Modeling the Ungauged Basin Using TOPMODEL in GRASS GIS. Brent Fogleman 30 November, 2009

Transcription

1 Modeling the Ungauged Basin Using TOPMODEL in GRASS GIS Brent Fogleman 30 November, 2009

2 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.

3 Area of Interest Rainfall: the 30 year average ( ) annual rainfall at Fort Bragg is inches Cumulative rainfall for study period (01Jan08 31Dec08) is 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

4 Falcon LZ Water in Water out

5 Falcon LZ Water out Wetland 6 3

6 TOPMODEL What 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

7 Upslope contributing area a i = upslope contributing area r is recharge rate a i r q i = T o tanβ exp(-d i /m) Where: T o is transmissivity D i is local storage deficit m is a model parameter controlling the rate of decline of transmissivity tanβ q i = a i r Topographic index = a / tanβ

8 TOPMODEL Why? 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

9 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

10 Additional Software Matlab Script for calculating potential evapotranspiration (P ET ) Python Scripts for formatting rainfall data MS Excel Calculations and hydrographs

11 Data USGS Gauge 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)

12 Data Pre-Processing DEM download from USGS Seamless Server import into GRASS reprojectto 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

13 Data Pre-Processing Identify the PourPoint E N

14 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

15 Elevation Flat Creek (gauged) 19.6 sq km ( acres) Falcon LZ (ungauged) 13 sq km ( acres)

16 Topographic Wetness Index Flat Creek Falcon LZ

17 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

18 Model Parameters GRASS version allows for 15 model parameters.too MANY! I used 8 model parameters A 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]

19 Model Input Preparation parameters.txt Flat Creek 10 # A # [m^2] 1.961E+007 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 # d Ad_r 00

20 Model Input Preparation input.txt # ntimesteps: Number of timesteps # dt: Time increment (timestep) [h] (in this case, day) # R: Rainfall [m/dt] # Ep: Potential evapotranspiration [m/dt] # # # ntimesteps dt # R Ep

21 GRASS GUI

22 Model Output # r.topmodel output file for "Flat Creek 10" Qt_peak: 3.64E+05 tt_peak: 254 Qt_mean: 1.82E+04 ncell: 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 tch Ad 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 E E E E E E E E E E E E E E E E E E 01..

23 Observed Max and Min Discharge

24 Subsurface flow is 90.8% of total flow Overland flow is 9.8% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe 0.09 # d Ad_r 0 0

25 Subsurface flow is 90.8% of total flow Overland flow is 9.8% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe 0.09 # d Ad_r 0 0

26 Subsurface flow is 87.73% of total flow Overland flow is 12.27% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

27 Subsurface flow is 87.73% of total flow Overland flow is 12.27% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

28 Subsurface flow is 64.60% of total flow Overland flow is 35.40% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

29 Subsurface flow is 64.60% of total flow Overland flow is 35.40% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

30 Subsurface flow is 61.24% of total flow Overland flow is 38.77% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 1 Nash-Sutcliffe # d Ad_r

31 Subsurface flow is 61.24% of total flow Overland flow is 38.77% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 1 Nash-Sutcliffe # d Ad_r

32 Subsurface flow is 44.47% of total flow Overland flow is 55.53% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

33 Subsurface flow is 44.47% of total flow Overland flow is 55.53% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

34 Subsurface flow is 29.06% of total flow Overland flow is 70.29% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

35 Subsurface flow is 29.06% of total flow Overland flow is 70.29% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

36 Subsurface flow is 26.66% of total flow Overland flow is 73.33% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

37 Subsurface flow is 26.66% of total flow Overland flow is 73.33% of total flow TM_FC E+07 # qs0 lnte m Sr0 Srmax td/alpha vch vr # infex K psi dtheta # nch 0 Nash-Sutcliffe # d Ad_r 0 0

38 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)

39 What s next? Determine parameters that more accurately model the hydrological response of the gauged watershed Apply model parameters to the ungauged watershed to model hydrological response Explore the RRMT (Rainfall Runoff Modeling Toolbox) in Matlab