additional data sources and model structure: help or hindrance? olga semenova state hydrological...
TRANSCRIPT
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
pre
cip
itat
ion
(m
m)
october january april july
963 mm77% Snow
High Elevation
0
50
100
150
200
pre
cip
itat
ion
(m
m)
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 model
R • 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 humidity
Output 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 ground
Grass
Shrubs
Trees
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
vo
lum
e w
ate
r c
on
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
vo
lum
e w
ate
r c
on
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
vo
lum
e w
ate
r c
on
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 stratum0.40 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
sn
ow
de
pth
, m
1.0
0 .8
0 .6
0 .4
0 .2
0 .0
2002-2003
05.200902.200911.200808.200805.200802.2008
sn
ow
de
pth
, 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
So
il m
ois
ture
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 . 00
0.95
0.90
0.85
0.80
0.75
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
?
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
Lo
ss
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
Mo
nth
ly P
ET
(m
m)
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.