1. 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

2. 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 societally-relevant 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

3. 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.

4. 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

5. 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

6. 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 – present
Basin scale hydrologic modeling (Gochis)

7. Development of a Long-Term Land Surface Data Set for Mexico
Surface forcing data: Daily precipitation, maximum and minimum temperatures
ERIC2 ( ) 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.

8. Guage Station Distribution (ERIC2)
• There is a hole in Northwestern Mexico since 1990, which can be filled somewhat by SMN near-real time data

9. Guage Station Distribution
1995-
SMN
ERIC2
Data322

10. 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.

11. Comparisons with SMN climatologic Tmean over some state’s capitals ( :
SMN ( ) Gridded( )
Lat Lon Tmean Lat Lon Tmean

12. Comparison of simulated and observed streamflow
The streamflow dataset is the Mexico acronym BANDAS (CNA and IMTA). 7 8 9 11 12 13 10 15 14 1 2 3 5 6 4

13. Realtime LDAS (over NAME Tier 1 & 2)
Target
Realtime report
by next day 9:00AM
Currently
Real-time
VIC runs
Met. data
EDAS realtime analysis
Ongoing
Initializing & comparing
Precipitation
SMN Mexico realtime
guage station data
USA Met. data
LDAS realtime
(lag 1 and ½ days)

14. Realtime LDAS Website (
                                                                                                                                                                                    

15. Realtime Simulation Plots: Apr. 18, 2004

16. Macroscale modeling status
Long-term LDAS forcing and derived (VIC) land surface fluxes and states completed for (to be extended to ) for NAME Tier 2
Summer 2004 (May-September) LDAS forcings and fluxes being updated to include NERN data

17. Predictability issues in the context of regional hydrologic forecasting

18. 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

19. 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)
RMSE (perfect fcst, uncertain IC)
RE =

20. Basin scale modeling

21. NAMS River Basin simulations (Francisco Muñoz)

22. Sonora Basins Features (INEGI, 1993)
River Basin
Surface
(Km2)
Surface Watera
(%)
Water Useb
Annual Precipitation
(mm)
Sonora
34,654
31.8
13:85.5:1.3:0.2
Yaqui-Matape
83,578
80.0
3.5:95:0.5:1
Mayo
26,500
87.8
2.5:96.5:0.5:0.5
Sonoita
18,346
9.2
29:69:1:1
0-300
Concepcion
40,770
26.2
4:93.5:1.5:1
0-400
a The proportion of Surface Water is with respect to Groundwater
b Proportions of water uses (Urban:Agriculture:Cattle:Industrial)

30. 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)