69th SWCS International Annual Conference July 27-30, 2014 Lombard, IL

1. Assessing Critical Source Areas in
Landscapes Affecting Downstream Water
Quality in North Jersey
Zeyuan Qiu, New Jersey Institute ofTechnology
Todd Walter and Brian Buchanan, Cornell University
Jonathan Kennen, U. S. Geological Survey
Michael Dosskey, National Agroforestry Center
July 29, 2014

2. Backgrounds on Streams and
Landscapes
 Water quality and stream ecological
integrity are closely related to land use
and landscape characteristics (Gomi et al.,
2002; Kennen et al., 2010).
 The understanding of the
interrelationship between stream
conditions and landscape change has
profound impacts on management
options (Brown et al., 2009; Coles et al.,
2010)

3.  Non-spatial Landscape characterization
◦ Assume equal contribution of landscape
◦ Percentage of land uses (Comelo et al, 1996;
Johnson et al., 1997)
◦ Impervious rate (Alberti et al., 2007; Schueler et
al., 2009)
 Spatial Landscape characterization
◦ Distinguish the heterogeneous landscape and
their differentiated roles
◦ Riparian health metrics (NRC, 2002)
◦ Zones of Influence (Bucher, 2009)
 Streamflow Regime (Kennen et al., 2014)
Characterization of Landscape and
Streams

4.  Hydrological and ecological processes
coupled with human interactions through
active land use management form various
hydro-ecological hotspots or critical source
areas (CSAs) in landscapes.
 Our objective is to test the hypothesis that,
relative to the rest of the landscape, CSAs
contribute disproportionately to ecosystem
degradation such as poor water quality and
stream integrity using monitoring data in
North New Jersey
Objective

5. Land and Water Connection

6. Let’s look at some landscape

7. Let’s look at some landscape

8. Let’s look at some landscape

9. Hydrologically
Sensitive Areas
(HSAs)
(Hewlett, 1982)
Variable Source Area (VSA) Hydrology
and Hydrologically Sensitive Area (HAS)
Concept

10. Terrain Indices
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Index Label Formulation Notes
1 TWI
Steady‐state upslope contributing area
by D8, hydraulic gradient=local surface
slope
2 TWI‐d
Time‐variable upslope contributing area
by D8, hydraulic gradient=local surface
slope
3 TWI‐DI
Time‐variable upslope contributing area
by D8, hydraulic gradient=downslope
index (DWI)
4 TWI‐d* As Index 2 with 3 x 3 low‐pass filter
5 TWI* As Index 1 with 3 x 3 low‐pass filter
6 TWIinf
Time‐variable upslope contributing area
by D‐infinity, hydraulic gradient=local
surface slope
7 STWI
Steady‐state upslope contributing area
by D8, hydraulic gradient=local surface
slope, soil tranmissivity (T)

11. Hydrologically Sensitive Areas
(HSAs)
Topographic Index
(Walter et al., 2002)

13. Managing Landscape Change
 Landscape management
◦ Mitigate the negative impacts of intensive
landscape disturbance such as
agriculture and built environment.
 Landscape planning
◦ Prevent the negative impacts of built
environment.
 Landscape design
◦ Minimize the negative impacts of built
environment.

14. HSAs and Locations for
Conservation Buffer Placement

15. Regional Scale Index Thresholds
Need: Identification of HSAs at the landscape level use terrain
thresholds.
Hypothesis: Regional soil moisture-index thresholds can be used to
define HSAs in the landscape.
Methods:
• Equipment: Manual and data-logging soil moisture probes
• Locations: Install across gradients of TIs, landuse, soil types.
• Statistical Analyses:
- Examine strengths of correlations (Spearman Rank
Coefficient)
- Evaluate spatial autocorrelation
• Compare to other indexing methods:
- Smoothed Dynamic Index (Lanni et al., 2011)
- TI (Beven and Kirby, 1977),
- ET-adjusted STI
- etc..

16. Overview of Study Sites

17. Equipment for Soil Moisture
Monitoring

18. Preliminary Results
TI
GameFarm,
NY
Coldbrook,
NJ
Christy Hoffman Farm, NJ
Bin
Orchards,
NY
TI Bin
Harford, NY

19. TIs vs Soil Moisture using DEMs
with Different Resolution

20. TIs at Different DEM Resolution
Aerial 10m 3m
There are strong correlation between the measured soil moisture in
various landscapes in both New York and New Jersey and the soil-based
topographic index derived from 3-meter LiDAR data (Buchanan et al.,
2014)

21. Qiu et al. (2014)

22. Qiu et al. (2014)

23. Further Work
 Identify the best index
and its threshold that
characterize the
hydrological process in
landscape
 Identify the threshold
metrics that represents
the land use changes for
water resource
management
 Provide stronger
scientific support to
……

24. Manage Landscape Changes
 Landscape management
◦ Mitigate the negative impacts of intensive
landscape disturbance such as
agriculture and built environment.
 Landscape planning
◦ Prevent the negative impacts of built
environment.
 Landscape design
◦ Minimize the negative impacts of built
environment.

25. Acknowledgements
 EPA National Center for Environmental
Research STAR (Science To Achieve
Results) program
 USDA National Institute of Food and
Agriculture AFRI (Agriculture and Food
Research Initiative ) Competitive Grants
Program