Hydrologic Modeling at Road-Stream Crossings · PDF fileHydrologic Modeling at Road-Stream Crossings: ... Department of Civil and Environmental Engineering 10 ... to gaged models

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

2 Department of Civil and Environmental Engineering

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


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!



§  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


Risk and Vulnerability


Figure Credit: MassDOT Project


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?



(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


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


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.


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


Approaches

§  Statistical Modeling Approaches §  Physical Modeling Approaches


Physically-Based Models Statistical Models


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.


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.


Approaches – Physical Modeling (scaling of flows)


Approaches – Physical Modeling

§  Physical models can be grouped into 3 categories:

(a)  Lumped conceptual (b) Hydrological response units (HRUs) (c) Distributed grid based



Determine appropriate stream

gages

Develop HRU’s and parameterize catchments

Assemble climate data for model

input

Calibration/Validation

Model daily streamflow output



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



From:Razavi,Coulibaly(2013)

Schematic of two main classes and subdivisions of continuous streamflow regionalization methods.

Regionalization --




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


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.


Thank you.





GEC – Fix y-axis Normalize by DA size and compare to gaged models in same graph


GEC – Fix y-axis Normalize by DA size and compare to gaged models in same graph


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



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



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)



Climate data: Current – Historical Observations

Future – Multimodel ensemble

Hydrology model suite: PRMS, SWAT, HSPF, HBV

Hydrological vulnerability: AEP analysis with Qcritical

Recap:

Documents

Hydrologic Modeling at Road-Stream Crossings · PDF fileHydrologic Modeling at Road-Stream Crossings: ... Department of Civil and Environmental Engineering 10 ... to gaged models