1. HL Distributed Hydrologic Modeling
Mike Smith
Victor Koren, Seann Reed, Ziya Zhang, Fekadu Moreda, Fan Lei, Zhengtao Cui, Dongjun Seo, Shuzheng Cong,
John Schaake
DSST
Feb 24, 2006

2. Overview Today: Next Call Goals, expectations, applicability R&D
Development Strategy
Implementation
RFC experiences
Goal is to provide information so that the DSST can ‘steer’ us.

3. Goals and Expectations
Potential
History
Lumped modeling took years and is a good example
We’re first to do operational forecasting
Expectations
‘As good or better than lumped’
Limited experience with calibration
May not yet show (statistical) improvement in all cases due to errors and insufficient spatial variability of precipitation and basin features… but is proper future direction!
New capabilities
Gridded water balance values and variables e.g., soil moisture
Flash Flood e.g., statistical distributed
Land Use Land cover changes
Lets be realistic. I will start with a few comments. Parameterization and Calibration are key too.

4. Expectations: Effect of Data Errors and Modeling Scale
Relative Sub -
basin Scale A/ A k 1 10
100 15 20 25 30 5
Relative error, Ek
, %
(lumped)
(distributed)
Noise % % % %
‘Truth’ is simulation from 100 sub-basin model
Simulation error compared to fully distributed
clean data
Initial thinking with distributed models was that the many grids would ‘smooth’ out the effects of data errors. However, most models are not linear and tend to magnify the errors rather than smooth them.
Data errors (noise) may mask the benefits of fine scale modeling.
In some cases, they may make the results worse than lumped simulations.

5. Rationale Scientific motivation Field requests
Goals and Expectations
Rationale
Scientific motivation
Finer scales > better results
Data availability
Field requests
NOAA Water Resources Program
NIDIS

6. Applicability Distributed models applicable everywhere Issues
Goals and Expectations
Applicability
Distributed models applicable everywhere
Issues
Data availability and quality needed to realize benefits
Parameterization
Calibration
Use dmip 2 to highlight data problems in mountainous areas.

7. Measures of Improvement
Goals and Expectations
Measures of Improvement
Hydrographs at points (DMIP 1)
Guidance from RFC
Spatial
Runoff
Soil moisture
Point to grid

8. HL R&D Strategy Conduct in-house work Collaborate with partners
Goal: produce models, tools, guidelines to improve field office operations
Conduct in-house work
Collaborate with partners
U. Arizona, Penn St. University
DMIP 1, 2
ETL
Work closely with RFC prototypes
ABRFC, WGRFC: DMS 1.0
MARFC, CBRFC: in-house
Publish results
NAS Review of AHPS Science
I could elaborate on each type separately, but we have a coherent program so I will discuss the research topics and how each partner fits in.

9. R&D Topics
Parameterization/calibration (with U. Arizona and Penn State U.)
Soil Moisture
Flash Flood Modeling: statistical distributed model, other
Snow (Snow-17 and energy budget models in HL-RDHM)
DMIP 2
Data assimilation (DJ Seo)
Links to FLDWAV
Impacts of spatial variability of precipitation
Data issues
R&D areas being investigated to support the RFC’s, WFOs and water Resources program. The following slides elaborate on a few of these. Data issues, space and time scale issues are covered in DMIP 2. Snow work overlaps with our plans to migrate to energy budget snow modeling for RFC river forecasting. HL-RDHM has been successfully linked to a stand alone version of FloodWave for the Tar River project.

10. 1. Distributed Model Parameterization-Calibration
Strategy for Sacramento Model
Explore STATSGO data as its has national coverage (available in CAP)
Explore SSURGO fine scale soils data for initial SAC model parameters (deliverable: parameter data sets in CAP)
Investigate auto-calibration techniques
HL: Simplified Line Search (SLS) with Koren’s initial SAC estimates.
U. Arizona: Multi-objective techniques with HL-RDHM and Koren’s initial SAC parameters.
Continue expert-manual calibration

11. HL-RDHM Parameterization - Calibration Steps
1. Distributed Model Parameterization/Calibration
HL-RDHM Parameterization - Calibration Steps
Water balance parameters
Spatial
data
Variable basin properties
Variable
Scale
(STATSGO 1x1 km grids:
Transform.
model
adjusted
Soil texture, Hydrologic Soil
relationships
parameters
parameters
Group, Land cover/use)
Lumped
Hourly
Calb.
Observed
Lumped,
Outlet
Rescaled
Observed
Area average
outlet
Semi -
lumped
calibrated
variable
outlet
parameters
hydrograph
calibration
parameters
parameters
hydrograph
Channel routing parameters
I will briefly discuss both types of parameters.
Channel
Variable
Measured data at outlet
Geomorpho -
Fitting curve
routing
channel
(discharge, top width,
logical
parameter
parameters
routing
cross -
section)
relations
adjustment
at outlets
parameters
Spatially variable basin properties
Observed
(slope, area, drainage density)
outlet
hydrographs

12. Parameterization and Calibration R&D Strategy
Combine Improved a priori parameter estimates with Auto-calibration techniques
a priori parameter estimates
auto-calibration techniques
HL: STATSGO
SAC parms. (in CAP at RFCs)
HL Lumped auto calibration using
SCE and SLS 1
HL: Mod STATSGO
SAC parms. 2
HL Dist auto
calibration of HL-RDHM adj.
factors: SCE, SLS
HL: SSURGO
SAC parms
Gridded
Parm
Values 3
Reduce uncertainty
HL: Climate SAC
Parm adjustment
(large area runs)
HL Dist auto
calibration of HL-RDHM grid parms: SLS
Down on right is complexity of auto calibration. A priori parameters are derived in absence of forcing data.
U. Az: Multi-objective
Optimization of 1) HL-RDHM adj. factors, 2) grid
parameters
U. Arizona:
Parameter
Uncertainty
SCE: Shuffled Complex Evolution SLS: Simplified Line Search

13. Soils Data for SAC Parameters Description of SSURGO data*
1. Distributed Model Parameterization/Calibration
Soils Data for SAC Parameters
Description of SSURGO data*
* The Penn State Cooperative Extension, Geospatial Technology Program (GTP) Land Analysis Lab
Polygon – a soil map unit; it represents an area dominated by one to three kinds of soil
Components – are different kinds of soil. Components are each separate soils with individual properties and are grouped together for simplicity's sake when characterizing the map unit.
Horizons – are layers of soil that are approximately parallel to the surface. Up to six horizons may be recorded for each soil component.
Polygon
Components
Horizons

14. Soils Data for SAC Parameters
1. Distributed Model Parameterization/Calibration
Soils Data for SAC Parameters
Demonstration of scale difference between polygons in STATSGO and SSURGO
SSURGO
STATSGO

15. 2 km Grid Connectivity for Distributed Channel Routing
1. Distributed Model Parameterization/Calibration
Results of SSURGO and STATSGO
Parameters for Distributed Modeling
Basin Locations and Land Cover 1 2 3 5 4 6 8 7 10 9 11
Oklahoma
Arkansas 1 2 3 5 4 6 8 7 10 9 11 12
2 km Grid Connectivity for
Distributed Channel Routing

16. Results of SSURGO and STATSGO Parameters for Distributed Modeling
Parameterization
Calibration
Results of SSURGO and STATSGO
Parameters for Distributed Modeling
Comparison of Rm for whole time series of 11 basins
Rm: Modified correlation coefficient. It is calculated by reducing normal correlation coefficient by the ratio of the standard deviations of the observed and simulated hydrographs.
Overall Rm--SSURGO-based > Rm--STATSGO-based for most basins
More physically-based representation of the soil layers!
More detailed spatial variability

17. Hydrograph Comparison
1. Distributed Model Parameterization/Calibration
Results of SSURGO and STATSGO
Parameters for Distributed Modeling
SSURGO
Hydrograph Comparison
__ Observed flow
__ SSURGO-based
__ STATSGO-based
Cave Springs
STATSGO

18. Comparison of SCE and SLS calibration processes
1. Distributed Model Parameterization/Calibration
Comparison of SCE and SLS calibration processes 1 3 2
1) SLS needs less function evaluations, but it leads to similar result;
2) SLS stops much faster and closer to the start point (a priori parameters);
3) On some basins, SCE misses the nearest ‘best’ solution.
4) SLS in AB-OPT
Distance from starting parameters

19. HL-RDHM Kinematic Wave Solution
1. Distributed Model Parameterization/Calibration
HL-RDHM Kinematic Wave Solution
Uses implicit finite difference solution technique
Need Q vs. A for each cell to implement distributed routing
Derive relationship at outlet using observed data
Extrapolate upstream using empirical/theoretical relationships
Two methods are available in HL-RDHM
‘Rating curve’ method : parameters a and b in Q = aAb estimated based on empirical relationship
‘Channel shape’ method: parameters estimated from estimates of slope, roughness, approximate channel shape, and Chezy-Manning equation
We havent seen significant differences/improvements in the two methods in basins studied.

20. Illinois River at Watts, OK
1. Distributed Model Parameterization/Calibration
Channel Width (a) and Shape (b) Parameter Estimation
1. Assume relationship between top width and depth:
2. Solve for a and b using streamflow measurement data:
Illinois River at Watts, OK
Example cross section
(a = 36.6, b = 0.6) B H
Channel top width parameter a is spatially variable within a basin
Channel shape parameter b is assumed constant
Note: data averaging was used to force low not to have undue influence on the results

21. Estimate Upstream Parameters Using Relationships from Geomorphology
1. Distributed Model
Parameterization
Calibration
Estimate Upstream Parameters Using Relationships from Geomorphology
Channel Model
Parameterization

22. Probabilistic Channel Routing Parameters
1. Distributed Model Parameterization/Calibration
1. Parameterization
Probabilistic Channel Routing Parameters
Basic concepts
Discharge – cross-section relationship obeys multiscale lognormal bivariate Gaussian distribution
The scale dependence of hydraulic geometry is a result of the asymmetry in channel cross-section (CS)
Application
Define CS geometry as a function of scale from site measurements
Define channel planform geometry as a function of scale
Define floodplain CS geometry as a function of scale from DEM
Monte-Carlo simulations to fit to multiscale lognormal model

23. 1. Distributed Model Parameterization/Calibration
DERIVED PROBABILISTIC HG: Accounting for the variability of channel and floodplain shapes.
Exp{E[lnCA|lnQ]}
Exp{E[lnV|lnQ]}
marginal PDFs of discharge

24. Probabilistic Channel Routing Parameters: BLUO2 Hydrographs
1. Distributed Model Parameterization/Calibration
Probabilistic Channel Routing Parameters: BLUO2 Hydrographs
With flood plain
Without flood plain
Observed

25. 2. Distributed Modeling and Soil Moisture
Use for calibration, verification of models
New products and services
NCRFC: WFO request
OHRFC: initialize MM5
NIDIS
NOAA Water Resources

26. Modified Sacramento Soil Moisture Accounting Model
(developed for Frozen Ground)
2. Soil Moisture
Modified Sacramento Soil Moisture Accounting Model
In each grid and in each time step, transform conceptual soil water content to physically-based water content
Physically-based
Soil Layers and
Soil Moisture
Sacramento
Model
Storages
Sacramento
Model
Storages
SMC1
UZTWC
UZFWC
LZTWC
LZFSC
LZFPC
UZTWC
UZFWC
LZTWC
LZFSC
LZFPC
SMC2
Gridded precipitation, temperature
SMC3
SMC4
Soil moisture is generated using V. Koren’s modified SAC model to compute physically based volumetric water content .
Sac model is one of the most well validated models around. Much more so than some of the newer so-called land surface models. Doesn’t mean we can learn from them, but SAC is still a good model.
SMC5
CONUS scale 4km gridded soil moisture products using SAC and Snow-17

27. Validation of Modified Sacramento Model
2. Soil Moisture
Validation of Modified Sacramento Model
Soil moisture
Computed and observed soil
Moisture and temperature:
Valdai, Russia,
Soil temperature

28. Validation of Modified Sacramento Model
2. Soil Moisture
Validation of Modified Sacramento Model
observed
Frozen ground
Non frozen ground
Comparison of observed, non-frozen ground, and frozen
ground simulations: Root River, MN

29. Modified SAC Publications 2. Soil Moisture
Koren, “Physically-Based Parameterization of Frozen Ground Effects: Sensitivity to Soil Properties” VIIth IAHS Scientific Assembly, Session 7.2, Brazil, April.
Koren, Parameterization of Soil Moisture-Heat Transfer Processes for Conceptual Hydrological Models”, paper EAE03-A HS18-1TU1P-0390, AGU-EGU, Nice, France, April.
Mitchell, K., Koren, others, “Reducing near-surface cool/moist biases over snowpack and early spring wet soils in NCEP ETA model forecasts via land surface model upgrades”, Paper J1.1, 16th AMS Hydrology Conference, Orlando, Florida, January
Koren et al., “A parameterization of snowpack and frozen ground intended for NCEP weather and climate models”, J. Geophysical Research, 104, D16, 19,569-19,585.
Koren, et al., “Validation of a snow-frozen ground parameterization of the ETA model”, 14th Conference on Hydrology, January 1999, Dallas, TX, by the AMS, Boston MA, pp

30. NOAA Water Resources Program: Prototype Products
2. Soil Moisture
NOAA Water Resources Program: Prototype Products
Initial efforts focus on CONUS soil moisture
HL-RDHM soil moisture for April 5m z
Soil moisture (m3/m3)

31. Comparison of Soil Moisture Estimates
MOSAIC
HL-RDHM
HL-RDHM:
Higher
Correlation
Upper 10cm
Lower 30cm
Source: Moreda et al., 2005.

32. Why a frequency- based approach?
3. Flash
Flood
A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations
Real-time
Historical
Why a frequency- based approach?
Frequency grids provide a well-understood historical context for characterizing flood severity; values relate to engineering design criteria for culverts, detention ponds, etc.
Computation of frequencies using model-based statistical distributions can inherently correct for model biases
Archived QPE
Real-time QPE/QPF
Distributed hydrologic model (HL-RDHM)
Initial hydro
model states
Distributed hydrologic model (HL-RDHM)
Max forecasted
peaks
simulated historical peaks (Qsp)
Statistical
Post-processor
Simulated peaks distribution (Qsp) (unique for each cell)
Forecasted
frequencies
Next step to define requirements for prototype

33. Statistical Distributed Flash Flood Modeling-
Example Forecasted Frequency Grids Available at 4 Times on 1/4/1998
14 UTC
15 UTC
In these examples, frequencies are derived from routed flows, demonstrating the capability to forecast floods in locations downstream of where the rainfall occurred.
16 UTC
17 UTC
Each cell contains the frequency of the maximum flow forecasted in the next 4 days at the given forecast time.
Note that the model can forecast floods in counties downstream of where the primary rainfall/runoff occurs.

34. Statistical Distributed Flash Flood Modeling - Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/ z
~11 hr lead time
Eldon (795 km2)
Implicit
statistical adjustment
~1 hr lead time
Dutch (105 km2)
Note again that all simulation and forecast hydrographs shown in this presentation are uncalibrated model results which show reasonable results at the interior point (Dutch mills). The blue diamond shows the forecasted peak back calculated from the statistical-distributed adjustment. For these two basins/cases the improvement is clear. Christi is an unusual basin (not shown here) where the statistical-adjustment actually degrades results in this case and based on average results (slide 8). I think precip data errors can explain this but I’m not sure. See notes for slide 8.
3. Flash Flood

35. Where does Site Specific fit?
3. Flash
Flood
Where does Site Specific fit?
In this domain:
-Statistical Distributed
-Distributed
-Site Specific with snow
Var, routing
RFC
modeling capability
WFO
All moving to finer scales, could be combination of lumped site specific and distributed modeling. FFGIT report: “ distributed modeling will be the long term scientific enhancement for flash floodmodeling. In mean time, statistical distributed …”
Site Specific, FFG, other
spatial scale
Perception of Modeling Trends

36. 4. Distributed modeling and snow
Transition from Snow-17 to Energy Budget Model
for RFC Operations: HL Activities
100
Snow-17 at RFCs
% Model Use
Energy Budget Model
Distributed modeling fits into our view of the transition from Snow-17 to energy budget models for RFC forecasting. Includes ideas from Eric Anderson.
Today
Time
Today + (?) years
Use of Snodas
Output in runoff
models
Distributed
Snow-17
Calb-OFS biases
Use of Snodas
Output in Snow-17 HL
Activities
New data for Snow-17
(wind speed, etc)
Sensitivity of Energy
Budget model to data
errors
Snow-17 MODs based
On Snodas

37. 4. Distributed modeling and snow
Distributed Snow-17
Strategy: use distributed Snow-17 as a step in the migration to energy budget modeling: what can we learn?
Snow-17now in HL-RDHM
Tested in MARFC area and over CONUS (delivered historical data)
Further testing in DMIP 2
Gridded Snow-17 parameters for CONUS under review (could be delivered in CAP)
Related work: data needs for energy budget snow models
Fekadu has developed gridded parameters of Snow-17 over Conus. These are based on Eric Anderson’s recommended values.

38. Current approach SNOW-17 model within HL-RDHM
4. Distributed modeling and snow
Current approach SNOW-17 model within HL-RDHM
SNOW-17 model is run at each pixel
Gridded precipitation from multi-sensor products are provided at each pixel
Gridded temperature inputs are provided by using DEM and regional temperature lapse rate
The area depletion curve is removed because of distributed approach
Other parameters are studied either to replace them with physical properties or relate them to these properties, e.g., SCF.

39. HL-RDHM Features: P, T & ET SNOW -17 Rain + melt
4. Distributed modeling and snow
HL-RDHM
P, T & ET
SNOW -17
Rain + melt
Features:
Gridded (or small basin) structure
Independent snow and rainfall-runoff models for each grid cell
Hillslope routing of runoff
Channel routing (kinematic & Muskingum-Cunge)
SAC-SMA or CONT-API
surface runoff
base flow
hillslope routing
HL-RDHM is basically optimize the experience of the existing lumped model concept and the usage of spatial data such as DEM and its derivatives.
Channel routing
Flows and State variables

40. Parameterization of Distributed Snow-17
4. Distributed modeling and snow
Parameterization of Distributed Snow-17
Min Melt Factor
Derived from:
Aspect
Forest Type
Forest Cover, %
Anderson’s rec’s.
If ok, wil be delivered via CAP.
Max Melt Factor

41. Energy-budget model assimilated
4. Distributed modeling and snow
Snow Cover Simulation
…Case Study
Snow cover obtained from energy-budget and Snow-17 model qualitatively agree well
Energy-budget model assimilated
Distributed Snow-17
December 12, Z

42. 4. Distributed modeling and snow
Flow simulation during snow periods (using lumped API model parms in each grid)

43. 5. DMIP 2
HL distributed model is worthy of implementation: we need to improve it for RFC use in all geographic regions
Partial funding from Water Resources
Much outside interest
HMT collaboration
Why do DMIP 2?

44. DMIP 2 Science Questions
Confirm basic DMIP 1 conclusions with a longer validation period and more test basins
Improve our understanding of distributed model accuracy for small, interior point simulations: flash flood scenarios
Evaluate new forcing data sets (e.g., HMT)
Evaluate the performance of distributed models in prediction mode
Use available soil moisture data to evaluate the physics of distributed models
Improve our understanding of the way routing schemes contribute to the success of distributed models
Continue to gain insights into the interplay among spatial variability in rainfall, physiographic features, and basin response, specifically in mountainous basins
Improve our understanding of scale/data issues in mountainous area hydrology
Improve our ability to characterize simulation and forecast uncertainty in different hydrologic regimes
Investigate data density/quality needs in mountainous areas (Georgakakos et al., 1999; Tsintikidis, et al., 2002)
Improve our model and understanding of processes and role of data.

45. Phase 2 Scope Distributed Model Intercomparison Project (DMIP) HMT
Nevada
Missouri
American
River
Kansas
Elk River
Carson
River
Illinois
River
HMT
Oklahoma
California
Arkansas
Blue River
The second phase of DMIP proposes to use the North Fork of the American River as one of its test basins. DMIP 2 will also contain additional tests on the basins forming DMIP 1.
ETL HMT data collection activities will be an exciting component of DMIP 2. Also, HMT will benefit from a multi-institutional evaluation of HMT products in an end-to-end fashion. Chandra Kondragunta will talk next about data requirements.
Texas
Tests with Complex Hydrology
Snow, Rain/snow events
Soil Moisture
Lumped and Distributed
Data Requirements in mtn West
Additional Tests in DMIP 1 Basins
Routing
Soil Moisture
Lumped and Distributed
Prediction Mode

46. 5. DMIP 2

47. DMIP 2 & HMT-West Research to Operations
Basic precip and temp data (gage only gridded)
Basic data enhanced by HMT observations:
-Network Density 1
-Network Density 2
-Network Density 3
Distributed model simulations
USGS
HL-RDHM
USBR
Others
Analyses,
conclusions,
recommendations for
data and tools for
RFCs
“What new data types are becoming available? What densities of observations
are needed? Which models/approaches work best In mountainous areas?”

48. DMIP 2: Potential Participants
Witold Krajewski
Praveen Kumar
Mario DiLuzio, ARS, TAES
Sandra Garcia (Spain)
Eldho T. Iype (India)
John McHenry, BAMS
Konstantine Georgakakos
Ken Mitchell (NCEP)
Hilaire F. De Smedt (Belgium) HL
Vincent Fortin, Canada
Robert Wallace, USACE, Vicksburg
Murugesu Sivapalan, U. Illinois
Hoshin Gupta, U. Arizona
Thian Gan, (Can.)
Newsha Ajami (Soroosh)
Vazken Andreassian (Fra)
George Leavesley (USGS)
Kuniyoshi Takeuchi (Japan)
Vieux and Associates
John England (USBR)
Andrew Wood, Dennis Lettenmaier, U. Washington
Martyn Clarke
South Florida Water Mngt. District
David Tarboton, Utah St. U.
David Hartley, NW Hydraulic Consultants
These groups have expressed interest in DMIP 2. John England has already registered, and Vazken is already setting up his models. Vitek Krajewski will help with data analysis.
Names in red have officially registered

49. Basic DMIP 2 Schedule Feb. 1, 2006: all data for Ok. basins available
July 1, 2006: all basic data for western basins available
Feb 1, 2007: Ok. simulations due from participants
July 1, 2007: basic simulations for western basins due from participants

50. 6. Data Assimilation for Distributed Modeling
Needed since manual OFS ‘run-time mods’ will be nearly impossible
Strategy based on Variational Assimilation developed and tested for lumped SAC model
Initial work in progress

51. Initial simulation 6. Data Assimilation WTTO2 in ABRFC
WTTO2 channel network
Assimilation period:
streamflow, PE, precip
Initial
simulation
6. Data Assimilation

52. Comparison of Unadjusted and 4DVAR-Adjusted Model States (WTTO2)
6. Data Assimilation

53. 7. Distributed Modeling and Links to
FloodWave
Rocky
Mount
Tarboro
rain depth
Channel Routing
and
Flood Mapping
Of Tar River below
Rocky Mount
Rainfall Data
Tarboro
Basic purpose of project is to show linkage of models
precip data will be generate on a grid and used a direct input to the distributed model
The distributed model will be set up over entire Tar Basin Domain. It will generate lateral inflows for direct input into the Flood Wave model. This is a new and important development: a distributed model directly linked to an advanced channel routing model in an operational setting. Very few instances of this as far as I know.
The Flood Wave model will be able to generate flood inundation maps for the Tar River Below Rocky mount. Flood Wave will have a two way linkage to the Estuary model. Floodwave provides flows into the Estuary model, while the Estuary model provides the down stream boundary condition for Flood Wave.
Distributed
Model of Tar
River Basin
Estuary Model

54. 7. Distributed Modeling and FloodWave
Inflow hydrograph
From HL-RDHM 1
HL-RDHM grid
In this example,
HL-RDHM provides:
Upstream inflow
hydrograph.at 1.
2. 5 lateral inflow
hydrographs to
floodwave between
cross sections 1 and 2.
floodwave
Floodwave
lateral inflow
reaches 2

55. 7. Distributed Modeling and FloodWave: Example
Hydrographs at Greenville, Tar River
400
350
300
SAC-SMA ‘warm-up’
250
200
150
100 50
Date
observed
simulated
Initial Simulation of Tar River using HL-RMS (no Flood Wave). No calibration.
After the warm up period, the simulation is good. Uses only Victor’s a priori
parameters.

56. 8. Impact of Spatial Variability
Question: how much spatial variability in precipitation and basin features is needed to warrant use of a distributed model?
Goal: provide guidance/tools to RFCs to help guide implementation of distributed models, i.e., which basins will show most ‘bang for the buck’?
Initial tests completed after DMIP 1: trends seen but no clear ‘thresholds’

57. 8. Impact of Precipitation Spatial Variability
precipitation at time t +Dt
precipitation at time t
input
‘filter’
precipitation at time t + 2Dt
Example,
When is the variability of precipitation great enough to overcome the filtering and damping effects of the basin (Obled et al., 1994; Smith et al., 2004) to cause variability in the outlet hydrograph that can’t be modeled well using lumped models? Base analyses on observed input and output data (not models) to generate diagnostic indicators.
flow
output
time

58. ’06 Funding HOSIP Stage Topic AHPS WR 1 2 3 4 DMIP 2 110
110
Parameterization:
SSURGO/STATSGO
100
Regionalized SAC-Snow Parameters 30
Auto Calibration: Arizona 75
Auto Calibration: HL
Snow-17 and HL-RDHM
Large Area Simulation for WR products 31
Statistical Distributed
VAR for Distributed Modeling
Spatial Variability
DHM 2.0 AWIPS (HSEB)
200 ?

59. Conclusions Distributed models are proper direction
Account for spatial variability:
Parameterization
Calibration
Better results at outlets of some basins
Amenable to new data sources
Scientifically supported flash flood modeling
New products and services