Mountain Hydrology of the Semi-Arid Wester n U.S.
Roger C. Bales1, Jeff Dozier2, Noah P. Molotch3, Thomas H. Painter4, and Robert Rice1
INTRODUCTION
Hydrologic feedbacks in mountainous regions control the availability of water across the landscape, influence the distribution of vegetation, dominate biogeochemical fluxes, and contribute to
global and regional climate variability. Improving knowledge of the processes controlling these feedbacks will promote clearer understanding of Earth’s water, energy and biogeochemical cycles; and
will enable sounder management of increasingly stressed natural resources. In the mountains of the
semi-arid western U.S. (Figure 1), the sharp wet-dry seasonal transitions, steep gradients in temperature and precipitation with elevation, and high interannual variability make the hydrologic feedbacks
quite different from those in lower-elevation or humid regions.
Making more informed decisions to manage water resources drives the need for better hydrologic information. Mountain river basins, their associated reservoirs, and their underlying aquifers
sustain the water demand of over 60 million people in the western U.S. Water allocations in the
West’s supply-limited systems rely on complex decision-support mechanisms that account for water
laws and markets, interstate compacts, international treaties, flood control, and agricultural, hydropower and municipal demands. Improvements in predicting the magnitude and timing of runoff—
i.e. narrowing the considerable uncertainty in runoff estimates—directly benefit regional economies.
Management of forest and other mountain resources also drives the need for new hydrologic
knowledge. Mountainous regions in North America contribute significantly to the continent’s carbon budget. Montane forest dynamics rely on the spatial and temporal variability of hydrologic variables, especially the variability in snow water equivalent and snowmelt [Nagler and Rott, 2000; Schimel
et al., 2002]. In addition, the albedo of mountain snow cover and in turn global warming are sensitive
to absorptive impurities such as dust and soot. Hansen and Nazarenko [2004] have asserted that about
a quarter of the observed increase in global temperature over the last century owes to absorption by
black carbon in snow.
School of Engineering, University of California, Merced
Donald Bren School of Environmental Science and Management, University of California, Santa Barbara
3 Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder
4 National Snow and Ice Data Center, University of Colorado, Boulder
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2
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Snow in mountains of the semi-arid West is the fundamental water source for the region’s hydrology, with downstream hydrologic processes (e.g. groundwater recharge) and interactions with
ecosystems being controlled by processes at higher elevations. A lack of process understanding and
a reliance on sparsely distributed observation networks together limit our ability to simulate hydrologic processes in mountainous regions and to make accurate predictions. As examples, we lack an
adequate description of factors controlling the partitioning of snowmelt into runoff vs. evaporation,
and we lack strategies to accurately measure the spatial variability of snow cover, particularly in
spring after most of the snow disappears from the lower elevations. Soil moisture in the mountains
likewise lacks strategies for adequate measurement. The volume of mountain-block and mountainfront recharge to groundwater and how recharge patterns respond to climate variability are poorly
known across the mountainous West.
Though topographically complex, the region’s thin soils, areas of either no forest or relatively
homogeneous forest cover, and snowmelt-driven water cycle offer opportunities for simplifying the
study of hydrologic processes. While research needs are many, three hydrologic issues represent
pressing questions in mountains of the semi-arid West:
1. Despite the hydrologic importance of mountainous regions, the processes controlling energy
and water fluxes within and out from these systems are not well understood.
2. There exists a need to realize, at various spatial and temporal scales, the feedback systems between hydrological fluxes and biogeochemical and ecological processes.
3. Lack of integrated measurement strategies and data/information systems for mountain hydrologic data hamper improvements to understanding the aforementioned processes.
ENERGY AND WATER FLUXES
In mountainous regions, the temporal and spatial distribution
of hydrometeorological conditions at and near Earth’s surface
controls the equilibrium state of water, dictating the distribution of
water inputs to and outputs from the system as well as the
residence times and pathways of water within it. Climate in the
semi-arid West exhibits considerable interannual variability, and the
Issue 1. Despite the hydrologic importance of
mountainous
regions,
the processes controlling
energy and water fluxes
within and out from
these systems are not
well understood.
temporal and spatial distributions of precipitation, snowmelt, soil moisture, evapotranspiration and
other hydrologic processes are sensitive to this variability. Sensitivity to climate change varies across
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gradients of physiography (e.g. elevation, vegetative community structure, and latitude) but the drivers and degree of this sensitivity in different mountainous landscapes are not fully comprehended.
Energy Fluxes
The spatial properties of basin-wide energy fluxes affect the way we interpret and forecast fluxes
of water across the varied mountain landscapes. Our understanding of land-surface/atmosphere energy exchange in mountains is particularly limited by their physiographic heterogeneity, which results
in complex mosaics of conditions that control the transfer of latent, sensible and radiative fluxes. On
the other hand, this land surface mosaic equilibrates with the boundary layer, which tends to homogenize fluxes over larger, topographically established scales.
Further motivating the study of these processes is recent evidence that these fluxes respond to
climate change in unknown ways. For example, average temperature has increased in California over
the past 50 years, and snow water equivalent (SWE) has decreased at lower elevations, with trends at
higher elevations unclear [Howat and Tulaczyk, in press].
Energy fluxes are closely linked with the water cycle. Perhaps the most important linkage is that
of snowmelt (Figure 2). Hydrologic and land surface models are increasingly including mass balance
and physically based snowmelt models [e.g., Cline et al., 1998].
Solar radiation. Above timberline the spatial and temporal distribution of net solar radiation largely
controls the timing and magnitude of snowmelt [Marks and Dozier, 1992]. The most important variables contributing to modeling the spatial and temporal variability in net radiation in seasonally
snow-covered systems are surface albedo and atmospheric attenuation. Snow albedo affects melt
rates more on high-elevation south-facing slopes than on lower elevation or north facing slopes
[Molotch et al., 2004a]. Anthropogenic increases in melt result from aerosols darkening snow [Hansen
and Nazarenko, 2004]. Explicit measurements of incident and reflected solar radiation are needed
across a range of physiographic conditions, particularly those that alter the distribution of snow
properties and vegetation—elevation, latitude, orientation, and exposure to wind. The spatial and
temporal distribution of atmospheric attenuation is critical to the accuracy of geometric terrain reflectance models used to distribute shortwave radiation in mountainous terrain [Dozier and Frew,
1990]. The lack of atmospheric observations inhibits the use of these terrain models over large regions. Thus, there is a need to further understand the processes that control the distribution of aerosols and clouds, particularly their relationship to synoptic-scale meteorology and to aerosol sources.
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Longwave radiation. Radiative fluxes often dominate the energy balance in mountain environments and in boreal forests [Blöschl, 1999; Link and Marks, 1999]. For the longwave radiation emitted
by the atmosphere, we currently lack the necessary observations to calculate the relationships between canopy structure and the distribution of longwave radiation beneath it. Observations of canopy structure (e.g. using airborne or future spaceborne LIDAR) and their relationship with radiative
fluxes are vital for understanding potential changes to feedbacks associated with anthropogenic and
natural disturbance, including fire and its suppression, forest thinning, and logging. Moreover, increased humidity and air temperature, associated with climate change, may cause greater longwave
emission from the atmosphere to the land-surface.
Sensible and latent heat. Latent heat fluxes are potentially altered by increased water vapor in the
atmosphere associated with an intensification of the hydrologic cycle. The observed decrease in
April 1 SWE at the lower elevations in the western US over the last 50 years [Mote, 2003] possibly
results from warmer air temperatures enhancing turbulent fluxes, resulting in earlier snowmelt, or
changes in precipitation type or quantity causing decreased accumulation. In the coming century,
model projections indicate that temperature increases may cause the April 1 snowpack to decline by
as much as one-third in the Sierra Nevada [Knowles and Cayan, 2002]. Long-term observations of sensible and latent heat fluxes across gradients of elevation and latitude are needed for the identification
of areas that are currently most sensitive to climate change.
Wind. The least-understood component of
turbulent fluxes is the spatial and temporal
distribution of wind fields [Liston, 2004].
Inhibiting understanding is a lack of
observations on wind fields at various scales
in mountainous environments, and how
Research questions related to energy balance
− How to represent & scale basin-wide energy balance in complex, heterogeneous terrain from
sparse point measurements?
− How this scaling varies across climate/vegetation
zones within a mountain range?
− How to blend satellite, surface & boundary-layer
measurements for scaling energy balance?
− What determines seasonal (transient) snowlines?
topography and vegetation affect the wind. The ability to physically represent topography and vegetation in modeling wind fields is limited by a lack of information on canopy microstructure, which is
sensitive to climate variability and forest management practices. Warmer air temperatures allow
vegetation to colonize new areas at the higher elevations and higher latitudes. Fire suppression results in increased canopy densities and provides enhanced shelter from air turbulence, whereas forest
thinning and clear cutting expose the underlying to wind. Observation strategies that evaluate the
sensitivity and distribution of wind fields across gradients of topography and forest succession are
needed.
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Water Fluxes into and out of the Mountain System
Precipitation. Direct measurements of precipitation in mountainous environments are particularly
difficult, given that so much of it falls as snow and that precipitation gauges catch snow poorly. Although it is possible to infer precipitation rates from snowpack observations, direct observations of
precipitation are needed to explore changes in precipitation type associated with climate variability.
Detailed observations of precipitation type and intensity, as well as the environmental conditions
controlling them, are needed across latitudes, vegetation zones and climate regimes. Elevation gradients need to be studied independent of latitudinal gradients. That is, rain/snow regimes vary along
both gradients, giving different patterns for total precipitation versus precipitation as snow. For example, annual precipitation in the Sierra Nevada decreases from north to south, but annual snow
accumulation is greater in the higher elevations in the south. The elevation at which precipitation
falls as snow (i.e. the “snowline”) varies from storm to storm and often varies on an hourly basis
during an individual storm. Thus, there are temporal as well as spatial controls on precipitation type.
These factors impact hydrologic processes
occurring across times scales from days to
weeks as rain-on-snow events can result in
catastrophic floods and dramatically reduce
snowpack water volume storage for spring
and summer runoff. Starting January 1,
1997, for example, much of the Sierra Ne-
Precipitation questions — research agenda
− Meteorological & climatic controls on precipitation
in mountains?
− Controls on & patterns of rain vs snow?
− How large-scale circulation & mesoscale forcing
influence the distribution & timing of precipitation?
− What mechanisms generate interannual variability
in precipitation?
− Importance of rain shadow, both seasonally &
storm to storm?
vada experienced huge floods associated with a warm storm on an early-season snowpack [National
Weather Service, 1997].
Snow distribution. The spatial distribution of SWE is a particularly important state variable, as
snow provides the majority of the water input to the system and dramatically influences energy exchange at the land surface. A pragmatic approach to estimating SWE distribution is needed at resolutions that reduce subgrid heterogeneity to a level where the majority of the variability in the system
can be modeled explicitly [Blöschl, 1999]. Small-scale studies have shown that regression-tree models
can successfully analyze the nonlinear relationships between snow accumulation and topographic
variables [Balk and Elder, 2000; Erxleben et al., 2002; Winstral et al., 2002] and therefore can be used to
determine how independent variables control the continuum of snow distribution over a given area.
Novel techniques for scaling these relationships and parameterizations of hydrologic state variables
to larger basins are needed, particularly as technological advances in observation capabilities emerge.
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A concept for addressing these scaling issues is the representative elementary area (REA) at which a
model element (or sub-catchment) contains a sufficient sample of the continuum of hillslope and
soil characteristics such that the pattern of those characteristics can be ignored [Wood et al., 1990].
Observations are needed at sub-REA scales to identify this continuum [Beven, 1995].
The coarse resolution of remotely sensed SWE estimates [Chang et al., 1991; Chang and Rango,
2000] cannot resolve small-scale SWE variability, so it is smoothed out across the grid element or
pixel scale . Hence, evaluation of these remotely sensed estimates has relied on the ability of ground
observations to represent the average conditions of subgrid element variability. Ground observations of SWE have been used in conjunction with remotely sensed snow covered area data to estimate the spatial distribution of SWE across mountainous watersheds [Fassnacht et al., 2003]. Remaining challenges include systematic, accurate estimation of snow under clouds [Molotch et al., 2004b],
canopy corrections for snow-covered area measurement, the design of adequate ground networks to
merge with remotely sensed information, and the merging of energy balance and snow-cover information to estimate the rate at which the snow melts.
Evapotranspiration. In semi-arid mountainous regions the greatest flux of water out of the system
is evapotranspiration. There is a need to understand the physiographic controls on how vegetation
responds to energy fluxes and water stress, across a range of vegetation types and climate regimes.
Current estimates of evapotranspiration at the regional scale are based on observing changes in soil
moisture from passive microwave data [Njoku et al., 2003] or by modeling the evolution of surface
energy and water exchanges and soil moisture. The coupling of energy and water flux with plant
physiologic measurements provides a link for using remote sensing to move from the plot to basin
scale. Particularly important is the emphasis on transition zones, interfaces, and the effects of climatic extremes in driving changes in ecological functioning. A lack of ground-based observations of
water vapor flux across these transitional zones hinders linking scales between point and remotely
sensed observations. Observational clusters, designed to characterize the continuum of evapotranspiration at sub-pixel scales, should be extended to basin and mountain range scales through blending with remotely sensed data.
Mountain front recharge. Output of water from the mountain system is a vital input to valley aquifers. In the Middle Rio Grande, this recharge is on the same order of magnitude (60-90 m3/yr) as
percolation from the Rio Grande [Anderholm, 1999]. Valley aquifer systems across the basin-andrange system of the southwestern U.S. similarly depend on runoff from the higher elevations. Het-
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erogeneous water inputs, topography, soil properties, bedrock permeability and vegetation in the
mountains largely control deep infiltration and hence aquifer recharge rates. The relationship between these hydrologic fluxes and the ecosystem controls on deep infiltration is still unknown.
Given that groundwater models assume that valley aquifer recharge occurs at the mountain front, we
lack a functional understanding of how the interactions vary across the heterogeneous mountain system.
Runoff. Empirical seasonal runoff forecasts (water supply outlooks) in snow-dominated systems are
based on historical relationships between point observations of snow and observed runoff. These
empirical models do best near mean conditions but perform poorly during climatic conditions that
are not well represented in the historical record, which are more likely to occur if there are trends in
the climate. Thus, there is a need to incorporate physical processes into these operational models.
Snow-area depletion curves have been used to empirically represent SWE in the snowmelt runoff
model (SRM) [Rango, 1992]. Subsequent inclusion of a radiation module improves the SRM by explicitly representing radiative fluxes, and inclusion of remotely sensed snow albedo further improves
it [Molotch et al., 2004a].
There are concurrent efforts to explicitly represent the entire system physically within landsurface schemes. In these heterogeneous systems land-surface schemes often misrepresent the partitioning of precipitation and snowmelt runoff into the various hydrologic pathways, leading to errors
in the timing and magnitude of runoff. Other modeling approaches such as the Modular Modeling
System [Leavesley et al., 1996] allow users to select modules to simulate individual processes. Such a
modular approach is critical for assessing algorithm accuracy and therefore, for evaluating our understanding of the physical processes represented within models.
Flow Paths and Residence Times within the Mountain System
In semi-arid regions, the partitioning of precipitation and snowmelt into surface runoff, infiltration and potential recharge is highly variable in space and time. Understanding the processes that
control partitioning into the various hydrohydrologic pathways is critical for realizing
linkages
between
hydrological,
biogeochemical and ecological functions.
Effort needs to be devoted toward
understanding the relationships between
Partitioning snowmelt & rainfall on the ground:
research questions
− How physical & ecological factors together determine the large variability in runoff vs. infiltration
across a mountain range?
− How to accurately estimate sublimation?
− Relative contributions of evaporation & transpiration to total ET in mountain ecosystems?
− How to predict hydrologic response of perturbed
systems, e.g. by fire?
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physical and hydraulic properties of soils and hydrologic processes (rainfall, infiltration, and runoff)
in upland areas. Developing relationships between measurable soil properties and vegetation characteristics and hydrologic processes is essential for computational scaling up from point, plot, and
headwaters to basin scale. Quantification of the relationships between rainfall or snowmelt intensity,
ecological site characteristics, and the resulting hydrologic processes (i.e. infiltration and runoff)
needs to be conducted through long-term field research in key soil-vegetation complexes within the
semi-arid mountainous west.
For hydrologic purposes, we need to understand the partitioning of snowmelt and rain into sublimation, runoff, evapotranspiration and infiltration. The conceptual models for alpine and subalpine
hydrology remain crude, and few measurements of the partitioning of snowmelt and rain at a range
of scales.
Hydrologic processes important at one scale may not necessarily be important at larger scales. At
the point scale, surface energy exchange and storage of melt water within the snowpack dominate
snowmelt and runoff. At larger spatial scales, lateral movement of water through the snowpack influenced by topography and stream network affects the timing of runoff. At longer temporal scales,
storage and routing will integrate short term variations in runoff caused by variations in melt rates.
Models developed based on an understanding of snowpack processes at the point scale will allow
point-scale processes to dominate when integrated to the basin scale. Therefore, different approaches must be used to gain an understanding of snowmelt runoff at point scales than those used
to gain an understanding at the basin scale (Figure 3).
Snowmelt infiltration into the substrate,
which depends on water content, strongly
controls the partitioning into runoff, storage, and
recharge. In Norway, more than 50% of
groundwater recharge occurs during snowmelt
[Jørgensen and Østmo, 1990]. The spatial and
Research questions: scaling
− How to blend remotely sensed & groundbased observations with integrated modeling schemes?
− What ground-based measurement strategy
to validate new remote sensing products?
− What role do snowlines play in the geomorphology, ecology, aqueous geochemistry &
hydrology of mountain catchments?
temporal variability of infiltration during snowmelt show localized infiltration. Future work to understand the partitioning of snowmelt into runoff, sublimation, infiltration, evapotranspiration
should use combinations of nested stream gauging and snowpack internal mass redistribution studies [Kattelmann and Dozier, 1999; Marsh, 1999].
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Spatially explicit models of snowpack evolution and snowmelt [Kirnbauer et al., 1994; Cline et al.,
1998; Luce et al., 1998; 1999; Colee et al., 2000] require field data and remotely sensed imagery for initialization, calibration, and validation. Field measurements are sparse or infrequent and subject to
disagreeable conditions or avalanche danger in many environments. Subject to availability of instrument duty cycles and constrained by cloud cover, remote sensing techniques can regularly provide
maps of snow properties for the model domain at a range of resolutions.
Mountain block recharge affects downstream hydrologic systems, including streamflow amounts
and timing, mountain-front recharge, and spring discharge. Observation of this variability, using
boreholes, in mountainous environments has not been conducted. Boreholes would also enable determining residence times and storage in the system. At the regional scale, fracturing and permeability at depth is not extremely variable, enabling borehole observations to be scaled up. Representation
of these mountain-block processes at the mountain front will dramatically improve our understanding of water flow paths in mountainous systems, and how upgradient recharge sustains downgradient baseflow. Such efforts are also critical for coupling physically based models surface- and
groundwater models.
ECOLOGICAL AND BIOGEOCHEMICAL FEEDBACKS
Fluxes of carbon and nitrogen in mountain systems have both local and regional importance.
For example, modeling suggests that in the Western U.S., net ecosystem exchange (NEE) is greatest
in the mountains [Schimel et al., 2002]. However, very few data are available to evaluate fluxes. NEE
is concentrated at high elevation, where the
terrain is usually rugged, whereas flux towers, the most widely used method for estimating these fluxes, are generally in flat ar-
Issue 2. There exists a need to realize, at various spatial and temporal scales, the feedback
systems between hydrological fluxes and biogeochemical and ecological processes.
eas.
Mountain catchments are receiving increasing fluxes of nitrogen other pollutants, through both
wet and dry deposition. Some are experiencing nitrogen saturation, yet the water-nutrient feedbacks
are understood in only selected locations. A compounding factor is the large organic nitrogen pool
in catchment litter, so the pool in soils is quite large relative to fluxes in atmospheric deposition.
Hydrologic Perturbations
Climate variability. Given the variable frequency and magnitude of extreme events like droughts,
flooding, severe storms and possible future shifts, hydrologic perturbations will alter biogeochemical
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cycles and the responses of ecosystems. In addition, the changing seasonal precipitation patterns
coupled with the hydrologic perturbations will result in alterations to the landscape as terrestrial and
aquatic systems adapt or change.
As snowmelt patterns shift and runoff occurs earlier, or if winter rain becomes more common,
one potential impact is a greater likelihood of floods [Miller et al., 2003]. An additional consequence
of the shift is longer summer droughts, stressing the terrestrial and aquatic ecosystem and the water
supply, affecting the distribution and
abundance of plants and animals [Stewart et
al., 2004].
Regional climate models are unable to
capture and predict extreme seasonal
climate events due to the coarse resolutions
Flow paths & residence times
− Do subsurface flow paths & residence times increase with scale, from headwater catchment a
river basin?
− How does baseflow respond to climate variability
across the various geologic & vegetation regimes
in a mountain range?
− Principal geologic factors controlling groundwater
storage, discharge to streams & chemical composition in mountains?
in the models. Improvements in the seasonal-to-interannual predictive capabilities of climate models
will come in part from more detailed understanding of the physical processes, resulting in more realistic representation at the subgrid scales, specifically the subsurface processes [Gordon et al., 2004].
Improvements in the predictability of the hydrologic processes in climate modeling and accurate inputs of the necessary boundary conditions such as soil moisture, groundwater, snow, and vegetation
will decrease errors and lead to more detailed understanding and predictions of summer and fall
stream baseflow.
Effects on ecosystems. Shifts in the mountain hydrologic cycle will affect terrestrial and aquatic
ecosystems. Besides earlier and more intense winter streamflow and persistent drought conditions,
terrestrial and aquatic ecosystems will respond to decreased baseflow. In addition, the potential exists for an increase in sediment transport and deposition resulting in altered river systems, with such
adverse effects of reducing the useful life of reservoirs and impacting fisheries. Further, such intensification of the hydrologic cycle can contribute to change in land geomorphology as the warm temperature coupled with more precipitation falling as rain and earlier snowmelt increase the likelihood
of soil erosion and landslides contributing to an increase in downstream sediment transport. However, ecosystems models based on climate variability have limited predictability given the uncertainty
of the interaction between hydrology and increased temperatures, changes in precipitation, alterations to the composition of vegetation, and increase in photosynthesis brought about by increased
concentrations of atmospheric CO2 [Gordon et al., 2004].
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We will need to improve the predictive capabilities of climate models with regards to hydrologic
perturbations and the response of ecosystems to abrupt changes to the hydrologic cycle. Such an
effort can only be accomplished by better parameterization and calibration of existing climate models, supported by new measurements and expanded knowledge of physical processes. Long-term
observations of the hydrologic cycle are critical, including remote sensing of land processes, installation of flux towers for monitoring energy and mass exchange, as well as the development of additional paleoclimate records, including sediment cores from alpine lakes and reservoirs [Melack et al.,
1997]. Of importance is the ability to scale up from point measurements along elevational and longitudinal transects to provide detailed observations on the spatial and temporal characteristics of the
hydrologic cycle.
Ecological and Biogeochemical Perturbations
Ecological perturbations. Considerable ecological issues remain unresolved when considering the
impacts of climate, fire, land use, and vegetation changes to the mountains of the semi-arid West.
For example, greater carbon sequestration occurs as reforestation increases and better land management practices are employed. However, logging can increase the release of CO2 to the atmosphere. Knowledge of post-fire hydrology is critical, including the effect of fire on infiltration rates,
erosion and water quality. Forest studies indicate that the timing and volume streamflow and soil
recharge are altered with a decrease in forest canopies [Ffolliott et al., 1989]. In addition long-term
studies on forest growth and succession could provide valuable insight on the water balance, were it
measured.
Increased nitrogen deposition from anthropogenic sources is perhaps damaging aquatic resources in high-elevation catchments [Williams and Tonnessen, 2000]. Such effects are evident in water
quality, aquatic organisms and other indicators of the degradation of terrestrial and aquatic resources. Further, phosphorous increases in high alpine lakes as a result of the transport of soils and
dust to the catchments have been reported.
The combined increases in nitrogen and
phosphorous
alter
aquatic
ecosystems
[Sickman et al., 2003a]. However, there are
still
unresolved
drometeorological
linkages
variables
between
(e.g.
hysnow
cover) and heterotrophic CO2 efflux, the
Carbon & nitrogen cycling research questions
− What is the importance of seasonal transitions to
carbon & nitrogen biogeochemistry at the scale of
a mountain range, i.e. across elevational & longitudinal gradients?
− How do changing seasonal transitions affect carbon & nitrogen cycling in & export from aquatic
ecosystems?
− How do they differ in rain vs. snow dominated
areas?
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variability in CO2 uptake by vegetation, and the efflux of nitrogen from mountainous basins [Baron
and Campbell, 1997].
Current understanding of the biogeochemical responses in ecosystems and feedbacks are particularly limited by the temporal and spatial variability in precipitation patterns, which affect deposition patterns of carbon and nitrogen and the release of these nutrients into the ecosystem. To resolve these issues of variability, a network designed to measure and capture spatial and temporal
variability is required.
Biogeochemical perturbations. Innovative measurements at the process level are needed to understand the biogeochemical evolution of snowmelt and surface waters in seasonally snow-covered
catchments. Intensive and extensive research conducted at the catchment scale in the last decade
shows that our understanding of the biogeochemical processes that determine surface water quality
in alpine basins is not sufficiently mature to model and predict how biogeochemical transformations
and surface water quality will change in response to climatic and anthropogenic changes in energy,
water, and chemicals [Sickman et al., 2003b; Meixner et al., 2004].
Linkages between climate change, historic and current landuse, forest dynamics, and resource
management practices impact the biogeochemical cycles of carbon, nitrogen, phosphorous, and sulfur. Research has indicated a rapid rise in nutrient concentrations, driven by climate and anthropogenic changes [Williams and Tonnessen, 2000]. It is clear that anthropogenic perturbations interact with
the atmosphere resulting in widespread deposition, resulting in increases in vegetation and plant
growth, but other factors such as soil acidification, increased ozone concentrations, loss of soil fertility through base cation loss, and their interaction with plant diseases and pests all reduce plant productivity.
High-elevation ecosystems in the western U.S. have seen increases in the atmospheric deposition
of nitrogen from the transport of nitrogen and carbon from lower to higher elevations resulting
from fires [Takemoto et al., 2001]. The alpine ecosystems’ transition from nitrogen-limited to saturated has changed water chemistry [Baron and Campbell, 1997]. With this evidence of increasing nitrogen to the system, knowledge of the hydrological and biological processes controlling the fluxes of
nitrogen is relatively unknown. The cycling of nutrients into the system is strongly linked to the hydrologic cycle, creating a strong seasonality (e.g. wet to dry) to the influx of carbon. Given the sensitivity of alpine regions, climatic perturbations in the form of warming and changes in precipitation
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patterns could eliminate the abrupt seasonality in mountainous environments, resulting in changes to
the biogeochemical cycle.
Seasonal snow cover influences the retention and release of nitrogen during the seasonal transition from cold to warm. The seasonal release of nitrogen through an ionic pulse at the onset of
snowmelt is a combination of both snowpack and soil nitrate. However, current research suggests
that the nitrate pulse is not simply a flushing of accumulated nitrate from the soils or snowpack, but
one of sequestration and release of nitrate from microbial activity [Sickman et al., 2003b]. Further,
research on the microbial activity suggest that immobilization of nitrogen in the soil is controlled by
the development of the seasonal snow cover and hence controls the release of nitrogen to surface
waters [Brooks and Williams, 1999]. These studies indicate that nitrate concentrations are controlled
by the seasonal snow cover and suggest that climate perturbations may directly control the availability of nitrogen with possible effects to the terrestrial ecosystem such as longer growing seasons in
alpine terrain. Thus allowing and supporting higher rates of nitrogen retention as well as decreasing
the aquatic export and redistribution of nutrients. As the climate changes and precipitation patterns
change the coupling of the carbon, nitrogen, and hydrologic cycle are likely to be affected as an increased presence of cold to warm seasonal transitions are replaced with wet to dry transitions.
More atmospheric deposition of pollutants are being deposited on sensitive alpine catchments.
In order to protect the sensitive alpine ecosystems, indices of critical loads of pollutants need to be
established [Williams and Tonnessen, 2000]. Understanding of the spatial and temporal distribution of
pollutants is vital to the ecological health of the environment. Research programs require long term
monitoring and since pollutants are spatially and temporally distributed, mountain range scale observations need to be initiated and expanded. Therefore, ground based surface observations, taking advantage of both latitudinal and elevational gradients are necessary to capture the temporal and spatial
heterogeneity [Bytnerowicz et al., 2002].
Since montane catchments are sensitive to changes from natural and anthropogenic perturbations and the linkage to biogeochemical cycles, i monitoring and feedback response to perturbations
must be studied in relation to land use change, nutrient inputs from atmospheric deposition, and
resource management practices. Reliable long-term measurements are essential to examine and
evaluate environmental changes and impacts to the system. For example, range-wide residence time
flow path measurements have the capability of indicating terrestrial disturbances in nutrient concentrations. By measuring residence times on a broad basis throughout several basins, geochemical
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composition can be identified with the age of the water. In addition, biogeochemistry can provide
insight into fine and long term temporal scales on events. At the fine temporal scale carbon, nitrogen, phosphorous, and sulfur can be measured during short lived high impact events (e.g. floods).
Longer time scales can be addressed by paleolimnological investigation in low elevation reservoirs
and high elevation lakes and coupled with tree-ring studies that document fire frequency, drought,
and flooding).
INTEGRATED MEASUREMENT STRATEGIES
Ground-based observations are heavily
used within operational water resource
management, yet they leave much of the
hydrologic cycle unmeasured. Streamflow is
Issue 3. Lack of integrated measurement
strategies and data/information systems for
mountain hydrologic data hamper improvements to understanding the aforementioned
processes.
accurately measured on most larger basins
and on some lower-order streams within these basins, but too many gauges are being taken out of
service and many tributary streams have never been gauged. Snow water equivalent, soil moisture
and the components of evapotranspiration remain poorly known, and prototype network designs are
lacking. Owing to complex, heterogeneous topography, hydrologic research is clearly needed to develop network designs that, together with advances in modeling, would improve understanding of
both the fluxes and reservoirs in the mountains, and also provide information on partitioning of
snowmelt into runoff, infiltration, sublimation, and evapotranspiration. New observations will enhance decision-support systems, especially when ground-based networks are integrated with advances in remote sensing and computational modeling.
Transferring knowledge of physical processes, scaling of climate model output data, and validating remotely sensed data are more difficult, though tractable, in mountainous regions because of
subgrid heterogeneity. Further, advances in ground-observations have not been on par with those in
remote sensing and computational modeling, and ground observation networks in mountainous regions have not been implemented with a water-balance focus.
Several sources of seasonal snow cover data exist, ranging from information collected as part of
weather monitoring to hydrologic data from networks dedicated to snow data collection, and more
recently to remotely sensed products from polar orbiting satellites. The research value of these timeseries data mandates their inclusion in future snow data archives, such as that to be associated with
the CUAHSI Hydrologic Observatories. However, none of these datasets encompasses enough sys-
14
tem interactions to be considered ‘snow system’ data. The need for integrated, interdisciplinary snow
system data collection and standardization is imperative to enable examination of system interactions
and feedbacks on global to local scales.
Precipitation
The spatial pattern of precipitation in mountainous terrain is nearly impossible to measure.
There are multiple networks in place, including NWS cooperative stations, RAWS (Remote Automated Weather Stations), NRCS SNOTEL (Snowpack Telemetry) stations, and a number of smaller
networks. Most of these stations lack the capacity to differentiate snow from rain. The National
Weather Service installed the Next Generation Weather Radar system (NEXRAD) in 1994 to improve operational measurements of precipitation around the country. However, NEXRAD signals
are occluded by mountains and thus are less reliable in the complex terrain where snowfall occurs.
There are also severe limitations with measuring precipitation accurately at a point. Traditional precipitation gauges have the inherent problems of undercatch, temporal lag due to the slow melting of
captured snow, and inability to discriminate solid and liquid precipitation. Turbulence at the catch
orifice inhibits capture of precipitation. Both heated-plate and optical sensors have been developed
to estimate snowfall more accurately. However, deploying them in remote locations away from
power may limit their use in the mountains.
Techniques to estimate precipitation using a combination of ground-based and remotely sensed
observations are being developed, but still at coarse resolutions and with poor abilities to distinguish
rain from snow. The research question is: how can a coupled satellite and ground-based precipitation estimation system meet the requirements for hydrologic applications in the semi-arid regions?
Snowpack Properties
Snow-covered area and albedo. Remote sensing is the only practical way to measure the spatial
extent of snow cover and albedo, and over the past decade methods for retrieving snow-covered
area from visible and infrared instruments on satellites have become well developed [Dozier and
Painter, 2004].
To the extent possible, remote sensing of snow properties derives from first principles about the
optical properties of snow. Radiative transfer models of snow reflectance and transmittance treat the
snow grains as independent scatterers, because the grain sizes and their center-to-center separations
are much larger than the wavelength of the light. Because of multiple scattering as light enters the
snowpack, penetration depths are restricted to no more than 0.5-m in the blue wavelengths and are
15
only a few millimeters in the near-infrared and infrared. Therefore, the possibility of remotely sensing SWE in the optical wavelengths is severely limited, and one must use active microwave remote
sensing to measure this important variable in alpine regions [Shi and Dozier, 2000a; 2000b]. In areas
where spatial resolution is not an issue, passive microwave remote sensing can estimate snow water
equivalent at values well below 1m, where the signal becomes asymptotic [Goodison and Walker, 1994;
Foster et al., 1997; Chen et al., 2001]. Remaining parameters come from a combination of in situ measurements and terrain-based energy-balance models [Dozier and Frew, 1990; Daly et al., 2000; Davis et
al., 2001].
Snow-covered area in alpine terrain often varies at a spatial scale finer than that of the ground
instantaneous field-of-view of the remote sensing instrument. This spatial heterogeneity poses a
“mixed pixel” problem in that the sensor may measure radiance reflected from snow, rock, soil, and
vegetation. To use the snow characteristics in distributed physical models, we must therefore map
snow-covered area at subpixel resolution in order to accurately represent its spatial distribution.
Otherwise, systematic errors may result. The snow cover algorithm for the Moderate Resolution Imaging Spectoradiometer (MODIS) [Hall et al., 2002], for example, identifies pixels whose snow cover
fraction is greater than about 50% as 100% snow-covered, whereas those pixels where the snow
cover fraction is less than 50% are mapped as snow-free (the algorithm can be tuned to adjust these
thresholds). For some applications, overestimates will balance underestimates. However, for snow
hydrologic applications in mountainous regions, the spatial distribution of snow cover with elevation
is needed, rather than the simple basin average.
The global snow albedo product derived from NASA Terra and Aqua MODIS data [Klein and
Stroeve, 2002] is also afflicted by the mixed pixel problem. The albedo model assumes complete snow
cover based on the MODIS snow cover product. For mountain snow cover, rarely continuous at the
500-m scale, the regional estimates of snow albedo will be systematically underestimated. Using remotely sensed fractional snow albedo dramatically improved estimates of spatial snowmelt patterns
and basin-wide snowmelt [Molotch et al., 2004a]. The same model for estimating snow properties has
been ported to use with MODIS surface reflectance data at 500m resolution (Figure 4) and is under
development for analysis of current and historical AVHRR data.
Snow water equivalent. SWE is measured accurately at over 1,700 points in the Western U.S.,
from a combination of manual snow surveys and transmitting snow pillows. While this large number
of samples can provide regional knowledge of the spatial distribution of SWE, it is insufficient to
16
resolve the variability of SWE at the basin scale. Moreover, the vast majority of snow courses and
automated stations are situated below timberline. In wet years, the lack of sampling of alpine snow
water equivalent manifests itself as a dramatic underestimate of late-spring runoff. The ideal spatial
resolution for estimating SWE distribution reduces subgrid element heterogeneity to a level where
most of the variability in the system can be modeled explicitly [Blöschl, 1999].
An acute problem is the lack of a network to measure the temporally and spatially continuous
distribution of SWE at basin and mountain range scales. The existing snow measurements are used
as indices of streamflow, rather than for directly measuring basin-scale snow volumes. The locations
are characterized by a homogenous snow cover and snowpack conditions specific to that elevation
in that basin, and all of the courses are on flat or nearly flat ground. Moreover, they must be accessible during winter without exposing the surveyors to avalanche danger. There is a pressing research
need to develop strategies to blend remotely sensed and ground-based data, including measurement
network design, to accurately estimate SWE. Maps such as those produced by the NWS National
Operational Hydrologic Remote Sensing Center come from spatial interpolation of precipitation,
snow telemetry, and snow-course measurements coupled with coarse-scale remote sensing of snow
cover. Yet the location and paucity of these sites necessarily undersample the SWE distribution.
Several investigators have explored multi-frequency, multi-polarization radar for retrieving SWE
and wet snow cover at spatial scales commensurate with the variability of snow properties in mountainous terrain [Nagler and Rott, 2000; Shi and Dozier, 2000a; 2000b]. However, layering of ice and liquid water, snowmelt columns, rough terrain, and vegetation cover present obstacles to quantitative
retrievals of SWE from radar. More importantly, we have no operational radar satellites with sufficient spectral and polarization capabilities to infer depth and density simultaneously. Passive microwave measurements of SWE are hugely important in measurements of continental-scale snow, but
their coarse spatial resolution limits their utility in the mountains.
Data integration. A ground-based observational network remains a viable and important component of the snow observing system that should not be replaced by the satellite system. Satellite-based
snow cover/depth observations probably will cover the future needs of many users of snow data,
but economics and scientific objectives now require a merging of all available snow information in a
superior enhanced data set. For example, Fassnacht et al. [2003] interpolated SNOTEL data using a
hypsometric regression with inverse distance weighting on residuals and subsequently masks the
continuous SWE field with fractional AVHRR snow-covered area. By incorporating fractional snow
17
cover maps from remote sensing, the interpolated SWE field more closely approximates the true
mass of snow. At the catchment scale, Elder et al. [Elder et al., 1998] mapped SWE with field survey
data through a regression tree model and then masked with 30 m Landsat Thematic Mapper fractional snow-covered area [Rosenthal and Dozier, 1996].
Opportunities for deploying new technologies exist, taking advantage of advances in spatially
distributed sensor networks comprised of smart wireless sensors and actuators. These have the capability of being deployed in a high-density measurement array providing both sampling in a wider
field or in a concentrated array. These low-cost, low-power systems will provide detailed spatial and
temporal data on such important issues affecting mountain hydrology such as land-air interactions,
ecosystems, and climate change. These systems are designed for continuous long-term hydrologic
measurements, with networks having the ability to be both adaptive and self-configuring. Individual
sensor signals are both archived and synthesized within the network to provide an integrated virtual
sensor capable of relaying more meaningful information than the individual sensors.
Soil Moisture
Soil moisture is measured at some Remote Automated Weather Stations (RAWS), but these generally do not lie in alpine settings. Remote sensing of soil moisture is made in the microwave frequencies. Passive microwave retrievals are too coarse for the spatial variability and rugged terrain in
mountain regions. Radar retrievals of soil moisture are in research mode now but the sensitivity of
backscattering to the soil dielectric constant holds significant promise for such retrievals [Oldak et al.,
2003; Western et al., 2004]. Again, rugged terrain and vegetation cover will confound retrievals. While
the technical difficulties are great, definition of the spatial variability of soil moisture is critical in
modeling hydrologic response in mountain catchments [Zehe and Blöschl, 2004]. Therefore, we recommend extension of the network of in situ soil moisture sampling for more spatially dense sampling. As radar retrievals of soil moisture advance, they should be integrated into the spatial interpolation schemes developed around the in situ network. Like SWE, network design for soil moisture
measurement is a research issue.
Evapotranspiration
Ogunjemiyo et al. [2002] have shown some promise that evapotranspiration may be retrieved in
rugged terrain with imaging spectroscopy. However, even at the plot scale, field spectroscopy has
limited potential to measure ET. Flux towers remain the method of choice for accurately estimating
land surface-atmosphere exchange at a point. Strategies for extending estimates across a mountain
18
range with gradients of precipitation and temperature, a mix of vegetation types, and varying management practices combine transects of flux towers with less-intensive instrumentation, both of
which are blended with information from remote sensing.
Data and Information Systems
Current computing environments, research practices, and data provided by agencies give too little support to integrating data between different systems. Typically, all these systems use scripts for
performing the required processing, along with idiosyncratic naming conventions for the files that
hold the products. It is therefore time consuming to extract data from a variety of systems, especially
when wants a synoptic view or wants to parse the data according to some criteria (e.g., data from all
snow courses above some particular elevation in a particular basin with more than some threshold
length of record). Our current modes of analysis usually require reorganizing the data and creating or
rediscovering metadata values for each product. Dissemination, especially where custom processing
such as subsetting, reprojection, or reformatting is required, is often treated in a similarly ad hoc fashion. The technologies to solve most of these problems are at hand already, but it will be necessary to
organize a concerted commitment among users and providers.
CONCLUDING THOUGHTS
Our current ability to quantitatively estimate water fluxes and reservoirs in mountains is inadequate. While there are a number of accurate streamflow measurements, many tributary rivers are ungauged. Precipitation and snowpack are measured accurately at a point, but the accuracy of rangescale estimates is unknown. There are also large knowledge gaps in other components of the water
cycle, biogeochemical fluxes and ecological linkages.
The economic value of and societal demand for new knowledge and tools for mountain hydrology is very large. Public agencies, private companies, watershed councils and other nongovernmental organizations, and a myriad of other stakeholders need and want new information on
with which to make water-sensitive decisions. Climate change and the high degree of climate variability experienced in the semi-arid west are important drivers for this new knowledge, along with
population growth and land use change.
Advances will require sustained investments in new measurements and infrastructure to enable
the research described above. The proposed CUAHSI hydrologic observatories will be an important
component of this investment, but the need is still broader. Adequate data and information systems
to support research are perhaps an even more critical need.
19
Measurement strategies for mountain systems will rely heavily on remote sensing. In most cases
the needed complimentary ground-based systems are not adequate to realize the full potential of remote sensing. Network design is in itself a research need.
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23
Figure 1. Relief map of the mountain west
24
data
cube
SWE
albedo SCA
incident
solar
relative wind
air
longwave temp humidity speed
t
y
x
vegetation
topography
soils
energy
balance
LSM
basin
potential
runoff
pixel by
pixel
SWE &
SCA
SWE
Time
pixel by
pixel
runoff
potential
Time
Figure 2. Energy balance modeling scheme
25
plot/hillslope
soil moisture
basin
remote sensing
SCA
albedo
vegetation
snow distribution
melt timing
snow distribution
partitioning
infiltration
ET
runoff
micromet
recharge
ground/RS
SWE
precip
radiation
EB
topography
fluxes
Blending measurements
from multiple scales
ground
soil moisture
infiltration &
recharge
micromet
bedrock
soils
Figure 3. Scaling the mountain water balance
26
Figure 4. Fractional snow-covered area and albedo from MODIS
27