kinds of patterns may not be comparable. Also, soil moisture anomalies

depict a more relative change from local conditions, while a change in the aridity

conditions reflect a more absolute change in the climate of a region {?}. The β index is

useful for the diagnosis of aridity in climate change studies, as the some of the result of

anthropogenic alterations in climate may be associated with absolute changes in the

climatology of a region.

6) IMPLICATIONS FOR CLIMATE CHANGE

As was mentioned in the previous section, there is large variability around the

climatological aridity of the West (figures 8 to 11). This large range of variability in

aridity also seem to be accompanied by the hint that drought affects the ecosystems over

large extensions of land with relatively high frequency (decadal to multidecadal; refs).

Although there is some resilience of the ecosystems to drought, longer term changes in

aridity such as the ones associated with severe and sustained droughts or climate change

can cause severe stress in their conditions.

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Previous hydrologic modeling experiments have studied the impacts of artificially

altering P and Tavg in the California mountains (Jeton et al. 1996). These studies

generally show result in earlier springtime runoff for the moderate warming (+3oC)

scenario in the energy-limited regions without too much change in the total volumes, and

a reduction of the runoff volumes without too much change in the peak timing for the

drying scenario (-5% to -10% P). The results of similar experiments for the West will be

shown in this section from the perspective of aridity. The study of parallel changes (such

as changes in the seasonality of runoff or changes in the SWE/P ratios) is out of the scope

of this study, and deserves a more detailed separate study.

In terms of aridity, the annual frequencies of occurrences for each of the three regions are

shown in Figure 11. Most of the northern mountainous regions have high frequency of

occurrence of energy-limited regions, and so -as expected- the deserts in southern

California are always classified as arid regions. Conversely, those regions that showed

some low to mid-frequencies, (for example because they tended to switch historically

from energy-limited to water-limited or from water-limited to arid during droughts) are

the ones more vulnerable to be affected by changes in climate.

Warming (+3oC) and drying (-5%P) would tend to convert the energy-limited regions to

water-limited and the water-limited to arid, but the warming effects are stronger. Also

the 5% P decrease scenario seems to relatively affect more the low lands, producing more

arid regions, compared to the reductions on the high elevations’ energy-limited regions.

Interestingly, the use of a Budyko-approximation (radiation based) PET equation, instead

of the Penman-Monteith, results in little desertification under 3oC warming, with all the

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effect confined to the reductions of the energy-limited regions (not shown). The reason

for this is because Rn in the VIC model is calculated from the daily P and temperature

data using a series of internal semi-empirical equations. For the 3oC warming

experiment, Rn increased significantly in the snow-covered regions. The sensitivity of

Rn to changes in P and Tavg suggests a feedback on aridity during droughts: that is a

reduction of the water supply due to the P deficit is augmented by an increase of the

demand (PET) due to increases in Rn due to the P reductions. This effect is most evident

in the Budyko approximation experiment, as the Rn changes are most evident in the

snow-covered regions.

The changes due to drought variability (figures 8 and 9) were associated with ecosystem

effects, in particular vegetation and also affecting other related variables such as wildfire

potential (Westerling et al. 2006). More permanent shifts in the mean aridity conditions

such as the ones expected from climate change can potentially worsen the situations that

will be experienced from individual future droughts.

Energy-limited regions that are likely to increase their frequencies of being catalogued as

water-limited during some of the years due to 3oC warming are located in most of the

high elevation regions, but in particular the Sierra Nevada and Colorado Rockies (Figure

12). Some of those regions that are more likely to increase their frequencies of years in

which they are considered arid due to decreases of P (-5%) are the semiarid lowlands of

the Upper Colorado river basin, as well as most of the state of Nevada, Southern

California and the San Joaquin River basin in California (Figure 12).

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In terms of the shifts in the climatological aridity, the overall changes in the arid regions

are on the order of 3-4% of the area of the West that would switch from semiarid to arid

for a change in 3oC or 5% decrease in P (Table 4). This is significant, as it constitutes an

expansion of around 18% of the current arid areas of the west. The reduction of the

energy-limited areas to water limited for the warming scenario represents a reduction of

17% of the available energy-limited (humid and semi-humid) areas. This represents an

important reduction in the mean moisture availability in the high-elevation regions.

7) CONCLUSIONS

Winter P is an important determinant of spring-summer drought not only in the snow-

covered areas, but also in a large part of arid and semi-arid regions. Water storage

provided by soils and slow melting of snowpack results in significant correlations

between winter P and spring-summer soil moisture at lags from three to five months. In

the areas with considerable winter snowpack, the delay caused by the “reservoir” of water

being locked as snow until mid-spring adds to the subsurface memory lag, resulting in a

total lag between winter P and soil moisture of around five months. The subsurface soil

moisture reservoir is enough to result in very strong correlations between winter P and

spring soil moisture in arid and semi-arid regions (lag three months), and in some cases

significant correlations were found at lags five months.

Drought can impose large variability in the aridity of the West landscape, so we expect

that it may be a challenge to distinguish the climate change signal in aridity from natural

variability unless the anthropogenic signal takes the form of significant climatic trends.

This also implies that severe and sustained droughts are still one of the primary concerns

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in terms of aridity because of the much larger area that can be affected during such type

of –temporary- events. This is particularly important in the case of those variables that

are more sensitive to shorter-term aridity changes such as wildfires. Shifts in the mean

state, associated with climate change, although small compared to the year-to-year

variability, are over-imposed on top of this variability and are particularly important for

the redistribution of the species and desertification. Warming of 3oC or reductions in P

of –5% (drying) would result in reductions of 17% of the semi-humid and humid areas of

the West and would increase the desert regions by 18%. Warming and drying have

effects on the aridity in the high and low elevations, although drying has smaller impacts

on the high elevations (as reductions of the energy-limited regions).

The modeled soil moisture index in the water-limited and arid regions correlates well

with the (also modeled) widely used PDSI throughout the year. Presumably, the soil

moisture index obtained from VIC would represent better cold-season processes due to

the explicit inclusion of the physics of snow accumulation and melting, compared to the

much simpler model used in the PDSI calculation. Unfortunately, the lack of a soil

moisture observation network in North America does not allow an extensive direct

validation of VIC soil moisture estimates. Fortunately, the model’s soil moisture

estimations seem to produce reasonable agreement with the few point measurements

available, while other VIC hydrological parameters validate well with observations when

the model has been calibrated using streamflow data, giving us confidence that a

significant part of the characteristics of hydrological processes are represented with

sufficient accuracy by VIC to make its results useful. An increase in the amount of

monitoring of soil moisture and other land-surface and ecological variables is needed in

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order to provide proper validation and improvements in land-surface models and the

calibration of remote sensed estimations. Such measurements would be of great value,

given the importance of drought impacts in the water resources, ecosystems and overall

economy in the West, and the possibility of increasing incidence of drought in the future

associated with climate change.

8. ACKNOWLEDGEMENTS

This work was funded by grants from the California Energy Commission through the

California Climate Change Center at Scripps.

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Table 1: Average water balance for the western United State as a percentage of annual P.

Modeled data from 1950-1999.

Runoff + snow + soil moisture + baseflow + deep inf. 30%

Evaporation + interception + sublimation 22%

Transpiration 48%

Table 2. Evaporation efficiency (β) corresponding to =1 according to various formulas

Formula β (=1)

Schreiber (1904) 0.63

Ol’dekop (1911) 0.76

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Budyko (1948) 0.69

Turc-Pike (1954; 1964) 0.70

Zhang et al. (2001) 0.60-0.75

Table 3. Climate classification limits for (Ponce et al. 2000) and corresponding limits

for β using Schreiber’s formula

Regime classification β classification

Arid 12>5 0.20β>0.08

Semi-arid 5>2 0.43β>0.20

Sub-humid 2>0.75 0.70β>0.43

Humid 0.75>0.375 0.83β>0.70

Table 4. Average percentages of the West of each hydrologic condition under historical,

and altered climate scenarios

Energy-limited Water-limited Arid

% % %

Historical 9.6 66.0 24.4

T = + 3oC 7.9 63.2 28.9

P = - 5%P 8.7 63.8 27.5

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