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>