the good, the bad, and the dirty · 2016-10-26 · the good, the bad, and the dirty . models for...
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The Good, The Bad, and the Dirty Models for Assessing Hydrologic Change in the Nation’s River
Ted Grantham
SCCWRP_v3
Assessing hydrologic change
in the nation’s rivers
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Collaborators
Daren Carlisle (USGS)
Ken Eng (USGS)
Dave Wolock (USGS)
Jeanette Howard (TNC)
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Environmental flows
the quantity, timing, and quality of water required to sustain freshwater and estuarine ecosystems and the human livelihoods and well being that depend on these ecosystems
∆flow ~ ecological indicator
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Limited flow data
Sparse gauging network, getting sparser
Flows altered by dams and land use
Limited pre-impact records
USGS Flow Gage, somewhere in Colorado
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Limited flow data
Sparse gauging network, getting sparser
Flows altered by dams and land use
Limited pre-impact records
USGS Flow Gage, somewhere in Colorado
models!
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Hydrologic models used for…
(1) Classifying stream types
(2) Predicting natural flows at ungauged sites
(3) Assessing flow alteration
(4) Establishing environmental flow standards
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Statistical modeling of flows
black box
precipitation
temperature
topography
streamflow
Methods: linear regression, principal components analysis, neural networks, support vector machine regression, regression trees (CART), random forest, etc.
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Assessing flow alteration
Step 1. Develop models to predict ”natural”, unimpaired flow metrics from basin features
Step 2. Apply models to predict unimpaired flows at gauged basins and compare with observed values
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Step 1
Develop models to predict natural, or unimpaired, flow metrics
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Methods
Reference-quality gauge stations with >20-year record
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Methods
1200 reference quality sites Compute 612 hydrologic metrics
98 “natural” geospatial predictors
• climate, soils, topography Random forest models
• 80% training / 20% validation Assess model performance: bias, accuracy, etc.
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climate topography soils geology
flowobs
reference climate topography soils geology
100s reference sites
model
model climate topography soils geology
flowobs
flowpred
flowobs
PERFORMANCE EVALUATION
Methods
Observed/Predicted Nash-Sutcliffe
Percent Bias
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O/E
MeanO/E =
1 1
good poor
Site
s Metric 1 Metric 2
STDO/E =
O/E
1.0 1.2 0.1 0.6
Predictive performance
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Eng et al. IN REVIEW Freshwater Science
Legend “popular” metrics
new metrics
n=153 n=531
n=398 n=173
Num
ber o
f flo
w m
etric
s
Nash-Sutcliffe Efficiency Percent Bias
STD(Observed/Expected) Mean Observed/Expected
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“Step 1” Findings
Many popular flow metrics cannot be reliably predicted, particular those representing low-flows
Most dimensions of the flow regime represented by at least one “predictable” metric
“Predictability” can be useful for guiding selection of metrics in river studies
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Step 2
Quantify dimensions and magnitude of flow alteration
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Methods
1,200 reference-quality gauge stations
3,800 altered gauge stations
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climate topography soils geology
flowobs
reference climate topography soils geology
100s reference sites
model
model
climate topography soils geology
flowobs
flowpred
flowobs
Methods
O/E
altered sites
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Change Detection
O/E 1 0 2
90th 10th
90th 10th
Site
s
inflated (+)
depleted (-)
depleted (-)
O/E 1 0 2
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Representative flow dimensions
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Alteration of high flows
national (n = 3,842)
median annual max flow
specific mean annual max flow
western mountains (n = 881)
median annual max flow
specific mean annual max flow
depleted
inflated
indeterminate
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High Flow Depletion in Western Mountain Region
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Flow Inflation in Sierra Nevada?
Spaulding Dam
Bowman Dam
Jackson Meadows Dam
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“Step 2” Preliminary Findings
Evidence of regional flow alteration “signatures”
Most “altered” gauges are impaired for multiple metrics (>90% of sites have more than 50 flow metrics that are depleted or inflated beyond natural range)
High flows and flow variability tend to be depleted and measures of low-flows inflated
BUT, there are often exceptions
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There is evidence of widespread alteration to multiple aspects of river flow regimes Modeling and impact assessment can inform targeted management of ecologically relevant metrics
predictable eco-
relevant
management- relevant