Runoff Simulations in Runoff Simulations in Region12 (or almost Region12 (or almost the State of Texas)the State of Texas)

Bryan Hong, Ph.D.

Outline of the Major TasksOutline of the Major Tasks

Preparing atmospheric forcing input for NOAH land surface model 1. Obtaining model predicted or observed data - Next Generation Rain data (NEXRAD) for precipitation and North American Land

Data Assimilation System (NLDAS) for others. - Research period : from 2004 to 2007 (4 years)

2. Data processing for model input - Spatial down-scaling - Converting data format

Producing more accurate runoff simulations for Cedric’s routing model (RAPID)

1. Analyzing various simulations based on different runoff schemes 2. Analyzing runoff changes based on vegetation or soil parameter changes

Meteorological VariablesMeteorological Variables

List of variables for model inputNLDAS from NCEP Eta Data Assimilation System (EDAS) output fields

via http://ldas.gsfc.nasa.gov

1. Air Temperature at 2 m (K)2. Specific Humidity at 2m (kg/kg)3. Surface Pressure (Pa)4. U Wind Component (m/s)5. V Wind Component (m/s)6. Downward Short Wave Radiation (W/m2)7. Downward Long Wave Radiation (W/m2)8. Total Precipitation (mm/s)

Projection typeGeographic lat/long

from Lambert-conformal of EDAS

Spatial Resolution0.125 degree (~ 15km) interpolated from 40km base

Temporal ResolutionHourly

interpolated from 3 hourly base

from Cosgrove, 2002 at GAPP meeting

Meteorological VariablesMeteorological Variables

NEXRAD rainfall

Precipitation NCEP/EMC 4km Gridded (GRIB)Stage IV Data

via http://data.eol.ucar.edu

Projection typePolar Stereo Graphic

Spatial Resolution~ 4km

Temporal ResolutionHourly

From http://www.dfanning.com/

Study Domain (Region 12)Study Domain (Region 12)

Map ProjectionAlbers

Spatial Resolution 4500m

Number of grid boxes 228 X 228

Total areaAbout 1M km2

Data ProcessingData Processing

Missing NEXRAD data

Complemented by Stage II data

Lost grids around coast linesdue to the coarse resolution of NLDAS

Filled with the values of nearest neighbor grids

NLDAS coverage

Study domain coverage

Lost grids

Lost grids around coast lines

Runoff Model ExperimentsRunoff Model Experiments

Based on Niu et al., 2009Dynamic

VegetationSM factor (β)

for stomatal Res. Runoff Schemes

EXP1

Dynamic Leaf Model

(Dickinson et al., 1998; Yang and

Niu, 2003)

NOAH TypeSIMGM

EXP2 SIMTOP

EXP3 Schaake 96

EXP4 BATS

EXP5 CLM Type

Oleson et al., 2004

SIMGM

EXP6 SIMTOP

EXP7 Schaake 96

EXP8 BATS

EXP9 BATS Type

Dickinson et al., 1993

SIMGM

EXP10 SIMTOP

EXP11 Schaake 96

EXP12 BATS

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TOPMODEL + groundwater scheme(Niu et al., 2007)

TOPMODEL +equilibrium water table(Niu et al., 2005)

Original NOAH surface runoff scheme(schaake, 1996)

Biosphere-Atmosphere Transfer scheme(Dickinson et al., 1993)

Runoff SimulationRunoff Simulation

Comparison to stream flow observations in Guadalupe and San Antonio River Basins

OBS_G EXP1_G EXP5_G EXP9_G EXP4_G EXP8_G EXP12_GMean 80.98 50.48 50.63 47.18 71.56 73.68 67.85STDV 138.89 130.05 133.73 124.69 263.58 272.57 255.82MIN 5.66 4.88 4.01 4.52 1.43 1.22 1.23MAX 2559.84 2073.76 2141.85 2024.09 3415.13 3458.50 3317.71

OBS_S EXP1_S EXP5_S EXP9_S EXP4_S EXP8_S EXP12_S

Mean 37.54 31.20 31.07 29.44 43.60 44.60 41.52

STDV 63.96 100.78 102.56 97.30 183.95 188.18 178.38

MIN 3.09 2.85 2.46 2.645 0.66 0.55 0.13

MAX 639.96 1766.23 1831.89 1714.09 2749.56 2851.07 2708.76

Guadalupe River Basin

San Antonio River Basin

Lower mean but closer STDV Closer mean but too high STDV

SYMGM Group BATS Group

2004

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2007

2007

Guadalupe River Basin

NOAH β + SYMGM

CLM β + SYMGM

BATS β + SYMGM

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2004

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2007

Guadalupe River Basin

NOAH β + BATS

CLM β + BATS

BATS β + BATS

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2004 2005 2006 2007

2004 2005 2006 2007

2004 2005 2006 2007

San Antonio River Basin

NOAH β + SYMGM

CLM β + SYMGM

BATS β + SYMGM

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San Antonio River Basin

NOAH β + BATS

CLM β + BATS

BATS β + BATS

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Other ExperimentsOther Experiments

Kim, 2008 at NASA IDS meeting

MAJOR LAND USEFor two river basins

GuadalupeBuilt-up land (4.0)Dry Crop/Pasture (51.8)Crop/Grass Mosaic (34.5)Mixed Forest (3.8)

San AntonioBuilt-up land (4.0)Dry Crop/Pasture (51.8)Crop/Grass Mosaic (34.5)Grassian (3.8)Changing vegetation properties (roughness length and

stomatal resistance) do not increase runoff much.

Land Cover Effect ?

Other ExperimentsOther Experiments

The land surface model uses only one soil texture either bottom or top soil

Runoff results does not show much difference between the use of soil texture maps.

Soil Texture Effect ?

Runoff vs. RainfallRunoff vs. Rainfall

Comparison of Runoff simulation to Rainfall inputsGuadalupe River Basin (m3/s) San Antonio River Basin (m3/s)

OBS_G EXP1_G EXP8_G NLDAS NEXRAD OBS_S EXP1_S EXP8_S NLDAS NEXRAD

Mean 80.98 50.48 73.68 47.46 51.27 37.54368 31.1995 44.6039 31.64 32.35

STDV 138.89 130.05 272.57 118.74 143.18 63.96166 100.7848 188.178 83.76 101.51

MIN 5.66 4.88 1.22 0 0 3.09 2.854 0.5494 0 0

MAX 2559.84 2073.76 3458.5 1245.18 1608.16 639.96 1766.226 2851.07 891.75 1121.84

OBS : Stream Flow Measurements for River Basins (m3/s)

EXP1 : Experiment with the ground runoff scheme and NOAH β (Niu et al., 2009)

EXP8 : Experiment with the BATS runoff scheme and CLM β (Niu et al., 2009)

NLDAS : 20% of NLDAS precipitation

NEXRAD : 20% of NEXRAD precipitation

cf. All experiments shown above have been conducted with the NEXRAD rain data.

Assumed Runoff ≈ 20% of rainfall

Choice of Rainfall Input?

Subsurface Runoff AnalysisSubsurface Runoff Analysis

A simple analysis for subsurface runoff and comparison to OBS.Assumptions1.During drying periods (no precipitation periods), subsurface runoff is the only source for stream flow. Standard deviations of subsurface runoff are generally low, indicating that water is steadily supplied from ground water.

2.When surface runoff is almost zero (less than 1.0 m3/s in this analysis), the observations also represent stream flows only from subsurface runoff.

OBS EXP1 EXP5 EXP9 EXP4 EXP8 EXP12

GuadalupeMean 73.33 20.37 19.97 18.47 8.47 8.35 7.29

STDV 129.73 16.00 16.05 15.42 7.86 7.75 7.15

San AntonioMean 32.40 11.72 11.33 10.64 4.35 4.25 3.72

STDV 55.81 11.24 11.17 11.23 5.59 5.51 5.57

Total analyzed dates for dry seasons : 1020 of total 1461 dates for Guadalupe river basin815 for San Antonio river basin

What Happen in Runoff?

SummarySummary

1. Experiments indicates that choice of β scheme is not much effective to runoff variation.

2. If the SYMGM runoff scheme is the best choice as shown in the previous research , we need more water for runoff simulation.

3. For the two river basin areas, changing vegetation and soil parameters is not substantially effective to increase runoff amount.

4. NEXRAD rain is a better choice to more water input than NLDAS rain.

5. The land surface model underestimates subsurface water in those two river basin areas.

Future WorksFuture Works

1. Need runoff output validation with stream flow observations for entire Region12

2. Comparison the routing simulations with different runoff outputs from SYMGM and BATS runoff schemes

3. Comparison runoff simulations between predicted vegetation from the dynamic leaf model and NASA remote sensing data such as NDVI and LAI from MODIS

4. Experiments with various saturated hydraulic conductivities to obtain more subsurface runoff

PLEASE, GIVE ME WATER!! THANKS

2004

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Guadalupe River Basin

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San Antonio River Basin

Cf. NEXRAD : 20% of NEXRAD precipitation