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

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.

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.

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 0% 25% 50% 75% ‘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.

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

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.

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

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.

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.

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

Validation of Modified Sacramento Model 2. Soil Moisture Validation of Modified Sacramento Model Soil moisture Computed and observed soil Moisture and temperature: Valdai, Russia, 1972-1978 Soil temperature

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

Modified SAC Publications 2. Soil Moisture 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

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 2002 12z Soil moisture (m3/m3)

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

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

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.

Statistical Distributed Flash Flood Modeling - Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/1998 15z ~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

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

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

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.

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.

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

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

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, 2003 12Z

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

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?

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.

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

5. DMIP 2

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?”

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

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

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

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

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

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

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

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.

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’

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

’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 ?

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