Structures and Implications for Flood Response in the Kromma Kill - - PowerPoint PPT Presentation

Geospatial Analyses of Urban Drainage Network Structures and Implications for Flood Response in the Kromma Kill Watershed

Katherine Meierdiercks & Michele Golden Department of Environmental Studies, Siena College

Gupta, 2010

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LINEAR V. NON-LINEAR RESPONSE ALONG A DRAINAGE NETWORK Can flood response be predicted from storm event magnitude and drainage area alone?

Heterogeneity of Hydrologic Response in Urban Watersheds

Meierdiercks et al., 2010

Runoff Coefficient = fraction of rainfall that becomes runoff

From Stream Corridor Restoration: Principles, Processes, and Practices (10/98). By the Federal Interagency Stream Restoration Working Group

Kromma Kill Watershed

QUESTIONS: (1) Can percent imperviousness explain heterogeneous flood response in the Kromma Kill and its subwatersheds? (2) Are there other geospatial characteristics that can be used to better predict flood response in the Kromma Kill and its subwatersheds?

Kromma Kill Watershed: 20 km2 Town of Colonie, Village of Menands Tributary to the Hudson River

Quantifying Flood Response

y = 5.0719x1.8075 R² = 0.99 2 4 6 8 10 0.5 1 1.5 Q (cfs) Stage (feet)

New Hall Rating Curve

y = 0.4695x R² = 0.7122 0.5 1 1.5 0.5 1 1.5 2 Runoff (in) Rainfall (in)

East Hills “Average” Runoff Ratio

Hydrologic Data

~35 rain events since 6/1/13 ~7.5 inches in June ~4.6 inches in July

Percent imperviousness as a predictor

• f flood response

Correlation Coefficient = 0.67

R² = 0.45296 0.1 0.2 0.3 0.4 0.5 0.6 10 20 30 "Average" Runoff Ratio Percent Impervious New Hall ARC North ARC South Lincoln Ave East Hills

Imperviousness is no better than slope at predicting flood response

R² = 0.4198 0.1 0.2 0.3 0.4 0.5 0.6 2 2.5 3 3.5 "Average" Runoff Ratio Slope (%) New Hall ARC North ARC South Lincoln Ave East Hills

Correlation Coefficient = 0.67

R² = 0.45296 0.1 0.2 0.3 0.4 0.5 0.6 10 20 30 "Average" Runoff Ratio Percent Impervious New Hall ARC North ARC South Lincoln Ave East Hills

Correlation Coefficient = 0.65

Are “pervious surfaces” really pervious?

Compacted urban soils

“Disconnected” Impervious Surfaces

(Roy and Shuster, 2009)

Distribution of impervious surfaces

(Mejía and Moglen, 2010)

Conclusions: The spatial distribution of impervious surfaces can impact peak magnitude and timing –

• All scenarios led to decreased peak
• Source clustering and uniform scenarios led

to delayed peak

Determine the probability that a rain drop with fall in an area draining into a stream of order ω and follow a path

• f a certain length

Geomorphic Instantaneous Unit Hydrograph (GIUH)

Hypothesizes flood response can be predicted from the geomorphic properties of the drainage basin 7.4 and 7.5 Q (t) = i(τ) f(t-τ)dτ Q determined by multiplying the rainfall by the GIUH

RA= Ai+1/Ai Drainage-area ratio (the larger RA, the larger the drainage area

• f higher order streams)

RL = Li+1/Li Length ratio (the larger RL, the longer higher order streams) Rb = Ni/Ni+1 Bifurcation ratio (the larger Rb, the “branchier” the watershed) L1 Average length of 1st order streams U Channel velocity

Rodriguez-Iturbe & Valdes, 1979

GEOMORPHIC PROPERTIES OF THE DRAINAGE BASIN

Rinaldo et al., 1995

R² = 0.9265 0.1 0.2 0.3 0.4 0.5 0.6 2 4 6 "Average" Runoff Ratio Area Ratio New Hall ARC North ARC South Lincoln Ave East Hills

Correlation Coefficient = 0.96 RA= Ai+1/Ai Drainage-area ratio (the larger RA, the larger the drainage area of higher order streams)

Implications for Stormwater Management??

SIENA RAIN GARDEN PROJECT

Future Work

• Include stormwater

pipes and roads as extensions of the urban drainage network

• Complete additional

GIS analyses

• Two additional

subwatersheds

• Integrate water

quality data

Concluding Remarks

• The processes that control flooding in small

urban watersheds are complex and not well understood.

• Percent impervious surface coverage has

traditionally been used as a predictor of flood

• response. It’s good, but not great.
• Geomorphic properties have been used to predict

flood response in natural watersheds.

• The geomorphic properties of urban watersheds

(as determined using GIS) can help us to better predict flood response and develop more effective watershed management plans.

Thank you

Michele Golden

This project is provided by the Principal Investigator (PI). Any opinions, findings, and conclusions or recommendations expressed in this presentation are those

• f the PI and do not necessarily reflect the views of Siena College; Siena College has not approved or endorsed its content.