modeling the ungauged basin using topmodel in...

Modeling the Ungauged BasinUsing TOPMODEL in GRASS GISBrent Fogleman 30 November, 2009

Purpose

Develop a predictive hydrological model to be

used by land managers at Fort Bragg in order for

them to better understand how the hydrological

characteristics of the watershed are affecting soil

erosion.

Presenter

Presentation Notes

My research project is to quantify the effects of erosion on Fort Bragg training areas; specifically Falcon Landing Zone. I plan to gain additional insight to how the hydrological processes affect erosion and sediment transport. Erosion is perhaps the biggest environmental concern to Army land managers. The goal is to develop mitigations in order to make the training lands sustainable.

Area of InterestRainfall: the 30‐year average (1971‐2000) annual rainfall at Fort Bragg is

47.34 inches

Cumulative rainfall for study period (01Jan08‐31Dec08) is

51.81 inches

Topography: flat to rolling

Cover: intermittent grasses, small shrubs and pine

Soil: 72.4 percent sand, 15.3 percent silt, and 12.3 percent clay

Use: heavy use by helicopters and tactical vehicles has caused depletion

of cover and has disturbed the soil

Presenter

Presentation Notes

Cumulative rainfall during the study period is consistent with the 30-year average annual rainfall at Fort Bragg. The topography is typical of the sandhills in the NC innr coastal plain region. Military training disturbs the area.

Falcon LZ

Water out

Water in

Falcon LZ

Wetland

6’3”

Water out

TOPMODELWhat is it?

• A semi‐distributed rainfall runoff model

• Basic assumption: all points in a catchment with the same topographic index

value will respond in a hydrologically similar way

• Theory assumptions:

1. saturated zone can be approximated by successive steady state

2. hydraulic gradient of the saturated zone can be approximated by local

surface topographic slope

3. distribution of downslope transmissivity with depth is an exponential

function of storage deficit or depth to the water table

Upslope contributing area

qi = Totanβ exp(-Di/m)

Where:To is transmissivityDi is local storage deficitm is a model parameter controlling the rate of decline of transmissivity

ai = upslope contributing area

r is recharge rate

ai

qi = airtanβ

r

Topographic index = a / tanβ

TOPMODELWhy?

– Renders good estimation of run‐off

– Suited for small catchments

– Best suited to catchments with shallow soils and moderate

topography which do not suffer from excessively long dry

periods

Software: GRASS(Geographical Resources Analysis Support System)

Why?

– Employs TOPMODEL function

– Developed by the U.S. Army Construction Engineering Research

Laboratory (CERL)

– Taught at NCSU

– Open source

Additional Software

Matlab

– Script for calculating potential evapotranspiration (PET)

Python

– Scripts for formatting rainfall data

MS Excel

– Calculations and hydrographs

Data• USGS Gauge 02102908 Flat Creek Near Inverness, NC

– One year of daily precipitation and discharge data (01 Jan 2008 to 31 Dec 2008)

• NC State Climate Office

– One year of mean daily temp used in the calculation of PET

• DEM USGS Seamless Server

– 1/3 arc second (10m) resolution

– Datum:NAD83

– Projection: Geographic Coordinate System (lat/lon)

Data Pre-Processing• DEM

– download from USGS Seamless Server

– import into GRASS

– reproject to WGS84, UTM Zone 17N

– create drainage direction map using command r.watershed with threshold=50000

– convert basin to vector format

– extract more detailed streams from flow accumulation

– thin streams and convert to vector format

– remove small spurs or "dangling lines" from the thinning process

– create a text file with the coordinates to the gauge/pourpoint

Data Pre-ProcessingIdentify the PourPoint

668195.00E 3893180.00N

Data Pre-Processing• DEM

– delineate the watershed and convert it to vector format using command r.water.outlet

– create a text file to reclassify the raster to indicate that values (1) are retained and values (0) are set to NULL

– convert reclassified basin raster to vector

– compute the watershed area

– create a MASK of the watershed using the reclassified basin as the MASK

– create a basin elevation map with MASK on

– create topographic wetness index with watershed MASK on

Flat Creek (gauged)• 19.6 sq km (4844.6 acres)

Falcon LZ (ungauged)• 13 sq km (3213.6 acres)

Elevation

Presenter

Presentation Notes

Similar in size, proportion and elevation and slope variance. Parameters used in the Flat Creek watershed should be applicable to Falcon LZ.

Topographic Wetness IndexFlat Creek Falcon LZ

Presenter

Presentation Notes

Also, similar topographic wetness index

Perceptual Model• During heavy rain events, water quickly finds its way into small

channels of near‐impermeable red clay

• During storm events, water builds volume and speed in a short time

Model Parameters• GRASS version allows for 15 model parameters…….TOO MANY!

• I used 8 model parametersA Total catchment area [m^2]qs0 Initial subsurface flow per unit area [m/h]

(close to baseflow)lnTe Areal average of ln(T0) = ln(Te) [ln(m^2/h)]

(transmissivity)m Model parameter [m]Sr0 Initial root zone storage deficit [m]Srmax Maximum root zone storage deficit [m]td Unsaturated zone time delay per unit storage deficit [h]

( > 0.0 )vch Main channel routing velocity [m/h]vr Internal subcatchment routing velocity [m/h]

Model Input Preparationparameters.txt

Flat Creek 10# A# [m^2]1.961E+007

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 8 0.025 0.0 0.1 80.0 3000 3000

# infex K psi dtheta0 0 0.0 0.0

# nch0

# d Ad_r0 0

Model Input Preparationinput.txt

# ntimesteps: Number of timesteps# dt: Time increment (timestep) [h] (in this case, day)# R: Rainfall [m/dt]# Ep: Potential evapotranspiration [m/dt]##

# ntimesteps dt366 1.0

# R Ep0.0000 0.001349700.0000 0.000805380.0000 0.000707110.0000 0.000819400.0000 0.00113164

GRASS GUI

Model Output# r.topmodel output file for "Flat Creek 10"Qt_peak: 3.64E+05 tt_peak: 254 Qt_mean: 1.82E+04 ncell: 196054 nidxclass: 27 ndelay: 0 nreach: 1 lnTe: 8.00E+00 vch: 3.00E+03 vr: 3.00E+03 lambda: 7.95E+00 qss: 1.05E+00 qs0: 7.50E‐05 tchAd 1.96E+07 Total flow Total flow/unit area Overland Flow/unit area Subsurface flow/unit area Vertical flux Mean saturation deficit timestep Qt qt qo qs qv S_mean

1 1.47E+03 7.50E‐05 0.00E+00 7.50E‐05 0.00E+00 2.39E‐01 2 2 1.47E+03 7.48E‐05 0.00E+00 7.48E‐05 0.00E+00 2.39E‐01 3 3 1.46E+03 7.46E‐05 0.00E+00 7.46E‐05 0.00E+00 2.39E‐01 ……..

Observed Max and Min Discharge

Presenter

Presentation Notes

You can see the three big events at the beginning of September We will now quickly look at several hydrographs produced by the model. You will see two hydrographs for each run; the first is on a linear scale, the second in on a log-10 scale so you can see the separation of the different flows. They are ordered by the percentage of subsurface flow of the total flow, with the greatest percentage of subsurface flow first.

Nash-Sutcliffe 0.09

TM_FC10 0.090241.96E+07

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 8 0.025 0 0.1 80 3000 3000

# infex K psi dtheta0 0 0 0

# nch0

# d Ad_r0 0

Subsurface flow is 90.8% of total flowOverland flow is 9.8% of total flow

Presenter

Presentation Notes

Let me orient you to the hydrograph: All measurements on the y-axis are in meters per day. The indices on the x-axis are Julian dates in 2008, so 0 indicates January 1 and 250 is September 6. Daily flows are represented primary axis and precipitation is on the secondary axis In the lower right corner you see the model parameters used in this run. This model is the only run that resulted in a Nash-Sutcliffe model efficiency greater than 0. This means the model is a better predictor of the hydrological response than that of the observed mean. An efficiency of 1 (E = 1) corresponds to a perfect match of modeled discharge to the observed data. An efficiency of 0 (E = 0) indicates that the model predictions are as accurate as the mean of the observed data An efficiency less than zero (E < 0) occurs when the observed mean is a better predictor than the model Essentially, the closer the model efficiency is to 1, the more accurate the model is.

Nash-Sutcliffe 0.09

TM_FC10 0.090241.96E+07



# nch0

# d Ad_r0 0


Presenter

Presentation Notes

You’ll notice the “gap” in simulated flow between May and August. I suspect this is the case because of the small amounts of precipitation and the high evapotranspiration during the hot summer months.

Nash-Sutcliffe -4.92

TM_FC11 ‐4.91818961.96E+07

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 8 0.025 0.04 0.001 80 3000 3000


# nch0

# d Ad_r0 0



TM_FC09 ‐1.868209081.96E+07



# nch0

# d Ad_r0 0



TM_FC02 ‐5.93241671.96E+07

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 4 0.0125 0.0025 0.041 60 20000 10000


# nch1

# d Ad_r8000 1



TM_FC04 ‐17.15271.96E+07

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 1.76 0.02 0.0425 0.0016 41.37 4477 4477


# nch0

# d Ad_r0 0



TM_FC08 ‐7.97703911.96E+07

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 0.01 0.025 0 0.1 80 3000 3000


# nch0

# d Ad_r0 0



TM_FC07 ‐13.6721.96E+07

# qs0 lnTe m Sr0 Srmax td/alpha vch vr0.000075 0.01 0.025 0 0.025 80 3000 3000


# nch0

# d Ad_r0 0


Challenges• Very little documentation on running TOPMODEL in GRASS

• Parameter Estimation and Predictive Uncertainty• GRASS apparently does not include analysis tools, making it not suitable for numerous runs (time consuming and high potential for human error)

Presenter

Presentation Notes

I found one document that helped me to define the inputs. Literature was useful to find parameter ranges, but as was a common theme: “There is no perfect parameter set” Required much copying and pasting of values to analyze outputs

What’s next?• Determine parameters that more accurately model the hydrological response of the gauged watershed

• Apply model parameters to the ungaugedwatershed to model hydrological response

• Explore the RRMT (Rainfall Runoff Modeling Toolbox) in Matlab

Presenter

Presentation Notes

Will I continue to use TOPMODEL in GRASS? -- Probably not for the sake that it is very time consuming and takes a lot of user input to analyze results

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