Hydrometeorological modeling in NAMS
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
for presentation at
Eighth Annual Meeting of WCRP/CLIVAR/VAMOS Panel
(VPM8)
Mexico City
March 8, 2005
NAME HYDROMETEOROLOGICAL WORKING
GROUP DRAFT RESEARCH STRATEGY
• Foster coordination of hydrologically-relevant NAME research within
NAME and with appropriate operational and research communities
• Engage stakeholders, creators and users of operational hydrological
forecasts
• Define a suite of key hydrological forecast parameters and make
measured improvements in their skill across a range of societallyrelevant space and time scales. (These parameters may include,
spatially-distributed and probabilistic forecasts of precipitation,
evaporative demand for agriculture, actual evaporation from forests
and rangelands, soil moisture and streamflow among others.)
• Foster transfer of hydrologically-relevant research for improvement
of operational hydrological forecasts from the NAME region.
• Support efforts to share research findings and prediction
systems between U.S. and Mexican collaborators
NWHG key science questions
•
Identify the dominant runoff generation mechanisms and
streamflow regimes in the NAM region and how different regions
respond to:
–
–
–
–
–
•
•
•
•
particular event characteristics
evolution of the monsoon season
modes of monsoon climate variability
anthropogenic control of the river networks via reservoirs and
diversions
modification of the stream network and landscape due to extreme
events, (e.g. flash floods, land-falling tropical storms, etc.)
Determine if basin scale water budgets from the NAM region can be
closed with any confidence and, if so, over what spatial and
temporal scales. Define the roles of vegetation, soils and shallow
aquifers in modulating basin- and regional-scale hydrologic
budgets.
Assess the sensitivity of hydrological predictions to uncertainty in
precipitation estimates and specification of land surface conditions
from event through seasonal time scales.
Quantify the impacts of enhanced observations obtained during the
2004 NAME EOP on short and long term Quantitative Precipitation
Forecasts S/I hydrological predictions.
Prioritize additional in-situ and remotely sensed observations that
are critical to improving hydrological forecasts from flash flood to
S/I time-scales.
Hydrological modeling goals
• Evaluate the utility of current hydrologic
modeling architectures for hydrological
prediction in both gauged and ungauged river
systems
• Demonstrate the utility of spatially-distributed
precipitation estimates and forecasts generated
from NAME climate process teams
• Demonstrate an improved capacity to advance
hydrological prediction capability throughout the
NAME region
Programmatic linkages
• IAHS Prediction in Ungauged Basins (PUBS):
PUBS is a decade-long program aimed at
improving the capacity to make hydrologic
predictions in ungauged river basins
• Hydrological Ensemble Prediction
Experiment (HEPEX): The aim of HEPEX is to
demonstrate how to produce reliable
hydrological ensemble forecasts that can be
used in operational water management decision
making. Its stated goal is to quantify forecast
uncertainty at each step of the forecast process
in a way that can be communicated to end users
Macroscale modeling
• LDAS retrospective (real time NAME 2004
field campaign (being updated with NERN
data); extension of Maurer et al (2002)
retrospective N-LDAS data over Tier 2
1925 – present
• Basin scale hydrologic modeling (Gochis)
Development of a Long-Term Land
Surface Data Set for Mexico
Surface forcing data: Daily precipitation, maximum and minimum
temperatures
ERIC2 (1940-1998) a product from Mexican Institute of
Technology of Water (IMTA) of the SEMARNAP, over 5,000
stations
Data322 (mainly for data pre-1940, and for Northwestern
mexico) produced by SMN (Servicio MeterorolÓgico Nacional,
2000), around 70 stations extending back to 1920s.
SMN daily historical precipitation data (1995 – near realtime)
provided courtesy of Miguel Cortez Vázquez of SMN, around
1,000 stations.
Guage Station Distribution (ERIC2)
• There is a hole in
Northwestern Mexico since
1990, which can be filled
somewhat by SMN near-real
time data 1995-2002.
Guage Station Distribution
1995-
1900-1940
SMN
ERIC2
Data322
Gridded Precipitation and Temperature Dataset
(1925-2003)
Long-term mean precipitation
spatial pattern
This spatial plot shows reasonable
pattern with lower precipitation in
desert region and higher magnitude
in coastal and south Mexico.
Long-term mean daily temperature
This plot shows lower temperature
over mountainous area (~10C), and
higher value over the coastal line
especially in South Mexico.
Comparisons with SMN climatologic
Tmean over some state’s capitals
(http://smn.cna.gob.mx/productos/norma
les/medias.html) :
Lat
24.8
24.4
19.2
18.8
17.9
17.38
16.37
15.5
22.83
SMN (1961-1990)
Lon
Tmean
-107.15 24.5
-106.7 24.9
-103.8 26.1
-103.67 26.2
-101.78 27.0
-101.07 26.7
-98.05 27.4
-93.07 27.4
-99.23 25.0
Lat
24.8125
24.4375
19.1875
18.8125
17.9375
17.4375
16.3025
15.4375
22.8125
Gridded(1925-2003)
Lon
Tmean
-107.1875
25.33
-106.6875
23.00
-103.8125
26.995
-103.6875
27.51
-101.8125
28.36
-101.0625
27.30
-98.0625
28.14
-93.0625
28.50
-99.1875
25.68
Comparison of simulated
and observed streamflow
The streamflow dataset is the Mexico
acronym BANDAS (CNA and IMTA).
1
2
3
4
5
6
6
7
10
13
8
11
14
9
12
15
Realtime LDAS (over NAME Tier 1 & 2)
Currently
Met. data
EDAS realtime analysis
Ongoing
Precipitation
SMN Mexico realtime
guage station data
Target
Realtime report
by next day 9:00AM
Real-time
VIC runs
Initializing & comparing
USA Met. data
LDAS realtime
(lag 1 and ½ days)
Realtime LDAS Website
(http://www.hydro.washington.edu/~chunmei/realtime/)
Realtime Simulation Plots: Apr. 18, 2004
Macroscale modeling status
• Long-term LDAS forcing and derived
(VIC) land surface fluxes and states
completed for 1950-2002 (to be
extended to 1925-2004) for NAME Tier
2
• Summer 2004 (May-September) LDAS
forcings and fluxes being updated to
include NERN data
Predictability issues in the context
of regional hydrologic forecasting
Western U.S. experimental hydrologic
forecast system
• ~100 forecast points
• 6-month forecasts (12
month for CPC) issued
monthly using VIC
model for ESP,
stratified ESP, NSIPP,
NCEP/GMS
• Planned
implementation of
weekly updates winter
2005
• Planned
implementation of
multi-model
hydrologic ensemble
Relative important of initial
condition and climate forecast
error in streamflow forecasts
Columbia R. Basin
fcst more impt
ICs more impt
Rio Grande R. Basin
RMSE (perfect IC, uncertain fcst)
RE =
RMSE (perfect fcst, uncertain IC)
Basin scale modeling
NAMS River Basin simulations (Francisco Muñoz)
Sonora Basins Features (INEGI, 1993)
Water Useb
(Km2)
Surface
Watera
(%)
(%)
Annual
Precipitation
(mm)
Sonora
34,654
31.8
13:85.5:1.3:0.2
100-500
YaquiMatape
83,57
8
80.0
3.5:95:0.5:
1
200-1000
Mayo
26,50
0
87.8
2.5:96.5:0.
5:0.5
300-1100
Sonoita
18,346
9.2
29:69:1:1
0-300
Concepcion
40,770
26.2
4:93.5:1.5:1
0-400
River Basin
a The
Surface
proportion of Surface Water is with respect to Groundwater
b Proportions of water uses (Urban:Agriculture:Cattle:Industrial)
ne
-9
5
A
br
-9
Ju 5
l-9
5
O
ct
-9
E 5
ne
-9
A 6
br
-9
6
Ju
l-9
6
O
ct
-9
E 6
ne
-9
7
A
br
-9
7
Ju
l-9
O 7
ct
-9
E 7
ne
-9
8
A
br
-9
Ju 8
l-9
8
O
ct
-9
E 8
ne
-9
A 9
br
-9
9
Ju
l-9
9
O
ct
-9
E 9
ne
-0
0
A
br
-0
0
Ju
l-0
O 0
ct
-0
E 0
ne
-0
1
A
br
-0
Ju 1
l-0
1
O
ct
E 01
ne
-0
2
A
br
-0
2
Ju
l-0
O 2
ct
-0
2
E
pptn (mm/day)
ne
-9
5
A
br
-9
Ju 5
l-9
5
O
ct
-9
E 5
ne
-9
A 6
br
-9
6
Ju
l-9
6
O
ct
-9
E 6
ne
-9
7
A
br
-9
7
Ju
l-9
O 7
ct
-9
E 7
ne
-9
8
A
br
-9
Ju 8
l-9
8
O
ct
-9
E 8
ne
-9
A 9
br
-9
9
Ju
l-9
9
O
ct
-9
E 9
ne
-0
0
A
br
-0
0
Ju
l-0
O 0
ct
-0
E 0
ne
-0
1
A
br
-0
Ju 1
l-0
1
O
ct
E 01
ne
-0
2
A
br
-0
2
Ju
l-0
O 2
ct
-0
2
E
pptn (mm/day)
Pre cipitation in the NAM S Riv e r Basins
10
9
8
7
6
5
Yaqui-Matape
Mayo
4
Fuerte
Conchos
3
2
1
0
time (months)
Runoff in the NAM S Riv e r Basins
3.5
3
2.5
2
Yaqui-Matape
Mayo
1.5
Fuerte
Conchos
1
0.5
0
time (months)
W
IN
T
SP ER
R -9
SU ING 5
M
M -95
ER
FA -95
W LL
I N -9
5
T
SP ER
9
R
SU ING 6
M
M -96
ER
FA -96
W LL
I N -9
6
T
SP ER
9
R
SU ING 7
M
M -97
ER
FA -97
W LL
I N -9
7
T
SP ER
R -9
SU ING 8
M
M -98
ER
FA -98
W LL
I N -9
8
T
SP ER
9
R
SU ING 9
M
M -99
ER
FA -99
W LL
I N -9
9
T
SP ER
R -0
SU ING 0
M
M -00
ER
FA -00
W LL
I N -0
0
T
SP ER
0
R
SU ING 1
M
M -01
ER
FA -01
W LL
I N -0
1
T
SP ER
R -0
SU ING 2
M
M -02
ER
FA -02
LL
-0
2
pptn (mm/day)
Water Balance in the Yaqui River Basin
12
10
8
Wdew
Baseflow
6
Runoff
Evap
Precip
4
2
0
seasons
W
IN
T
SP ER
R -9
SU ING 5
M
M 95
ER
FA -95
W LL
I N -9
5
T
SP ER
9
R
SU ING 6
M
M 96
ER
FA -96
W LL
I N -9
6
T
SP ER
9
R
SU ING 7
M
M 97
ER
FA -97
W LL
I N -9
7
T
SP ER
R -9
SU ING 8
M
M 98
ER
FA -98
W LL
I N -9
8
T
SP ER
9
R
SU ING 9
M
M 99
ER
FA -99
W LL
I N -9
9
T
SP ER
R -0
SU ING 0
M
M 00
ER
FA -00
W LL
I N -0
0
T
SP ER
0
R
SU ING 1
M
M 01
ER
FA -01
W LL
I N -0
1
T
SP ER
R -0
SU ING 2
M
M 02
ER
FA -02
LL
-0
2
pptn (mm/day)
Water Balance in the Mayo River Basin
12
10
8
Wdew
Baseflow
6
Runoff
Evap
Precip
4
2
0
seasons
W
IN
T
SP ER
R -9
SU ING 5
M
M 95
ER
FA -95
W LL
I N -9
5
T
SP ER
9
R
SU ING 6
M
M 96
ER
FA -96
W LL
I N -9
6
T
SP ER
9
R
SU ING 7
M
M 97
ER
FA -97
W LL
I N -9
7
T
SP ER
R -9
SU ING 8
M
M 98
ER
FA -98
W LL
I N -9
8
T
SP ER
9
R
SU ING 9
M
M 99
ER
FA -99
W LL
I N -9
9
T
SP ER
R -0
SU ING 0
M
M 00
ER
FA -00
W LL
I N -0
0
T
SP ER
0
R
SU ING 1
M
M 01
ER
FA -01
W LL
I N -0
1
T
SP ER
R -0
SU ING 2
M
M 02
ER
FA -02
LL
-0
2
pptn (mm/day)
Water Balance in the Fuerte River Basin
12
10
8
Wdew
Baseflow
6
Runoff
Evap
Precip
4
2
0
seasons
W
IN
T
SP ER
R -9
SU ING 5
M
M 95
ER
FA -95
W LL
I N -9
5
T
SP ER
9
R
SU ING 6
M
M 96
ER
FA -96
W LL
I N -9
6
T
SP ER
9
R
SU ING 7
M
M 97
ER
FA -97
W LL
I N -9
7
T
SP ER
R -9
SU ING 8
M
M 98
ER
FA -98
W LL
I N -9
8
T
SP ER
9
R
SU ING 9
M
M 99
ER
FA -99
W LL
I N -9
9
T
SP ER
R -0
SU ING 0
M
M 00
ER
FA -00
W LL
I N -0
0
T
SP ER
0
R
SU ING 1
M
M 01
ER
FA -01
W LL
I N -0
1
T
SP ER
R -0
SU ING 2
M
M 02
ER
FA -02
LL
-0
2
pptn (mm/day)
Water Balance in the Conchos River Basin
12
10
8
Wdew
Baseflow
6
Runoff
Evap
Precip
4
2
0
seasons
-0.1
ar
-9
Ju 5
n9
Se 5
p9
D 5
ic9
M 5
ar
-9
Ju 6
n9
Se 6
p9
D 6
ic9
M 6
ar
-9
Ju 7
n9
Se 7
p9
D 7
ic9
M 7
ar
-9
Ju 8
n9
Se 8
p9
D 8
ic9
M 8
ar
-9
Ju 9
n9
Se 9
p9
D 9
ic9
M 9
ar
-0
Ju 0
n0
Se 0
p0
D 0
ic0
M 0
ar
-0
Ju 1
n0
Se 1
p0
D 1
ic0
M 1
ar
-0
Ju 2
n0
Se 2
p0
D 2
ic02
M
mm/day
Original minus Crop landsurfaces'Runoff in the NAMS
0.7
0.6
0.5
0.4
0.3
Yaqui-Matape
0.2
Mayo
Fuerte
0.1
Conchos
0
-0.2
-0.3
time (months)
Original minus Grass landscapes'Runoff in the NAMS
0.5
0.4
0.3
0.2
Yaqui-Matape
97
94
91
88
85
82
79
76
73
70
67
64
61
58
55
52
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
0
1
pptn (mm/day)
0.1
Mayo
Fuerte
-0.1
Conchos
-0.2
-0.3
-0.4
-0.5
-0.6
time (months)
Comments on land surface hydrologic
prediction issues in the NAM region
• Sources of predictability (IC vs climate
forecast)
• Role of space-time variability of
precipitation, and its interaction with basin
scale
• Hydrologic processes, and their
representation (e.g. infiltration excess
overland flow, groundwater/baseflow)