`

Printing:ArcGIS Python-based Hybrid Hydrologic Model (Distributed-Clark)

for Spatially Distributed Rainfall-Runoff Generation and Routing

Younghyun Cho1, 2 and Bernard A. Engel2 | Email: yhcho@kwater.or.kr1Hydrometeorological Cooperation Center, K-water (Korea Water Resources Corporation), Gwacheon, Korea, The Republic of2Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907

Introduction

• A GIS-based hybrid hydrologic model (conceptually lumped and

distributed feature model), Distributed-Clark, was developed for spatially

distributed rainfall-runoff flow prediction (Cho, 2016). In this model, the

SCS curve number estimated spatially distributed excess rainfall and

GIS-derived time-area diagram (isochrones) based on a set of separated

unit hydrographs are utilized to calculate a direct runoff hydrograph.

• Development of the Distributed-Clark model includes four main steps:

watershed pre-processing, spatially distributed excess rainfall

estimation, which includes NEXRAD precipitation data processing (Cho

and Engel, 2017), spatially distributed unit hydrograph derivation,

and direct runoff hydrograph convolution.

• For this implementation, ArcGIS 10.1 was used as the GIS platform to

build and execute the Python script tools (DistributedClark_10.1;

Python Toolbox, storm event ver.) for each step.

Model Description

• Procedures: Distributed-Clark development & NEXRAD data processing

Implementation Application

Results

Seventh Symposium on Advances in Modeling and Analysis Using Python | Seattle, WA

Advances in Modeling and Analysis Using Python, Poster 316 | January 23, 2017

References

• Cho, Y., 2016: Development and evaluation of a watershed-scale hybrid

hydrologic model. Ph.D. dissertation, Dept. of Agricultural and Biological

Eng., Purdue University, 211 pp.

• Cho, Y. and B. A. Engel, 2017: NEXRAD Quantitative Precipitation

Estimations for Hydrologic Simulation Using a Hybrid Hydrologic Model.

J. Hydrometeor., 18, 25-47.

NEXRAD data

Runoff generation

: Spatially distributed excess rainfall

• DistributedClark_10.1 (storm event version)

• A model case study of single storm event application for a river basin was

conducted; the Mucatatuck River near Deputy, IN using NEXRAD

precipitation product demonstrated relatively good fit (direct runoff ENS

0.94, R2 0.96, and PBIAS -0.60%) against observed streamflow as well as

a slightly better fit (direct runoff; ENS of 0.2% and R2 1.0%) in comparison

with the outputs of spatially averaged rainfall data simulations.

Runoff routing

: Separated unit hydrograph

Time-Area Histogram Map(Isochrones)

• Deriving time-area histogram and unit hydrograph using Watershed Pre-

processing and Unit Hydrograph Toolbox (Python Script Tools)

- input data: DEM, outlet point, NLCD land use, and NEXRAD grid

Spatially distributed unit hydrograph

DRH: ENS 0.97, R2 0.97, PBIAS - %(NEXRAD: ENS 0.01↓, R2 0.01↓)

DRH: ENS 0.99, R2 0.99, PBIAS -0.38%(NEXRAD: ENS 0.02↑, R2 0.02↑)

DRH: ENS 0.98, R2 0.99, PBIAS -0.15%(NEXRAD: ENS 0.09↑, R2 0.06↑)

DRH: ENS 0.82, R2 0.87, PBIAS -1.87%(NEXRAD: ENS 0.04↓, R2 0.03↓)

Observed Simulated (distributed; NEXRAD data) Simulated (averaged; gauged data)

0

30

60

90

120

150

23 47 71 95 119 143

MUSCATATUCK (event #2: Nov. 09-14, 2002)

0

40

80

120

160

200

8 32 56 80

MUSCATATUCK (event #4: Oct. 18-21, 2004)

0

70

140

210

280

350

12 36 60 84 108

MUSCATATUCK (event #5: Mar. 27-31, 2005)

0

40

80

120

160

200

3 27 51 75

MUSCATATUCK (event #6: Oct. 27-30, 2006)• NEXRAD precipitation data processing for spatiotemporally varied rainfall

inputs; Runoff can be obtained using Excess Rainfall Toolbox

- input data: NEXRAD NCEP Stage IV product and soil map for CN

Cumulative precipitation map for 27-30 Oct 2006

• Watershed, Gauge, Radar Locations, Land use, Soil, and CN map

Convolution tool for Direct Runoff Hydrograph

• Storm event (4 cases, 2002 to 2006) simulation results

[ Muscatatuck River near Deputy, IN ]

#1 #2 #3 #4

< Parameters: i 2.25 mm/hour, R 7.5 hour >

λ: 0.112 (II) λ: 0.213 (I) λ: 0.068 (II) λ: 0.261 (III)