HL Distributed Hydrologic Modeling

1. 1/59 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. 2/59 Overview Today: Goals, expectations, applicability R&D Next Call Development Strategy Implementation RFC experiences Goal is to provide information so that the DSST can �steer� us. Goal is to provide information so that the DSST can �steer� us.

3. 3/59 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. Lets be realistic. I will start with a few comments. Parameterization and Calibration are key too.

4. 4/59 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.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.

5. 5/59 Rationale Scientific motivation Finer scales > better results Data availability Field requests NOAA Water Resources Program NIDIS

6. 6/59 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. Use dmip 2 to highlight data problems in mountainous areas.

7. 7/59 Measures of Improvement Hydrographs at points (DMIP 1) Guidance from RFC Spatial Runoff Soil moisture Point to grid

8. 8/59 HL R&D Strategy 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. 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. 9/59 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. 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. 10/59 1. Distributed ModelParameterization-Calibration 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. 11/59 I will briefly discuss both types of parameters.I will briefly discuss both types of parameters.

12. 12/59 Down on right is complexity of auto calibration. A priori parameters are derived in absence of forcing data.Down on right is complexity of auto calibration. A priori parameters are derived in absence of forcing data.

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19. 19/59 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. We havent seen significant differences/improvements in the two methods in basins studied.

20. 20/59 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 resultsChannel 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

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22. 22/59 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

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24. 24/59 Probabilistic Channel Routing Parameters: BLUO2 Hydrographs

25. 25/59 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. 26/59 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. 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.

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29. 29/59 Modified SAC Publications Koren, 2005. �Physically-Based Parameterization of Frozen Ground Effects: Sensitivity to Soil Properties� VIIth IAHS Scientific Assembly, Session 7.2, Brazil, April. Koren, 2003. Parameterization of Soil Moisture-Heat Transfer Processes for Conceptual Hydrological Models�, paper EAE03-A-06486 HS18-1TU1P-0390, AGU-EGU, Nice, France, April. Mitchell, K., Koren, others, 2002. �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., 1999. �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., 1999. �Validation of a snow-frozen ground parameterization of the ETA model�, 14th Conference on Hydrology, 10-15 January 1999, Dallas, TX, by the AMS, Boston MA, pp. 410-413. http://www.nws.noaa.gov/oh/hrl/frzgrd/index.html

30. 30/59 NOAA Water Resources Program:Prototype Products Initial efforts focus on CONUS soil moisture

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33. 33/59 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.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. 34/59 Statistical Distributed Flash Flood Modeling -Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/1998 15z 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.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.

35. 35/59 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 �� 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 ��

36. 36/59 Distributed modeling fits into our view of the transition from Snow-17 to energy budget models for RFC forecasting. Includes ideas from Eric Anderson.Distributed modeling fits into our view of the transition from Snow-17 to energy budget models for RFC forecasting. Includes ideas from Eric Anderson.

37. 37/59 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. Fekadu has developed gridded parameters of Snow-17 over Conus. These are based on Eric Anderson�s recommended values.

38. 38/59 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. 39/59 Gridded (or small basin) structure Independent snow and rainfall-runoff models for each grid cell Hillslope routing of runoff Channel routing (kinematic & Muskingum-Cunge) 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.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.

40. 40/59 If ok, wil be delivered via CAP. If ok, wil be delivered via CAP.

41. 41/59 Snow Cover Simulation

42. 42/59 Flow simulation during snow periods(using lumped API model parms in each grid)

43. 43/59 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?Why do DMIP 2?

44. 44/59 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. Improve our model and understanding of processes and role of data.

45. 45/59 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. 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.

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47. 47/59 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 Analyses, conclusions, recommendations for data and tools for RFCs

48. 48/59 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. 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.

49. 49/59 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. 50/59 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

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52. 52/59 Comparison of Unadjusted and 4DVAR-Adjusted Model States (WTTO2)

53. 53/59 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. 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.

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56. 56/59 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. 57/59 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. 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.

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59. 59/59 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