
NSF Org: |
EAR Division Of Earth Sciences |
Recipient: |
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Initial Amendment Date: | April 20, 2015 |
Latest Amendment Date: | October 26, 2018 |
Award Number: | 1445889 |
Award Instrument: | Continuing Grant |
Program Manager: |
Laura Lautz
llautz@nsf.gov (703)292-7775 EAR Division Of Earth Sciences GEO Directorate for Geosciences |
Start Date: | May 1, 2015 |
End Date: | April 30, 2020 (Estimated) |
Total Intended Award Amount: | $339,379.00 |
Total Awarded Amount to Date: | $351,379.00 |
Funds Obligated to Date: |
FY 2016 = $12,000.00 FY 2017 = $95,685.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
11440 W BERNARDO CT STE 208 SAN DIEGO CA US 92127-1643 (858)798-9440 |
Sponsor Congressional District: |
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Primary Place of Performance: |
12555 High Bluff Dr. # 255 San Diego CA US 92130-3005 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
EDUCATION AND HUMAN RESOURCES, Hydrologic Sciences |
Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT 01001718DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.050 |
ABSTRACT
Changing snowmelt patterns associated with climate variability pose challenges to water resources management in river basins around the world. Hydrologic models are useful to assess impacts of expected climate variability on snowmelt-driven watersheds, but are hampered by limited data. Even in well-instrumented basins data records on snow variables are too short to describe the long-term variability of snow cover, snow depth, and snowmelt. Tree rings, with a long history of application in hydrology, offer a solution to the data limitations. Tree-ring properties, such as ring-width, wood density, and the anatomical structure of cells, are sensitive to changes in hydrologic variables. Moreover trees that can provide such data are often widely distributed over forested watersheds, and the records they provide can extend from centuries to millennia. This project is the first effort at assembling and interpreting a network of tree-ring chronologies specifically for the purpose of studying snowmelt properties and snowpack. The research is being conducted along the American River, a vital source of water supply to the State of California. A goal of the research is to develop transferrable, generally useful, research tools for better understanding of snowmelt processes and their variability in space and time. The scientific understanding of the spatiotemporal variability of the hydrologic variables will benefit society through improvements in sustainable water resources planning and watershed management practices. The research will include field sampling and development of tree-ring chronologies and reconstructions of snowpack-related variables. Resulting data will enhance the infrastructure for research and education on hydroclimatic variability by expanding the available palaeoclimatic data networks. The project includes training of undergraduate and graduate students and a post-doctoral researcher. In addition, it incorporates meetings with stakeholders to further the public understanding of science.
Changing snow-accumulation and snowmelt regimes in recent decades pose an increasing challenge to water resources management in the United States. Earlier melt and decreases in accumulation have been linked to both increasing winter and spring temperatures and an increasing fraction of precipitation falling as rain instead of snow. The existing observational networks in many basins do not have the spatiotemporal resolution to adequately characterize the variability of parameters relevant to these changes. Specifically, for the western USA, the intra-annual and spatial distribution of hydrological variables is poorly understood, as existing in-situ observations in the mountains are scarce and of short duration. The hypothesis of this interdisciplinary research is that key hydrological characteristics of the Sierra Nevada such as snow pack properties, precipitation, soil moisture and temperature can be constrained using the intra-annual features of tree rings. In addition, basin scale characteristics such as seasonal snow-line evolution and features of extreme events such as droughts can be identified. The novelty of this research is identification of key hydrological characteristics within a few days to weeks of occurrence. The research will explore the use of intra-ring properties of tree rings from multiple tree species and sites along an elevation transect in a snowmelt-driven watershed with goals of (a) improving the representation and scaling of land surface processes important to snowmelt processes in complex terrain, and (b) developing a method to detect the spatial variability and seasonality of evolving snowpack and soil moisture. The study basin is the North Fork of the American River, which drains the western side of the Sierra Nevada Mountains, California. Hydrometeorological variables will be derived for the sampling locations by merging a dense observational network and state-of-the-art land-surface model simulations. The cross association among hydrological variables and tree ring indicators will enable a description of the seasonal evolution of hydrological processes and provide for the development of proxy records that describe the spatial variability of various snow pack features.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
A shift toward less snowpack and earlier snowmelt since the latter half of the 20th century has dramatically impacted water resource management in the western United States. At the same time, the shifting snow regime has influenced ecosystems, which play a role in the water balance of river systems. This project explores novel applications of tree-ring data to gain a better understanding of how snowpack variation affects the growth of conifers in the Sierra Nevada of California and how tree-ring data can be applied for a long-term context of recent and projected changes in snow regimes.
Our project focused on the watershed of the North Fork of the American River, which drains to the Sacramento River from headwaters west of Lake Tahoe. We collected tree-ring samples from 11 different sites in and around the watershed using increment borers. Our most intensively studied site is Carpenter Ridge, California (Figure 1). There we conducted a detailed comparative study of the cell anatomy and snow response of two species, mountain hemlock (Tsuga mertensiana) and red fir (Abies magnifica), growing interspersed on the same slope. We applied a hydrologic model to generate daily time series of snowmelt and soil water content at the tree site from readily available inputs of daily precipitation and temperature, and then modeled the statistical relationship between the tree-ring and hydrologic variables. We used cell anatomical measurements as well as the more conventional annual ring width in this analysis. Two important cell-anatomy variables are size of the cells and the thickness of the cell walls (Figure 2).
Key findings based on relationships estimated from annual time series covering the 51 years 1964-2014 are: 1) tree-ring variables for the species studied have useful information on snowpack variability; 2) tree-ring cell anatomy can significantly contribute to our understanding of variability of snowpack and snowmelt regimes; and 3) individual tree-ring cells, developing at different times of the growth year, reflect hydrologic variations occurring at the times of development of the cells (Figure 3). This last finding is promising for cell anatomy as a tool for studying long-term variability of snowmelt regimes.
An unexpected finding was that that the two species -- red fir and mountain hemlock -- differ greatly in the way that growth responds to snowpack. For example, a deep snowpack this winter and spring favors high tree-growth in fir, but low growth in hemlock in the current year. It is not until the next growth year that hemlock growth responds positively to the deep snowpack. This finding has huge implications for the interpretation of snowpack variability from tree rings and for the design of statistical models to reconstruct past snowpack. A second unexpected finding was a strengthening of the dependence of tree growth on soil moisture in recent decades. We hypothesize that this shift in signal strength may be due to increasing water stress in the trees, possibly forced by regional warming. In essence, the trees are becoming more water limited with climate change.
Another part of our study addressed the snow signal in giant sequoia (Sequoiadendron giganteum), which is found in scattered groves at lower elevations on the west side of the Sierra Nevada south of the latitude of Lake Tahoe. This species is of interest for long-term snowpack information because sequoia depend heavily on snow and can live for more than three thousand years. Moreover, the Laboratory of Tree-Ring Research over the past three decades has collected and stored wood samples from giant sequoia and developed tree-ring chronologies at more than 23 sites. Using hydrologic modeling, we found that sequoia ring width has a strong signal for May 1 snow-water equivalent. We were able to generate a reconstruction of that variable for a site in the central Sierra Nevada back to the year 90 CE (Figure 4). The reconstruction, unprecedented in length for a snow-water equivalent, reaches its highest and lowest annual values before the start of the 20th century, and offers some support for century-long drought that other researchers have inferred in the Sierra Nevada in medieval times from exposed stumps in lakes and river beds.
Last Modified: 07/01/2020
Modified by: Eylon Shamir
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