Texas A&M University, Zachry Department of Civil Engineering

Instructor: Dr.Francisco Olivera, CVEN658 Civil Engineering Applications of GIS

Streamflow Analysis Using ArcGIS and HEC-GeoHMS

Jeongwoo Han

Dec.06.2010

Abstract

Streamflow is the one of the components consists of water cycle and useful water resources to sustain

human life. There have been efforts to estimate, analysis and predict the streamflow to make stable water

use and flood control. This study focuses on the streamflow estimation and comparison of peak discharge

and discharge volume as results of different transform methods in HMS. To proceed the study through the

ArcGIS and HEC-GeoHMS selecting study area and collecting data, basically, are needed. This study

chose San Antonio basin as study area and gathered the various raster and feature class data. These

collected data were used in preprocessing in the ArcGIS 9.3 and ArcHydro 9 for computing Hydrologic

parameters through HEC-GeoHMS will be used in estimating streamflow runoff in HEC-HMS. SCS

transform and Clark transform method were adopted for calculating runoff in HMS. SCS loss and

Muskingum method were chosen for loss method and routing method, respectively, in HMS. As input

data for the rainfall-runoff model, this study selected the total 18 number of gage station. To reflect

spatial rainfall characteristics of precipitation data of specific hydrologic events periods Thiessen weight

method was used.Finally, results of runoff from different transform methods were estimated and

compared.

Introduction

The uses of geographic information systems (GISs) to facilitate the estimation of runoff from watershed

have gained increasing attention in recent years. This is mainly due to the fact that rainfall–runoff models

include both spatial and geomorphologic variation (Melesse and Shih, 2002). To reflect the spatial

information, effectively, to the streamflow estimation this study was conducted using ArcGIS 9.3,

ArcHydro 9 and HEC-GeoHMS 4.3. Streamflow estimation requires Rainfall-Runoff modeling which

need the precipitation data, land use data, soil data and topologic data. These required data are basis of

computing hydrologic parameters of Rainfall-Runoff model. Land use data from Natural Resources

Conservation Service (NRCS) of US Department of Agriculture (USDA), DEM raster data from US

Geological Survey (USGS), Soil data from SSURGO of USDA and Hydrologic Unit Code (HUC) from

Texas Water Development Board (TWBD) comprise the basic spatial geomorphologic data set. Using

collected geospatial data this study conducted the computing Hydrologic parameters. Basin area, River

length, Longest flow path, centroid of subbasin, CN lag time, Time of concentration, Curve Number(CN)

and Impervious area are resulted from HEC-GeoHMS of US Army Corps Engineering (USACE)

Computed Hydrological data will be the input parameters of Rainfall-Runoff model in HEC-HMS. SCS

Transform, Clark Transform, SCS loss, Muskingum methods were adopted for the transform method, loss

method and routing method, respectively in HEC-HMS. In addition, to reflect the spatial characteristics of

precipitation this study employed the Thiessen weighted precipitation method under specific hydrological

event periods. Rainfall stations which were chosen are located within 100mi radius from San Antonio city.

Rainfall gage station data were gathered from National Oceanic and Atmospheric Association

(NOAA).HEC-HMS results the discharge of streamflow based on the computed hydrologic parameters

and precipitation data. Further this study compared the results of streamflow from different transform

methods for analyzing the variation of amount which are resulted from SCS and Clark hydro graph

method. The ArcGIS and HEC-GeoHMS supply efficient and reducing time consuming tasks for

reflecting the spatial characteristic information to computing hydrologic parameters and streamflow

processes. Also, Hec-GeoHMS relates the hydrologic data and parameters from GIS with HMS interface.

So, User can execute the runoff calculation without extra process for setup the HMS schema.

2. Literature review

Jain et al (2000) studied on the design flood estimation for ungagged basin using GIS. Study of Jain et al

is focused on applying Geographical Information System (GIS) supported Geomorphological

Instantaneous Unit Hydrograph (GIUH) approach for the estimation of design flood. The National

Institute of Hydrology has developed a mathematical model, which enables the evaluation of the Clark

Model parameters using geomorphological characteristics of the basin (Jain et al, 2000). Study of Melesse

and Shih (2002) considered Land use from Landsat images for 1980, 1990 and 2000.Using GIS and

image processing software the process of determining spatially distributed runoff curve numbers from

Landsat images is displayed. Spatially distributed runoff curve numbers and runoff depth were

determined for the watershed for different land use classes (Melesse and Shih,2002). Baed on the

literature review this study also estimated the parameters of hydrograph and subbasin using GIS.

Furthermore, using ArcHydro and HEC-GeoHMS parameters and schema of river reach and subbasin

were directly related with HEC-HMS. This straight forward method enhances the efficiency of estimating

streamflow. Comparing the SCS hydrograph and Clark hydrograph this study could figure out the

variation between two methods.

3. Methodology

Computing loss, hydrograph, routing methods are included to estimate streamflow. Among several

methods to proceed the rainfall-runoff calculation SCS loss method, SCS hydrograph, Clark hydrograph

and Muskingum routing methods were adopted. These above methods essentially need parameters which

can reflect the spatial and geomorphologic information to execute the streamflow estimation. Hydrologic

parameters were acquired by computing through ArcGIS, ArcHydro and HEC-GeoHMS. Based on the

preprocessed data and computed parameters HEC-HMS was operated and resulted in the runoff.

3.1 NRCS Curve Number Method

“The NRCS CN method is described by the NRCS (1985, 1986). Basin can be characterized by a single

parameter called Curve Number (CN)” (Wurbs and James, 2002). The SCS runoff equation is

(1)

Where is Runoff (in), is rainfall (in), is Potential maximum retention after runoff begins (in) and

is Initial abstraction (in) (Maidment, 1993).

“Initial abstraction is all losses before runoff begins. is highly variable but from data from many

small agricultural watershed, was approximated by the following empirical equation”

(Maidmement,1993).

(2)

“By eliminating as an independent parameter, this approximation allows use of a combination of

and to produce an amount of runoff. Substituting Equation 3.2 into Equation 3.1 gives” (Maidment,

1993).

(3)

“Where the parameters is related to soil and land use condition of subbasin through the Curve Number

which has range of 30 to 100. is related to CN by Equation 3.4” (Maidment, 1993).

(4)

Equation 3 is the rainfall-runoff equation used by NRCS for estimating depth of direct runoff storm

rainfall (Melesse and Shih, 2002). Through Equation 3 and Equation 4 we can verify that CN is directly

related with rainfall-runoff model. The major factors determine the CN are hydrologic soil group, Land

use and antecedent runoff condition (Maidment, 1993). CN indicates that higher CN value represent

higher runoff. Soil has been classified in to four hydrologic group (A, B, C and D) according to their

infiltration rate (Maidment, 1993). Group A represents highest infiltration rate. In other word, group A

represents lowest runoff rate. Reversely Group D represents highest runoff rate. CNs are also affected by

antecedent moisture condition (AMC). According to AMC CN can be converted value under AMC I or

AMC III. Basically CNs were computed under AMC II. Lower AMC represent drier condition and

Higher AMC represent wet condition (Melesse and Shih, 2002, Maidment, 1993).

3.1.1 Computing SCS Curve Number in GIS

Based on the theory of 3.1 CN can be estimated. Land use raster data, Soil data and Basin boundary

polygon are needed.

(1) Select the study area by attribute

Hydrologic Unit Code (HUC) has origin code number of specific catchment. Using select by attribute

study area is extracted from HUC. To save permanently selected features exported to shape file.

(2) Land use Reclassification and delineation

Land use raster data has attribute categorized in accordance with land cover. Origin land use data has

many land cover category to facilitate the process it is needed to simplify the category. To reduce the

category raster reclassify is used. To extract the land use data which fit in the extent of study area raster

calculator or extract by mask can be used. Extracted land use raster is converted to polygon.

(3) Soil data modification

Downloaded soil data are merged to cover the study area. After merging soil data will be clipped to fit in

the extent of study area. To compute CN soil data has to include information of hydrologic soil group.

However, origin soil geodatabase files don’t have hydrologic soil group. Downloaded soil data have table

data which have hydrologic soil data separately. So, through joining process attribute of hydrologic soil

group can be added to the soil geodatabase file.

(4) Union soil and land use data

Treated soil and land use data have union processing. Through union processing attributes of soil and

land use data combined to one shapefile. This union file and CNLookup table are used computing CN

grid process in HEC-GeoHMS. CNLookup table is like index can related the land use and soil group

attribute with CN.

3.2 Unit Hydrograph Method

The Purpose of unit hydrograph is to generate the hydrographs for the storm in the hydrologic events

periods. SCS unit hydrograph, Snyder synthetic unit hydrograph and Clark Synthetic hydrograph methods

are commonly used (Wurbs and James, 2002). This study adopted the SCS unit hydrograph method and

Clark unit Hydrograph method.

3.2.1 SCS Unit Hydrograph

SCS Unit Hydrograph was developed by NRCS in the 1950’s based on analyses of many unit

hydrographs for gaged watersheds in various conditions. Due to simplicity and easy to use SCS

hydrograph have been used and applied throughout the United States and the world. The SCS Unit

hydrograph only has two parameters, which are watershed area and lag time . The time to peak is

estimated as a function of rainfall duration and the peak of the unit hydrograph is estimated as

follow (Wurbs and James, 2002).

(5)

(6)

Where, Equation 5 has unit in hours and Equation 6 has English units.

3.2.2 Clark Synthetic Unit Hydrograph

The Clark method was developed based on the concept of routing a time-area relationship through a

linear reservoir. A Depth of 1 unit of water which is covering the watershed is allowed to runoff.

Watershed characteristic, such as size, shape and surface roughness are effect on time-area relationship

which represents the transition hydrograph of runoff. To estimate the Clark Hydrograph time of

concentration is needed. However, estimating is difficult enough and developing an isochrone map

and fulfilling time-area relationship is much more difficult. To simplify and make easy to apply

Hydrologic Engineering Center has developed the following time-area relationship.

(7)

(8)

Where,

is the contributing area at time as a function of the total watershed area . is a fraction

of the time of concentration .

Based on the time-area relationship a translation hydrograph is routed through linear reservoir (Wurbs and

James, 2002).

3.2.3 Computing Parameters for Unit Hydrograph in GIS

Basin area, channel length, centroid of basin, longest flow path, CN basin lag time, Time of

concentration can be computed for the unit hydrograph. These parameters are computed through the

HEC-GeoHMS. Before operating HEC-HMS, DEM preprocessing is needed through ArcHydro.

1. ArcHydro process

(1) Fill Sink

Fill sink process fills the sink in the grid. Cell is in the lower than elevation of neighbor cell trap the

water. So, through the fill sink this study fills the sink (Merwade, Maidment and Robayo, 2004).

(2) Flow Direction

This process results in direction of flow (Merwade, Maidment and Robayo, 2004).

(3) Flow Accumulation

This process results in the accumulated number of cells of upstream of cell (Merwade, Maidment and

Robayo, 2004).

(4) Stream Definition

This process results in the stream line. Stream definition computes the grid which has a value of 1 of

flow accumulation (Merwade, Maidment and Robayo, 2004).

(5) Stream Segmentation

This process results in grid of segment of stream. The computed segments have unique identification,

that is all the cells in the segment have same grid code (Merwade, Maidment and Robayo, 2004).

(6) Catchment Grid Delineation

This process results in the catchment grid carries the grid code. The grid code corresponds to the value

of cells carried by stream segments polygon (Merwade, Maidment and Robayo, 2004).

(7) Catchment Polygon Processing

This Process converts the catchment grid to polygon polygon (Merwade, Maidment and Robayo, 2004).

(8) Drainage line processing

This process converts the stream link grid to polyline polygon (Merwade, Maidment and Robayo, 2004).

(9) Adjoint Catchment processing

This process results in the aggregated catchment from the catchment polygon (Merwade, Maidment and

Robayo, 2004).

Above processing generate the input data will be used in HEC-GeoHMS to compute the Hydrologic

parameters.

2. HEC-GeoHMS Process

(1) Subbasin Merge and Split

Basin is divided on the basis of stramflow gage station. To make single subbasin the catchments in the

same subbasin is merged.

(2) River Merge

After merge and split of basin river reach can be divided. Trough merge we can avoid the multi routing.

(3) River Length

River length is calculate to be used in HMS

(4) Basin Area

Subbasin area is calculate to be used in HMS

(5) Longest Flow Length

Longest Flow Length of each subbasin is calculate to be used in HMS

(6) Centroid of Basin

Centroids of each subbasin are computed

(7) CN Lag time

CN Lag time is calculate to be used in SCS transform method in HMS. Using CN Lag time time of

concentration is computed.

After computing CN grid and other hydrologic parameters HMS setting is executed. HMS setting assign

the loss method, transform method and routing method will be used in HMS and generate the HMS

schematic, such as river reach, junction and subbasin.

3.3 Routing method

Routing is procedure to predict the changing magnitude, speed and shape of flood wave as s function of

time at the points along the watercourse. Routing is classified into lumped and distributed. Hydrologic

routing belongs to the lumped routing and Hydraulics routing belongs to distributed routing (Maidment,

1993). This study adopted the Muskingum routing method of hydrologic routing method.

3.3.1 Muskingum River Routing

Muskingum routing is based on the storage-outflow relationship and relates the storage to both inflow

and outflow. Muskingum routing method is represented as follow

(9)

(10)

Where, is storage, is inflow and is outflow

(11)

(12)

(13)

3.3.2 Routing in GIS

Muskingum routing method in HMS needs travel time and . is assumed 0.2. K is assume CN lag

time.

3.4 Thiessen Polygon

Mean depth over a particular area is obtained by averaging the precipitation depth at multiple gaging

stations. The thiessen method is based on weighting the precipitation at each gages in proportion to the

land area within the basin that is closer to that gage than any other gage. The portions of the basin which

are assigned to each gage are represented by constructing the thiessen polygons (Wurbs and James, 2002).

3.4.1 Thiessen Polygon in GIS

Thiessen polygons are constructed based on the rainfall gage point and basin polygon. Using create

thiessen polygon in ArcHydro tool thiessen polygon can be constructed. After constructing the thiessen

polygon basin polygons which have portions of each gage are created using intersecting process of

thiessen polygon and basin polygon.

4. Application

This study selected the study area as San Antonio River Basin. San Antonio River Basin has area of

3,861 mi2. Figure 1 represents the study area. Land use data, Soil data, DEM data, HUC data,

precipitation data and stream gage data were collected table 1 represents the source of data.

Table 1. Source of data

Data Source

Land Use USDA

Soil USDA (SURRGO)

DEM USDA(NED 30M)

HUC TWBD

Precipitaion NOAA

Streamflow data USGS

Fig.1 Study area

4.1 Preprocessing in ArcGIS

Basin extraction, Land use reclassification and soil data modification was operated in ArcGIS

Step 1.

San Antonio basin was extracted from HUC

Fig.2 San Antonio basin extraction

Step 2.

Landuse Extraction and reclassification

Land use raster data extracted by raster calculator to fit in the extent of study area. Figure 3 displays the

raster calculator.

Fig.3 Raster Calculator

Figure 4 represents the Land use data before reclassifying. Through Rater reclassify Figure 5 was

generated. This has simplified land use categories.

Fig.4 Before Reclassifying Fig.5 After Reclassifying

Reclassified land use raster data was converted to polygon.

Step 3: Soil data modification

To combine table to Soil geodatabase file joining process was done

Fig.6 Joined Soil data

Step 4: Union soil and land use data

Figure 7 represents the combined table through union process

Fig.7 Union soil and land use table

Step 5: CNLookup table

To give index for relating the land use and soil data with CN CNLookup table was made like figure 8.

Fig.8 CNLookup table

4.2 Preprocessing in ArcHydro

DEM preprocessing was executed through ArcHydro, such as Fill sink, flow accumulation, flow

direction, stream definition, Stream Segmentation Catchment Grid Delineation, Catchment Polygon,

Drainage line, Adjoint Catchment processing

Step 1: Fill Sink

Fig.9 Fill Sink

Step 2: Flow Direction

Fig.10 Flow Direction

Step 3: Flow Accumulation

Fig.11 Flow Accumulation

Step 4: Stream Definition

Fig.12 Stream Definition

Step 5: Stream Segmentation

Fig.13 Stream Segmentation

Step 6: Catchment Grid Delineation

Fig.14 Catchment Grid Delineation

Step 7: Catchment Polygon Processing

Fig.15 Catchment Polygon Processing

Step 8: Drainage line processing

Fig.16 Drainage line processing

Step 9: Adjoint Catchment processing

Fig.17 Adjoint Catchment processing

4.3 Computing Hydrologic parameters in HEC-GeoHMS

Step 1: Generating Subbasins through Merge and Split

Figure 18 represents the subbasin which was generated through HEC-GeoHMS

Fig.18 Generated Subbasins

Step 2: River Merge

Figure 19 shows the rivers in subbasins

Fig. 19 Generated Rivers

Step 3: River Length

Table of river has attribute of characteristic of rivers, such as length and slope

Fig.20 Table of rivers

Step 4: Basin Area

Table of Area has attributed of the characteristic of subbasins, such as area and slope of basin.

Fig.21 Table of Area

Step 5: HMS Setting

Through HMS setting loss, transform and Muskingum methods are selected. To import the results of

HEC-GeoHMS results to HEC-HMS it makes interface file and background file.

Fig.22 HMS Schematic

4.4 Selecting Rainfall gage and collecting rainfall data

Table 2 shows the hydrologic event periods. Under these periods rainfall data were collected. Figure 23

shows the Rainfall gage stations were chosen.

Table 2 Hydrologic event period

Event Periods

Jun.27.2004~JUL.27.2004 (AMC2)

MAR.15.2005~APR.14.2005 (AMC1)

MAR.01.2009~APR.01.2009 (AMC3)

JUN.24.2010~JUN.24.2010 (AMC1)

Fig.23 Rainfall gage station

4.5 Creating Thiessen polygon

To reflect the spatial characteristics of precipitation thiessen polygon was created

Fig. 24 Thiessen polygon

5. Results

Runoff of streamflow was finally estimated using HEC-HMS. All parameters needed for the HMS were

computed through HEC-GeoHMS. HEC-HMS Results in the hydrograph of each subbasins and routed

hydrograph at each junction. This results show the hydro graph of W990 basin located in the downstream

in the periods from Jun.24.2010 to Jul.24.2010. Figure 25 represents the runoff of SCS transform method

and Figure 26 represents that of Clark transform method. Table 3 represents the summary of Peak

discharge and discharge volume from different two methods. The mean relative error represents the

variation of the results between two methods.

Fig. 25 streamflow of SCS Transform method

Fig. 26 streamflow of Clark Transform method

Table 3. Summary of streamflow of W990 Basin

SCS Transform Clack Transform

Peak Q (cfs) 5774.5 5297.5

Discharge (AC-FT) 102676.4 101703.6

Mean Relative Error of Q 7.8%

6. Conclusions

ArcGIS, ArcHydro and HEC-GeoHMS supply efficient and pragmatic environmental to compute

Hydrologic parameters which have spatial characteristic. Especially, AcrHydro and HEC-GeoHMS

supply easy and straightforward method to treat the raster data for Hydrologic computing. The

Streamflow results imply the clack method estimate the relative lower runoff than SCS Transform method.

Reference

1. Assefa M. Melesse and S.F. Shih. (2002), “Spatially distributed storm runoff depth estimation using

Landsat images and GIS”, Computers and Electronics in Agriculture 37 173-/183

2. David R. Maidment. (1993). Handbook of Hydrology, McGraw-Hill

3. Rlph A. Wurbs and Wesley P. James. (2002). Water Resources Enginnering, Prentice Hall

4. S. K. JAIN, R. D. SINGH and S. M. SETH. (2000), “Design Flood Estimation Using GIS Supported

GIUH Approach”, Water Resources Management 14: 369–376

5. Venkatesh Merwade, David Maidment and Oscar Robayo.(2004). “Watershed and Stream Network

Delineation”, <http://www.crwr.utexas.edu/gis/gishydro05/Introduction/Exercises/Ex3.htm>