Montana State Watershed Lab Montana State University - Bozeman Hydrologic connectivity from hillslope to landscape scales: Implications for runoff generation.

Slides:

Advertisements

Similar presentations

Minimum temperature mapping in complex terrain for fruit frost warning Jin I. Yun Kyung Hee University Suwon, Korea.

Advertisements

Precipitation - Runoff Relations Watershed Morphology

Bankfull / Effective / Dominant

Peatscapes: Monitoring of Hydrology and Water Quality at Geltsdale and Priorsdale Report of Phase 1: Nov 2007 – Mar 2009 Dr Jennine Jonczyk, Dr Mark Wilkinson,

Predicting the likelihood of water quality impaired stream reaches using landscape scale data and a hierarchical methodology Erin Peterson Geosciences.

Active layer depth as a key factor of runoff formation in permafrost: process analysis and modelling using the data of long-tem observations Lyudmila Lebedeva.

The Effects of Site and Soil on Fertilizer Response of Coastal Douglas-fir K.M. Littke, R.B. Harrison, and D.G. Briggs University of Washington Coast Fertilization.

Tifton Georgia Florida Gulf of Mexico Atlanta Athens SE Environmental Flows Conference Roles of Farm Ponds and Potential Impacts on Streams in the Coastal.

Michael J. Brayton MD/DE/DC Water Science Center Hydrologic Controls on Nutrient and Pesticide Transport through a Small Agricultural Watershed, Morgan.

Plot Scale © Oregon State University Isotope Hydrology Shortcourse Prof. Jeff McDonnell Richardson Chair in Watershed Science Dept. of Forest Engineering.

Soil CO 2 Efflux from a Subalpine Catchment Diego A. Riveros-Iregui 1, Brian L. McGlynn 1, Vincent J. Pacific 1, Howard E. Epstein 2, Daniel L. Welsch,

Applications of Scaling to Regional Flood Analysis Brent M. Troutman U.S. Geological Survey.

The central theme of this project is to attempt to quantify the effect of riparian cover on stream water temperature at small spatial scales, with a view.

Monitoring the hydrologic cycle in the Sierra Nevada mountains.

Pacific Southwest Research Station, Fresno, CA Kings River Experimental Watersheds KREW.

Using the Maryland Biological Stream Survey Data to Test Spatial Statistical Models A Collaborative Approach to Analyzing Stream Network Data Andrew A.

UC Marine Council Santa Barbara Coastal LTER - NSF Nutrient Export Coefficient Modeling in Mediterranean Coastal Streams Timothy H. Robinson, Al Leydecker,

Biological and Environmental Engineering Soil & Water Research Group Spatial Variability of Groundwater Soluble Phosphorous on an Alluvial Valley-Fill.

Hydrologic Mixing Models Ken Hill Andrew McFadden.

Hydrological Modeling FISH 513 April 10, Overview: What is wrong with simple statistical regressions of hydrologic response on impervious area?

Alan F. Hamlet Dennis P. Lettenmaier Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering.

Mark Williams, CU-Boulder Using isotopes to identify source waters: mixing models.

Biological and Environmental Engineering Soil & Water Research Group Hydrological pathways in a glaciated watershed in the Catskill Mountains Adrian Harpold.

Integrated Approach for Assessing the Characteristic of Groundwater Recharge in Basin Scale Hsin-Fu Yeh*, Cheng-Haw Lee, Kuo-Chin Hsu Department of Resources.

Washington State Climate Change Impacts Assessment: Implications of 21 st century climate change for the hydrology of Washington Marketa M Elsner 1 with.

WaterSmart, Reston, VA, August 1-2, 2011 Steve Markstrom and Lauren Hay National Research Program Denver, CO Jacob LaFontaine GA Water.

Modeling Variable Source Area Hydrology With WEPP

Strategic sampling of microclimate, soil moisture and sapflux for improving ecohydrological model estimates in the California Sierra Kyongho Son and Christina.

Modeling water and biogeochemical cycles in the Front Range, Colorado: effects of climate and landuse changes Landrum, Laura L., Natural Resource Ecology.

Temporal and spatial patterns of basin scale sediment dynamics and yield.

Watershed Assessment and Planning. Review Watershed Hydrology Watershed Hydrology Watershed Characteristics and Processes Watershed Characteristics and.

Sensitivity of Oregon's Watersheds to Streamflow Changes due to Climate Warming: A Geohydrological Approach Mohammad Safeeq Department of Geosciences,

Southern Sierra Critical Zone Observatory Activities from 2009 annual report Investigators: UCM: R. Bales (PI), M. Conklin, S. Hart, A. Behre UCB: J. Kirchner,

Streamflow Predictability Tom Hopson. Conduct Idealized Predictability Experiments Document relative importance of uncertainties in basin initial conditions.

CZO. Southern Sierra CZO: snowline processes CZO.

Pathways for nitrate release from an alpine watershed: Determination using  15 N and  18 O Donald H. Campbell Carol Kendall, Cecily C. Y. Chang, Steven.

The Southern Sierra Critical Zone Observatory (CZO) is designed as a platform for integrated, multi-disciplinary research and scientific information that.

Summer Synthesis Institute Vancouver, British Columbia June 22 – August 5 Overview of Synthesis Project Synthesis Project Descriptions Summer Institute.

Objective: Have a working knowledge of the relationship between the vegetative cover in a watershed and water yield and water quality.

1 Factors influencing the dynamics of excessive algal blooms Richard F. Ambrose Environmental Science and Engineering Program Department of Environmental.

VFR Research - R. Hudson Basic Hydrology Streamflow: Hydrographs; Case studies of logging effects on streamflow; Peak flow.

Coupling between fire and permafrost Effects of permafrost thaw on surface hydrology between better- drained vs. poorly- drained ecosystems Consequences.

Source waters and flow paths in an alpine catchment, Colorado, Front Range, United States Fengjing Liu, Mark W. Williams, and Nel Caine 2004.

RipStream: Quantifying stream temperature response to Oregon timber harvest practices Jeremy Groom 1, Liz Dent 2, Lisa Madsen 3 1 OSU Dept. of Forest Engineering.

To main objectives: Build a seamless, routed stream network across WAM tiles Apply a process based, riparian zone delineation tool Riparian Processes.

HYDROGRAPH SEPARATION

Spatial distribution of snow water equivalent across the central and southern Sierra Nevada Roger Bales, Robert Rice, Xiande Meng Sierra Nevada Research.

Additional data sources and model structure: help or hindrance? Olga Semenova State Hydrological Institute, St. Petersburg, Russia Pedro Restrepo Office.

Ed Maurer Partitioning precipitation into rain and snow for event-based hydrologic modeling in the Pacific Northwest U.S. Edwin Maurer Civil Engineering.

Streamflow Response to Climate: Why Geology Matters –Tim Mayer, US Fish and Wildlife Service Presented at the Oregon Water Conference Corvallis, OR May.

Seasonal variation in surface- groundwater exchanges in an urban floodplain with active gravel-bar formation Dorothea Lundberg Karen Prestegaard University.

Parameterisation by combination of different levels of process-based model physical complexity John Pomeroy 1, Olga Semenova 2,3, Lyudmila Lebedeva 2,4.

Seasonal and elevational variation of surface water  18 O and  2 H in the Willamette River basin J. Renée Brooks 1, Parker J. Wigington 1, Jr., Carol.

Dr Bill Cotching Tasmanian Institute of Agricultural Research Managing our Land and Water Resources A research perspective.

Flow prediction accuracy given DEM resolution  Model accuracy for snow-rain transition watersheds was more sensitive to DEM resolution than for snow-dominated.

A GIS approach to understanding groundwater – surface water interactions in the Logan River and Red Butte Creek, Utah Trinity Stout CEE 6440.

Mapping the Thermal Climate of the HJ Andrews Experimental Forest, Oregon Jonathan Smith Spatial Climate Analysis Service Oregon State University Corvallis,

RACC High School Training June 26, 2012 Jody Stryker University of Vermont Introduction to Watershed Hydrology.

Managing Natural Water Storage for Climate Change Adaptation.

Trends in floods in small catchments – instantaneous vs. daily peaks

Approach in developing PnET-BGC model inputs for Smoky Mountains

Precipitation-Runoff Modeling System (PRMS)

Assessing spatial and temporal snowpack evolution and melt with time-lapse photography Caitlin Bush.

Snowfall Runoff Forecasting in San Juan County, UT

in the Neversink River Basin, New York

Terrain analysis for stream hillslope morphology

Watersheds as Integrators of Climate: The Hydrogeomorphic Template as

Jacob Piske, Eric Peterson, Bill Perry

Problem: Interpolation of soil properties

Forests, water & research in the Sierra Nevada

Presentation transcript:

Montana State Watershed Lab Montana State University - Bozeman Hydrologic connectivity from hillslope to landscape scales: Implications for runoff generation and water quality Brian McGlynn – Montana State University (MSU) Kelsey Jencso, PhD student (MSU) Kristin Gardner, PhD student (MSU) Collaborators Mike Gooseff – Penn State Ken Bencala – USGS, NRP – Menlo Park Steve Wondzell – USFS, Olympia Ward McCaughey – USFS RMS Jan Seibert – Stockholm University, Sweden EAR McGlynn EAR Gooseff RM-4151: Ecology & Management of Northern Rocky Mountain Forests, Tenderfoot Creek Experimental Forest and the USDA, Forest Service, Rocky Mountain Research Station R832449

Montana State Watershed Lab Montana State University - Bozeman Does spatial location of change influence watershed response to perturbation? Big Sky, Montana an outdoor laboratory Requires : 1)understanding of hydrologic connectivity across landscape 2)relationships between pattern of change and landscape structure Residences (septic systems) increasing by 100s per year

Map area ~ 22 km 2 7 gauged watersheds OBJECTIVES Investigate hydrologic connectivity over space and time Develop conceptual model of runoff generation–watershed structure Test ideas in a developing watershed (Big Sky) – applied example Tenderfoot Creek Experimental Forest

Hydrologic instrumentation  24 transects of nested wells and piezometers (140 recording GW wells)  7 flumes with real time specific conductance (SC), temperature, and stage recorders  ALSM 1m topography data  9 water content probe nests across riparian hillslope transitions  >8 rain gauges  4 snowmelt lysimeters  2 SNOTEL sites  2 H 2 O/CO 2 eddy-covariance towers w/ full energy budget instrumentation.  600 m 2 plot w/ intense water content (64 TDR probes) soil and snow temperature (80)  Frequent stream and GW sampling with a focus on solutes, 18 O, D, and DOC Little Belt Mountains, Montana ~850 mm precipitation with ~550 mm ET ~75% as snow 0 degrees C average temperature Soil depths 1-2 meters Elevation range ~500m from 2300m base Highly instrumented USFS nested catchments with a focus on water and carbon research from the plot to multiple watershed scales

Montana State Watershed Lab Montana State University - Bozeman 0 ha Log ha Terrain-based riparian mapping Topographically-driven redistribution of water

Montana State Watershed Lab Montana State University - Bozeman Combining upland drainage and local riparian area along the stream network Hilllsope area accumulation Hillslope area accumulation Upland area accumulation pattern 0 ha Area accumulation 40 ha > area accumulation > water accumulation > increase in streamflow stream Low riparian buffer potential High riparian buffer potential Low to High riparian buffer potential Buffering potential f (riparian area : hillslope area)

Montana State Watershed Lab Montana State University - Bozeman Lateral inflows vary along the channel network Riparian buffering potential varies along the channel network Riparian buffering potential frequency Buffering potential

Hillslope-riparian-stream hydrologic connectivity South hillslope South riparian 10/64/0710/7 Kelsey Jencso NO CONNECTIVITY North hillslope North riparian Water table elevation m Connectivity

Date R 2 =0.91 n=24

Montana State Watershed Lab Montana State University - Bozeman Examining watersheds in 4 th dimension (temporal connectivity) Max bar height = 100% Of the year Each side of the stream separated

Montana State Watershed Lab Montana State University - Bozeman How does upland connectivity relate to streamflow magnitude?

Montana State Watershed Lab Montana State University - Bozeman Obj. 1: Investigate hydrologic connectivity over space and time Obj 2: Develop conceptual model of runoff generation–watershed structure  Topographically driven lateral redistribution of water drives transient upland-stream connectivity and runoff generation  Riparian buffering potential spatially variable Intermediate summary

Principles to apply to analysis of landuse change in the Big Sky watershed Hydrologic connectivity Riparian buffering potential  Suggests location of change in watershed could be significant

Montana State Watershed Lab Montana State University - Bozeman Does spatial location of change influence watershed response to perturbation? Big Sky, Montana - an outdoor laboratory Resort Residences (septic systems) increasing by 100s per year Sampling locations

Montana State Watershed Lab Montana State University - Bozeman Weekly nitrate time series from 4 of 9 watersheds Residences Area km 2 NF = 1 SF = 151 MF = 1690 WF = 1880 NF = 21 SF = 118 MF = 85 WF = 207

Montana State Watershed Lab Montana State University - Bozeman Winter nitrate Maximum Value 2.17 mg/l Yellowstone Club – no access - Runoff mm/hr Nitrate mg/l

Montana State Watershed Lab Montana State University - Bozeman Late summer nitrate Maximum Value 1.31 mg/l Runoff mm/hr Nitrate mg/l

Montana State Watershed Lab Montana State University - Bozeman Stream nitrogen sources  15 N of dissolved N Septic Atmospheric Deposition Geologic sources Natural range Septic impacted Human-derived nitrogen can be tracked with stable isotope analysis Stream samples across Big Sky watershed

Flow connected Not flow connected Spatial structure of stream N Synoptic sampling variograms October SeptemberJune March February August SUGGESTS N immobilization in the growing season, leads to complex spatial patterns and a lack of spatial correlation Distinct Seasonality in Spatial Dependence No spatial correlation

Montana State Watershed Lab Montana State University - Bozeman Spatial Linear Models Generalized Least Squares Estimation: Potential explanatory variables for stream nitrogen  # septics in subwatershed  # septics weighted by connectivity potential  geology (% shales)  stream order  % forest  riparian buffer potential (riparian area/hillslope area)  elevation  slope  roads  bare rock and talus  aspect  watershed area  and more… Methods: Cressie et al., 2006; Ver Hoef et al., 2006; Peterson et al., 2007.

Montana State Watershed Lab Montana State University - Bozeman Seasonal Influences on Streamwater Nitrate Dormant Season # Septics Geology Growing Season Septic connectivity Riparian buffer pot. Geology N loading N processing potential R 2 = 0.9R 2 =

Montana State Watershed Lab Montana State University - Bozeman Spatial Data Analysis Conclusions  Seasonality in variograms suggest N immobilization in uplands, riparian areas and stream network break down spatial patterns during growing season.  Spatial linear models indicate seasonality in the influences on streamwater NO 3 - N loading variables significant during dormant season Hydrologic connectivity and riparian buffer potential are significant during growing season Summer Winter

Montana State Watershed Lab Montana State University - Bozeman Take home message  Transient connectivity drives runoff generation (source areas change through time)  Watershed structure strong control on runoff generation and riparian buffering potential  Spatial location of change matters and intersection of change pattern and watershed hydrology influences response to perturbation *Gardner, K.K. and B.L. McGlynn. In revision. Spatio-Temporal Controls of Stream Water Nitrogen Export in a Rapidly Developing Watershed in the Northern Rockies. Water Resources Research. *Jencso, K. J., B. L. McGlynn, M. N. Gooseff, S. M. Wondzell, and K. E. Bencala. In revision. Hydrologic Connectivity Between Landscapes and Streams: Transferring Reach and Plot Scale Understanding to the Catchment Scale, Water Resources Research. EAR McGlynn EAR Gooseff R832449

Montana State Watershed Lab Montana State University - Bozeman Extra slides to follow in case there are specific questions

Montana State Watershed Lab Montana State University - Bozeman Spatial Linear Models 1) Flow Connected vs Flow Unconnected Site B and C are flow-connected Site A and C are flow-connected Site A and B are not flow connected 2) Downstream Flow Distance (DFD) BC = 20 AC = 18 AB = 19 A C B 10 9 [Cressie et al., 2006; Ver Hoef et al., 2006; Peterson et al., 2007.

Montana State Watershed Lab Montana State University - Bozeman Spatial Linear Models 3) Proportional Influence of upstream site on downstream site ABC A100 B010 C FROM SITE TO SITE A C B WatershedArea A10 B40 C50

Montana State Watershed Lab Montana State University - Bozeman Spatial Linear Models Covariance matrix (  ) is a function of downstream distance (DFD),flow connectedness, and proportional influence.  = parameter estimates X = known explanatory variables z = known dependant variable (NO 3 - ) Generalized Least Squares Estimation: