Overview

Strategy and Initial Results

Primarily, work under Phase I  focused onapplying the   Sacramento Soil Moisture Accounting Model (SAC-SMA) in a lumped and semi-distributed manner.  Before any modeling wasperformed, several issues were  investigated.  First, we firstcarried out several analyses of the mean areal precipitation values derivedfrom traditional rain gage networks and Stage III data.  We foundthat seasonal and annual biases on the order of 5-10% often exist betweenthe two products, indicating that the SAC-SMA calibrated at a 6-hour timestep with rain gage data must be recalibrated for use with hourly meanareal values from NEXRAD Stage III.  These tests have been expandedto include several more years of radar data as well as data from the calibrationrain gage network (Wang, et. al, 2000)   In addition, extensive tests were performed to analyze the sensitivity of the SAC-SMA and threeother models to spatial scale.  These tests revealed that the fastresponding runoff  components  from the SAC-SMA are very sensitiveto changes in the scale of precipitation forcing.  Also, it was determinedthat the Reformulated SAC-SMA was the least sensitive to changes in spatial scale.   Multi-year continuous  lumped simulations drivenby Stage III data and traditional rain gage data  were derived forseveral basins in the Illinois River watershed in Oklahoma.  In thesetests, it was found that the radar forced simulations were better thanthe gage forced simulations. Lumped parameter simulations of these basinsdriven by Stage III data  compared  well to the observed streamflowrecords. Thus, for these basins, lumped hourly applications of theSAC-SMA proved to be an effective way to use the NEXRAD Stage III data.

Simulations from semi-distributed applicationsof the SAC-SMA to the same basins were also generated.  Theintent of  this work was to capture the spatial variability  of precipitation while keeping the Sacramento model parameters spatiallyuniform.  Multi-year simulations of lumped and semi-distributed applicationsof the SAC-SMA were evaluated.  These tests are among the first comparisonsof lumped vs. distributed continuous   modeling approaches usinghigh resolution radar estimates of precipitation.  Surprisingly, itwas found that semi-distributed modeling approach did not provide significantimprovement over the hourly lumped simualations, even in cases with identifiedspatial variability of precipitation.  Problems related to sub-basinSAC-SMA parameter identification and calibration are identified and discussedin the Phase 1 report.

 The results from Phase I  investigations have led to theitems under Current Projects
 

Current Projects

   A.  Within HL

 
1. Distributed Modeling with HL-RMS

HL has begun development of the Research Modeling System (RMS). Click here to see a brief overview and actual results.
 
2. Development and Testing of the Reformulated SAC-SMA

This work is a short term practical  effort to modify a lumpedmodel to exploit the spatial variability of precipitation as measured byNEXRAD. ( Reformulationof the SAC-SMA  Model to Account for the Spatial Variability of Rainfall)   This allows the lumped SAC-SMA to generate fast response surface runoffsomewhere in the basin even though the  mean areal precipition amountis not enough to generate surface runoff from the entire basin. The upperzone tension water (UZTWM)  and upper zone free water (UZFWM) componentsof the SAC-SMA have been modified to accept a distribution function ofrainfall derived from hourly  Stage III data. Analysis of the spatialvariability of hourly precipitation indicates that a gamma distributioncan be used as and approximation of the empirical distriubution. The parametersof the distribution are computed for the rainy area and are based on meanareal precipitation and coefficient of variation.  Preliminary testingof the model has shown that it is less sensitive to changes in spatialscale ( DistributedModeling: Phase 1 Results, ScaleDependencies of Hydrologic Models to Spatial Variability of Precipitation).  Initial testing is currently underway with the Elk River above the USGSgage at Tiff City, MO.  Plans are to test this approach further withBaron Fork above Eldon, OK, Peacheater Ck. above Christie, OK, and the Blue River above Blue, OK.
 
3. Development and Testing of the LinearDistributed Routing Model (LDRM)

The LDRM has a grid point structure. Connectivity of the grid pointsis determined based on both stream network definition and elevation datausing an eight direction rule. It is assumed that each grid cell consistsof a number of the hillslopes which are the source of water for the ‘main’grid channel. All hillslopes have the same characteristics inside eachgrid cell but they may be different from pixel to pixel. It is also assumedthat all surface and sub-surface runoff of a pixel reaches the ‘main’ channelof that pixel. The ‘main’ channel is the only source of water exchangebetween neighboring pixels. A linearized kinematic wave model integratedover each pixel is used to route runoff within the cell to the stream (hillsloperouting), and to move water from one grid cell to another (channel routing).The grid cell size can be a few hundreds of meters to tens of kilometers.The lower limit of grid cell size is determined by the resolution of inputdata and geographical information. The upper limit is determined by thedegree of heterogeneity and stream channel network structure of the basin.Two parameters should be estimated for each grid cell:  hillslopeand channel lag time parameters, although constant values throughout allpixels can provide reasonable practical results in some cases. Lag timeparameters can be estimated based on an available unit hydrograph at abasin outlet (case of constant parameters for all pixels), or by usingempirical relationships between GIS data and water velocity. A preliminaryversion of the LDRM is available with the HRAP grid resolution. Distributed (with HRAP grid resolution) or lumped over entire basin or a number ofsub-basins total runoff can be used as an input to  LDRM. DistributedHRAP grid rainfall can be used as a pattern to distribute runoff if lumpedinput version is used.
 
4. Semi-Distributed Modeling of the BlueRiver, OK.

We are continuing our work with SAC-SMA semi-distributed modeling withan investigation of the Blue River above the U.S.G.S. gage at Blue, OK.  Preliminary lumped modeling of this basin with Stage III data indicatedpotential improvements to be gained by distributed modeling approaches.This elongated basin has been disaggregated into 8 sub-basins, each withits own  synthetic unit hydrograph and SAC-SMA parameters.  TheSAC-SMA parameters were derived  using a procedure developed by VictorKoren ( Useof Soil Property Data in the Derivation of Conceptual Rainfall-Runoff ModelParameters)  Initial testing of this semi-distrbutedapproach has shown clear improvement in basin outlet simulation accuracycompared to lumped modeling.  A Muskingum-Cunge routing scheme isbeing tested for improved channel routing of runoff. Channel cross sectiondata has been obtained from local agencies as well as a field visit. A scheme for calibrating a semi-distributed model is also being developed.
 
5. Derivation of A Priori Estimates ofSAC-SMA Model Parameters from GIS-Soils Data

This study is focused on developing a procedure to derive the SAC-SMAmodel parameters based on soil texture data.  It is hoped that thismethod can be used to derive initial estimates of SAC-SMA parameters forboth lumped areas and for sub-basins in a semi-distributed applicationof the SAC-SMA.  To quantify relationships of model parameters withsoil properties, the assumption was made that the SAC-SMA tension waterstorages relate to an available soil water, and that free water storagesrelate to gravitational soil water.  Porosity, field capacity, andwilting point derived from STASGO dominant soil texture for eleven standardlayers were used in estimating available and gravitational water storages. SCS runoff curve numbers and saturated hydraulic conductivity of differentsoils were also used.  Analytical relationships were derived for 11SAC-SMA model parameters.  Preliminary tests on a few basins in differentregions suggest that most parameters derived from soil properties agreedreasonably well with calibrated parameters for those basins.  Accuracystatistics of hydrographs simulated using calibrated and derived parameterswere also close.  Although calibrated parameter simulations usuallygive higher accuracy, the gain is not significant.  It means thatparameters derived from soils data are very reasonable, and  can beimproved by using calibration if observed historical data are available. The procedure has been tested on several basins so far with promising results.
 
6. GeospatialDatabases and Programs Developed to Assist with Flash-Flood Guidance (derivingthreshold runoff values) and Distributed Modeling

 

 
 
 
 
 

Overview:    The Hydrologic Research Lab is activelyengaged in cooperative research with a number of organizations.  Throughthese efforts and HRL internal research, it is hoped that we can achieveoptimal use of  high resolution NEXRAD precipitation products to improvethe ability of the NWS to forecast river flows.

 
1. MIT-HRL Cooperative Research

The purpose of this collaborative research is to compare simulationsfrom a fully distributed, physically-based hydrologic  model withthose from a lumped application of the Sacramento Soil Moisture AccountingModel. Comparisons will be performed on several basins representing differentclimatic regimes across the country. Initially, tests will be conductedon  Baron Fork at Eldon, OK (307 sq. mi), Peacheater Creek at Christie,OK, (25 sq. mi. sub-basin of Baron Fork, and the Blue River at Blue, OK( 476    sq. mi.). Following these,  tests will be conductedon  the Cheat River in  W.VA.,  the Marais Des Cygnes Riverin Kansas and a small river basin in  Massachusetts.  The MITmodel uses a TIN representation of the landscape and computes runoff usingboth the Hortonian Infiltration Excess and Saturation Excess mechanisms.
 
2. HRC-HRL Cooperative Research

HRLis continuing its research with theHydrologic Research Center.  This project focuses on Monte-Carlo randomsampling of parameters and radar rainfall input perturbations in the generationof an ensemble of flow traces from a distributed model. Several basinsaround the U.S. will be examined.   Questions such as "Is spatiallydistributed model event response different from the spatially-lumped modelresponse in view of parametric and rainfall input uncertainty"? and "Howdependent are the answers on model discretization scale?" will be addressed. We hope to identify  characteristics that make basins good candidatesfor distributed modeling.
 
3. University of Arizona Cooperative Research

HRL has had a long standing cooperative research program with the Universityof Arizona regarding automated methods for calibrating lumped hydrologicmodels.  Lately, this work has been expanded to include approachesfor calibrating semi-distributed versions of the SAC-SMA.

• 2002 Federal Interagency Hydrologic Modeling Conference (FIHMC) Paper Describing HL-RMS

• HL-RMS User Manual

• Whats required to get started with HL-RMS?

Future plans
HRL is planning to host an "Intercomparison of distributed, semi-distributed, and lumped hydrologic models for operational forecasting."  We plan to announce this intent during the AMS Hydrology Committee meeting Wednesday January 12, 2000.  We will invite others (other Federal agencies, universities, the private sector) to help us plan this activity.  We will have final details in place for announcement during the Spring 2000 AGU meeting (May 30-June 3 in D.C.) at the NWS/OH organized session on distributed modeling.

In general, we plan to provide Intercomparison objectives and test procedures,as well as test data sets via the web.  We anticipate Intercomparisonparticipants to download data, conducts tests at their facilities, andpost results on the Intercomparison web site.  We will host a workshopafter tests have been completed for Intercomparison participants to discussmodel pros and cons, and to record strengths and weaknesses in an operationalsetting.
 

Tools
In the course of  research, we have developed the followingtools for use in HRL and in the RFCs:

 
• GIS tools
• Displaying and using XMRG/HRAPfiles with ArcView or Arc/Info GIS
• MAPX preprocessor:  This program is used to derive multi-year timeseries of mean areal values of precipitation derived from the Stage IIIxmrg files.  These time series  are called MAPX time series inthe NWS, and are used in calibration and simulation experiments. . A basinboundary in Lat/Lon coordinates is required.  A beta version of thisprogram has been released to the RFCs, and can be used to derive MAPX timeseries for lumped basins as well as semi-distributed sub-basins. A formal version of this program is planned for this year.
• Plotting Routines for Manual Calibration:  The Interactive CalibrationProgram (ICP) has been enhanced with the addition of the PLOT-TS function. This graphical routine allows the user to display up to six panes, eachcontaining a number of plotted time series. Time series with differenttime steps can be plotted.
• Hourly Statistics program:  This code is used for evaluating resultsfrom hourly time step modeling experiments, both lumped and semi-distributed.It is planned that a final version of this code will be incorporated intothe ICP.

• NOAA Technical Report NWS 51: NOAA Distributed Model Research and Development (2004) 2.7mb
• NOAA Technical Report NWS 50: Distributed Modeling: Phase 1 Results (1999) 221 pages (ordering info: contact Mike Smith)
• B.D. Finnerty, M.B. Smith, V. Koren, D.J. Seo, G. Moglen, Space-Time Scale Sensitivity of the Sacramento Model to Radar-Gage Precipitaton Inputs, Journal of Hydrology, 203 (1997) 21-38.
• Distributed Parameter Hydrologic Modeling and NEXRAD for River Forecasting: Scale Issues Facing the National Weather Service
• D. Johnson, M.B. Smith, V. Koren, and B. Finnerty, Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks, Journal of Hydrologic Engineering, Vol. 4, No. 2 April, 1999,  117-124.
• D. Wang, M.B. Smith, Z. Zhang, S. Reed, V. I. Koren, Statistical Comparison of Mean Areal Precipitation Estimates from WSR-88D, Operational,and Historical Gage Networks, 80th Meeting of the AMS, Long Beach, Ca., January 10-14, 2000.
• M.B. Smith, V. Koren, Z. Zhang, D. Wang, Semi-Distributedand Lumped Modeling Approaches: Case Study of NEXRAD Data Application toLarge Headwater Basins in the Arkansas River Basin, 1999, Spring Meeting of the AGU, Boston.
• V. I. Koren, B.D. Finnerty, J. C. Schaake, M. B. Smith, D. J. Seo, Q. Y. Duan, Scale Dependenciesof Hydrologic Models to Spatial Variability of Precipitation
, Journal of Hydrology 217(1999) 285-302.
• ThePotential for Improving Lumped Parameter Models using Remotely Sensed Data, V. Koren Paper J1.10, 13th Conference on Weather Analysis and Forecasting, August 2-6, 1993, Vienna, Virginia, 397-400.
• V. Koren, M.B. Smith, D.J. Seo, B.D. Finnerty, Reformulation of the SAC-SMA Model to Account for the Spatial Variability of Rainfall. (HRL internal publication. Contact Mike Smith for a copy)
• V. I. Koren, M. Smith, D. Wang, Z. Zhang, Use of Soil Property Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters, 80th Annual Meeting of the AMS, Long Beach, Ca. January
• S. Lindsey, Strategy for Utilizing Radar-Based Precipitation Estimates for River Forecasting, ASCE International Symposium on Engineering Hydrology, San Francisco, Ca., July 25-30, 1993, 940-945
• Wang, D., M. Smith, Z. Zhang, S. Reed, V. Koren, Statistical Comparison of Areal Precipitation Estimates from WSR-88D, Operationaland Historical Gage Networks, AMS 15th Conferencein Hydrology, Jan. 10-14, 2000, Long Beach, CA

Current Personnel

Victor Koren

1) Mathematical models and  their application to flash and spring flood predictions
2) Numerical solution of unsteady flow equations and numerical experiments

 
• Koren, V., J. Schaake, K. Mitchell, Q.-Y. Duan, F. Chen, J. Baker. A parameterizationof snowpack and frozen ground intended for NCEP weather and climate models,JGR, Vol. 104, No. D16, 19.569-19585, 1999.
• Koren, V., Q.-Y. Duan, J. Schaake, and K. Mitchell.  Validation ofa snow-frozen ground  parameterization of the Eta model.  14thConference on Hydrology, 10-15 January 1999,  Dallas, TX, AMS, J1.3,410-413, 1999.
• Koren, V., F. Kogan, C. Barrett.  Parameterization of hydrologicalmodel using NOAA/AVHRR data. Adv. Space Res., Vol. 19, No. 3, 507-510,Pergamon, 1997.
• Schaake, J., V. Koren, Q.-Y. Duan, K. Mitchell, and F. Chen.  Simplewater balance   model for estimating runoff at different spatialand temporal scales, JGR, vol. 101, No.  D3, 7461-7475, 1996.
• Koren, V., J. Schaake, Q.-Y. Duan, b. Koch, F. Kogan.  Parameter estimationof large- scale hydrological models using land surface characteristics.Extended abstract on  “Second International Scientific Conferenceon the Global Energy and Water Cycle”, 17-21 June, 1996, Washington, DC,USA, 1996.
•  Koren, V., C. Barrett.  Satellite Based, Distributed Monitoring,Forecast, and Simulation  (MFS) System for the Nile River, in Book‘Applications of Remote Sensing in  Hydrology’, Canada, 187-200, 1995.

        Other Publications:

 
• Reed,S.M., and D.R. Maidment Use of Digital SoilMaps in a Rainfall-Runoff Model, CRWR Online Report 98-8, 265 pp., December1998, http://www.ce.utexas.edu/org/crwr/reports/online.html.
• Reed,S.M., D.R. Maidment, and J. Patoux, Spatial Water Balance of Texas,CRWROnline Report 97-1, 126 pp., February 1997, http://www.ce.utexas.edu/org/crwr/reports/online.html(Also published as Texas Water Resources Institute Technical Report TR-177,1997)
• Reed, S.M., and D.R. Maidment A GIS Procedure for Merging NEXRAD PrecipitationData and Digital Elevation Models to Determine Rainfall-Runoff ModelingParameters, CRWR Online Report 95-3, 119 pp., September 1995, http://www.ce.utexas.edu/org/crwr/reports/online.html.Reed,S.M., and Maidment, D.R., 'Coordinate Transformations for Using NEXRADData in GIS-Based Hydrologic Modeling', Journal of Hydrologic Engineering,April, 1999, 174-182

         Other Publications:

 
• Smith, M.B. and Vidmar, A., 'Data Set Derivation for GIS-Based Urban HydrologicalModeling', Photogrammetric Engineering and Remote Sensing, Vol.60, No. 1, January 1994, 67-76.
• Smith, M.B., 'A GIS-Based Distributed Parameter Hydrologic Model for UrbanAreas', Hydrological Processes, Vo. 7, 1993, 45-61.
• Smith, M.B., and Brilly, M., 'Automated Grid Element Ordering for GIS-BasedOverland Flow Modeling', Photogrammetric Engineering and Remote Sensing,Vol. 58, No. 5, May  1992,     579-585.
• Smith, M.B., Koren, V. Zhang, Z., and Wang, D. 'Lumped and Semi-distributedModeling Using NEXRAD Stage-III Data: Results From Continuous Multi-yearSimulations', Proc. of the 1999 Georgia Water Resources Conference, pp.359-362, Kathryn J. Hatcher (Ed.), March 30-31, 1999.
• Berich, R.H. and M.B. Smith, 1985, ‘LandSAT and Micro-GIS for WatershedModeling’, Proceedings of the ASCE Specialty Conference - Hydraulics andHydrology in the Small Computer Age, Lake Buena Vista, Florida, August12-17, pages 668-673.

        Other Publications:

 
• Ziya Zhang,  Douglas L. Kane and Larry D. Hinzman. Development andApplication of a Spatially Distributed Arctic Hydrologic and Thermal ProcessModel (ARHYTHM), An International Journal of Hydrological Processes, inpress, 1999.
• D. J. Morton, Ziya Zhang, L. D. Hinzman, and S. O'Connor.  The Parallelizationof a Physically Based, Spatially Distributed Hydrologic Code for ArcticRegions.  In proceedings of the 1998 ACM Symposium on Applied Computing,pp. 684-689, Atlanta, Georgia, 1998.
• Ziya Zhang,  Douglas L. Kane and Larry D. Hinzman.  Developmentof a Physically Based,  Spatially Distributed Hydrologic Model forArctic Regions. Abstract and poster presented at 1996 Fall meeting of theAmerican Geophysical Union.
• Ziya Zhang,  Douglas L. Kane, Larry D. Hinzman and Douglas J. Goering.Ground Dominates Arctic Hydrologic Response. Proceedings of the Workshopon Environmental Technology and Resource Development in Alaska, M. A. Tumeoand C. R. Woolard (Eds.), Fairbanks, Alaska, May 22-24, 1996.
• Ziya Zhang,  Doug Kane and Larry Hinzman. Simulations of Hydrologicand Thermal Processes in Arctic Regions.  Landscapes, American Associationfor the Advancement of Science, The 46th Arctic Division Science Conference,Abstract and presentation, 19-22 September 1995.
• Larry D. Hinzman, Douglas L. Kane and Ziya Zhang. A Spatially DistributedModel for Arctic Regions.  International GEWEX Workshop on Cold-Season/RegionHydrometeorology, Summary Report and Proceedings, 22-26 May 1995, Banff,Alberta, Canada, Int. GEWEX Project Office Publication, Series, No. 15,Washington, D.C., 236-239.
• Douglas J. Goering,  Ziya Zhang,  Larry D. Hinzman and DouglasL. Kane. A Spatially Distributed Hydrologic Model Applied to an ArcticWatershed. Wadati Conference on Global Change and the Polar Climate, 7-10 November, 1995,  Tsukuba Science City,  Japan.  GeophysicalInstitute, University of Alaska and Wadati Conference Local OrganizingCommittee, Japan.  p.138-141.

         Other Publications:

 
• Wang, D., N.J. Rosenburg, R. Brown, L., Vail, R. Srinivasan, 1999. Sensitivityof US Water Resources to ENSO Persistent Climate Scenarios.  Proc.Specialty Conference on Potential Consequences of Climate Variability andChange to Water Resources of the United States, Atlanta, Georgia.
• Rosenburg, N., R.A. Brown, D.Wang, 1999.  Annual water yield in theconterminous United States as affected by two GCM scenarios of greenhouse-forcedglobal climate change. Proc. Specialty Conference on Potential Consequencesof Climate Variability and Change to Water Resources of the United States,Atlanta, Georgia.
• Rosenberg, N.J, D. Wang, R. Srinivasan, 1999.  Possible impacts ofglobal warming on the hydrology of the Ogallala aquifer region. J. CclimateChange, 42: 677-692.
• Wang, D., P. Dawson, G. Johnson, 1999: Simulations of  hydrologically-significantwinter storms using an explicit cloud model.  Submitted to J. AppliedMeteorology.
• Wang, D., G. Johnson, P. Dawson, 1996: Employing a coupled land-atmospheremesoscale  model for precipitation prediction of  ydrologically-significantwinter storms in the inland Pacific Northwest.   Proc. 77th AMS annualmeeting, Long Beach, Ca.
• Wang, D., G. Johnson, P. Dawson, and K.T. Chang, 1995: Integrated use ofGIS and a comprehensive meteorological modeling system applied to winterstorm in Idaho, Proc. 64th Western Snow Conference, Bend, Oregon.
• Wang D., P. Dawson, G. Johnson, 1995: Simulation of a winter storm overa mountainous watershed, Proc. 7th Conf. on Mountain Meteorology. AmericanMeteor. Soc., July 16- 20, 1995, Breckenridge, Co., pp. 162-167.
• Dawson, P., G.L. Johnson, C.L. Hanson, D. Wang, 1995: Meteorological studiesin a mountainous watershed, Proc. 7th Intl. Symp. on Measurement and Modelingof Environmental Flows, IMEC&E, Nov. 12-17, 1995, San Francisco, CA, 6pp.
• Dawson, P., G.L. Johnson, and D.Wang, 1995: Analysis of a winter stormin Idaho, Proc., Intl. GEWEX workshop on cold-season/region hydrometeorology.Banff, Alberta, Canada, May 22-26, 1995.
• Johnson, G.L., P.J. Dawson, D.Wang, 1995: Derivation of wind and precipitationfields at the watershed scale using two atmospheric models. Proc., theUSDA-ARS Workshop on Weather and Climate Research, July, 1995, Denver,Colorado.

 

PAPERS AT AGU SPRING CONFERENCE 2001

Presentations in MS PowerPoint format
 
 
• TRANSITION FROM LUMPED TO DISTRIBUTED SYSTEMS, Victor Koren, Michael Smith, Seann Reed, Ziya Zhang
• Comparisons of Simulation Results Using the NWS Hydrology Laboratory's Research Modeling System (HL-RMS), Ziya Zhang, Victor Koren, Seann Reed, Michael Smith, and David Wang
• Kinematic Channel Routing Parameter Estimation for a NEXRAD Cell Based Model, Seann Reed, Victor Koren, Ziya Zhang, Michael Smith
• COMPARISON OF MEAN AREAL PRECIPITATION ESTIMATES FROM WSR-88D AND HISTORICAL GAGE NETWORKS OVER CHEAT RIVER BASIN, WV, David Wang, Michael Smith, D.J. Seo, Victor Koren, Seann Reed, Ziya Zhang

 

 
 
 

Presentations in Acrobat format
 
 

• TRANSITION FROM LUMPED TO DISTRIBUTED SYSTEMS, Victor Koren, Michael Smith, Seann Reed, Ziya Zhang
• Comparisons of Simulation Results Using the NWS Hydrology Laboratory's Research Modeling System (HL-RMS), Ziya Zhang, Victor Koren, Seann Reed, Michael Smith, and David Wang
• Kinematic Channel Routing Parameter Estimation for a NEXRAD Cell Based Model, Seann Reed, Victor Koren, Ziya Zhang, Michael Smith
• COMPARISON OF MEAN AREAL PRECIPITATION ESTIMATES FROM WSR-88D AND HISTORICAL GAGE NETWORKS OVER CHEAT RIVER BASIN, WV, David Wang, Michael Smith, D.J. Seo, Victor Koren, Seann Reed, Ziya Zhang