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Drought – how the western U.S. is transformed from
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energy-limited to water-limited landscapes.
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Hidalgo H.G.1, Cayan D.R.1,2 and Dettinger M.D.2,1
1Scripps
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Institution of Oceanography
2United
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States Geological Survey
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SUBMITTED TO:
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Journal of Hydrometeorology (?)
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MAY 2006
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Abstract
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Keywords: Evapotranspiration, drought, climate change, desiccation, desertification, Variable Infiltration
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Capacity, VIC, regional hydrology.ABSTRACT
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Simulations of the western United States’ (US) hydrology using the Variable Infiltration
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Capacity were used to quantify the effects of drought and climate change on the
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landscape’s aridity. An index of aridity based on the ratio of actual to potential
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evapotranspiration (AET/PET) was calculated for years of extreme droughts and pluvials
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and for altered conditions of precipitation (P) and average temperature (Tavg). In the
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high elevations increases in aridity are related to reductions in the areas where AET rates
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can be considered energy-limited, while in the low elevations increases in aridity are
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associated with increases in aridity that in the long-term can lead to desertification. In
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terms of the shifts in the climatological aridity, the overall changes in the arid regions are
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on the order of 3-4% of the area of the West that would switch from semiarid to arid for a
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change in 3oC or 5% decrease in P. This is significant, as it constitutes an expansion of
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around 18% of the current arid areas of the west. The reduction of the energy-limited
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areas to water limited for the warming scenario represents a reduction of 17% of the
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available energy-limited (humid and semi-humid) areas. This represents an important
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reduction in the mean moisture availability in the high-elevation regions. Drought can
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impose large year-to-year variability in the aridity of the West landscape, so we expect
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that it may be a challenge to distinguish the climate change signal in aridity from natural
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variability unless the anthropogenic signal takes the form of significant climatic trends.
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1) INTRODUCTION.
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Actual evapotranspiration (AET) consumes approximately 70% of precipitation (P) that
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is delivered annually to the western United States (the West; Table 1). The amount of
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annual AET is determined by a) the amount of P, which represents the upper limit of
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AET; and b) the amount of energy available to convert water to vapor. This water
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volume is stored in the soil, in the snow or intercepted by the canopies, and exchanged to
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vapor phase through the processes of evaporation, transpiration and sublimation. In cases
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where the supply of water is exceeded by the demand to evapotranspire it, the
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evapotranspiration may be thought of as “water limited”.
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water and not sufficient of energy to consume all of it, AET can be thought of as “energy
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limited”.
In cases where there is ample
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Potential evapotranspiration (PET) represents the atmospheric demand of water from the
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soil and free water surfaces. PET is estimated from the energy available for the
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vaporization of water with no regard for the actual availability of water to evaporate.
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Data from a meteorological network in California, has shown that PET is strongly
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controlled by the variability of net radiation (Rn = shortwave + longwave), more than
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Tavgs (Hidalgo et al. 2005). The ratio of AET/PET, known as the evaporative efficiency
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or  (Equation 1) will be used here as an indicator of aridity. Semi-humid and humid
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regions in the high elevations of the West present the highest s. Those same regions
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commonly present winter snowpack and energy-limited AET rates during large part of
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the year. Very low  values are associated with deserts, or regions in which the AET rates
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are extremely water-limited.
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that influences the character and sustainability of natural vegetation and terrestrial
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ecosystems (Rind et al. 1990).
Climatological aridity is a critical environmental factor
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β
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(1)
AET
PET
3
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As such,  is an index that links several aspects of the soil-vegetation-atmosphere
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continuum. For example, it is thought that  plays a role in indexing surface soil-
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vegetation feedbacks (Rind et al. 1990). That is, an initial P deficit would result in a
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reduction in  that would cause plant desiccation, which will cause further reductions in
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the  values (Rind et al. 1990). Also,  has been associated with vegetation conditions,
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or as a proxi indicator of vegetation distribution (Specht 1972; 1981). The AET
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efficiency is also used as an index for vegetation conditions in the Idso-Jackson Crop
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Water Stress Index (Idso et al. 1981; Jackson et al. 1981), as well as in the Water Deficit
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Index by Moran et al. (1994).
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Drought can stress different aspects of the water resources, agricultural and ecological
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systems of a region. Therefore several conceptual definitions of drought are commonly
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used to aid in the quantification of the impacts. For example, deficits in runoff (or
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streamflow) have been associated with hydrological drought, soil moisture deficits are
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usually associated with agricultural drought and P deficits are linked to meteorological
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drought (Dracup et al. 1980). In our particular case, we are interested in the variability of
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the landscape’s aridity and its relation to the variability of drought over a large part of the
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20th century. One of the most noticeable consequences of drought is the temporary
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increase in the aridity in the low-elevations. As it will be seen in following sections,
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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
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high-elevations, severe and sustained droughts can have severely impact the ecosystem’s
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balance in those regions. For example, the reductions of energy-limited AET regions can
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produce negative consequences for the vegetation, ecosystems in general, wildfires and
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land erosion in the high-elevations.
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Climate change can potentially bring (more permanent) shifts in the mean aridity of the
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West across its full range of elevations and climates. Average temperature (Tavg)
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projections from General Circulation Models for the 21st century consistently suggest
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warming trends for the region, while the sign and magnitude of the P trends are less
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unanimous across the different models and greenhouse gases emission scenarios
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(Dettinger 2005).
There is evidence from historic observations of the second half of the
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20th century that warming in snow-covered areas translates in a greater fraction of winter
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P falling as rain (instead of snow), earlier start of the spring-summer evapotranspiration
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season, earlier timing of spring streamflow peaks, as well as reductions in summer soil
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moisture (Aguado et al. 1992; Dettinger and Cayan 1995; Stewart et al. 2004; 2005;
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Manabe and Wetherald 1987; Findell and Delworth 2005). The hydrological impacts due
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to warming are mainly confined to the high elevations since these areas have the highest
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yield, and also because the processes related with snow accumulation and melting are
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quite sensitive to temperature changes. Conversely, even though the hydrological
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impacts under warm scenarios in the lower-elevations are generally smaller, it will be
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shown here that there are significant increases on the aridity of these regions under warm
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scenarios.
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The objectives of this study are: 1) to examine the spatial structure of energy and water
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limited regions (at the same time incorporating extremely water-limited (arid) category),
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within the West; 2) to describe temporal variability of energy-limited, water-limited and
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arid regions, and consider changes from years that are relatively wet to those that are
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relatively dry; and 3) to investigate changes in these hydrologic characteristics when
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Tavg is increased and P is decreased.
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2) DATA SOURCES AND HYDROLOGICAL MODEL
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The main hydroclimatic dataset used in this analysis was produced using the Variable
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Infiltration Capacity (VIC) model (Liang et al. 1994) macroscale land-surface
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hydrological model originally developed at the University of Washington and Princeton.
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A variety of hydrological parameters such as soil moisture, snow water equivalence
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(SWE) and runoff can be estimated with the model using as input daily meteorological
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data along with soil and vegetation properties. The land-surface is modeled using a tiled
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configuration of vegetation covers, while the subsurface flow is modeled using three soil
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layers of different thicknesses (Sheffield et al. 2004). Defining characteristics of VIC
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are the probabilistic treatment of sub-grid soil moisture capacity distribution, the
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parameterization of baseflow as a nonlinear recession from the lower soil layer, and that
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the unsaturated hydraulic conductivity at each particular time step is a function of the
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degree of saturation of the soil (Sheffield et al. 2004; Campbell 1974; Liang et al. 1994).
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Details on the characteristics of the model can be found elsewhere (Liang et al. 1994;
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Cherkauer et al. 2002). VIC has been used extensively in a variety of water resources
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applications, from studies of climate variability, forecasting and climate change studies
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(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
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obtained from the North American Land Data Assimilation Systems (NLDAS;
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http://ldas.gsfc.nasa.gov), along with the daily gridded meteorological data (maximum
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and minimum temperature, P and windspeed) for the West from 1950 to 1999 obtained
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from the Surface Water Modeling group at the University of Washington from their web
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site (http://www.hydro.washington.edu/Lettenmaier/gridded_data/index_maurer.html),
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the development of which is described in Maurer et al. (2002). We also used a separate
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meteorological dataset from 1915 to 2003 from Hamlet and Lettenmaier (2005) and
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Hamlet (Alan Hamlet, University of Washington personal communication 2005). This
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longer dataset is used to provide a longer term variability perspective of the West’s
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hydrology, even though the spatial extend of the data only covers the four main major
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water resources regions: the Colorado, the Columbia river basin, the state of California
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and the Great Basin. The longer dataset is mainly used in Section 5, in the rest of the
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sections the VIC simulations are computed using the data from Maurer et al. (2002).
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The daily output from the model for each grid-point was averaged (or aggregated) to
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produce monthly averages (or totals). The daily soil moisture for each layer was divided
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by their respective local soil capacity (obtained from NLDAS) and integrated to produce
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monthly time-series of a fractional index for each layer. The average fractional index for
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all three layers was averaged to produce a soil moisture index that will be used
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subsequently in this study as a drought index time-series at every grid-point. Thus, this
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index represents the variability of moisture in the subsurface without considering
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infiltration to the deep aquifers (i.e. all water from the three layers can be used for
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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
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windspeed obtained from the Maurer et al. (2002) or Hamlet and Lettenmaier (2005)
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dataset using a Penman-Monteith equation (Penman 1948; Monteith 1965) as described
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in Shuttleworth (1993). For each gridpoint, PET was estimated as the weighted sum of
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the contributions from all vegetation types -as well as bare soil- at daily time-scales. The
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daily PET values were then aggregated to produce monthly totals.
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A PDSI dataset for the contiguous US was computed using the 1/8 degree P and Tavg
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data from the Maurer et al. (2002) dataset using a computer program from the National
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Climatic Data Center (NCDC). The Thornwaite coefficients (Thornwaite and Mather
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1955), needed for estimation of PET by the program, were fitted using the Penman-
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Monteith based PET data calculated from VIC along with the Tavg and P data from
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Maurer et al. (2002). The soil capacities were obtained from NLDAS. The resulting
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monthly PDSI data at 1/8 degree cover the period from 1950 to 1999, and compared
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fairly well the PDSI patterns obtained from Climate Division data from NCDC.
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Monthly remotely sensed Normalized Difference Vegetation Index (NDVI) data at ¼ x ¼
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degree resolution from 1981 to 1999 were obtained from the Global Inventory Modeling
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and Mapping Studies (GIMMS) data set (Pinzon and Tucker 2004; Tucker et al. 2005).
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The GIMMS data form part of the International Satellite Land-Surface Climatology
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Project (initiative II) data archive (Hall et al. 2005). An alternative VIC dataset was
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interpolated to the ¼ x ¼ degree resolution from the original 1/8 degree resolution to
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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
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satellite-measurements of surface light absorption that are later used to estimate an
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standardized index of the fraction of absorbed photosynthetically active radiation or
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photosynthetic capacity of vegetation. NDVI data are generally useful only during the
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warm season, as the snow cover can obstruct satellite sensors. The NDVI data will be
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used in section 3.3 to provide a linkage between aridity and vegetation conditions.
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3) THE EVAPORATIVE EFFICIENCY (β)
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3.1 Relationship of the evaporative efficiency the a commonly used aridity index
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The AET /PET ratio (evaporative efficiency or β; Equation 1) will be used here as a way
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to differentiate between energy-limited and water-limited regions and also to measure the
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extension of the deserts (extremely water-limited limited regions). The advantage of
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using β to delimit regions compared to the -more commonly used- aridity index ,
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defined as the ratio of PET and P (Equation 2) is that β could be calculated at monthly
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time-scales allowing to track seasonal changes in the soil moisture footprint of snowpack
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and to look at intraseasonal changes in aridity. In addition, β is more intrinsically related
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to physiological plant processes, as transpiration is a large fraction of AET (Table 1), and
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therefore its variations are more directly associated with ecosystem health.
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φ
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(2)
PET
P
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At annual time-scales, β for snow-and-ice free regions is approximately related to the
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aridity index , through the following empirical formulas (see also Arora 2002 and Sharif
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and Miller 2005; Figure 1):
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1
β
1
1 e
φ
(3) Schreiber (1904)
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3
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β
tanh
1
φ
(4) Ol’dekop (1911)
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β
φ
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(5) Budyko (1948)
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1
β
2
φ
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1
(6) Turc-Pike (1954; 1964)
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1 wÏ †
β
2
1 wÏ † φ
(7) Zhang et al. (2001)
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Where all the symbols were previously defined, except for w, the plant available water
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coefficient (2.0 for forests, 0.5 for short grass or crops). The threshold between water-
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limited and energy limited AET is more intuitively defined using  than β, as it would
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correspond to the points were the annual demand of water represented by PET is equal to
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the annual supply represented by P (and therefore =1). The corresponding β for =1, for
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each of the formulas 3 to 7 are shown in Table 2. According to these formulas the
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corresponding threshold AET efficiencies range from 0.60 to 0.76.
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φ
1
2
By definition  is calculated using annual totals. For consistency, β will be defined using
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annual totals here too. Therefore  index represents the ratio of the total accumulated
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water demand divided by the total accumulated annual supply of water irrespective of
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their seasonality. The analysis of the effects of seasonality on aridity is out of the scope
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of this study, but it should be noted, that the peaks of the seasonal cycles of P and PET in
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the coastal regions of the West are out of phase by almost six months, while in the more
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continental regions their peaks tend to be more synchronous.
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AET rates with efficiencies higher that a certain threshold β, are limited by the
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availability of energy, suggesting that there is ample water in the surface or in the
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subsurface to sustain such high efficiencies. From all equations tested relating β and 
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(equations 3 to 7), the equations that fit better the western US hydrology computed using
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VIC are Schreiber’s (Equation 3) in the coastal West regions (including the mountains)
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and parts of the northern Rockies and Ol’dekop’s (Equation 4) in most of the arid and
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semiarid regions (not shown). Therefore an initial general limit of β=0.63 (Table 2) will
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be used (according to as Schreiber’s formula) as the defining point between energy
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limited regions and water-limited regions (Figure 1). However, the empirical equations
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(3 to 7) seem to generally overestimate β for  <2 in the coastal regions, and in particular
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the Sierra Nevada mountain range (not shown). A final adjustment was performed on the
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β limits so that the classification of regions defined by β matched more closely the 
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classification. The reasons for these discrepancies seem to be related to relatively high
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climatological PET rates in these southern coastal regions, related to high Rn estimations
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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
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latent heat of vaporization (Budyko’s approximation), the calculated βs are still lower
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than predicted by the empirical formulas 3 to 7 (not shown). It is unknown whether there
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are particular characteristics of the climate and hydrology of the coastal western regions,
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that make the empirical equations 3 to 7 unsuitable for describing the relationship
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between β and  or if this is a characteristic of the generation of Rn from this particular
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hydrological model. Note that strictly speaking the relationships from equations 3 to 7
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should not be applied to ice/snow regions, and therefore the equations may not provide a
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good fit in the very high elevation regions such as the gridpoint in Figure 1a for example.
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This may affect the determination of the boundary between energy-limited and water-
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limited regions. This, however, does not completely explain why the equations 3 to 7 are
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still inadequate for the gridpoint shown in Figure 1b, which has little to almost no snow,
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as the βs computed from the VIC data (points) were still underestimated (at low aridity
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values) compared to the values predicted from the equations (lines).
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The threshold between water-limited and arid regions is more consistently defined using
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any of the equations 3 to 7, as there is large similitude between the extent of the desert
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areas defined using either β or  (not shown), and to other sources (e.g. Goodall et al.
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1979). A threshold of =0.20 will be used here to define the extremely water limited
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regions (arid regions), obtained from the  classification from Ponce et al. (2000),
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converted to a β limit using the Schreiber equation (Table 3). This limit is also consistent
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with the value suggested by Rind et al. (1990). The resulting classifications of regions
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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
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In a similar way as , β can be interpreted in terms of balance berween the demand and
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supply of water. However as will be seen next, in many cases β also is related to the
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energy balance near the surface, and in particular it indexes the partition of energy into
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latent heat and sensible heat components (Bowen’s ratio):
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From the energy-balance near the surface for an ice/snow free gridpoint:
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AET Î ”G
Rn H λ
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(7)
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Where λ is the latent heat of vaporization, H is the sensible heat flux and ΔG is change in
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the net ground heat flux.
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Dividing by λ:
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Rn
AET
λ
AET λ
(8)
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Assuming that the ground flux is small and that PET is approximated by the left hand
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term of equation (8) according to Budyko’s approximation.
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1
PET AET
2
3
H
AET
AET λ
(9)
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Defining the Bowen ratio as:
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H
γ
9
AET λ
(10)
10
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From 9 and 10:
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1
β
1 γ
(11)
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Scatterplots of  versus 1/(1+) result in the expected linear relationship suggested by
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Equation 11 for snow/ice free gridpoints even at monthly time-scales (not shown).
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Therefore  indexes the partition of energy near the surface in the same way as  for
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these gridpoints below snowline and at monthly and longer time-scales. For gridpoints
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with significant winter snowpack, Equation 7 needs to incorporate extra terms related to
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the exchanges of energy between the snowpack and the environment and therefore
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1
equation 11 is not strictly valid for those cases. High s are associated with small
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Bowen ratios and vice versa according to (11). Small Bowen ratios are associated with
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relatively shallow boundary layers (Schar et al. 1999). Therefore the surface fluxes of
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heat and moisture in the energy-limited regions are thus concentrated in a relatively small
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volume of air. In a similar way as , Bowen ratios are used to provide an index for
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vegetation condition and distribution (Mahrt et al. 2001; Wilson et al. 2002).
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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
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to be related to the vegetation greenness indexed by the NDVI (Figure 3). It is interesting
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to note that for β values higher than a certain threshold, there is not much change in the
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NDVI anomalies. This suggests that the primary control of vegetation greenness in the
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West is water (Nemani et al. 2002), and that decreasing AET rates below the β threshold
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can have consequences for vegetation conditions (Figure 3).
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According to Figure 3, during droughts or altered climatic conditions imposed by climate
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change, the vegetation in the highest regions will not be affected as much, as long as
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these regions hold sufficient moisture to maintain energy limited AET rates. However,
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the vegetation in regions around the transitional zone between energy-limited and water-
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limited, and all the way down to the arid regions would be vulnerable to be affected by
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climate alterations. The specific impacts on vegetation would depend on the type of
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vegetation and biological resilience to drought at each particular elevation.
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1
4) DIFFERENCES IN THE HYDROLOGICAL VARIABILITY OF ENERGY
2
AND WATER LIMITED REGIONS
3
4
In water limited (and arid) regions soil moisture, PDSI and AET tend to present a strong
5
seasonal correlation with P throughout the year, and generally weak correlations with
6
Tavg (Figures 4 and 5). These indices are therefore intercorrelated to each other and each
7
of them provides similar characterization of drought variability (in this case P deficits or
8
surpluses). With the exception of the significant negative correlations between soil
9
moisture and AET during the summer (that are partially associated with significant
10
intercorrelations between P and Tavg during this season), the strongest control of the
11
intraseasonal variability of many important hydrological variables in water limited (and
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arid) regions is precisely P.
13
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In contrast the presence of snowpack on the energy limited regions makes similar
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correlation plots more complex (Figure 6). For example the significant positive
16
correlation between Tavg and AET is an indication of the availability of water during the
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spring-summer, and therefore of the presence of energy-limited AET rates.
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high AET efficiency regions, the effect of the snow accumulation produces a distinct
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correlation lag between winter P and spring-summer soil moisture. This contrast with the
20
results of the simpler PDSI model that does not consider snow physics, but that tends to
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have a stronger autocorrelation (stronger soil memory). The PDSI model is evidently
22
inadequate to describe drought variability in snow-covered (energy-limited) regions.
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VIC’s soil moisture presents a more realistic version of the correlations between winter P
16
In the
1
and spring summer drought due to the explicit consideration of the physics of snow
2
processes (Figure 6).
3
4
The connection between winter P and soil moisture in the energy-limited regions is
5
delayed compared to the water-limited regions because of the effect of the snowpack.
6
Significant correlations were found in the energy-limited regions between OND P and
7
JJA soil moisture, (although the strongest connection is between DJF P and MJJ soil
8
moisture). Winter P is still an important factor in the water-limited regions, but at shorter
9
lags. Significant correlations were found between early winter (NDJ) P and AMJ soil
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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
12
from previous’ winter P, particularly in the energy-limited-regions.
13
14
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5) VARIABILITY OF LAND SURFACE ARIDITY
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In this section the changes aridity of the West from water years 1916 to 2003 will be
17
explored. Aridity in the west was very high from 1916 to 1940 and then decreased slowly
18
until 1999 (Figure 7). Then it increased sharply up to 2002, the year with the largest
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percentage of land considered under arid conditions. In 2002, more than 35% of the
20
West was considered arid. This contrasts with the wettest year on record: 1983, when
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only 10% would be considered arid (figures 8 and 9).
22
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Due to the presence of snow, the extent of the energy limited areas should be more
24
sensitive to the warming during the last half of the 20th century than the arid regions.
17
1
That is, it would be expected that changes in the timing of the spring associated with
2
earlier snowmelt would result in less total annual AET and therefore there would be
3
decreasing β trends from 1950 to 1999. However other processes, such as the increasing
4
P trends in from 1950 to 1999 may have compensated for the effect of Tavg, causing
5
increasing AET rates and decreasing aridity of the West during that period (Sheffield et
6
al. 2004).
7
8
Notice also that the soil moisture anomaly patterns for the same years (Figure 9) tend to
9
be consistent in certain aspects with the aridity patterns, but there are differences. The
10
variability of soil moisture is not only related to climate, but also to soil properties. For
11
that reason gridpoints in different regions would have different mean states for a constant
12
degree of aridity (Figure 10). The  index does not depend on soil properties (only on
13
climate) and therefore is better suited for climate classification. This classification shifts
14
from year-to-year according to alterations in climate around its climatogical mean. For
15
example, the desert areas would extend northwards during drought from their original
16
positions in the southwest. The sensitivity of these aridity patterns and their shape is
17
different from a generalized anomalous pattern of soil moisture during droughts,
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therefore both kinds of patterns may not be comparable. Also, soil moisture anomalies
19
depict a more relative change from local conditions, while a change in the aridity
20
conditions reflect a more absolute change in the climate of a region {?}.
21
useful for the diagnosis of aridity in climate change studies, as the some of the result of
22
anthropogenic alterations in climate may be associated with absolute changes in the
23
climatology of a region.
24
18
The β index is
1
6) IMPLICATIONS FOR CLIMATE CHANGE
2
As was mentioned in the previous section, there is large variability around the
3
climatological aridity of the West (figures 8 to 11). This large range of variability in
4
aridity also seem to be accompanied by the hint that drought affects the ecosystems over
5
large extensions of land with relatively high frequency (decadal to multidecadal; refs).
6
Although there is some resilience of the ecosystems to drought, longer term changes in
7
aridity such as the ones associated with severe and sustained droughts or climate change
8
can cause severe stress in their conditions.
9
10
Previous hydrologic modeling experiments have studied the impacts of artificially
11
altering P and Tavg in the California mountains (Jeton et al. 1996). These studies
12
generally show result in earlier springtime runoff for the moderate warming (+3oC)
13
scenario in the energy-limited regions without too much change in the total volumes, and
14
a reduction of the runoff volumes without too much change in the peak timing for the
15
drying scenario (-5% to -10% P). The results of similar experiments for the West will be
16
shown in this section from the perspective of aridity. The study of parallel changes (such
17
as changes in the seasonality of runoff or changes in the SWE/P ratios) is out of the scope
18
of this study, and deserves a more detailed separate study.
19
20
In terms of aridity, the annual frequencies of occurrences for each of the three regions are
21
shown in Figure 11. Most of the northern mountainous regions have high frequency of
22
occurrence of energy-limited regions, and so -as expected- the deserts in southern
23
California are always classified as arid regions. Conversely, those regions that showed
24
some low to mid-frequencies, (for example because they tended to switch historically
19
1
from energy-limited to water-limited or from water-limited to arid during droughts) are
2
the ones more vulnerable to be affected by changes in climate.
3
4
Warming (+3oC) and drying (-5%P) would tend to convert the energy-limited regions to
5
water-limited and the water-limited to arid, but the warming effects are stronger. Also
6
the 5% P decrease scenario seems to relatively affect more the low lands, producing more
7
arid regions, compared to the reductions on the high elevations’ energy-limited regions.
8
Interestingly, the use of a Budyko-approximation (radiation based) PET equation, instead
9
of the Penman-Monteith, results in little desertification under 3oC warming, with all the
10
effect confined to the reductions of the energy-limited regions (not shown). The reason
11
for this is because Rn in the VIC model is calculated from the daily P and temperature
12
data using a series of internal semi-empirical equations. For the 3oC warming
13
experiment, Rn increased significantly in the snow-covered regions. The sensitivity of
14
Rn to changes in P and Tavg suggests a feedback on aridity during droughts: that is a
15
reduction of the water supply due to the P deficit is augmented by an increase of the
16
demand (PET) due to increases in Rn due to the P reductions. This effect is most evident
17
in the Budyko approximation experiment, as the Rn changes are most evident in the
18
snow-covered regions.
19
20
The changes due to drought variability (figures 8 and 9) were associated with ecosystem
21
effects, in particular vegetation and also affecting other related variables such as wildfire
22
potential (Westerling et al. 2006). More permanent shifts in the mean aridity conditions
23
such as the ones expected from climate change can potentially worsen the situations that
24
will be experienced from individual future droughts.
20
1
2
Energy-limited regions that are likely to increase their frequencies of being catalogued as
3
water-limited during some of the years due to 3oC warming are located in most of the
4
high elevation regions, but in particular the Sierra Nevada and Colorado Rockies (Figure
5
12). Some of those regions that are more likely to increase their frequencies of years in
6
which they are considered arid due to decreases of P (-5%) are the semiarid lowlands of
7
the Upper Colorado river basin, as well as most of the state of Nevada, Southern
8
California and the San Joaquin River basin in California (Figure 12).
9
10
In terms of the shifts in the climatological aridity, the overall changes in the arid regions
11
are on the order of 3-4% of the area of the West that would switch from semiarid to arid
12
for a change in 3oC or 5% decrease in P (Table 4). This is significant, as it constitutes an
13
expansion of around 18% of the current arid areas of the west. The reduction of the
14
energy-limited areas to water limited for the warming scenario represents a reduction of
15
17% of the available energy-limited (humid and semi-humid) areas. This represents an
16
important reduction in the mean moisture availability in the high-elevation regions.
17
18
7) CONCLUSIONS
19
Winter P is an important determinant of spring-summer drought not only in the snow-
20
covered areas, but also in a large part of arid and semi-arid regions. Water storage
21
provided by soils and slow melting of snowpack results in significant correlations
22
between winter P and spring-summer soil moisture at lags from three to five months.
23
the areas with considerable winter snowpack, the delay caused by the “reservoir” of water
24
being locked as snow until mid-spring adds to the subsurface memory lag, resulting in a
21
In
1
total lag between winter P and soil moisture of around five months. The subsurface soil
2
moisture reservoir is enough to result in very strong correlations between winter P and
3
spring soil moisture in arid and semi-arid regions (lag three months), and in some cases
4
significant correlations were found at lags five months.
5
6
Drought can impose large variability in the aridity of the West landscape, so we expect
7
that it may be a challenge to distinguish the climate change signal in aridity from natural
8
variability unless the anthropogenic signal takes the form of significant climatic trends.
9
This also implies that severe and sustained droughts are still one of the primary concerns
10
in terms of aridity because of the much larger area that can be affected during such type
11
of –temporary- events. This is particularly important in the case of those variables that
12
are more sensitive to shorter-term aridity changes such as wildfires. Shifts in the mean
13
state, associated with climate change, although small compared to the year-to-year
14
variability, are over-imposed on top of this variability and are particularly important for
15
the redistribution of the species and desertification. Warming of 3oC or reductions in P
16
of –5% (drying) would result in reductions of 17% of the semi-humid and humid areas of
17
the West and would increase the desert regions by 18%.
18
effects on the aridity in the high and low elevations, although drying has smaller impacts
19
on the high elevations (as reductions of the energy-limited regions).
Warming and drying have
20
21
The modeled soil moisture index in the water-limited and arid regions correlates well
22
with the (also modeled) widely used PDSI throughout the year. Presumably, the soil
23
moisture index obtained from VIC would represent better cold-season processes due to
24
the explicit inclusion of the physics of snow accumulation and melting, compared to the
22
1
much simpler model used in the PDSI calculation. Unfortunately, the lack of a soil
2
moisture observation network in North America does not allow an extensive direct
3
validation of VIC soil moisture estimates. Fortunately, the model’s soil moisture
4
estimations seem to produce reasonable agreement with the few point measurements
5
available, while other VIC hydrological parameters validate well with observations when
6
the model has been calibrated using streamflow data, giving us confidence that a
7
significant part of the characteristics of hydrological processes are represented with
8
sufficient accuracy by VIC to make its results useful. An increase in the amount of
9
monitoring of soil moisture and other land-surface and ecological variables is needed in
10
order to provide proper validation and improvements in land-surface models and the
11
calibration of remote sensed estimations. Such measurements would be of great value,
12
given the importance of drought impacts in the water resources, ecosystems and overall
13
economy in the West, and the possibility of increasing incidence of drought in the future
14
associated with climate change.
15
16
8. ACKNOWLEDGEMENTS
17
This work was funded by grants from the California Energy Commission through the
18
California Climate Change Center at Scripps.
19
20
9.0 REFERENCES
21
Aguado E, DR Cayan, LG Riddle and M. Ross. 1992. Climatic fluctuations and the
22
timing of West Coast streamflow. Journal of Climate. 5:1468-1483.
23
24
Arora V.K. 2002. The use of the aridity index to assess climate change effect on annual
runoff. Journal of Hydrology. 265:164-177.
23
1
2
3
4
5
Budyko, M.I. 1948. Evaporation under natural conditions, Gidrometeorizdat, Leningrad,
English translation by IPST, Jerusalem.
Campbell, G. S. (1974), A simple method for determining unsaturated conductivity from
moisture retention data, Soil Sci., 117, 311–314.
Cherkauer, K. A., L. C. Bowling, and D. P. Lettenmaier (2002), Variable Infiltration
6
Capacity (VIC) cold land process model updates, Global Planet. Change, 38, 151–
7
159
8
9
Dettinger M.D. 2005. From climate-change spaguetti to climate-change distributions for
21st century California. San Francisco Estuary and Watershed Science. Vol. 3,
10
Issue 1 (March 2005). Article 4.
11
http://repositories.cdlib.org/jmie/sfews/vol3/iss1/art4
12
13
14
15
16
Dettinger MD and DR Cayan. 1995. Large-scale atmospheric forcing of recent trends
toward early snowmelt runoff in California. Journal of Climate. 8:606-623.
Dracup JA, KS Lee, and EG Paulson. 1980. On the definition of droughts. Water
Resources Research. 16: 297-302.
Findell KL and TL Delworth. 2005. A modeling study of dynamic and thermodynamic
17
mechanisms for summer drying in response to global warming. Geophysical
18
Research Letters. 32, L16702, doi:10.1029/2005GL023414.
19
Goodall DW, RA Perry and KMW Howes (eds.). 1979. Arid-land ecosystems: structure,
20
functioning and management. Cambridge University Press. Cambridge, New
21
York. 881pp.
22
Hamlet, A.F. and D.P. Lettenmaier, Effects of Climate Change on Hydrology and Water
23
Resources in the Columbia River Basin, Am. Water Res. Assoc., 35(6), 1597-
24
1623, 1999.
24
1
2
3
Hidalgo HG, Cayan DR, Dettinger MD. 2005. Sources of variability of
evapotranspiration in California. Journal of Hydrometeorology, 6: 3-19.
Idso, S.B., Jackson R.D., Pinter P.J.Jr., Reginato R.J. and Hatfield J.L. 1981. Normalizing
4
the stress-degree-day parameter for environmental variability. Agric. Meteorol.
5
24:45-55.
6
7
8
9
10
11
12
Jackson R.D., S.B. Idso, R.J. Reginato, P.J. Printer. 1981. Canopy temperature as a crop
water stress indicator. Water resources research 17:1133-1138.
Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994.A Simple
hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs,
J. Geophys. Res., 99(D7), 14,415-14,428.
Mahrt L, Vickers L, Sun J. and McCaughey JH. 2001. Calculation of the area-averaged
fluxes: Application to BOREAS. Journal of applied meteorology. 40:915-920.
13
Manabe, S., and R. T. Wetherald. 1987, Large-scale changes of soil wetness induced by
14
an increase in atmospheric carbon dioxide, J. Atmos. Sci., 44, 1211–1235
15
Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen (2002), A
16
long-term hydrologically-based data set of land surface fluxes and states for the
17
conterminous United States, J. Clim., 15, 3237–3251
18
19
20
Monteith, J. L, 1965: Evaporation and the environment. Symp. Soc. Expl. Biol., 19, 205–
234
Moran MS, Clarke TR, Inoue, Y and Vidal A. 1994. Estimating crop water deficit using
21
the relation between surface-air temperature and spectral vegetation index.
22
Remote Sensing of Environment, 49: 246-263.
23
Nemani RR, CD Keeling, H Hashimoto et al. 2002. Climate-driven increases in global
24
terrestrial net primary production from 1982 to 1999. Science. 300: 1560-1563.
25
1
Nijssen, B., D. P. Lettenmaier, X. Liang, S. W. Wetzel, and E. F. Wood (1997),
2
Streamflow simulation for continental-scale river basins, Water Resour. Res.,
3
33(4), 711–724.
4
5
6
7
8
9
10
11
12
13
14
Nijssen, B., G. M. O'Donnell, D. P. Lettenmaier, D. Lohmann, and E. F. Wood (2001),
Predicting the discharge of global rivers, J. Clim., 14, 3307–3323.
Ol’dekop, E.M. 1911. On evaporation from the surface of river basins. Trans. Met. Obs.
Lur-evskogo, Univ. Tartu 4 in Russian.
Palmer, W.C. 1965. Meteorological drought. Research Paper No. 45, US Department of
Commerce Weather Bureau, Washington, D.C.
Penman, H. L, 1948: Natural evaporation from open water, bare soil and grass. Proc.
Roy. Soc. London., A193, 129–145.
Pike J.G. 1964. The estimation of annual runoff from meteorological data in a tropical
climate. Journal of hydrology.2:116-123.
Pinzon, J., M. E. Brown and C. J. Tucker. 2004. Satellite time series correction of
15
orbital drift artifacts using empirical mode decomposition. In Hilbert-Huang
16
Transform: Introduction and Applications, eds. N. Huang, pp. Chapter 10, Part
17
II. Applications (in press).
18
19
20
Ponce VM, Pandley RP, Ercan S. 2000. Characterization of drought across the climate
spectrum. Journal of hydrologic engineering. ASCE. 5:222-224.
Rind, D, Goldberg R, Hansen J, Rozenzweig C, and P Ruedy, 1990: Potential
21
evapotranspiration and the likelihood of future drought. J. Geophys. Res., 95,
22
9983–10004
23
24
Schar C, Luthi D, Beyerle U et al. 1999. The soil-precipitation feedback: A process study
with a regional climate model. Journal of Climate. 12: 722-741.
26
1
Schreiber, P. 1904.Uber diw Beziehungen zwischen dem Niederschlag und der
2
Wasserfuhrung der Flube in Mitteleuropa. Z meteorol. 21: 441-452.
3
Sharif H. and N.L. Miller. 2005. Hydroclimatological predictions based on basin’s
4
humidity index. Proceedings of the 18th Conference on Climate Variability and
5
Change. American Meteorological Society.
6
Sheffield J, G Goteti, F Wen, and EF Wood. 2004. A simulated soil moisture based
7
drought analysis for the United States. Journal of Geophysical Research –
8
Atmospheres. 109 (D24). D24108.
9
10
11
12
13
14
15
Shuttleworth WJ. 1993. Evaporation, Chapter 4. In Handbook of Hydrology, Maidment
DR. (editor), McGraw-Hill Inc., New York.
Specht R.L. 1972. Water use by perennial evergreen plan communities in Australia and
Papua New Guinea. Aust. J. Botany. 20. 273-299.
Specht R.L. 1981. Growth indices – their role in understading the growth, structure and
distribution of Australian vegetation. Oecologia. 50. 347-356.
Stephenson NL. 1998. Actual evapotranspiration and deficit: biologically meaningful
16
correlates of vegetation distribution across spatial scales. Journal of
17
Biogeography. 25 (5): 855-870
18
Stewart IT, Cayan DR, Dettinger MD. 2004. Changes in snowmelt runoff timing in
19
western North America under a 'business as usual' climate change scenario.
20
Climatic Change. 62: 217-232.
21
22
23
Stewart IT, Cayan DR, Dettinger MD. 2005. Changes toward earlier streamflow timing
across western North America. Journal of climate. 18 (8): 1136-1155.
Thornthwaite CW and JR Mather. 1955. The world water balance. Pub. Climatol. 8. 1-86.
27
1
Tucker, C. J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, E.
2
Vermote and N. El Saleous. 2005. An Extended AVHRR 8-km NDVI Data Set
3
Compatible with MODIS and SPOT Vegetation NDVI Data. International
4
Journal of Remote Sensing (in press).
5
6
Turc, L. 1954. Le bilan d’eau des sols. Relation entre la P, l;’evaporation et
l;’ecoulement. Ann. Agron. 5, 491-569.
7
Westerling AL, Citation to Science paper or to one of Tony’s conferences.
8
Wilson KB, Baldocchi DD, Aubinet M. et al. 2002. Energy partitioning between latent
9
heat and sensible heat flux during the warm season at FLUXNET sites. Water
10
11
resources research. 38(12).1294.
Wood A.W., L. R. Leung, V. Sridhar, D. P. Lettenmaier,. 2004. Hydrologic
12
Implications of Dynamical and Statistical Approaches to Downscaling Climate
13
Model Outputs, Climatic Change, 62: 189 – 216
14
Wood EF, D. Lettenmaier, X Liang, B. Nijssen, and S W Wetzela. 1997. Hydrological
15
modeling of continental-scale basins. Annual Review of Earth and Planetary
16
Sciences. 25: 279-300. doi:10.1146/annurev.earth.25.1.279.
17
18
Zhang L., Dawes WR, Walker G.R. 2001. response of mean annual evapotranspiration to
vegetation changes at catchment scale. Water resources research. 37: 701-708.
19
20
Table 1: Average water balance for the western United State as a percentage of annual P.
21
Modeled data from 1950-1999.
22
23
Runoff + snow + soil moisture + baseflow + deep inf.
30%
24
Evaporation + interception + sublimation
22%
28
1
Transpiration
48%
2
3
Table 2. Evaporation efficiency (β) corresponding to =1 according to various formulas
β (=1)
4
Formula
5
Schreiber (1904)
0.63
6
Ol’dekop (1911)
0.76
7
Budyko (1948)
0.69
8
Turc-Pike (1954; 1964)
0.70
9
Zhang et al. (2001)
0.60-0.75
10
11
Table 3. Climate classification limits for  (Ponce et al. 2000) and corresponding limits
12
for β using Schreiber’s formula
13
14
Regime
 classification
β classification
15
Arid
12>5
0.20β>0.08
16
Semi-arid
5>2
0.43β>0.20
17
Sub-humid
2>0.75
0.70β>0.43
18
Humid
0.75>0.375
0.83β>0.70
19
20
Table 4. Average percentages of the West of each hydrologic condition under historical,
21
and altered climate scenarios
22
23
24
Energy-limited
Water-limited
%
%
29
Arid
%
1
Historical
9.6
66.0
24.4
2
T = + 3oC
7.9
63.2
28.9
3
P = - 5%P
8.7
63.8
27.5
4
5
30