Additional data sources Additional data sources and model structure: and model structure: help or hindrance?help or hindrance?

Olga SemenovaOlga SemenovaState Hydrological Institute, St. Petersburg, RussiaState Hydrological Institute, St. Petersburg, Russia

Pedro RestrepoPedro RestrepoOffice of Hydrologic development, NOAA, USAOffice of Hydrologic development, NOAA, USA

James McNamaraJames McNamaraBoise State University, USABoise State University, USA

ObjectivesObjectives

• Test the Hydrograph model in semi-arid snow-dominated watershed

• Study the effect of additional observations on the quality of the streamflow simulation results

• Answer the question, if the model developed for completely different geographical settings can handle the additional data in a satisfactory way without change of its fixed structure?

Dry Creek watershed, Idaho, USADry Creek watershed, Idaho, USA

Dry CreekDry Creek

Catchment Area: 28 km2

Elevation Range: 1030-2130 m

Grasses, shrubs, and conifer forests vary with aspect and elevation

Low Elevation Grass

Mid Elevation Shrub

High Elevation Forest

Available dataAvailable data

0

50

100

150

200

prec

ipita

tion

(mm

)

october january april july

963 mm77% Snow

High Elevation

0

50

100

150

200

prec

ipita

tion

(mm

)

october january april july

335 mm32% Snow

Low Elevation

• Air Temperature• Relative Humidity• Wind Speed/Direction• Solar Radiation• Net Radiation• Soil Moisture• Soil Temperature• Precipitation• Snow Depth

Hydrometeorological Data

State Hydrological Institute, St. Petersburg, Russia

Hydrograph modelHydrograph modelR

• Single model structure for watersheds of any scale

• Adequacy to natural processes while looking for the simplest solutions

• Minimum of manual calibration

Forcing data: precipitation, temperature, relative humidityOutput results: runoff, soil and snow state variables, full water balance

Slope transformationof surface flow

Initial surfacelosses

Infiltration andsurface flow

Heat dynamicsin soil

Snow coverformation

Heat energy

Interception

Heat dynamicsin snow

Snow melt andwater yield

EvaporationWater dynamics in soil

Channel transformation

Runoff at basin outlet

Underground flow

Transformation of underground flow

PrecipitationRain Snow

Watershed discretizationWatershed discretization

Bare groundGrassShrubsTrees

Representative points Runoff formation complexes

01.200910.200807.200804.200801.200810.200707.200704.200701.2007

Te

mp

era

ture

, d

eg

ree

C

35

30

25

20

15

10

5

0

-5

volu

me

wa

ter

con

ten

t

0.25

0.20

0.15

0.10

0.05

0.00

observed simulated observed simulated

Lower Weather station (1151 m), Lower Weather station (1151 m), soil, 5 cm depth, 2007-2008soil, 5 cm depth, 2007-2008

Lower Weather station, soil state variables Lower Weather station, soil state variables 30 cm depth, 2007-200830 cm depth, 2007-2008

01.200910.200807.200804.200801.200810.200707.200704.200701.2007

Te

mp

era

ture

, d

eg

ree

C

25

20

15

10

5

0

volu

me

wa

ter

con

ten

t

0.25

0.20

0.15

0.10

0.05

observed simulated observed simulated

Lower Weather station, soil state variables Lower Weather station, soil state variables 100 cm depth, 2007-2008100 cm depth, 2007-2008

01.200910.200807.200804.200801.200810.200707.200704.200701.2007

Te

mp

era

ture

, d

eg

ree

C

20

18

16

14

12

10

8

6

4

2

volu

me

wa

ter

con

ten

t

0.25

0.20

0.15

0.10

0.05

observed simulated observed simulated

Main soil characteristics and parametersMain soil characteristics and parametersInitial

(SSURGO DB)

Calibrated value Observations

Soil type Loam Sandy loam Sandy loam to loam

Soil depth 70 cm 120 cm 130 cm

Density (kg/m3) 2700 No change

Porosity (volume content = VC) 0.40 0.50 for upper stratum 0.40 in average, 0.48 for upper stratum

Specific heat conductivity (Wt/m degree)

1.7 1.3

Specific heat capacity (J/kg degree)

830 840

Water holding capacity (VC) 0.12 – 0.30 0.21 – 0.25

Wilting point (VC) 0.03 – 0.080.01 – 0.08

(calibrated by strata)Infiltration coefficient (mm/min) 7.1 No change 0.2 – 11 (2 in average)

Evaporation coefficient (10-8 m/mbar s)

0.40 – 0.60 0.35 – 0.40

Strata evaporation ration 0.40 for the 1st 0.35

1) solar radiation input to effective air T changed from 1 to 0.5

2) added correction factor to snow 1.4, rain 1.2

Additionally calibrated:

Snow state variables, Tree Line station (1651 m)Snow state variables, Tree Line station (1651 m)

observed simulated

05.200303.200301.200311.200209.200207.200205.200203.200201.200211.2001

sno

w d

ep

th,

m

1 .0

0 .8

0 .6

0 .4

0 .2

0 .0

2002-2003

05.200902.200911.200808.200805.200802.2008

sno

w d

ep

th,

m

1 .4

1 .2

1 .0

0 .8

0 .6

0 .4

0 .2

0 .0

2008-2009

Simulated and observed snow depth (m)Simulated and observed snow depth (m)

Lower Gauge Annual Water BalanceLower Gauge Annual Water Balance

Precipitation mm(% of P)

Streamflow mm(% of P)

Groundwater Recharge

mm(% of P)

ET mm(% of P)

635 (1) 169 (0.23) 37 (0.09) 429 (0.69)

Aishlin and McNamara (2010)

Groundwater recharge assessed by chloride mass balance

QR

P-(ET+Q+R) =0

0%

20%

40%

60%

80%

100%

BG TL C1E C2E C2M LG

Catchment Partitioning of Precipitation 2005-2009

Distributed Water BalanceDistributed Water Balance

C1E

C2EC2M

C1W

TLBG

LG

Evapotranspiration (ET)

Groundwater Recharge (R)

Streamflow (Q)

Treeline catchment “loses” approximately 44% of annual precipitation to deep groundwater recharge

RUNOFF LW 0.15 m soil moisture TL 0.15 m soil moisture

06.0305.0304.0303.0302.0301.0312.0211.0210.0209.0208.0207.02

Soi

l moi

stur

e

0.30

0.28

0.26

0.24

0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

m3/

s

1 . 000.950.900.850.800.750.700.650.600.550.500.450.400.350.300.250.200.150.100.050.00

?

Riparian vegetationRiparian vegetation

Handling of Riparian VegetationHandling of Riparian Vegetation• Assume Riparian vegetation transpires at the

potential rate from May through August• Increases linearly from 0 on 1 May to the

potential rate on 31 May• Decreases linearly from the potential rate on Sept

1st to 0 on Sept 30.• Assume evapotranspiration losses from riparian

vegetation directly affect streamflow• Used climatological pan evaporation, with k=0.7.• Average seasonal water use• Approach followed compares favorably with

measured cottonwood water (966mm) and and open water evaporation (1156mm) use in the San Pedro River Basin (Arizona)1

1“Hydrologic Requirements of and Evapotranspiration by Riparian Vegetation along the San Pedro River, Arizona” Fact Sheet 2006-3027, USGS, May 2007

RiparianRiparian Vegetation EvapotranspirationVegetation Evapotranspiration

Assumed Evapotranspiration Losses from Riparian Vegetation

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

1 28 55 82 109 136 163 190 217 244 271 298 325 352

Julian day

Loss

es (m

3/s)

Monthly Pot. Evapotranspiration

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12

Mon

thly

PET

(mm

)

PET from Pan (K=0.7) Fitted

RiparianRiparian Vegetation Losses-DetailVegetation Losses-Detail

observed simulated simulated + correction

11.200610.200609.200608.200607.200606.200605.2006

m3/

s

0 . 5

0.4

0.3

0.2

0.1

0.0

Runoff: final resultsRunoff: final results

01.200501.200401.200301.200201.200101.2000

m3/

s

2 . 5

2.0

1.5

1.0

0.5

0.0

observed simulated

01.201001.200901.200801.200701.200601.2005

m3/

s

2 . 5

2.0

1.5

1.0

0.5

0.0

2000-2004

2005-2009

Runoff: final resultsRunoff: final results

01.200501.200401.200301.200201.200101.2000

m3/

s

2 . 5

2.0

1.5

1.0

0.5

0.0

observed simulated

01.201001.200901.200801.200701.200601.2005

m3/

s

2 . 5

2.0

1.5

1.0

0.5

0.0

2000-2004

2005-2009

Model versus wrong observations…Model versus wrong observations…

lower gage 2mgage

01.200801.200701.200601.2005

m3/

s

2 . 8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

#0

#0

#0

#0#0

#0#0

Lower gage

2mgage

Model versus wrong observations…Model versus wrong observations…

lower gage 2mgage

07.200605.200603.200601.2006

m3/

s

2 . 8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Model versus wrong observations…Model versus wrong observations…

lower gage 2mgage simulated

07.200605.200603.200601.2006

m3/

s

2 . 8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Model versus wrong observations…Model versus wrong observations…

lower gage 2mgage simulated

07.200605.200603.200601.2006

m3/

s

2 . 8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

ConclusionsConclusions• The Hydrograph model produces reliable soil

moisture and temperature, snow water equivalent and streamflow simulations without changes to the model structure.

• We handled water usage from riparian vegetation by post-processing the data. The model can handle that situation with its algorithm for simulating shallow groundwater. This will be done later on.

• Use of models which require modest amount of parameter adjustment serves also as a quality control for observations

• Overall, simulation results were satisfactory, with minor amount of parameter calibration.