Department of Civil and Environmental Engineering
Gordon E. Clark and Dr. Richard N. Palmer
Hydrologic Modeling at Road-Stream Crossings:
Challenges and approaches in estimating flood flows at ungauged locations in the Northeast region
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Outline
§ Description of Problem
§ Challenges Explained
§ Our Approaches
§ Moving Forward
Does model choice make a difference in the:
(1) Culvert Sizing
(2) Ranking of Vulnerability
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Description of Problem
Why do we need runoff predictions ?
- sizing infrastructure
- estimating flood flows
- global change research
- adaptation strategies
- vulnerability analyses
- ecological flows
Sustainable Management of River Basins and Our Water Resources!
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Description of Problem
§ Why do we care about road-stream crossings? • Road Resiliency • Aquatic Organism Passage • Ubiquity: Culverts are pervasive!
Photo Credit: Gordon E Clark 2015.
https://www.youtube.com/watch?v=EUc5WXdnImA Figure Credit: Bryan Sojkowski USFWS
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Risk and Vulnerability
Description of Problem
Figure Credit: MassDOT Project
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Description of Problem The goal is:
To estimate flood flows for the 2-, 5-, 10-, 25-, 50-, and 100-year recurrence intervals using physical and statistical methods at ungauged locations and determine the effects of future climate change on these high flows.
Does the approach make a difference in decision making?
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Description of Problem
(1) Use the Deerfield River Basin as a pilot study to assess road-stream crossing hydrologic/ hydraulic vulnerability
(2) Apply methods to assess vulnerability at a broader regional scale
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Challenges Why are predictions difficult? § Lack of data
§ Inconsistently of solution approaches across processes, places and scales
§ Modeling approaches often over simplify complex systems.
à Simply put, there are so many places in which we simply have no data!
“Far better an approximate answer to the right question , which is often vague, than an exact answer to the wrong question, which can always be made precise.” - John W. Tukey, 1962
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Challenges Background on Predictions in Ungauged Basins (PUB) Methods: § Research active area regionally and
globally § No single paradigm exists for all
settings
§ Recent book published and organized to synthesize research from across the world
à We are working on a very important and topical problem.
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Challenges Background on PUB methods:
§ We define two broad categories: Statistical and Process-Based
§ Most common method is the drainage-area (DA) scaling ratio method *ASSUMPTION à runoff at donor and recipient ungauged catchment only differ because of size of drainage-areas!
Sta$s$calMethodsProcess-Based
Methods
IndexMethods Regression Geosta$s$cal
LumpedConceptual
HydrologicResponseUnits
DistributedGrid
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Approaches
§ Statistical Modeling Approaches § Physical Modeling Approaches
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Approaches – Statistical Modeling
§ We chose the following models: a. RPFE (USGS)
b. Jacob’s Equations
c. CRUISE
d. DA ratio method
e. CliFSS (Climate Forced
Statistical Streamflow)
Hydrologic models at crossing site.
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Approaches – Physical Modeling (scaling of flows)
Regional Analysis of DA ratio method
§ For physically based models, we need to have a way to scale the data to the ungauged locations.
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Approaches – Physical Modeling (scaling of flows)
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Approaches – Physical Modeling
§ Physical models can be grouped into 3 categories:
(a) Lumped conceptual (b) Hydrological response units (HRUs) (c) Distributed grid based
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Approaches – Physical Modeling
Determine appropriate stream
gages
Develop HRU’s and parameterize catchments
Assemble climate data for model
input
Calibration/Validation
Model daily streamflow output
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Approaches – Physical Modeling
For Deerfield River Basin § CRUISE (Archfield, 2013)
is used to estimate flows in the larger subbasins in the Deerfield River Basin that is used to calibrate physical models.
Goodness-of-Fit SizeandLoca$on
gages overlappingyears R2 NSE KGE NRMSE PBIAS DA
(SqKm) STATE
01139800 45 0.71 0.67 0.83 57.4 -6.5 22.80 VT
01141800 37 0.66 0.65 0.75 59.5 11.9 12.28 NH
01153500 25 0.92 0.92 0.93 28.6 4.7 263.33 VT
01154000 27 0.92 0.91 0.92 29.2 5.7 187.33 VT
01162500 45 0.94 0.93 0.89 25.6 1.1 49.71 MA
01165500 23 0.81 0.78 0.85 46.6 9.8 32.86 MA
01169000 45 0.83 0.83 0.87 41.5 -3.7 230.64 MA
01169900 39 0.87 0.86 0.82 37.8 -6 62.42 MA
01170100 38 0.8 0.78 0.88 47.2 -2.8 106.99 MA
01174000 23 0.88 0.77 0.72 48.4 5.5 9.15 MA
01174565 13 0.87 0.81 0.75 44.1 17.1 32.99 MA
01174600 34 0.74 0.74 0.82 51.1 -4.6 1.62 MA
01174900 37 0.75 0.74 0.8 50.7 -10.9 7.11 MA
01180000 15 0.9 0.86 0.78 38 16.9 3.61 MA
01181000 45 0.83 0.82 0.8 42.3 8 243.50 MA
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Approaches – Physical Modeling
From:Razavi,Coulibaly(2013)
Schematic of two main classes and subdivisions of continuous streamflow regionalization methods.
Regionalization --
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Moving Forward
To complete over next 3 months –
§ Finish applying methods to Deerfield and compare models
§ Take approaches that perform the best and apply to broader Northeast region
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Moving Forward
Does model choice make a difference in the:
(1) Culvert Sizing
(2) Ranking of Vulnerability
Comparing hydrological modeling approaches where we have data.
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GEC – Fix y-axis Normalize by DA size and compare to gaged models in same graph
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GEC – Fix y-axis Normalize by DA size and compare to gaged models in same graph
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Climate Driven Vulnerability Analysis
Qcritical
Culvert hydraulic modeling to establish
critical discharge value
Qcritical
Hydrologic vulnerability analysis (for every crossing location)
Annual exceedance probability curve
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Climate Driven Vulnerability Analysis
Future Climate
Design Overview:
Mid-Century (2040-2070)
Late-Century (2070-2100)
Goal - To explore the range of uncertainties with future climate associated with annual storm flows and road-stream crossing vulnerability.
Time frame -
Daily Precipitation and
Temperature Data
Physical hydrological
model suite (4 models)
Change in flood flow analysis
Vulnerability analysis
(xN)
(xN) N = number of crossings (subset)
Ensemble of climate data (either statistically or
dynamically downscaled)
We are interested in: i) Failure risk ii) Confidence
(GCM, emissions, model predictions)
iii) Model agreement
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Climate Driven Vulnerability Analysis
Example: HSPF
(1971-2000) Current climate data downscaled
from GCMs 2 emissions scenarios
HSPF model
(2040-2070) Future climate
data from GCMs 2? emissions scenarios
Determine Bias
HSPF model
Hydrologic vulnerability
analysis for all crossing locations
Based on ensemble mean for each emission scenario
Scenario A Scenario B
(examples)