Flooding in Flathead County and Kalispell, MT

Initiation to Geo-HMS and HEC-HMS

Remi CandaeleHydrology - CE394K

Outline

1. Context & Flood Events Characteristics

2. ArcGIS Process

3. Geo-HMS Process

4. HMS Model

5. Future WorkEvergreen – Kalispell Suburb - 1995

Context

• Flathead county subject to flooding

• Located in northwest corner of Montana State.

• Total area of 13,614 km² (5,256 mi²),

• 410 km² (158 mi²) of it (3.01%) is water.

• As of 2000, the population was 74,471.

Motivations

• Being familiar with a Hydraulic Model !!!

Flood Events CharacteristicsWhitefish river nr Kalispell

0

5

10

15

20

25

30

35

40

Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06

Flo

w (

m3/

s)

Stillwater Rv nr Whitefish

0

20

40

60

80

100

120

140

Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06

Flo

w (

m3

/s)

Peak Flow : 2 events at 35m3/s

Peak Flow : 2 events > 100m3/s

Flood Events Characteristics

• Occurring mostly during spring time,

• Focus on 2 major events :– 04/13/1996– 04/20/1997

• Low Rainfall intensity : computation over a long period,

• Difficulty : 1 meteorological station and 2 gauge stations.

Date Time Location Precipitation (inc)4/13/1996 3:00 0.14/13/1996 4:00 04/13/1996 5:00 0.14/13/1996 6:00 0.24/13/1996 7:00 0.14/13/1996 8:00 0.14/13/1996 9:00 0.14/13/1996 10:00 0.24/13/1996 11:00 0.14/13/1996 12:00 0.14/13/1996 13:00 0.2

Hungry Horse Dam

Time Series

Flathead Valley

Catchments & Streams Delineation

• Projected Coordinate System : NAD1983_StatePlane_Montana• 7 Subwatersheds selected

OBJECTID NAMEBasinSlope

(%)BasinCN

BasinLag (hr)

Area_HMS (km2)

1 W90 24.89 67.02 5.78 476.79

2 W100 20.58 70.21 8.28 498.30

3 W110 22.92 60.90 5.46 211.40

4 W120 6.39 66.50 1.68 3.83

5 W130 22.06 63.12 6.71 557.40

6 W140 10.09 67.78 8.46 282.61

7 W150 13.08 67.39 5.84 115.68

SubWatershed Characteristics

Reclassification of Land Cover

• Data downloaded from NLCD

• Reclassification in 4 main categories thanks to Dr Venkatesh Merwade

Original NLCD classification Revised classification (re-classification)

Number Description Number Description

11 Open water

90 Woody wetlands

95 Emergent herbaceous wetlands

1 Water

21 Developed, open space

22 Developed, low intensity

23 Developed, medium intensity

24 Developed, high intensity

2 Medium Residential

41 Deciduous forest 42 Evergreen forest

43 Mixed forest

3 Forest

31 Barren land

52 Shrub/scub

71 Grassland/herbaceous

81 Pasture/hay

82 Cultivated crops

4

Agricultural

Land Use Grid Obtained

Soil Data – Characterization of the Soil Type

• SSURGO (scale 1:24,000) dataset doesn’t cover the entire watershed: use of the Statsgo dataset (1:250000).

• 4 types of soil: A/B/C/D. Soil B assigned to the unknown soils

Classification Type of soil

A (low runoff potential)

Soils having a high infiltration rate (low runoff potential) when thoroughly wet. These soils have a high rate of water transmission.

B Soils having a moderate infiltration rate when thoroughly wet. These soils have a moderate rate of water transmission.

C Soils having a slow infiltration rate when thoroughly wet. These soils have a slow rate of water transmission.

D (high runoff potential)

Soils having a very slow infiltration rate (high runoff potential) when thoroughly wet. These soils have a very slow rate of water transmission.

Curve Number & Grid

• CN obtained from SCS TR55 (1986)

• Generation of a CN Grid

• Statistics– Mean : 66.26– Std Deviation : 11.06

Geo-HMS ArcGIS Interface to create input files to HMS

• Processes– River Length– River Slope– Basin Slope– FlowPaths,– Basin Centroids

• HMS Inputs/Parameters– Loss, Transform methods by

SCS,– Route method by Muskingum– Time Lag– Basin Curve Numbers

Selection of the contributing catchments

-GeoHMS Interface

Geo-HMS ArcGIS Interface to create input files to HMS

Basin Curve Numbers

Basin Lag in Hours

Geo-HMS ArcGIS Interface to create input files to HMS

Schematic

Export to HMS

Running the model under HMS

• Meteorologic model : – Weight gages,– Muskingum routing,– Time Lag,– Time Series.

Results – Calibrating the model

• Computations for exercising.

• Next steps :– Find accurate numbers for Muskingum

routing,

– Calibration factor : Streamflow = Model

Acknowledgments

• Venkatesh Merwade – Purdue U.

• Christine Dartiguenave – ESRI

• Ron Schlagenhaufer – Flathead County

• Folks : Eusebio, Tyler, Brian & others

Questions ????