Observations and mechanisms of change in seasonally snow- covered mountain environments: Linking process to pattern Paul D. Brooks Hydrology and Water Resources University of Arizona, Tucson, AZ

Outline of talk: Briefly describe a couple important characteristics of seasonally snow- covered mountains Present their geographical distribution with an emphasis on major water sources for lower elevations Provide a few examples of observed changes in these regions Discuss challenges in evaluating the mechanisms of change needed for prediction of hydrological (and ecological) responses to future climate

Mountain environments are characterized by transitions in space where vegetation reflects local climate Problem when you are trying to define boundary conditions Possible advantage when you are trying to identify process or mechanism Can we use spatial variability to predict critical transitions in time?

Seasonally snow-covered mountain environments store precipitation and release it during periods of high demand “Gradual” melt aids management Characterized by sharp, seasonal transitions Can we use temporal transitions to understand internal system function?

Arid and semi-arid environments of the world Many of the major mountain ranges of the world co- located in these regions

Regional Example AVHRR-derived frequency of snow cover Figures and data from Smith et al, 2003 and SAGE database The Tigris and Euphrates rivers arise in the seasonally snow- covered mountains of Turkey and Iraq Euphrates River Discharge at Firat

Indus River Discharge at Kotri Regional Example The Indus river originates in the western Himalaya in Pakistan and southwest Asia Data from SAGE database

Regional Example Both the Colorado and the Rio Grande originate in the Rocky Mountains of the southwestern U.S. Figures from USGS and NASA online reports

Mountain block and mountain front recharge in seasonally snow-covered mountains also replenishes regional aquifers in semi-arid environments Percentage of population of each State in the contiguous Western United States dependent on ground water for domestic water needs. From U.S. Geological Survey (1998). Regional Example

Between 32% and 80% of running freshwater originates in the mountain environments (Viviroli et al. 2003) As much as 50% of the human population depends directly or indirectly on fresh water resources originating in mountain environments (Beniston et al. 1997) Global importance of seasonally snow-covered mountains What are the observed changes in seasonally snow-covered mountain environments? What do these changes mean for water resources in the future?

Mountain environments seem to be warming throughout the world at rates faster than lower elevations Observations of change: Temperature Alps Sierra Nevada Beniston et al. 1997

Trends in Nov–Mar temperature and total precipitation. For temperature, red indicates warming and blue cooling since 1930; for precipitation red indicates decreases and blue increases since Mote et al Observations of change: Temperature & Precipitation TemperaturePrecipitation

Linear trends in 1 Apr SWE in the western United States and Canada for the period 1950–97, with negative trends shown by red circles and positive by blue circles Mote et al Observations of change: SWE

Data from Stewart et al. 2004, Mote et al Observations of change: Snow Melt Snowpacks are melting earlier and faster

Data from Stewart et al. 2004, Mote et al Observations of change: Streamflow Streamflow is higher in winter and early spring and lower in the summer

Observations of Change: Vegetation The type, amount, and activity of vegetation changes in response to climate, humans, and other biota

Vegetation directly affects the hydrologic cycle through the partitioning of incoming precipitation into soil moisture, evaporation, transpiration, sublimation, stream flow, and groundwater Vegetation indirectly affects the hydrologic cycle by affecting soil and topographical development Vegetation is changing on time scales of relevance to water resource management Why focus on Vegetation?

A closer look at mountain water balance: What do we know and where do we need either more data or improved understanding? P = S + E + T + GW R + R P – (S + E + T) = GW R + R

A closer look at mountain water balance: P – (S + E + T) = GW R + R Widespread lack of meteorological observations in mountainous terrain, complicated by the fact that variability increases as terrain complexity increases 6 of 404 coop climate stations above 2500m ~ 600 SNOTEL sites in western U.S.

A closer look at mountain water balance: P – (S + E + T) = GW R + R SNOTEL sites are located in areas of preferential snow accumulation km N SNOTEL April: 2001 April: 2002 Snow depth Snow water equivalent historical mean SWE, cm survey SNOTEL (Molotch et al. 2004)

A closer look at mountain water balance: P – (S + E + T) = GW R + R Results from Hood et al Continental mountain snowpacks may lose highly variable and potential large (40% or more) fraction of precipitation to sublimation

A closer look at mountain water balance: P – (S + E + T) = GW R + R By shading the snowpack and limiting turbulent exchanges vegetation can limit sublimation and increase net snow water input (~25%) Canopy Cover Normalized SWE Shading Interception

A closer look at mountain water balance: P – (S + E + T) = GW R + R From Monson et al Mountain forests strongly respond to melt with peak carbon uptake and high rates of transpiration

Temporal scale Hydrologic Impact Climate Vegetation Temporal scale Hydrologic Impact Climate Vegetation What do we know and where do we need either more data or improved understanding? P – limited spatial meteorological data, but ongoing efforts are developing scaling rules for snow; rain is more limited S, E, and T – large and variable fluxes mediated by poorly understood relationships between physical and ecological processes

The Need for Coordinated Research Observatories Incoming Precipitation as Rain/ Snow Atmosphere-Land Surface H 2 O, CO 2, and Energy Balance Partitioning of Precipitation into Infiltration, Runoff, and Recharge Vegetation Water Use/ Plant Physiological Ecology Ecological Dynamics Groundwater flow Nested Catchments Distributed data collection Integrated modeling

Assessing the integrated ecohydrological response Both physical and ecological processes leave a “signature” in water chemistry; Simultaneous analysis of hydrology and chemistry can help give the “right” answer for the correct reasons

Assessing the integrated ecohydrological response Conservative tracers (and isotopes) can be used to infer water sources, residence time and routing Eq 1 [Cl - ] 1 f 1 + [Cl - ] 2 f 2 +[ Cl - ] 3 f 3 = [Cl - ] stream Eq 2 [SO 4 2- ] 1 f 1 + [SO 4 2- ] 2 f 2 +[SO 4 2- ] 3 f 3 = [SO 4 2- ] stream Eq 3 f 1 + f 2 + f 3 = 1

Assessing the integrated ecohydrological response Conservative tracers (and isotopes) can be used to infer water sources, residence time and routing; simultaneously, biogeochemically cycling solutes provide information about the ecological response

Ground based measurements indicate that both temperature and precipitation are changing, but observations are sparse Both ground based and remote sensing data indicate that vegetation is changing, but reasons are not clear Snowmelt hydrologic response appears to be changing, but large fluxes (S, E, T, GW R ), and how they may change as vegetation and climate change are unknown Simultaneously quantifying hydrological and biogeochemical fluxes provides a tool for linking observed changes in pattern with underlying processes Summary

Ratio of snowfall to runoff. The red lines indicate the regions where streamflow is snowmelt-dominated, and where there is not adequate reservoir storage capacity to buffer shifts in the seasonal hydrograph. The black lines indicate additional areas where water availability is predominantly influenced by snowmelt generated upstream. Barnett et al Global significance of seasonally snow-covered mountains

What do we know and where do we need either more data or improved understanding? To understand the effects of climate and ecological change on mountain systems we need a coordinated, interdisciplinary research strategy that provides both: 1)new understanding of relationships between energy, water, and vegetation and, 2)distributed data to drive predictive models of these interactions under future scenarios

Quantify how eco-hydrological interactions control the water fluxes and storage that constitute the basin scale water balance (New Process Knowledge) Integrated field and modeling work Accurately incorporate the important hydrometeorological, physiographic, and physiological interrelationships in a distributed hydrologic watershed model that includes snow and vegetation processes (Combining Process Knowledge, Data, and Modeling) Represent eco-hydrological interactions, which are the outcome of processes that occur at the meter to hillslope scale, at the scale of landscapes to basins (Distributed Data)