Susquehanna Shale Hills: Update May 2012

S. L. Brantley and the team

Education and Outreach

Hydrogeophysics Field Camp 2012 at Shale Hills and greater watershed led by Kamini Singha with CAREER grant funds

• 3 weeks • 8 undergraduates • Wireline logs for temp, gamma and caliper • optical televiewer • slug tests

CZO Shale Transect

600

500

400

300

200

100

0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Zr, Na

Augera

ble

soil

depth

(cm

)

Wales

Pennsylvania

Virginia

Tennessee

Alabama

Puerto Rico

Understanding the water cycle

Henry Lin, Jun Zhang, J. Doolittle, Chris Graham, Chris Duffy, Ken Davis, Yuning

Shi, Dave Eissenstat, Katie Gaines

Hydropedology Group Selected Highlights (2011)

• Preferential flow occurred during >90% of 175

precipitation events in the past 3 years. This study

was the first that has attempted to simultaneously

determine the temporal and spatial patterns of in

situ preferential flow and its underlying controls

• The Hydropedograph Toolbox we developed offers

comprehensive and streamlined analysis of

automatic soil moisture monitoring datasets,

which can facilitate cross-CZO studies Soil Moisture Storage

within 1 m Deep Solum (m)

> 0.5

< 0.01

• Time-lapsed GPR in combination with real-time

soil water monitoring have revealed flow paths in

fractured shales and the impacts of soil layering on

subsurface lateral flow

Hydropedograph

Shale Hills Lidar-derived Canopy Height Model: Katie Gaines, Dave Eissenstat

-18

(p

er

mil)

Oxygen-18 value of tree xylem water across 9 species: Katie Gaines, Dave Eissenstat

Natural abundance stable isotopes of oxygen and hydrogen from plant xylem water help determine the sources of water used by plants and tree rooting depth. Data from 2009 and 2011. ACSA is Acer saccharum (sugar maple), QUVE is Quercus velutina (black oak). More negative)18O values are associated with deeper water sources (deep soil water, ground water), and less negative values are associated with shallower water sources such as shallow soil water.

“Flux-PIHM” was created as a merger of 1) a land-atmosphere model of fluxes of water, energy and momentum;

2) a ground-water hydrology model

Qu and Duffy 2007

NOAH

+ PIHM

at Shale Hills CZO

Optimize key model parameters using an ensemble Kalman filter method and observations of: discharge ground water depth soil moisture surface energy flux

+

Water summary

• Shale Hills CZO data used to optimize flux-PIHM model using the ensemble Kalman filter

• Importance of results: – Whole-watershed water balance constructed from synthesis of observations and model – This model includes hydrologic interaction between ground water dynamics and the

atmosphere. It may be the most physics-based model of its kind (and not many exist). It should be superior to more empirical models for flood/drought forecasting in a changing climate, for example, or for evaluation of ecosystem-hydrologic interactions (roots tapping groundwater).

– Ensemble Kalman filter methodology available to optimize the model for any other watershed – e.g. explore the degree to which the model parameters for the Shale Hills CZO are applicable to other watershed (e.g. vary location, scale of watershed)

– Model is highly data constrained (soil moisture, stream discharge, ground water table depth, evapotranspiration – all used to optimize the model parameters), taking advantage of the CZO observational array

– Ensemble Kalman filter methodology/dense CZO array is suited to network design questions – that is, what observations are essential (type, number, duration) to characterize ‘bedrock to land surface’ hydrology to a given degree of accuracy/precision? (this has not yet been evaluated but could be with ‘data removal’ or ‘data degradation’ experiments)

Putting the geology into the hydrologic models: Weathering may be very deep at Susquehanna Shale Hills CZO

(Brantley, Holleran, Jin, in review, ESPL special issue)

Landscape evolution

Rudy Slingerland, Yu Zhang, Eric Kirby, Nikki West, Lin Ma, Sue

Brantley

North ridgecrest South ridgecrest

Comparison of residence times and downslope regolith flux, inferred from meteoric 10Be with residence times and regolith production flux, inferred from U-series disequilibrium, reveal that regolith transport is of the same order as production

North hillslope South hillslope

Regolith fluxes were successfully measured using meteoric 10Be concentrations in regolith. Regolith fluxes appear to increase linearly downslope (Nikki West)

Paper submitted by Nikki West to JGR

Describing 10Be at SSHO

• To interpret our meteoric 10Be measurements from regolith and bedrock in SHO we re-derived the transport equation for the concentration of 10Be (C) as:

where: C and Cr are the meteoric 10Be concentrations in regolith and bedrock (atoms g-1 sample) σre and σro are the regolith and bedrock densities, respectively, h is the regolith thickness, e is the elevation of the regolith-bedrock interface, D is the delivery rate meteoric 10Be via precipitation (atoms cm-2 y-1), Po is the regolith production rate on bedrock, U is the uplift rate, λ is the radioactive decay constant of 10Be

( ) hro r

re re re r

a ro ro rr

e

o

C e C C Dm e C CV C C P e

t h t x h h h

Deriviation by Nikki West and Rudy Slingerland

Results

• Regolith at SHO moves at relatively slow creep

velocities (~ 0.2 cm/yr) • Fluxes do not vary linearly with gradient • Meteoric 10Be inventories at the north and south

ridge top sites imply minimum soil residence times of 11 ± 3 ky and 9 ± 2 ky, respectively

• These timescales imply steady erosion rates of 16 ±5 m/My and 19 ± 5 m/My, and an approximate balance between regolith production and transport during the Holocene

Landscape Evolution: Yu Zhang, Rudy

Slingerland

Landscape Evolution

Tree throw Uplift Sediment transport

Erosion Soil

production

𝜕z

𝜕t=

σro

σre− 1 P0e−αh cos θ sec θ −

𝜕 𝑞𝑡𝑥 + 𝑞𝑏𝑥

𝜕𝑥−

𝜕 𝑞𝑡𝑦 + 𝑞𝑏𝑦

𝜕𝑦+ U

𝜕𝑒

𝜕𝑡= −P0e−α h cos θ secθ + 𝑈

𝑞𝑡 = 𝐾𝑠𝑖𝑛 𝜃

𝑞𝑏 =𝐶𝐷𝐵

𝐷𝑠𝑒𝑑0.75

𝜏0 𝜏0 − 𝜏𝑐

𝜏0 = 𝛾𝑤𝑅𝑆0

z(x,y,t) elevation of ground surface [m]

e(x,y,t) elevation of bedrock[m]

h(x,y,t) thickness of regolith

𝑞𝑏 sediment flux [m2/s]

𝑞𝑡 regolith lateral movement flux [m2/s]

U uplift rate[m/s]

Sketch of landscape evolution model in x-direction

Model testing and application: 1D

250

260

270

280

290

300

310

1 4 7 1013161922252831343740434649525558

bedrock

groundsurface

• 1-D steady state testing for a cross-section

Steady state solution

• At the beginning, weathering rate is greater than regolith lateral movement

rate. So the thickness of regolith is increased.

• With the increasing of thickness of regolith, the weathering rate is

decreased.

• After a long time simulation, the system reaches steady state condition

under which the net regolith lateral movement rate is equal to uplift rate

and weathering rate on hillslope and the net regolith lateral movement rate,

sediment transport rate, uplift rate and weathering rate are in balance in

channel

Model testing and application: 2D • 2-D simulation for Shale Hills Observatory

Data for 2-D simulation

Simulation result

(Initial condition) (1000 years simulation)

• Integrate the model into PIHM

• Add hillslope sediment transport part and creep part

Biotic cycling: C, Mn, and Fe

Dave Eissenstat, Katie Gaines, Beth Herndon, Sue Brantley, Tiff Yesavage, Aaron Thompson, Margot Kaye, Jason

Kaye, Henry Lin

100

200

300

400

500

600

250 260 270 280 290 300 310

Litt

erf

all (

g m

-2)

Elevation (m)

South

North

South y=1128.2-2.9x, R2=0.22, p=0.047 North y=447.4-0.3x, R2=0.01, p=0.88

SSHO leaf litter, Fall 2011 • Leaf litter input to watershed: 293 g

m-2 year-1 dry mass

• South slope: leaf litter input negatively correlated with elevation (Fig 1)

• North slope: no elevation trend (Fig 1)

• Dominant species contributing to leaf litter inputs

Quercus rubra (27%) Quercus prinus (23%) Quercus alba (15%) Acer saccharum (13%) Carya spp. (12%)

Fig 1. Dry mass leaf litter fall (g m-2 yr-1) across elevation ranges (valley bottom to ridge top) of north and south slopes in the Shale Hills watershed.

Future work - SSHO terrestrial carbon •Aboveground (AG) carbon pool estimated from forest structure •Temporal trends in AG carbon pools (Net primary production) estimated from tree radial growth

Toward closure of the C budget at SSHO: We have SOM, DOC, ground-based chamber flux

measurements, litter flux, eddy flux, soil gas C vs depth

Litter flux work by Lauren Alexander and Margot Kaye

Mn in the catchment: Elizabeth Herndon

Fe isotopes in the catchment: Tiff Yesavage

Yesavage et al. accepted with revisions, Geochimica

Five hypotheses for the next year

• Spatially intensive measurements of soil moisture, tree sap flux, sapwood area, LAI, ground water depth and physical properties can be synthesized with spatially integrated measures of eddy flux and landscape-level soil moisture (COSMOS) within a distributed modeling framework to understand and predict physical processes.

• Water isotopes and chemistry document the age and flow paths of water in Shale Hills.

• Tree roots and associated mycorrhizas are causing the soil thickness to deepen at rates of ~26 m/My on the ridgetops at SSHO as the catchment recovers from the periglacial conditions imposed during the last glacial maximum.

• Feedbacks among frost shattering, weathering reactions, and the evolution of topography have resulted in an asymmetric distribution of fractures that in turn controls the observed differences in fluid flow in the subsurface between the sun-facing and shaded sides of the catchment.

• Hillslopes become progressively less steep to the south along our Rose Hill shale climosequence because chemical weathering rates increase with increasing temperature and rainfall while erosion rates are relatively constant.