1. 1 Brian Cosgrove Collaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang, Zhengtao Cui, Ziya Zhang NOAA/NWS/OHD Brian Cosgrove Collaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang, Zhengtao Cui, Ziya Zhang NOAA/NWS/OHD Distributed Modeling DHM-TF: Monitoring and Predicting Flas h Floods with a Distributed Hydrologic Model Eastern Region Flash Flood Conference June 3 rd 2010 Photo: NOAA

2. 2 Focus: Leveraging distributed modeling to more effectively analyze and predict flash flooding Hydrologic Modeling: Distributed versus lumped Overview of OHD’s Distributed Hydrologic Model Threshold Frequency (DHM-TF) flash flood application DHM-TF Precipitation forcing data Visualization and interpretation of DHM-TF data DHM-TF Flash flood case studies Summary and future plans Outline:

3. 1. Rainfall and soil properties averaged over basin 2. Single rainfall/runoff model computation for entire basin 3. Prediction/verification at one point Lumped Distributed Lumped Versus Distributed Models 1. Rainfall, soil properties vary by grid cell 2. Rainfall/runoff model applied separately to each grid cell 3. Prediction/verification at any grid cell 4. Advantages over lumped—cell-to-cell routing, higher resolution, ingest gridded observations Distributed models are well-suited for flash flood prediction and monitoring, offering high-resolution streamflow at outlet and interior points with ability to route flow 3

4. 4 DHM-TF: An application of distributed modeling What is DHM-TF? ◦ A generic approach to leverage strengths of distributed modeling and statistical processing to monitor and predict flash floods ◦ Provides way to cast flood severity in terms of return period by converting model flow forecasts to frequency (return period) ◦ Similar approach to that used/developed at CBRFC Why this method? ◦ Fills gaps in existing flash flood tools (routing, rapid updates, interior pts) ◦ Return periods directly relate to existing engineering design criteria ◦ Resistance to uniform bias in modeled flow (only rankings used) Distributed Hydrologic Model Frequency Post Processor Gridded Frequency (Return Period) Gridded Discharge DHM-TF

5. 5 0-1 Hour HPN Forecast (mm) 23Z April 21st to 00Z April 22 nd 2009 HPE Precipitation (mm) 23Z April 21st to 00Z April 22 nd 2009 MPE Precipitation (mm) 23Z April 21st to 00Z April 22 nd 2009 1-2 Hour HPN Forecast (mm) 23Z April 21st to 00Z April 22 nd 2009 Observations Forecasts DHM-TF Ingests MPE, HPE, and HPN Precipitation

6. 6 DHM-TF Output Both discharge and return period output available Return period superior for flash flood depiction ◦ Resistance to bias in flow values versus raw discharge ◦ Relates directly to existing engineering design criteria DHM-TF Return Period (Years)DHM-TF Discharge (m 3 /s)

7. Interpreting DHM-TF Output Return Period (Years) Uniform 2-Year Value DHM-TF Output Return Period (Years) Compare DHM-TF Return Period Map -with- Return Period Threshold Map Flooding judged to occur in grid cells which exceed two year return period Flooding judged to occur in grid cells which exceed values on varying threshold map -or- Spatially Varying Values (Generated from local knowledge, engineering design criteria) Superior Choice: Better-reflects actual channel conditions 7

8. 8 DHM-TF Performance Factors leading to good DHM-TF simulations: ◦ Temporally static (or zero) model flow bias ◦ Hydrologic model which accurately represents flow distribution ◦ Adequate length of underlying precipitation record (need ≥ 10 years) ◦ High-quality precipitation forcing data ◦ Good fit of Log Pearson Type III distribution to actual flow values ◦ Few instances of water regulation in simulation domain Skill of end-user ◦ Interpretation of return period map affected by local knowledge  Low water crossings  Vulnerable infrastructure  Well-protected / highly engineered areas  Water regulation structures Photo credit: NOAA APRFC 8

9. 9 How is DHM-TF currently implemented? ◦ Sacramento model with kinematic wave routing…but generic approach which can be applied to any distributed model ◦ Executed with and without cell-to-cell routing DHM-TF pilot studies are underway in coordination with NWS Weather Forecast Offices (WFOs) and River Forecast Centers (RFCs) ◦ DHM-TF executed over Baltimore/Washington WFO domain on OHD server ◦ Pittsburgh WFO domain DHM-TF simulation run on Pittsburgh WFO server ◦ Imminent expansion to Binghamton WFO domain (on BGM server) Current Status of DHM-TF Pittsburgh, Binghamton, and Balt/Wash WFO Domains 89,000 km 2 57,500 km 2 11,000 km 2 Pittsburgh Binghamton Balt/Wash

10. 10 OHRFC MPE (4km, high confidence) OHRFC MPE or PBZ HPE No Precipitation T-24 hrsT-23 hrsPresentT+3 hrs DHM-TF Run 1 (state update) DHM-TF Run 2 (forecast run) Model States Saved *Cycle automatically repeated every hour in current setup Switch Time Return Periods Calculated Real-time Pittsburgh DHM-TF Prototype Optional HPN T+1 hr

11. 11 DHM-TF Verification Two flash flood case studies from the Pittsburgh WFO ◦ August 9 th- 10 th, 2007: 25 flash flood warnings issued, large event with two waves of rain ◦ March 22 nd -23 rd, 2010: 4 flash flood warnings issued, smaller event Following slides will detail several comparisons: ◦ Location of spotter-reports versus DHM-TF output ◦ DHM-TF output with and without cell-to-cell routing ◦ Model-produced flow versus measured USGS stream gauge flow ◦ DHM-TF timing versus timing of WFO flash flood warnings Highlights: ◦ Good overall results versus observations ◦ Cell-to-cell and local routing each have unique strengths

12. 12 Standard Cell-to-Cell RoutingLocal Routing (only internal cell routing) Maximum DHM-TF Return Period Values (Years) 12Z 8/9/07 through 12Z 8/10/07) Overall, good match between areas of high DHM-TF return periods and spotter-reported events (wave symbols) Local routing performs slightly better than cell-to-cell routing Difficult to determine storm report location DHM-TF Verification: August 9 th, 2007 Flash Flood Reported Flash Floods

13. Pittsburgh Area DHM-TF maximum event return period difference plot (years) over 12Z 8/9 to 12Z 8/10 time period Computed as: Local Routing minus Cell-to-Cell Routing DHM-TF Verification: August 9 th, 2007 Flash Flood Local routing yields higher return periods over main stem rivers, better representing flash floods in pixels that include large channels Reported Flash Floods 13

14. 14 Girty’s Run discharge with input precipitation derived with tropical Z-R relationship Girty’s Run discharge with input precipitation derived with standard Z-R relationship Precipitation forcing greatly impacts modeled flows Local = Only internal cell routing Std = Standard cell-to-cell routing Local = Only internal cell routing Std = Standard cell-to-cell routing 14

15. 15 NWS FF Warning DHM-TF Verification: August 9th, 2007 Flash Flood County-wide comparison of DHM-TF with FF warnings Simulations used MPE data NWS Flash flood warnings ◦ Westmoreland County (3 issued, 3rd not verified) ◦ Allegheny County (4 issued, 4th not verified) DHM-TF peaks (and time above 2 year return period threshold) agree well with verified warning periods Local routing performs better toward end of event

16. 16 Standard Routing Option 3/22 23:59Z – 3/23 03:00Z3/23 13:48Z – 3/23 22:45Z 3/23 01:09Z – 3/23 07:15ZReported Flash Floods 3/22 23:42Z – 3/23 02:45Z 3/22 23:42Z – 3/23 03:45Z Pittsburgh WFO-Issued Warnings and Spotter-Reported Flash Floods DHM-TF Return Periods (Years) at 12Z on March 23rd, 2010 FF AF FF = Flash Flood Warning AF = Areal Flood Warning Local Routing Option DHM-TF Verification: March 22-23, 2010 Flash Flood PBZ WFO: Use of cell-to-cell routing enabled accurate depiction of flood extent

17. 17 DHM-TF: Summary and Future Work Summary ◦ DHM-TF: Combines distributed hydrologic model with threshold frequency post-processor  return periods ◦ Capitalizes on strengths of distributed modeling ◦ Fills gaps in existing flash flood tools (routing, rapid updates, interior pts) ◦ Collaborative development and promising assessment effort Future Work ◦ Validation and deployment at additional field locations ◦ Operation at higher temporal and spatial resolutions ◦ In-depth validation using NSSL SHAVE data ◦ Collaborative Assessment…Further refine DHM-TF to better match the needs of forecasters

18. 18 Thank You

19. 19 Extra slides that follow are only for reference if needed

20. 20 Return Period Calculation Distributed model outputs flow within each grid cell (m 3 /s) Method needed to translate flow into return period DHM-TF uses Log Pearson Type III (LP3) procedure ◦ Established procedure with good availability of supporting data sets ◦ Create probability distribution curve for each grid cell from log of annual max flow values (over ≥ 10 years) ◦ Mean, standard deviation, and skew of flow data control shape of curve ◦ Use cumulative probability distribution and flow for each grid cell to compute annual exceedance probability (AEP) and return period (1/AEP) ◦ Procedure is automated within DHM-TF subroutines LP3 Probability DistributionCumulative LP3 Probability Distribution probability p(y)

21. 21 Sacramento Soil Moisture Model Cell-to-Cell Channel Routing Snow17 Snow Model Precipitation Temperature Potential Evaporation Precipitation Temperature Potential Evaporation surface/impervious/direct runoff rain + melt Flows and State Variables base flow / interflow Hillslope Routing (delays within-cell flow into channel) Hillslope Routing (delays within-cell flow into channel) *** Currently, full version only available as separate package from OHD (not within AWIPS) but will eventually be integrated in upcoming Community Hydrologic Prediction System (CHPS). Specifics: OHD Research Distributed Hydrologic Model (RDHM) Optional DHM-TF Flash Flood Post Processor Optional DHM-TF Flash Flood Post Processor

22. 22 = Basin boundary = Model grid cell = Channel network = Outlet Point= Interior Point= Headwater Point Various types of output locations RDHM ingests temperature, precip, and PE and produces discharge, soil temperature and soil moisture at each cell Routes flow between cells via channel network Accurately reflects impact on flow (timing/magnitude) of non-uniform precipitation Produces verifiable discharge values at any location (including interior points.) HRAP (16km 2 ) resolution most common, but ½ and ¼ HRAP are future possibilities Distributed Model Overview

23. 23 Application of OHD Distributed Model to Blue River, OK April 3, 1999 Model Parameters Rainfall Surface Runoff Flow Direction Heavy Rain Distributed Modeling for Improved River Forecasts

24. 24 Hydrologic Response at Different Points in the Blue River Basin 0 40 80 120 160 200 4/3/99 0:004/3/99 12:004/4/99 0:004/4/99 12:004/5/99 0:004/5/99 12:004/6/99 0:00 Flow (CMS) 0 40 80 120 160 200 4/3/99 0:004/3/99 12:004/4/99 0:004/4/99 12:004/5/99 0:004/5/99 12:004/6/99 0:00 Flow (CMS) Distributed Lumped Observed Flow (CMS) 0 40 80 120 160 200 4/3/99 0:004/3/99 12:004/4/99 0:004/4/99 12:004/5/99 0:004/5/99 12:004/6/99 0:00 B A Blue River, Oklahoma Heaviest Rain O Lumped model output limited to basin outlet, distributed model able to output at interior points Lumped model underestimates and delays peak at outlet due to basin averaged precip Distributed model captures spatial variability and produces better simulation Hydrograph at Location A Hydrograph at Location B Hydrographs at Basin Outlet (O) Distributed Modeling for Improved River Forecasts

25. 25 Current DHM-TF Requirements Model operation ◦ OHD RDHM software package (obtain from NWS LAD)  Operating System: Red Hat Enterprise Linux 4.0  Compiler: GNU GCC/G++ 3.4.6 or later and PGF90 4.1-2  Software Libraries  C++ BOOST library 1.36.x  GNU Scientific Library (GSL) 1.6 or later  Miscellaneous  Autoconf 2.13  Automake 1.4-p5  GNU Make 3.79.1 ◦ RDHM Supporting data sets  Meteorological: Precipitation (long-term ~10 years, quality controlled), potential evaporation (can use monthly climatology), temperature (if using Snow17)  Parameters: Can often use pre-defined a priori data sets as solid starting point Visualization of output ◦ Google Earth (KML)  Google Earth software (runs best on PC, Pro version ingests shapefiles)  xmrgtoasc and a2png conversion utilities, luxisr.ttf font, Linux zip utility ◦ Simple PNG image  GRASS GIS

26. 26 Domain = 11,000 km 2 Sterling WFO DHM-TF Prototype DHM-TF with cell-to-cell routing currently running in real-time on OHD server over LWX WFO domain Analyzed June and September 2009 flash flood events with both cell-to-cell and local routing simulations Monitoring real-time DHM-TF simulations Sterling WFO DHM-TF Domain

27. 27 DHM-TF Verification: August 9 th, 2007 Flash Flood Three mesoscale convective systems caused widespread flooding over Ohio, Pennsylvania, West Virginia, and Maryland ◦ 25 Flash flood warnings issued by Pittsburgh WFO 12Z 8/9 to 02Z 8/10 ◦ 24 Reported flash flood events ◦ 10 Flash flood warnings with no corresponding reported event in county Verification: Difficult to determine storm report location Warned counties outlined in green Wave symbol indicates reported flash flood PBZ WFO CWA outlined in red Warned counties outlined in green DHM-TF domain covers shaded area MPE Precipitation (mm) 12Z 8/9 to 12Z 8/10

28. 28 Girty’s Run Discharge Modeled flows (using local and cell-to- cell routing options) are too small in magnitude Precipitation input was too small (PBZ WFO has provided updated precipitation) Two HRAP pixels cover Girty’s Run (upstream pixel and pixel at gauge) USGS Gauge at Millvale

29. 29 DHM-TF Precipitation Forcing: Multisensor Precipitation Estimator (MPE) Data Description ◦ One hour temporal resolution, 4km spatial resolution, > 1 hour latency ◦ Uses a combination of radar, gauge, and satellite rainfall estimates Production ◦ Produced in AWIPS environment by each field office ◦ Bias correction factors developed from a comparison of radar and gauge data ◦ Bias-corrected radar blended with gauge-only field to produce merged radar/gauge product ~18 pixels within City of Baltimore MPE Precipitation (mm) 23Z April 21st to 00Z April 22 nd 2009 Characteristics ◦ Several hour latency time may exist due to repeated manual adjustments and quality control of input fields as additional gauge reports are received ◦ Latency makes real-time use in flash flood forecasting impractical

30. 30 DHM-TF Precipitation Forcing: High Resolution Precipitation Estimator (HPE) Description ◦ Sub-hourly temporal resolution, 1km spatial resolution, < 1 hour latency ◦ Uses radar rainfall estimates Production ◦ Produced in AWIPS environment at each field office ◦ HPE leverages recent MPE gauge/radar bias information to automatically generate radar-based rainfall and rain rate products statistically corrected for bias ◦ A user-defined radar mask determines how overlapping radars will be blended for each pixel within domain of interest Characteristics ◦ No manual quality control ◦ Low latency, and high spatial/temporal resolution makes real-time use practical for flash flood forecasting ~72 pixels within City of Baltimore HPE Precipitation (mm) 23Z April 21st to 00Z April 22 nd 2009

31. 31 DHM-TF Precipitation Forcing: High Resolution Precipitation Nowcaster (HPN) Description ◦ Sub-hourly temporal resolution, 4km spatial resolution, 1 hour (operational) or 2 hour (research) forecast lead time Production ◦ Dependent on HPE, produced in AWIPS environment at each field office ◦ Local motion vectors are derived through a comparison of radar rain rates spaced 15 minutes apart, and are used to project current radar echoes forward in time out to two hours ◦ Rain rates are then variably smoothed by a method based on the observed changes in echo structure over the past 15 minutes, as well as the current observed rain rate field Characteristics ◦ High spatial/temporal resolution well- suited for flash flood forecasting HPN 15 minute precipitation forecasts (mm) out to 2 hours

32. 32 Monocacy at Jug Bridge (2116 km 2 ) Cumulative Bias, Monocacy River at Jug Bridge (2100 km 2 ) Bias Correction of Precipitation Time-changing bias detected in MARFC MPE archives prior to 2004 Bias corrected precipitation needed to support unbiased simulation statistics for a reasonable historical period (~10 years) Analysis of Monocacy River flow shows reduction in cumulative bias and improved consistency when bias corrected precipitation is used Consistent bias can be removed through calibration or addressed through DHM-TF approach

33. 33 Monthly RFC MPE Precipitation 03/97 (mm) Monthly PRISM Precipitation 3/97 (mm) Monthly Bias (ratio, log scale) RFC Hourly MPE Precipitation 03/01/97 12z (mm) Adjusted RFC Hourly MPE Precipitation 03/01/97 12z (mm) Bias Correction of Precipitation Key end result: time-changing, inconsistent precipitation biases are removed